This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability . After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol , Hydroxyprogesterone caproate , Retosiban , Ritodrine  and Terbutaline . Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25–40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various  Indicated or Iatrogenic PTB accounts for the remaining 25% of preterm deliveries [Preterm birth (PTB) defined as birth before 37 weeks of gestation is the leading cause of neonatal morbidity and mortality worldwide . Spontanliveries . IatrogePROM described as the rupture of amniotic membranes before labor begins , can resPakistan sees 748,100 PTB cases annually, ranking fourth after India, China, and Nigeria, with an estimated high incidence of PTB of up to 16.5% [The metabolic proteins (pathway) leading to PTB are overlapping between multiple etiologies; thus, understanding PTB initiators and effectors is difficult, preventing the discovery of biomarkers as usual predictors for this state . Their eComplement factors B, H and the coagulation factors IX and IX ab have been identified as the highest-ranking proteins with distinguishing cases of PTB . SimilarTherefore, the research objective was to search the genes for the most potent PTB protein/biomarkers as well as to investigate the occurrence of region-specific genetic variations, their impact on proteins’ stability, evolutionary conservativeness, and changes in properties of protein associated with PTB. Moreover, to identify potential therapeutic targets with their corresponding protein cavities and their binding interactions with intervening bioactive compounds was also explored to manage the challenges of PTB.https://www.ncbi.nlm.nih.gov/genes). The search strategy included the key terms and synonyms, adapted to retrieve PTB-related genes and proteins, were “preterm birth OR preterm labor OR preterm delivery”. All the relevant genes along with their corresponding proteins were extracted from the period of 1st January 2009 till 30th June 2021. Overall, 55 different proteins from 20 different genes were found . All required genomic information, such as, genes names, aliases, HGNC (HUGO Gene Nomenclature Committee) Name/ID, NCBI accession number, ENSEMBL genes ID, genomic coordinates, gene type, transcripts counts, exon counts, genetic variation counts, exonic variation counts, filtered exonic variants, protein name with amino acids counts, UniProtKB Identifiers, reference sequence status, and gene expression were retrieved from NCBI https://www.ncbi.nlm.nih.gov/gene; ENSEMBL http://asia.ensembl.org/Homo_sapiens/Gene/Summary; HGNC (HUGO Gene Nomenclature Committee) https://www.genenames.org/; and GeneCards (https://www.genecards.org/)  databases (https://asia.ensembl.org/index.html) database , further validated by dbSNP (https://www.ncbi.nlm.nih.gov/snp/). VEP helps in the determination of downstream functional effects of variants  on genes, transcripts, and protein sequence, as well as for regulatory regions. The relevant information of all SNPs included SNP ID, chromosome loci, nucleic acid changes, amino acid changes and reported transcript sequence ID. All SNPs were retrieved from ENSEMBL database, followed by the extraction of exonic variants by applying filter in the VEP module of ENSEMBL. The extracted data was downloaded as default comma-separated values (CSV) excel file.Variations of each gene were extracted from Variant Effect Predictor (VEP) module  of ENSEMin-silico algorithms:1000 genome project database (1KGD)  was usedhttps://sift.bii.a-star.edu.sg/) is used to distinguish pathogenic mutations having significant involvement with disease from neutral polymorphisms. In simple way, SIFT predictions helps to analyze the amino acid substitution effects on the function of protein [SIFT ( protein . SIFT schttp://genetics.bwh.harvard.edu/pph2/dbsearch.shtml). The prediction (score) output is categorized as ‘most likely damaging’, ‘may be damaging’, and ‘benign’ with specificity and sensitivity values for a mutation [The functional impact of substitution of an amino acid on collective proteins function and its stability was scored through PolyPhen2  is one of the largest and most comprehensive aggregated variant datasets of allele frequency with open access available from the NCBI database of Genotypes and Phenotypes (dbGaP) , 24. ALFin silico approaches include:All exonic missense SNPs, retrieved and analyzed through earlier mentioned strategies were further investigated by the transcript sequence-based computational analysis through various Machine Learning (ML) algorithms, to further scrutinized the nsSNPs as whether pathogenic or not. These http://www.pantherdb.org/tools/csnpScore.do. It measures the length of time (in millions of years ‘my’), a position in a current protein has been preserved by tracing back to its reconstructed direct ancestors. The longer a position has been preserved, the more likely that it will have a deleterious effect. The thresholds were: "probably damaging" , "possibly damaging"  and "probably benign" (time < 200my) [PANTHER-PSEP was used to calculate the score of position-specific evolutionary preservations through online classification system, such as, < 200my) .https://snps.biofold.org/phd-snp/phd-snp.html). The Swiss-Prot database was used for training and the prediction was used with 20-fold cross validation [PhD-SNP is a pathogenicity-predicting tool based on support vector machines through web server  terms, and outperforms other available predictive methods. SNPs&GO includes information extracted from the protein 3D structure (SNPs&GO3d) with respect to sequence-based method [SNPs&GO from a protein sequence predict whether a variation is disease related or not by exploiting the corresponding protein functional annotation available on d method .https://rostlab.org/services/snap2web/) that distinguishes between effect and neutral variants/nsSNPs by taking a variety of sequence and variant features into account. A high score , white indicates weak signals (-50<score<50), and a low score  of single amino acid substitutions on protein function (o effect .http://mmb.irbbarcelona.org/PMut/) is a web-based tool for the annotation of pathological variants on proteins, was trained and tested by the manually created database SwissVar (October 2016 release), which includes 27,203 harmful and 38,078 benign mutations for 12,141 proteins. The prediction scores of PMut are from 0 to 1, and the cutoff value is set to 0.5  [PMut (.5 to 1) .http://mutpred.mutdb.org/. The scores range from 0.5 to 1.0 are classify as ‘pathogenic mutation’ whereas, values less than 0.5 are ‘benign’ [A web-server tool developed to categorize and predict amino acid substitutions on functional protein as pathogenic or benign in human, available through ‘benign’ .http://mupro.proteomics.ics.uci.edu/). It was developed on the basis of two machine learning methods i.e., Support Vector Machines (SVM) and Neural Networks (NN). The advantage of this in-silico algorithm is that they do not require tertiary structures to predict protein stability changes [MUpro is a set of ML programs to predict how single-site amino acid mutation affects protein stability ( changes .http://gpcr2.biocomp.unibo.it/~emidio/I-Mutant3.0/I-MutantSuite_Help.html). Through I-Mutant predictions, the ternary classificatory has an extra class, namely Neutral, that refers to small ΔΔG values changes upon (WT/New) single point protein mutations. The range of ΔΔG values for a Neutral mutation classification is: -0.5 = <DDG = <0.5, while changes <-0.5 are classified as Large Decrease and changes >0.5 are classified as Large Increase [I-Mutant v.3.0 is a predictor of protein stability changes upon mutations. I-Mutant v.3.0 is an SVM-based tool for the automatic prediction of protein stability changes upon single point mutations at pH 7 and temperature equal to 25°C. The predictions were performed from the protein sequence and can be used both as a classifier for predicting the sign of the protein stability change upon mutation and as a regression estimator for predicting the related DeltaDeltaG (ΔΔG) values (Increase .http://ncblab.nchu.edu.tw/iStable2/seqsubmit.html). This tool also computes the secondary structural information and surface accessibility of protein sequence with mutation [iPTREE-STAB/iStable v.2.0 is an interpretable decision tree-based method for discriminating the stability of proteins stabilizing or destabilizing  upon single amino acid substitutions from amino acid sequence. The discrimination and predictions are mainly based on decision tree coupled with adaptive boosting algorithm, and classification and regression tree, respectively, using three neighboring residues of the mutant site along N- and C-terminals  is a method used to predict stability of protein variants from sequences and structures. The INPS-MD predictor using sequences is based on a simplified support vector (SVR) as implemented by the libsvm package, which was only tested by linear and radial basis function (RBF) kernels. INPS-MD predictions can be interpreted to identify stabilizing (ΔΔG>0) and destabilizing (ΔΔG<0) variations [INPS-MD  was used to analyze the evolutionary conservation of amino acids by calculating the conservation score through unique algorithm. The algorithm calculates normalized conservative score using Bayesian method for calculating rates with a confidence interval to each of the inferred evolutionary conservation scores. The amino acids with scores between 7 and 9 were evolutionary conservative amino acids [ConSurf software (no acids .https://npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=npsa_sopma.html) used five independent algorithms to compute the secondary structural details of proteins [The SOPMA—a neural network method (PHD), jointly predict the secondary structure  of amino acid residues. This software (proteins .http://www.cmbi.ru.nl/hope/input/) was used to predict the effect of amino acid mutation/SNPs on physical and chemical properties, hydrophobicity, spatial structure, and function of proteins [The HOPE software (Have (y) Our Protein Explained, proteins .The Genotype-Tissue Expression (GTEx) database cataloged a large number of tissue-specific genotypes and shared regulatory expression quantitative trait loci (eQTL) variants. We used GTEx to identify the best expressed gene in the female reproductive tissues  that could impact PTB status at protein level . After iProtein Homology/analogY Recognition Engine V 2.0 (Phyre2) tool was used for homology modeling of concerned PTB protein . FollowiProtein highly expressed with a large number of tissue-specific and shared regulatory variants in female reproductive tissues.Protein with highest structural coverage information (retrieved through PDB) among others (found with rare coding variants).After following the above-mentioned inclusion criteria, CNN1 was selected for 3D homology modeling. The target sequence of Calponin1 (CNN1 Isoform 2) protein was searched from UniProt database (Entry No. P51911-2). PHYRE2 tool was used for homology modeling that consisted of sub-algorithmic stages:Stage 1—Gathering Homologous Sequences: CNN1 isoform 2 sequence was scanned against the specially curated NR20  protein sequence database with HHblits [ HHblits . The res HHblits , and botStage 2—Fold Library Scanning: The models were scanned against a database of HMMs [ of HMMs  of proteStage 3—Loop Modeling: Indels in these models were corrected by loop modeling.Stage 4—Side-chain Placement: Amino acid side chains were added to generate the final PHYRE2 models.https://saves.mbi.ucla.edu/) web server [z-score of the chosen model. SMART-EMBL tool (http://smart.embl-heidelberg.de/) was used to compute the confidently predicted domains, repeats, motifs and low complexity region (LCR) in protein. The SuperPose webserver (http://superpose.wishartlab.com/) was used to calculate both sequence alignment between template and 3D homology model through structure superposition using modified quaternion eigenvalue approach to generate RMSD statistics of the superimposed molecules [The best homology models were selected based on the top-ranked modeled structures (according to similar template pattern). The alignment description of templates obtained through manual protein-blast comparison with the highest percent identity at 100% confidence was generated by Phyre2 tool. The quality assessment of stereochemical properties of 3D homology models were carried out by PROCHECK . Ramachab server  to know olecules .To detect protein binding cavities, cavity-detection guided Blind Docking approach was used to explore the binding cavities of homology models. CB-Dock is an automatic protein-ligand docking approach which identifies the binding cavities/sites. CB dock compared cavities and ranked through a method called ‘CurPocket’ with state-of-the-art protein–ligand binding site prediction methods using the benchmark set of COACH , as pred“preterm birth OR preterm labor OR preterm delivery”. The visual inspection of all compounds was performed to ensure the arrangements of respective constituting atoms as per 3D molecular structures. Furthermore, stable molecular conformations with arrangement in space was carried out by the energy minimization of 3D structures of PTB therapeutic compounds. According to ‘drug type’ category, only small molecules were filtered by Lipinski’s rule of five [Drug used in PTB management were explored from the publicly accessible DrugBank database having comprehensive information about drugs and drug targets . The sea of five . After aAutoDock Vina software (version 4.2) was used for protein-drug analysis and to estimate the molecular interactions of PTB proteins with selected drugs (ligand) molecules . The beshttps://discover.3ds.com/discovery-studio-visualizer-download.The interactions of the docked complexes and 2D ligand-protein interaction plots were drawn with the help of Discovery Studio 2017 R2 Client (v17.2.0.16349) available at in-silico pharmacokinetic predictions of the selected PTB drugs. ADMET lab platform (http://admet.scbdd.com/home/index/) [The ADMET  approach was used for /index/)  was usedst January, 2009 to 30th June, 2021)  of 20 genes were found with PTB-related search query from NCBI duration from 1e, 2021) . Out of e, 2021) .Through ENSEMBL gene ID of each gene, the genetic variations were extracted. ROBO1 gene has the highest numbers of SNPs, i.e., 278,967, while ADAMTS4 has very least numbers of SNPs, i.e., 5,757 . LikewisAll exonic variants of 20 PTB-related genes were further filtered based on MAF≤0.01 and downstream functional effects . Based oWe retrieved 36 transcript-level high-risk PTB-related nsSNPs in four genes, followed by further validation with six different algorithms to evaluate the functional impacts . MissensnsSNP rs532147352 of CNN1 with all four transcripts showed the similar output about the structural and functional changes of a protein. This SNP result in the large decrease in the stability of protein computed by MUpro, I-Mutant, iPTREE and INSPS based on integrated predictor for protein stability change upon single mutation (R>H) . The Con(9) located in random coil region of protein and were highly conserved and exposed  mutation also exhibited the decrease in stability of protein, as computed by the relevant software, which might affect the function and associated with disease occurrence . The SNP exposed . The alp exposed . The mutIn IQGAP2 gene, the nsSNP rs140724393 resulted in the large destabilization of protein structure due to Y>C mutation, which might lead to severe pathophysiological consequences . This murs200940839 through various analytical tools was predicted as higher protein structural changes which could resulted in the destabilization of protein function . The mutThe mutation rs34950321 (T>I) was predicted to cause higher protein structural changes which could resulted in the destabilization of protein function . This muin silico estimation.The mutation rs34968964 (E>Q) in all six transcripts were found highly conserved, exposed, and located in alpha-helix region of protein . Around In SLIT2 gene, the mutation rs547207452 (G>R) in all five transcripts was not computed by ConSurf software, however, transcript IDs ENST00000273739.9, ENST00000503837.5, ENST00000622093.4 were in beta-turns and the rest two were with random coil region as this protein has >82% coil in secondary structure distribution. The ~63% residues were exposed to surface, and mutation results in decreasing the hydrophobicity . The HOPGTEx database identified the CNN1 Isoform 2 as the highly expressed gene in female reproductive tissues when compared against the pattern of gene expression to the rest of all 4 genes . The GTEThe protein-wise structural coverage of selected proteins was extracted from the PDB. The highest protein structural coverage of CNN1 was 41% with amino acids length of 136 residues positioned at 20–142 through NMR method . However−86, and 94% identity)  formed alpha-helix, 14.80% (41 residues) were involved in beta-strand, 7.58% (21 residues) were involved in beta-turn and random coil were 48.01% (133 residues). CNN1 isoform 2 was found to be the highly expressed protein  with maximum structural coverage available on PDB database . Therefodentity) . Afterwasoform 2 . The 3D soform 2 . Only 10soform 2 . The CNNi.e., 1WYP. However, RMSD value for α-carbons was 2.27, back-bone atoms were 2.22 and heavy atoms were 2.35.Finally, we selected the highest percent similarity (94%) model of 123 aligned residues as our target protein for further investigation of this work . For subin-silico interaction of homology model (CNN1 Isoform 2) with progesterone in cavity-graded pose rank 1   was examined through the al/mole) . The detal/mole) .From DrugBank database, initially 17 drugs were extracted as small drug molecules . All 17 in-silico analyses of all five drugs interactions with homology model (CNN1). The estimated energies and molecular mechanics of all five drugs showed a significant binding interaction with homology model  on Finnish population reported the occurrence of fetal SLIT2 genomic variation . SLIT2 aPredicting the effect of genomic variations on protein stability such as amino acid substitution is useful for screening of disease-causing SNPs  in proteCNN1, thin filament-associated protein known to regulate smooth muscle contraction, includes regulation, modulation of contraction processes, and activation of downstream pathways. It is reported to bind actin, calmodulin and tropomyosin , 61. TheThe current pharmacological basis for managing PTB with progesterone is well reported . UnfortuDrug target identification describes the process of identifying the direct molecular target available as protein or on nucleic acid, prominent for continuing interaction with usually small drug molecule . An effeWe selected five main drugs, including Hydroxyprogesterone caproate, Allylestrenol, Terbutaline, Ritodrine, and Retosiban with potential drug-likeness attributes, and performed residue specific molecular docking with our PTB target protein (CNN1 isoform 2). The obtained energetic estimate plays decisive role regarding the drug designing approach based on the binding interactions of PTB protein with therapeutic compound to identify protein targets through structural conformation held with non-covalent interactions . The assin-silico investigation approach in assessing the pharmacokinetics  properties of the drug of interest. The in-silico screening markedly decreased the rate of attrition before subjecting the drug to clinical trials [Drug discovery and drug development processes are preliminary linked with an l trials , 69.The investigated physicochemical properties of Terbutaline, Ritodrine, and Retosiban showed high drug solubility properties, while Allylestrenol and Hydroxyprogesterone caproate were less soluble with elevated distribution coefficient values. The consideration of drug solubility plays a significant role in the selection of drug molecule, and its evaluation as best possible usage , 69. Besin vivo absorption of drugs, as it measures the quantification rate of a drug across polarized Caco-2 mono-layers [in-silico outcome for Caco-2 permeability regarding human intestinal absorption of selected PTB-drugs showed the optimal range, further validated through HIA  pharmacokinetic percentage scores [After administration of the drug, the main focus of interest in drug discovery is the availability strength of drug into the bloodstream and then how fast the required magnitude of drug reaches its specific target site (receptor) for the purpose of therapeutic action , 69. Theo-layers , 69. Thee scores , 69.in-silico pharmacokinetics for the bioavailability (F 30%) of five PTB drugs showed the highest values for Ritodrine (64.30%), Allylestrenol (48.20%), and Retosiban (44.50%). Terbutaline (26.20%) and hydroxyprogesterone caproate (19.30%) had lower range values. This generally strengthens our point of selection of small molecules and should be replicated in future novel drug compounds for finding best therapeutic role in relation for the management of PTB.Bioavailability represents the administered drug fraction which reaches the systematic (bloodstream) circulation as a sub-category of drug absorption variable/factor . Hydrophin-silico drug binding energetic estimates. When a drug enters the systematic (bloodstream) circulation, it is distributed to the different tissues of the body. The rate of drug entry into the tissues mainly depends on the rate of blood flow to the respective tissues and the various partition characteristics between blood and tissue. Drugs reach the CNS  through brain capillaries and CSF , but the restriction of drug penetration to this vicinity is mainly regulated by the brain’s permeability characteristics [The investigational outcome regarding volume distribution of all five screened PTB-related drugs showed an evenly distributed pattern, and the drug Ritrodine is highly lipophilic in nature, among the rest. Except for Terbutaline, the plasma protein binding interaction of all four other drugs was satisfactory as it indicates the high binding affinity with CNN1 isoform 2, also cleared from the eristics , 69. AllWhen any drug or pharmacological compound is administered to the body by mouth, the first pass metabolism occurs in the liver tissues. This phenomenon typically occurs when a drug gets metabolized at a particular location in the body that ultimately results in the reduced active concentration of the drug upon reaching its target site of action or the systemic circulation. This pre-systemic metabolism may greatly reduce the bioavailability of the drug and serve as the basic justification for most of the drug’s withdrawal from the market, in addition to the reported drug-induced liver injury. Similarly, cytochrome P450 (CYP-450) serves as a super-family of proteins containing heme as a cofactor, which constitutes its significant role in metabolism and detoxification of xenobiotic , 69. DurThe half-life time and clearance rate of any drug play a vital role in the drug development process as it involves the removal of drug/substance from the body either as bio-transformed metabolites or in unchanged form (intact drug form). This whole process is supported by a variety of factors which impact drug excretion, such as intrinsic drug properties  and may induce kidney tissue injuries , 69. Morin-vitro challenge by using variable levels of perturbing variants of PTB. Furthermore, this study will lay the groundwork for future research into the gene-associated quest to better understand the role of CNN1 protein, particularly its CH domain, specifically probing its relevance with PTB via functional genome-wide sequencing and detailed molecular analysis. Finally, the role of identified consensus interacting residues of this in-silico experimentation will be further tested with novel derivatives, which will pave the way for discovering new therapeutic compounds to manage the challenge of PTB.In future, a follow up study may look for rationalization and optimization of PTB protein/biomarkers (obtained from the present effort) through in-silico genes-based strategies not only developed a better understanding to improve access to cost effective obstetric and neonatal care but also reduce the neonatal and childhood mortality. This research work comprising the in-silico transcript-based analyses of nsSNPs of CNN1, COL24A1, IQGAP2 and SLIT2 genes highlighted that 7 nsSNPs with 36 different transcripts have shown the most pathogenic among all known and reported nsSNPs. These nsSNPs possess marked energetic differences in terms of their impact on the stability of protein. The structural docking-based experimentation of CNN1 gene and its molecular interaction  analysis could serve as an intervention target for the prevention of PTB. Therefore, on a country level, if more efforts are made in terms of exploring novel genetic variations to identify non-synonymous pathogenicity from the same set of genes with further development and incorporation of in-silico tools will pave the way for managing PTB.PTB is an important perinatal health issue and addressing this global health problem through S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Effective organization of proteins into functional modules  requires systems-level coordination between transcription, translation and degradation. Whereas the cooperation between transcription and translation was extensively studied, the cooperative degradation regulation of protein complexes and pathways has not been systematically assessed. Here we comprehensively analyzed degron masking, a major mechanism by which cellular systems coordinate degron recognition and protein degradation. For over 200 substrates with characterized degrons , we demonstrate that degrons extensively overlap with protein-protein interaction sites. Analysis of binding site information and protein abundance comparisons show that regulatory partners effectively outcompete E3 ligases, masking degrons from the ubiquitination machinery. Protein abundance variations between normal and cancer cells highlight the dynamics of degron masking components. Finally, integrative analysis of gene co-expression, half-life correlations and functional relationships between interacting proteins point towards higher-order, co-regulated degradation modules (‘degronons’) in the proteome. Systematic bioinformatics analysis of cooperative degradation of protein complexes indicates that degrons extensively overlap with protein-protein interaction sites, hiding degrons from ubiquitination machinery and suggesting the existence of co-degrading functional modules in the proteome. Synergy between transcription, translation and degradation is important for the operation of proteome modules , as demonstrated by transcriptional networks organized into modules of co-expressed genes6. Previous proteome-wide studies showed that functionally related proteins have correlated turnover and similar half-lives9, possibly due to mutual stabilization of the interaction partners and/or the masking of their proteolytic determinants11. Clearly, more work is required to characterize the systems-level coordination of protein degradation.Precise control over the abundance of proteins is critical for cellular homoeostasis and the regulation of cellular pathways. In vivo protein abundance, often thought to be primarily regulated by gene transcription and mRNA translation12. Target protein (substrate) selection is based on the specific recognition of degradation elements (degrons) by E3 ubiquitin ligases13. Substrate degrons display a tripartite hierarchy14 consisting of a primary degron (a sequence motif that recruits an E3 ligase), secondary degron , and tertiary degron   sites that modulate binding preferences17. We reasoned that if binding sites for alternative partners overlap with degrons, the resulting competing interactions would mask and interfere with degron function, preventing their recognition by UPS components  regulates biological processes via targeted degradationme) Fig. . Cooperants Fig. . Thereby18. In fact, MATα2 possesses multiple degrons that all overlap with corepressor binding sites19. E2F1, an important cell cycle regulatory TF, is stabilized by degron masking upon binding to the retinoblastoma tumor suppressor protein20. Among C/EBP TFs, NF-IL6 (C/EBPβ) is stabilized by homodimerization, and C/EBPγ and C/EBPζ are stabilized upon heterodimerization with NF-IL621. Other important examples include β-catenin and p53, both of which are stabilized against proteasomal degradation by multiple PPIs, with important functional consequences  stabilize interacting proteins and coordinate their turnover. Degron masking was observed among transcription factors (TFs), for example, the yeast transcriptional repressors, MATa1 and MATα214, onto which we mapped experimentally derived partner binding sites to explore potential degron masking . Physically interacting proteins had correlated half-lives, mRNA co-expression and showed functional similarity. We propose that degron shielding by PPIs results in mutually correlated turnover (degradation) profiles between functionally connected, physically interacting groups of proteins , highlighting the importance of this basic regulatory mechanism in cellular proteostasis.Although these examples hint at widespread tuning of degron accessibility by PPIs, degron masking has not been systematically assessed. Here we used our previously assembled datasets of experimentally validated degronsing Fig. . This st14, onto which we projected experimentally derived PPI binding site information . Our dataset contained 157, 42 and 34 proteins with annotated primary, secondary and tertiary degrons, respectively  targeted by the E3 Mdm2/Hdm2. This degron is masked in the interface between p53 TAD and the nuclear receptor coactivator binding (NCBD) domain of CREB-binding protein, CBP26  they exhibit any tendency towards functions similar to that of the substrates whose degradation they potentially regulate. Taking the proteins analyzed in Fig. 24, we queried UniProtKB33 for curated annotations such as binding sites, functional sites, PTMs and other relevant biochemical information indicating physical degron masking and functional interference  may mask multiple degrons simultaneously, especially when they are located proximally, as illustrated by β-catenin and HIF1α , we queried the ELM database35 for motifs that overlap with degrons (see Methods). We demonstrate here how multiple, clustered motifs act as input sites for signal integration and enable complex regulatory decisions on substrate degradation. Depending on their functional type and context, overlapping motifs influence degron function by providing binding sites for negative regulators (the interaction initiates/enables substrate degradation) or positive regulators (whose binding prevents degradation) of half-life. Although experimentally verified ELMs remain limited36 which precludes a statistical analysis, nevertheless, we found numerous degron overlapping ELMs , PLK-1 and GSK3β can negatively regulate half-life, phosphorylating and activating substrate phosphodegrons, which are then recognized by SCF E3s37. Conversely, kinases also function as positive regulators in cases where phosphorylation, either of the degron or its neighborhood, prevents degron recognition by forming phospho-inhibited modules38. An example is Cdc6, an essential licensing factor involved in DNA replication origin firing. Adjacent to its KEN degron are a cyclin docking motif and a Cdk2 phosphorylation site  docking motif that negatively regulates Mdm2 degradation42. Lys446 is the major site for Mdm2 auto-ubiquitination43 and the tertiary degron, containing the Usp7 docking motif, is located just upstream (AA 370–428)  Fig. . Therefo34. Incorporation into complexes would stabilize the substrate by masking its degrons and thereby determine its functional lifetime  could alter the localization of substrates vis-à-vis cognate E3s  typically form part of the regulatory module. Finally, the overlap of a cleavage motif with an internal degron can also result in several scenarios 17. We also collected available binding (Kd) data of substrate-E3 and substrate-AP pairs for primary degron containing substrates . Overall, E3s were significantly lower in abundance compared to substrates (P = 1.25E–12), and, more strikingly, E3s were ~10-fold less abundant than APs (P = 1.13E–62)  binding, the measured ranges of Kd values for AP interactions are either very similar to or smaller than the Kd’s measured for E3 interactions (except for p53) activation and/or changes in cellular localization.We obtained protein concentrations from PaxDb62) Fig. . PaxDb d62) Fig. , 9. On arS = 0.4; P = 1.8E-21)    variability since the analysis included all PaxDb datasets, substrate–E3 pairs exhibited a moderate, but statistically significant correlation (Spearman 21) Fig. . Balancepes Fig. . In cont14) Fig. , indicatPaxDb provides abundance datasets for normal human tissues as well as cell lines; among the latter, the majority were cancer cell lines  and bottom 1% (least abundant), respectively, of that dataset (see Methods). p53 abundances were very low in normal (unstressed) tissues and no data were available for almost all tissue datasets   are also higher in cell lines  Fig. . In contces Fig. , could ines Fig. . ATM phones Fig.  and stabTherefore, the approach we propose underlines the importance of studying the relative expression of alternate complexes within each substrate’s degradation regulatory network  can be identified. Yeast is an extensively studied model organism with multiple high-coverage, high-quality datasets  available. We selected two PPI networks (see Methods): (1) a high-confidence network of S. cerevisiae soluble proteins (“Collins” network55), and, (2) the multi-validated BioGRID yeast network56.The modular organization of the proteome requires the co-regulation of functional units  at all levels: transcription, translation and protein degradation. Such coordination clearly exists for protein productionEach network was decomposed into binary PPI pairs  and their relevant properties were analyzed Fig. . FirstlyT1/2 value): resulting in pairs with similar (0.8–1.0), different (0.5–0.8) and very different (0.0–0.5) half-lives. The number of pairs in the ‘similar’ category were much higher than in the other categories . Moving along these paths, the strong T1/2 ratio signal, seen for direct neighbors, dropped significantly as from the 3rd neighbor onwards, to levels observed for random PPI pairs . Remarkably, correlated stabilities extended as far as 6th (or farther) neighbors, satisfying the criterion for degronons, coordinated degradation sub-networks within the interactome. For degronons of length ≥6, we identified protein members and their participating complexes  interaction might physically stabilize proteins, all the data on degron masking presented in this paper collectively argue that stabilization mediated by PPIs is necessarily specific and regulated. Fig. Protein turnover receives dynamic input from multiple pathways, manifesting in degron masking by alternate PPIs that integrate cell state and signaling information. In vivo dynamics of complexes in which degrons get shielded determines the functional longevity of proteins. Complex assembly affects degron exposure and activity either by direct masking within the interface Fig.  or by in15, both local dynamics and long-range structural rearrangements upon binding could contribute to masking. Locally, folding-upon-binding of IDRs influences degron accessibility: for example, free IκBα has a C-terminal IDR responsible for its Ub-independent, 20 S proteasomal degradation. However, in complex with NF-κB, the IDR gets folded and masked in the interface, stabilizing IκBα57. Local conformational preferences are also influenced by PTMs58, which can impact degron accessibility. IDRs host multiple PTM sites, allowing interplay between modifying enzymes and regulatory partners  is clearly important for Ub-dependent degradation, conferring specificity to substrate recruitment13 and making primary degron masking an important regulatory step. However, Ubsites and degradation initiation segments are equally important for the complete degradation cycle. For example, Ubsite mutations block substrate degradation leading to disease14. Masking substrate Ubsite(s) will therefore make them inaccessible (or less accessible) for E2-catalyzed Ub-transfer and can stabilize substrates, independent of primary degron recognition. Furthermore, the dynamics of Ubsite-masking complexes could result in differences in the rates of synthesis of polyUb chains on substrates. That could hypothetically have interesting implications, such as influencing the ordering of substrate degradation in cell cycle regulation by the anaphase-promoting complex/cyclosome62 or cell-type specific regulation of protein stability. Similarly, if the tertiary degron  is bound by a partner 63. In summary, the masking of each of the tripartite degron elements (both by intra and inter-molecular PPIs) may independently regulate degradation, with combinatorial masking of multiple substrate degrons also possible  based on degron-overlapping binding sites Fig.  and analns Figs. , 5, wher11 and individual studies have demonstrated that sequestration within complexes stabilizes subunits, and in certain cases, subunit stabilities were mutually interdependent . Furthermore, in many co-regulated complexes, the loss of one subunit reduced the protein (but not mRNA) levels of the other subunit(s)64. Here, we observed that yeast PPI pairs having highly similar half-lives were: (1) functionally similar lar Fig. . Thus, ilar Fig. , the sub14 were used  for partner proteins. Only experimentally identified PPI sites/motifs that overlapped with known degron(s) were considered. We considered strictly overlapping as well as adjacent binding sites/motifs; the latter defined as binding sites whose boundary was within 10 amino acids  of a known degron. IntAct data (in psimitab format) was parsed to keep only interactions between protein partners. We generated a distribution of their Interaction Confidence scores  levels47. PaxDb provides abundance data expressed in parts per million (ppm), describing relative abundances of each protein with reference to all the other protein molecules in the sample . Therefore, within datasets, ppm values reflect proportional abundances and are useful for comparison-based statistics. Plots of dataset consistency, dataset coverage, size and other relevant properties are shown in Supplementary Fig. https://pax-db.org/help). Nevertheless, for cross-dataset comparisons, for example when comparing degradation regulatory module component abundances across cell and tissue types pes Fig. , we devipes Fig. , proteinpes Fig.  or rank pes Fig. .Saccharomyces cerevisiae were taken from two sources:55, built by merging interaction datasets (obtained using affinity purification/mass-spectrometry approaches) from two high-throughput studies by Krogan et al.66 and Gavin et al.67 The Collins et al. study used a Purification Enrichment (PE) statistical scoring system to identify high confidence interactions55, resulting in 9070 interactions among 1622 distinct proteins. We also used the Collins dataset in a recent publication68 and concluded that the network was of high quality.The high confidence PPI network of soluble yeast proteins derived by Collins et al.S. cerevisiae PPI network (version March 2018) from the BioGRID database56. These included literature-annotated PPIs detected using both high and low-throughput methods and included co-complex and binary interactions. Interaction reliability criteria were based on the number of different experimental techniques used to detect each physical interaction and the number of publications reporting the interaction. This dataset contained 14,285 interactions among 3737 proteins, and similar to the Collins network was curated for our recent publication68.The multi-validated (MV), Genome-wide protein-protein interaction (PPI) datasets of 68. First, binary interactions  in the input network were shuffled randomly using the sample_degseq function of the igraph R package (http://www.igraph.org/r), avoiding repeated interactions and self-loops. Next, the node identities  were randomly shuffled using the sample function, performed without replacement, using the R base package (http://www.r-project.org). Finally, the resulting networks were edited to eliminate any (few) remaining interactions that were also present in the original (starting) PPI network.Ten different, random PPI networks were generated based on each of the Collins and BioGRID PPI networks, preserving the total number of protein nodes and the node degree distribution of each input network. The same protocol was also used in our recent publicationS. cerevisiae were used from: (1) Martin-Perez and Villen69 and, (2) Christiano et al.70 From the Martin-Perez dataset, we obtained relative protein half-life values  averaged over two replicates for the prototrophic strain. From the Christiano dataset, protein half-lives (provided in minutes) were obtained.Two recent half-life datasets from 71 (July 2016). Only GO terms based on the evidence codes ‘IDA’ (Inferred from Direct Assay) and ‘IPI’  from the Biological Process (BP) ontology were considered. We used the semantic similarity measure developed by Wang et al.72, available as an implementation in the R-package, GOSemSim73. The GO BP SemSim score provides a numeric estimate of the similarity of two sets of GO terms  based on their location in the GO hierarchical graph and relationships to ancestor terms.Functional similarity between two interacting proteins was evaluated using the GO semantic similarity measure. Functional annotations were obtained from the Gene OntologyS. cerevisiae gene pairs, obtained from COXPRESdb74, were used to represent gene co-expression levels (1.0 indicates highly correlated co-expression). COXPRESdb computes mRNA expression profiles from expression (microarray) data measured under different conditions.Pearson’s correlation coefficients calculated between mRNA expression profiles of https://networkx.github.io) as a graph object. Then the all_pairs_shortest_path algorithm was run on the graph to define shortest paths between all pairs of nodes (proteins). The output paths provide information on the starting and final members as well as intermediate path members (proteins).The Collins network was input into the NetworkX  Python package  between groups were performed using the Mann-Whitney U-test (two-sided), unless otherwise specified.All statistical tests for calculating statistical significance (Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary


A super-secondary structure (SSS) is a spatially unique ensemble of secondary structural elements that determine the three-dimensional shape of a protein and its function, rendering SSSs attractive as folding cores. Understanding known types of SSSs is important for developing a deeper understanding of the mechanisms of protein folding. Here, we propose a universal PSSNet machine-learning method for SSS recognition and segmentation. For various types of SSS segmentation, this method uses key characteristics of SSS geometry, including the lengths of secondary structural elements and the distances between them, torsion angles, spatial positions of Cα atoms, and primary sequences. Using four types of SSSs , we showed that extensive SSS sets could be reliably selected from the Protein Data Bank and AlphaFold 2.0 database of protein structures. Protein folding mechanisms have fascinated scientists for a half of a century ,2,3. AccThe use of SSSs to solve biomedical problems is rather desirable, as the alpha-helical and beta-hairpin types of SSSs can serve as initial unique structures for the construction of protein epitope mimetics (PEMs) ,11. ThesIn previous studies, we reported the possibility of studying SSSs in aberrant protein forms caused by post-translational modifications (PTMs). We observed that PTMs that have been detected in patients with various types of cancer are frequently localized in the SSS  . So far,Predicting the secondary and supersecondary structures of proteins by their 3D-structures  is becoming a top priority in structural biology research. Numerous approaches for prediction are currently known but the most commonly used are based on (1) probabilistic models, such as kernel density estimation (KDE) ,14 and nModels based on the sequence-to-sequence architecture, where the protein structure is considered as a sequence of amino acids with the main characteristics of their localization, i.e., contact map. Featured sequences are processed using a group of recurrent layers .Models based on 3D-CloudSegmentation, in which each atom of a molecule is represented as a point in a 3D space. PointNet, PointNet++, and dynamic graph CNN (DGCNN) architectures  are usedModels based on the representation of a protein molecule as a 3D volumetric object (protein voxelization) with subsequent processing by 3D-Convolution family networks .Models based on the representation of a protein molecule as a graph with subsequent processing by graph neural networks (CGNs) .Neural networks (NN) have recently been applied to the problem of structures classification and segmentation. Neural networks are typically designed to classify and/or predict one or two types of SSSs, though several NN-utilizing methods are now capable of predicting β-hairpin and βαβ-units (StackSSSPred) ,22,23. Thttps://psskb.org/ (accessed on 28 October 2022). In addition to the secondary structure prediction, PSSNet can also be applied for the prediction of free energy, solvent availability, contact maps, and searching for stable protein structures.Here, we present a new approach to classify different types of SSSs, specifically βαβ-unit, α-hairpin, β-hairpin, and αα-corner, and the approach was tested on standard format files extracted from the public Protein Data Bank (PDB) . The neuhttps://psskb.org/, accessed on 28 October 2022). After training the model, we applied it to complete dataset maintained in the PDB and AlphaFold. The results were selectively assessed by expert researchers, entered into a database, and finalized as publicly available. The number of SSSs defined by the model are listed in This model was the basis for the filling of gaps in the open knowledge base of SSSs Capture of excess structure sections;(b)Breakages in structure element links;(c)Incorrect definition of corners ).The main issues that arose from the classification and segmentation of structural motifs were as follows :(a)CaptuDespite the results of the model validation being quite satisfying (IoU = 0.92), the estimated accuracy ranged between 0.83–0.85. Thus, we established several problems specific to the topology of a specific type of SSS.Capturing extra sections is a prerequisite for SSS with α-helix elements, and it usually manifests if the distance between the last and first Cα atoms of the first and the second structures, respectively, is 9.7 Å. However, the network at the output from the GRU layer generates a feature map that captures both structures e. This iBreakages in structures generally occurred in low-resolution (>4.0 Å) PDB files, but the proportion was insignificant and relatively narrow compared to the total size of the consolidated databank.The network identified curved helices with a large angle of inflection as two elements with incorrectly defined angles for αα-corner structures. Rigorous analysis revealed that the issue can be effectively resolved only if we performed retraining of the model on a meaningfully larger representative sample that covers all such elements; the retraining and sample collecting are currently in progress.Despite the difference in accuracy between the actual and validation datasets, the model managed SSS segmentation and classification tasks well. The difference in accuracy suggests relatively high folding variability among the structures downloaded from PDB and AlphaFold. Hence, the training dataset must be sufficiently extended, especially in terms of negative examples, to improve the accuracy of segmentation and classification.The composition of super-secondary structures is simple combinations of α-helices, β-hairpins, and short loops with a well-defined hydrophobic core involved in SSS stabilization. The loop–helix, β-hairpin, and Greek key motifs are prominent representatives of SSSs ,31,32. CMedical proteomic research is mainly focused on the extensive study of the molecular basis of a disease associated with the arrival of aberrant forms of proteins that are regularly not found under normal  conditions. Aberrant proteins are frequently caused by genetic polymorphisms, alternative splicing, and PTMs ,34. SuchHere, we present a new approach to frame the problem of SSS recognition and segmentation based on the geometric characteristics of structures and spatial relationships within a protein sequence. The main advantage of our method is a low requirement for computing sources. We used a standard personal desktop computer with a typical GeForce GTX 1650—4Gb video card for training and data processing in the PDB and AlphaFold2.0 databases. We also operated the PSSNet model with high recognition accuracy  and annotation capability of >1.9 million SSSs of βαβ-unit hairpin, β-hairpin, and αα-corner. This opens up wide margins for investigation on the PSSKB resource. The model does not require a large training set, since sets of 2000 specimens were used to train the model. Plots of GPU memory usage as a function of protein sequence length can be found in A distinctive feature of our model is its ability to recognize and segment the SSSs within a protein sequence of arbitrary length, i.e., regardless of the sequence length. The model can operate directly with any file in PDB format, including those with low data quality, poor resolution, and sparse protein sequences. The only limitation of the model is the amount of graphics processing unit (GPU) memory. Likewise, most current models focus on recognition of only one certain type of structure and work only with a few prepared datasets, resulting in relatively low numbers of recognized structures within the range of thousands to tens of thousands.The architecture of the proposed method is powered by a comprehensive combination of CGNs, CNNs, Bi-GRUs, self-attention, and multi-head attention mechanisms, which encourage network flexibility and easy adaptation to solve problems in structural biology and bioinformatics. Primary examinations have shown that a model with minimal modifications can predict the structural alphabet based on geometric characteristics for differentiable molecular modeling problems. A subsequent investigation will target this and other issues.Training, test, and validation datasets downloaded from the Protein Data Bank were represented by the following types of SSSs: a βαβ-motif , a β-hairpin, an α-hairpin, and an αα-corner (70°–90°).https://doi.org/10.6084/m9.figshare.21529812.v1  ) predicted by the model coincide according to the following equation:The model training process lasted 24 epochs for each SSS and models were assessed on the validation datasets. During the learning process, the learning rate was changed from 1 × 10We considered the position of SSS in the reference and predicted structures and evaluated the coincidence of their positions. The harmonic mean of the recall and precision metrics (F1) were also evaluated .small datasets for rapid, efficient learning, and retaining;ability to generalize features on a relatively small training set;good recognition accuracy (mean IoU > 0.83);huge amounts of information (such as that in the PDB and AlphaFold) can be assessed within a reasonable timeframe.Super-secondary structures are blocks of protein molecules with unique and compact spatial arrangements. Such structures are stable outside the protein globule due to pronounced hydrophobic cores. Structural biology considers SSSs as the nuclei of protein folding and as starting structures when looking for the possible folding pattern of polypeptide chains while modeling protein structures. Our model combines GNN, CNN, and RNN methods and suggests the following advantages:https://psskb.org/, accessed on 28 October 2022); however, the model is generic and can be applied to a wider set of SSS types. The assembled set of SSS structures opens up new options for studying the uniqueness and compactness of protein spatial packing and folding nuclei, and can also act as starting structures for searching for possible polypeptide chain folding while modeling protein structures.Our model can classify more than 2.3 million SSSs for all protein structures available in the PDB and AlphaFold databases. The reliability and accuracy of the model were demonstrated on four types of SSSs taken from the public Structural Elements Database  in the segmentation model and replenishing the database. We will also focus on improving annotations and ensuring the quality of SSS presentations. Furthermore, we will generate sufficient information for users with extensive experience in structural biology and new entrants into that. We will also tailor the database to meet the needs of the research community and provide accurate SSS information for future updates.


However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes . We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook  The accuracy of AlphaFold decreases with the number of protein chains and the available GPU memory limits the size of protein complexes that can be predicted. Here, the authors show that complexes with 10–30 chains can be assembled from predicted subcomponents using Monte Carlo tree search. By incorporating protein-interaction information from many co-purification experiments, the human protein complex map, hu.MAP 2.04, provides a set of 4779 complexes with more than two chains. However, only 83 of these complexes are present in PDB. There are only 372 structurally resolved human protein complexes with over two chains, and of the 3130 eukaryotic core complexes in CORUM5 only 800 have homologous structures covering all chains in PDB, suggesting a gap in our structural knowledge of protein complexes.Large protein complexes govern many cellular processes, performing complicated tasks such as mRNA splicingThere are only 265 hetero and homomeric, non-redundant complexes in the PDB with 10–30 chains. Although it is unknown how many large complexes may exist, following the relationship between the known human complexes from hu.MAP and the structural coverage of these, one can extrapolate that there may indeed be a low structural coverage across different species.6 for modelling the structure of protein complexes, template-based modelling7, shape complementarity docking8 and integrative modelling10. Template-based modelling and docking methods have recently been shown to be outperformed by a combined fold and docking methodology using AlphaFold11 for dimeric complexes, even if the bound form of each monomer is known12. Further, few docking programmes handle more than two protein chains, i.e., these methods are not suitable for building large complexes with no close homology to known complexes. There is currently (to our knowledge) no available docking benchmark for complexes with more than two chains, and previous studies only report results on a few examples14.There are at least three approaches15. This type of guided assembly is typically based on a Markov process9 or Gaussian mixture models16, where many different potential configurations are explored and scored. This process makes it possible to assemble complexes with up to 1000 protein chains17. However, obtaining electron density maps can be very difficult, as some protein complexes are hard to express, purify and crystallise. Still, many recent assemblies of large protein complexes exist, such as the human nuclear pore complex18 and 26 S proteasome19.Assembling large protein complexes with integrative modelling generally requires electron density maps or other experimental information to guide the assembly process20. This method has been trained on proteins of up to nine chains or 1536 residues and can predict complexes of up to a few thousand residues, where memory limitations come into play. However, the performance declines rapidly for proteins with over two chains  or the FoldDock protocol12 using AlphaFold11 (AF). First, we examine the use of predicted subcomponents of the native dimeric or trimeric subcomponents. We then continue without assuming knowledge of the interactions, presenting the final protocol based on all possible trimeric subcomponents.Here, we begin with an outline of the protein complex assembly using Monte Carlo tree search (MCTS). We then explore the success rates using either AlphaFold-multimer version 2To analyse the possibility of assembling large protein complexes, we extracted all high-resolution non-redundant complexes from the PDB with more than nine chains not containing nucleic acids or interactions from different organisms . We start by analysing the possibility of assembling these protein complexes assuming that exact pairs of interactions between protein chains are known. Using either AFM or FoldDock, we predict the structure of all unique pairs of interacting protein chains as subcomponents and create assembly paths from these.As an example, the assembly of 6ESQ (acetoacetyl-CoA thiolase/HMG-CoA synthase complex) is shown in Fig. 23 or predictions4. Next, potential assembly paths are created by starting at a randomly selected chain and adding all possible connections through superposition. Often overlaps occur among the predictions due to imperfect subcomponents, resulting in that, e.g. atoms from chains B and C occupying the same spatial position in a given complex ABCD. Therefore, an assembly path is discontinued when over half of the alpha carbons from two different chains are within 5 Å of each other. An assembly path is complete when all chains in a complex can be linked together. For 6ESQ, the assembly results in a model with a TM-score of 0.96.During the first part of this paper, we assume that the interaction graph is known, i.e., we limit the assembly paths only to include interactions existing in the complex. Although this is a simplification, the number of assembly paths is still huge, and it could at least be theoretically possible to obtain this information from other types of experiments24  chains are added randomly to expand the path, thereby creating new nodes. From these expansions, complete assemblies are simulated. Simulations are stopped when no additional subunits can be added, see Methods. The simulated assemblies are scored by their cumulative mpDockQ  score, and the scores are backpropagated to yield support for the previous selections. The path with the most support is selected, creating a complex that is the most likely to be correct. Due to the statistical nature of the search procedure, no aspect of a specific complex is being “learned” in the backpropagation, i.e., all 175 complexes can be used for evaluation.Due to the high number of possible paths to explore, searching all paths is unfeasible. Therefore, we search for an optimal path using MCTSOnly fifteen out of 175 complexes could be assembled to completion using native pairwise interactions with both AFM and FoldDock  modelling pipeline often caused clashes  for AFM, while for the FoldDock pipeline, the corresponding fraction is 23.2% (520/2242). All dimeric interactions were extracted from the trimers and assembly paths were constructed as previously. Out of 175 complexes, 58 (33%) and 55 (31%) could be assembled to completion with median TM-scores of 0.80 and 0.74 for FoldDock and AFM, respectively  problem of predicting the complexes without knowing interactions (full approach). In addition to the problem of possibly incorrectly identified interacting pairs, this also increases the number of possible erroneous paths dramatically. Anyhow, we find that 91/175 (52%) of structures can be assembled with a median TM-score of 0.51 , the median scores are 0.76 and 0.80 for the full and native trimer approaches, respectively. For AFM, the corresponding scores are 0.77 and 0.79 (45 complexes). Using native interactions thereby results in slightly higher scores overall, but to a lower fraction of complete assemblies. Models generated by FoldDock outperforms AFM overall, mainly because of fewer complete assemblies from AFM , we analyse the ROC curve , the number of effective sequences (Neff), the subcomponent accuracy for each complex, the type of symmetry and the interface accuracy between predicted and assembled interfaces Fig. . We perf12  with a median TM-score of 0.85 Fig. . Eukaryo12 Fig. . When an12 Fig. . There a12 Fig.  provides with a mn = 58). The asymmetric complex displayed in Fig. https://www.rcsb.org/structure/1l0l) has a TM-score of 0.75, however, and most chains are in their correct positions with the exception of one membrane helix and a small cytosolic chain causing the decrease in TM-score.The symmetry of the complexes is also found to be significant Fig. . The domThe only Octahedral complete assembly has a TM-score of 0.99 and the Tetrahedral median is 0.92. The two complexes with helical symmetry display varying quality . All types of symmetry display successful examples Fig. , suggest29. Only the complexes that could be assembled to completion and that had interfaces resulting from the assembly were analysed (n = 55). The predicted interfaces are found to be of higher quality on average .As the assembly proceeds, new interfaces are generated. To provide an estimate of the quality of the assembled interfaces we compare these to those predicted directly with FoldDock using the DockQ scoreSo far, we have only addressed the possibility to assemble complexes with 10–30 chains, outside of current computational limitations. However, it is possible that the assembly approach with MCTS can be used for smaller complexes as well. To analyse the possibility to improve the accuracy of AFM end-to-end (E2E) on smaller complexes we created a set of 278 complexes with 4–9 chains that have less than 30% sequence identity to the proteins in the AFM training set (see Methods). We predicted all possible trimeric subcomponents using FoldDock and assembled them using MCTS. We find that the performance of AFM E2E is higher than the MCTS assembly across all oligomers of 4–9 chains Fig. . The perTo predict the structure of large complexes directly from sequence information is currently a difficult challenge. Here, we present a method that suggests that one possible approach is to predict subcomponents and assemble them into a larger complex. Using subcomponents predicted from native dimers, native trimers and all possible trimeric interactions, the median TM-scores are 0.77, 0.80 and 0.50, respectively . The scoring function mpDockQ can distinguish if assemblies are complete and predict their accuracy, making the blind approach with all trimers feasible. FoldDock based on AF outperforms AFM in predicting trimeric subcomponents in combination with being much faster , it can also be noted that AF was not trained for assembly of proteins either, yielding support to the robustness of this method.We find that when the subcomponents are accurately predicted using native trimers, so are the complete assemblies. This suggests it is possible to assemble complexes as long as their subcomponents are accurate. The symmetry of the complex affects the outcome and some symmetries  are more abundant and easier to predict than others . Not all trimers can be folded using two NVIDIA A100 Tensor Core GPUs with 40 Gb of RAM each. The limit of AF (and AFM) on this computational platform appears to be roughly 3000 residues, and 73/175 (42%) of all complexes are larger than that. We find that the assembly approach suggested here is outperformed by the current state-of-the-art AFM for complexes with 4–9 chains whenever it is possible to run AFM.In summary, we have shown that assembling large complexes with different symmetries is possible using only protein sequence information and stoichiometry. Modelling large complexes in parts and assembling them converts the problem of predicting large complexes to the prediction of their subcomponents. This suggests an exciting future where models of all protein complexes in entire cells may be modelled.One limitation of predicting protein complexes using the approach proposed here is stoichiometry. It is often not known how many copies of a protein are in a given complex, a requirement for the assembly. Once this limitation is overcome either by computational or experimental studies of complexes, it will be possible to assemble many different protein complexes, possibly in novel configurations.20 in its training and testing data, and there is no available method validated for modelling larger complexes, we obtained all complexes with 10–30 chains from the PDB on 2022-01-10 to extend the current limit . E.g. if the sequences from complex 1 map to clusters A, B, and C and those of complex 2 map to clusters A, B, C, and D, then complex 2 will be kept and complex 1 excluded. After the clustering, we ensure that no complex contains any chain shorter than 50 residues (193 complexes), to remove protein-peptide interactions. We then download the first biological assemblyTo create interaction networks for the guided assembly of the complexes, interactions between different chains with CBs (CA for Glycine) within 8 Å from each other were extracted. Interactions are defined when 10% of the beta carbons  of the shortest of two protein chains are within 8 Å from the other. On average, each interaction pair consists of 70 residue pairs, and within each complex, there are 22 interacting pairs of chains Fig. .n chains.To assemble entire complexes, we predict all dimeric and trimeric interactions in a set of The number of possible dimers follows:The number of possible trimers follows:n) dimers is D(n) and in T(n) trimers:From these dimers and trimers, we extract all edges (pairwise interactions). The number of edges in D and uniclust30_2018_0837 jointly with HHBlits38 (from hh-suite v.3.0-beta.3 version 14/07/2017). By using the species and genetic positional information, the results from the Uniprot search are paired. All results from the other searches are instead block-diagonalized. All of the created MSAs  are used to predict the structure of a protein complex.AlphaFold-multimer (v2.0)12, based on AlphaFold (v2.0)11, was run as well. This protocol creates two MSAs constructed from a single search with HHblits38 version 3.1.0 against uniclust30_2018_0837 using the options:The FoldDock protocolhhblits -E 0.001 -all -oa3m -n 2The first of the two MSAs is constructed by extracting the organism identifiers (OX) from the resulting a3m file and pairing sequences using the top hit from each OX. The second is constructed by block diagonalizing the resulting a3m file. An extension to three chains was made here also, following the same pairing and block diagonalizing procedure as has been done for two chains. The folding was performed using AlphaFold model_1, 10 recycles and one ensemble structure. The recycles refer to how many times the intermediate output is fed back into the network and the MSAs are resampled. The ensemble structure entails how many times the information within the network is processed before it is averaged.The structural prediction was performed on two NVIDIA A100 Tensor Core GPUs each with 40 Gb of RAM with a time limit of 24 h per prediction. Three sets of different subcomponents for the complexes with 10–30 chains were modelled, all native dimeric, all native trimeric and all possible trimeric subcomponents. For the complexes with 4–9 chains, all possible trimeric subcomponents were modelled to compare with predicting these directly using AFM E2E.For the complexes with 10–30 chains, the unique dimer subcomponents of 656/656 could be predicted for AFM and FoldDock, respectively. For FoldDock, 2242/2246 unique native trimers were predicted, and 2234/2246 for AFM. The four that did not work using FoldDock had the error message “Cannot create a tensor proto whose content is larger than 2GB.” and the 13 that did not work for AFM ran out of time on two NVIDIA A100 Tensor Core GPUs each with 40 Gb of RAM with a time limit of 24 h per prediction. For the approach using all trimers, 8049/8049 unique subcomponents were successfully modelled using FoldDock. The all-trimer approach using AFM resulted in 7999/8049 unique subcomponents due to the 24 hour time limitation.For the 278 selected complexes of 4–9 chains (see section “Non-redundant complexes from the PDB with 4–9 chains without homology to the AFM training set”), there were 2400 subcomponents for the all-trimer approach of which 2375 could be predicted with FoldDock. Memory limitations resulted in that 25 could not be predicted. In total, 62 complexes could not be assembled to completion using MCTS. For AFM E2E, 67 complexes could not be predicted due to memory limitations .n = 1733, supplementary Fig. For the analysis of the entire dataset  and from these 28 possibilities for each node and so on.n’ nodes and E(n) edges, the number of unique paths that contain all nodes follow:If there are no overlapping interfaces in a complex of Note that n’ here is the number of nodes extracted from the predicted subcomponents, which are more than the number of unique nodes since e.g. the trimers ABC and ABD both contain the nodes A and B. Equation 28 ≈ 2.3⋅1041 possible paths at the upper bound considering all dimers from 30 protein chains . This is a very large number that is not possible to search in a feasible amount of time with our available computational resources. However, it is very unlikely this number of paths has to be explored due to overlaps in the subassemblies.According to Eq. When the subpaths that contain overlaps are excluded during assembly, the number of possible paths reduces quickly. Let’s assume there are only three possible interactions for each chain. Then the number of possible paths becomes much fewer, depending on how the network is connected. If all branches in a network contain unique chains Fig. , there i24. MCTS applies a heuristic search method through a graph to find an optimal path :Selection: start at a randomly chosen node 1 .1, .., eN, deemed “children” to node 1 and create N different paths. Expand the new nodes added through the edges by randomly selecting new edges. If the new nodes do not have any edges, they are deemed “leaf nodes”. In this case, the best scoring node according to Eq. Expansion: obtain all edges eSimulation: add chains randomly to the path until the overlap criterium is obtained or the complex is complete. An overlap is defined as when over 50% of the alpha carbons in the shortest of two protein chains are within 5 Å from each other.Backpropagation: score the simulated complex using Eq. From the interactions in the predicted subcomponents, we add chains sequentially following a path through the interaction network (graph) constructed using MCTSath Fig. . MCTS coVi is the average complex score  accordingly:The complex 6LNI could not be assembled using trimers due to no interactions between the chains being present in the predictions. This protein is an amyloid protein and should thereby not occur naturally in the cell. For the assembly approach using all possible trimers, there are very many paths to assemble for some complexes. Six additional complexes  could not be assembled due to time constraints (72 h on a 2.6 GHz processor).Input:          - Predicted edges and their structuresOutput:          - Largest possible assembled complex1 Select an edge connected to node (chain) A and make this the current node nwhile(edge_available(n)):          #Expand          while(not is_leaf(n))                     n = best_child(n)          end          #Simulate and score using UCB          score = roll_out(n)          #Back propagate the score          back_propagate(score)end#Return the path taken to reach the current node nreturn get_path(n)12. This score for multiple interfaces, we deem “multiple-interface predicted DockQ” or mpDockQ. The interface contacts are taken as beta carbons  from two different protein chains being within 8 Å of each other. These metrics are calculated for the entire interface of each chain, as in the DockQ29 score for multiple interfaces. E.g., if chain A interacts with both chains B and C, the score is taken over both of these interfaces simultaneously. This is done for all interfaces and chains and summed over the entire complex. The complexes with the highest sums are favoured. Favouring complexes with higher scores, results in complexes with both larger interfaces and with more reliably predicted residues.We score the interfaces of the complexes being assembled in the MCTS using:40 with the following sigmoidal equation:x = average interface plDDT⋅log10(number of interface contacts) , we fit a simple sigmoidal function towards the TM-score Fig.  using thTo analyse if the atoms from different chains in the same prediction overlap, we calculate the distance between all atoms in all chains in a given prediction. We count clashes as two-atom positions from different chains being within 1 Å of each other (the size of one hydrogen atom).29 programme is too slow to be run on large complexes if all interfaces are to be compared (minutes-hours for a single complex). Therefore, the programme MMalign41 is used to score entire complexes, as compared to the scoring of dimeric complexes with FoldDock previously12. MMalign performs optimal structural alignment between the model and native structures, computing a score (TM-score) normalised to be between zero and one, where one indicates a perfect match.The DockQSince MMalign performs optimal structural superposition, it is also possible to evaluate models of different sizes. This is important since the predictions are based on full-length protein sequences (and to score incomplete assemblies), while the PDB structures generally do not contain all residues from these, meaning that loops and other disordered regions are not present in the PDB structures. This also means that for most proteins, the score can never be 1, depending on how similar the SEQRES sequence is to the sequence present in the PDB structure. Since we assess the real sequences here, our approach represents a more realistic modelling scenario.42. The clustering was performed using MMseqs2 version fcf52600801a73e95fd74068e1bb1afb437d719d30rs was used to indicate the Neff. MMseqs2 was run with the following command:The Neff is a measure of the information present in a multiple-sequence alignment. To calculate the Neff, we clustered sequences from each MSA independently (the paired versions) at 62% sequence identity, following the rationale behind the BLOSUM62 matrixMMseqs2 easy-cluster msa outname /tmp --min-seq-id 0.62 -c 0.8 --cov-mode 1The clustering was done for all predicted subcomponents in each complex. To obtain a Neff score for each complex, we averaged the scores for all subcomponents.We create receiver operating characteristic (ROC) curves using the metrics average interface plDDT (predicted lDDT from AF), the number of interface residues, contacts, and interactions between chains normalised with the number of chains in each complex and the mpDockQ . The positive examples are taken either as complete assemblies  or above the median TM-score (only for mpDockQ). The metrics are used to distinguish between true and false positives  by creating thresholds of all possible metric values. From the thresholding we calculate the true- and false positive rates:Using the thresholds and corresponding TPR and FPR, the TPR is plotted against the FPR. This creates a ROC curve. For each metric, the area under the ROC curve (AUC) is computed as:20) and the resulting scarcity of complexes. The dataset was obtained by querying PDB on 2022/06/02 for structures having between 2 and 9 chains with release dates after 2018/04/30. For completeness, we included structures with 2–3 chains as well, although no assembly was performed for these as a minimum of 4 chains is required to create trimeric subcomponents.The procedure for selecting complexes between 4–9 chains is different from that of selecting 10–30 chains due to homology reduction towards the AFM training set 30 within each oligomeric state with a sequence identity threshold of 30%. If all the chains from a protein structure in this dataset were mapped to a single protein in the training dataset, it was removed. This generated a dataset with 931 dimers, 164 trimers, 269 tetramers, 103 pentamers, 91 hexamers, 74 heptamers, 73 octamers, and 28 nonamers. We then sampled up to 50 complexes from each set randomly to reduce the computational cost , resulting in 278 complexes. All MSAs are generated on 8 Intel Xeon E5-2690v4 cores for a maximum 18 h . See above for the folding GPU hardware limitations.The proteins with less than 50 amino acids and structures containing DNA or RNA were excluded. To perform homology reduction between this dataset and the AlphaFold-MultimerTo analyse the number of available human PDB files (reported in the introduction), we downloaded all human entries from the PDB on the 14th of October 2021 and counted the number of chains occurring in each entry. In total, there are 2649 human PDB files, 1557 with one chain, 720 with two, and 372 entries with over two chains.4 were selected. hu.MAP is the result of a machine learning framework that identifies protein complexes using data from over 15,000 mass spectrometry experiments. In total, there are 6956 complexes and 30,572 protein chains, from 9962 unique genes. There are 4779 complexes with at least three chains, of which only 83 have all chains together in the same PDB entry.To analyse the gap in complex structural knowledge for human proteins (introduction), all complexes with at least three chains from hu.MAP 2.043). The folding takes 1–2 h on NVIDIA A100 Tensor Core GPUs, largely depending on the size of the complexes and MSAs. The complete prediction per subcomponent is thereby in the range of 1–2 h for FoldDock and 3–4 h for AFM. The average number of subcomponents is 13 and 49 using all native and possible trimers for the 175 complexes with 10–30 chains (8561/175 and 2246/175), respectively. This results in a total prediction time of 13–26 or 50–100 and 39–52 or 150–200 h for the guided or all trimeric constituents of each complex with FoldDock and AFM, respectively. The assembly time is identical regardless of how the subcomponents are predicted, on average 0.34 and 3.73 h per complex using the native trimeric and all trimeric subcomponents, respectively, predicted with AFM on a 2.35 GHz CPU (16 cores of AMD Epyc 7742 CPUs), scaling exponentially as with the number of possible paths. The assembly time is therefore negligible compared to the time needed to predict the structure of the subcomponents and only relies on CPU.The computational time required to predict a complex is mainly limited by the number of subcomponents. For AFM, more MSAs are generated, resulting in ~20× longer runtimes compared to running only HHblits against Uniclust30  and Haddock .In order to obtain a performance comparison for the presented method, we tested similar existing multi-chain assembly algorithms over the same dataset. We selected Multi-LZerDConcerning Haddock, we found that the usage of this tool is oriented toward adopting all available knowledge in the form of restraints. While this procedure may be beneficial to drive docking and reduce computational time, it is impractical to adopt for a large number of structures composing our dataset. To circumvent this limitation, Haddock also allows the possibility to include random surface restraints or centre of mass restraints to force contacts between molecules. As suggested by the authors, we attempted these strategies while increasing the sampling in it0 and it1 stages to 10,000 and 400 models, respectively. For Haddock, 77 complexes were completed.The Multi-LZerD pipeline required on average 5 h per chain on its preparation stage running standard LZerD, plus several additional hours for the Multi-LZerD stage. This running time was enough to exceed our 72 h maximum allocation time limit, while trying to dock a 10-chain complex, one of the smallest in our dataset. Test runs have been executed over an entire cluster node with 2 Intel Xeon Gold 6130 CPU having 16 cores each and 96GB RAM.Supplementary InformationPeer Review File


Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design. Protein-protein interactions play a central role in biological activities, including signal transduction, cell metabolism, and immune system . Determid) or Gibbs free energy (ΔG), which can be derived from the Kd, is commonly used to quantify protein-protein interactions (d or inhibition constant (Ki) measurements of protein complexes are sometimes performed simultaneously with X-ray crystallography experiments, some datasets contain protein-protein binding affinity data together with the 3D structures  and antigens . Using t barcode . Models  barcode . Althougcode  will be the prediction target (Machine learning models are designed based on the type of data they will be used on. For datasets that are extracted from the Protein Data Bank (PDB) , both thn target . Recentln target . For then target . In thisn target .d, Ki, or IC50 values recorded in the dataset are the prediction targets.The PDBbind dataset is a collection of complex structures extracted from PDB with binding affinities determined experimentally . The fird and ΔG are reported in the dataset along with the pH, temperature, and experimental methods used. As the protein-protein interaction can induce a conformation change, both the structures of complexes and unbound components are available in this dataset. Compared with other datasets, the structure-based benchmark for protein-protein binding affinity is a small dataset, but the included additional information of pH and temperature are very valuable for improving the prediction accuracy. In addition, the structure data of unbound components can be used to construct models that predict the binding affinity of two proteins from different structure resources. This is the most common case in practice.The structure-based benchmark for protein-protein binding affinity is an assembled dataset of 144 protein-protein complexes . The datSKEMPI  is a database describing the changes of binding affinities and other kinetics characteristics upon mutations . SKEMPI The ATLAS (Altered TCR Ligand Affinities and Structures) database is a collection of the binding affinity of a TCR to a pMHC. The datvia single-cell RNA-sequencing  on the dextramer. Although the data obtained using this method are noisy, this method represents a high-throughput method for identifying TCRs with high binding strength to a target pMHC.Unlike the previously described datasets, the 10x single cell immune profile dataset is generated quencing . T cellsMachine learning is a research field that focuses on the data to find patterns, build models for prediction or explanation, and understand the relationship underlying the data . Machinep) of the experimental ΔΔG (binding affinity change caused by mutations) and predicted ΔΔG was 0.73. Rodrigues et al. proposed a method to predict binding affinity based on graph-based signatures, which described the distance patterns between atoms on the binding interface  with a gradient-boosting tree (GBT) . The GNNp of 0.85 and 0.79 on SKEMPI and SKEMPI 2.0 datasets, respectively.Wang et al. developed a topology-based network to capture the geometric and topological pattern of the complex efficiently . The feap of 0.853 on SKEMPI dataset.Another topology-based model named PerSpect-EL was proposed by Wee and Xia who combined persistent homology with ensemble learning to improve the prediction accuracy . CNN modp = 0.77 on SKEMPI dataset. As the ProtDCal is a general-purpose program for generating 3D-structure descriptors, it is said that some machine learning models can extract binding information from the universal descriptors to predict the binding affinity.PPI-Affinity is a web tool that predicts the binding affinity using support vector machine and other classic machine learning models . Acceptip of the measured and predicted ΔG was 0.44 on the structure-based benchmark for protein-protein binding affinity. Chen et al. developed an end-to-end model to predict the binding affinity from the amino acid sequence based on a recurrent convolutional neural network (RCNN) (p = 0.873). Another model based on pre-trained embedding and residual RCNN was proposed by Xue et al. (p = 0.264) was reported using a homology and structure similarity-base data splitting method on a subset of SKEMPI 2.0. Fischer et al. considered the UMI counts in 10x single cell immune profiling dataset as the binding strength of TCR-pMHC complex (R2 of the prediction was 0.63 on the 10x dataset.Sequence-based methods take the amino acid sequence as the input and directly predict the binding affinity. The featurization methods include substitution matrix representation (SMR), position-specific scoring matrix (PSSM), and other embedding methods developed for natural language processing . Yugandhk (RCNN) . Comparee et al. . This me complex . A modelIn this study, we introduced several datasets and models for binding affinity prediction. In this section, we will discuss issues regarding the practical application in protein design. As machine learning methods offer high-throughput prediction with high accuracy, they are desirable tools for screening newly designed proteins for specific binding to a target. Recent reviews on this topic cover prediction models of protein-protein interaction that are mainly classification models ; howeverThe most pressing problem is that there is no widely accepted evaluation method for protein-protein binding affinity models. As shown in Since the task of binding affinity prediction for protein design is different from general protein-protein binding affinity prediction, in which the prediction targets are in the same protein family, such as antibody, TCR, or enzyme, the evaluation method for prediction models used in protein design should be specified accordingly. As an example, in antibody or TCR design, mutations are introduced to a specific protein to enhance the binding affinity to a particular range . TherefoWhile this model evaluation is relatively simple, models can perform poorly in the high binding affinity region. This is because the binding affinities of most antibodies or TCRs are in the low affinity range, and training data for the model may not be sufficient to learn the pattern of high-affinity proteins. This type of problem also exists in other fields, such as materials design , where oIn addition to simulations and experiments, recently developed highly accurate structure prediction models are also important tools in the study of protein-protein interactions . As the In order to improve the prediction accuracy, various techniques have been used in the models. Rodrigues et al. included the experimental conditions and other auxiliary information in the model . As tempAlthough machine learning methods have been extensively used in various fields of science to address pertinent issues, it has some limitations as well. One of the common problems is the lack of data, which is also present in the protein-protein binding affinity prediction. However, the rapid development of related research fields raises expectations for the emergence of large datasets that can enable highly accurate prediction models. Experimental methods such as cryo-electron microscopy increase the accumulation of structure data, prediction models for protein structures are used to generate virtual protein structures, and simulation methods can predict the binding affinities based on protein structures. Therefore, a high-performance model trained on a large dataset is expected to appear in the near future and make itself an indispensable tool for protein design, which can be further applied to various fields associated with healthcare, material sciences, and energy.


The recent successes of AlphaFold and RoseTTAFold have demonstrated the value of AI methods in highly accurate protein structure prediction. Despite these advances, the role of these methods in the context of small-molecule drug discovery still needs to be thoroughly explored. In this study, we evaluated whether the AI-based models can reliably reproduce the three-dimensional structures of protein–ligand complexes. The structure we chose was NLRP3, a challenging protein target in terms of obtaining a three-dimensional model both experimentally and computationally. The conformation of the binding pockets generated by the AI models was carefully characterized and compared with experimental structures. Further molecular docking results indicated that AI-predicted protein structures combined with molecular dynamics simulations offers a promising approach in small-molecule drug discovery. High-resolution X-ray crystallographic or NMR structure predictions of proteins and protein–ligand complexes are critical for understanding the interactions between small-molecule drug candidates and their targets, and therefore, are pivotal in the drug discovery process . HoweverAI methods for protein structure predictions, including AlphaFold (AF)  and RoseIn the current study, we explored whether AI-predicted protein structures can be employed to determine the binding modes of known NLRP3 inhibitors. The target, NLRP3  , is a prModels generated by locally installed AlphaFold and RoseTTAFold were used as initial structures for subsequent docking and MD simulations; those models can be downloaded from the The docking poses were obtained using MOE . All MD The whole system was first minimized using a steepest descent algorithm, with the maximum number of cycles set as 10,000. Subsequently, the system was heated from 297 K to 300 K over a period of 50 ps. This was followed by 100 ps NVT and 200 ps NPT equilibrations, with all heavy atoms restrained during this process.The production phase was simulated in an NPT ensemble with the Berendsen barostat  and LangThe human NLRP3 protein contains subunits PYD , FISNA , LRR  and a central NACHT domain comprising NBD , HD1 , WHD  and HD2  . For a lGiven the difficulties in obtaining the structure of the multi-subdomain NLRP3 protein using experimental and traditional theoretical methods, we chose this protein to test the accuracy of the AF and RF models when predicting protein conformations, and whether correct protein–small-molecule binding modes could be generated based on these structures. Five models were generated by two locally installed programs each. The pLDDT  scores  were calFor multi-domain or -subdomain protein targets such as NLRP3, the predicted Templated Modeling (pTM) score  can be uNext, we computed the RMSD values between the predicted models and one of the monomers in the experimental NLRP3 decamer (PDB code: 7PZC). As shown in MCC950 has long been identified as a selective NLRP3 inhibitor that blocks NLRP3 inflammasome activation ,20 Figud, yet thThe results showed that R578 and its adjacent residue F575 in the experimental structure of the NLRP3-NEK7 complex (PDB code: 6NPY) were located in the solvent-exposed region, relatively distant from the ADP-binding site a. The diIn addition to the hydrogen bonds formed between the amide group of MCC950 and the protein, the tricyclic head group of the small molecule also participated in hydrophobic interactions with neighboring residues involving I411, Y443 and F575. In addition, the terminal carboxylate group engaged H-bonds with G629 a. We, thTo further assess the use of AI models in small-molecule drug discovery, we performed molecular docking of MCC950 for each model of AF and RF. However, even the top-ranked models produced only partially correct binding modes. So, molecular dynamics (MD) simulations were carried out to refine the poses by considering the induced fit and protein dynamic effects. To keep the Walker A moiety from shifting, ADP was docked back into its binding site, as in the AI models, then simulated by MD for 200 ns. It turned out the RF model 1 structure could hardly accommodate ADP at the corresponding site. So, we only performed a 200 ns MD simulation of AF model 4 bound to ADP a. AnotheCompared to the experimental structure, ligand MCC950 engaged similar hydrogen-bonding interactions with R578, A228 and G629 . The disDespite the use of protein structure templates during both training and prediction procedures, AlphaFold and RoseTTAFold are intrinsically more advanced than the conventional template-based methods such as homology modeling and threading. The neural network architecture enables AI methods to learn deeply from the coevolution of protein sequences, and detect the underlying correlations between pairs of amino acids through the construction of multiple sequence alignment (MSA) and pair representation, instead of merely patching the templates together. Therefore, the AI methods can accurately predict protein structures using templates with a sequence identity and structural coverage below 30%. This study demonstrated that the structures predicted by the AI methods did not heavily rely on the existing experimental structure, meaning they could be useful complements for experimental approaches including X-ray crystallography, NMR and cryo-EM.AlphaFold has been shown to predict protein backbone as well as side chain coordinates with high accuracy. However, accurately predicting the protein structure was particularly challenging in the context of this work, as the NLRP3 protein consists of multiple subdomains, and the small-molecule binding pocket is located in the interdomain region. It was shown that both AlphaFold and RoseTTAFold can predict confidently at the subdomain level. Nevertheless, the subdomains were more compact in RoseTTAFold models in the present study, whereas AlphaFold successfully reproduced a key salt bridge interaction observed in the experimental structure. Our study highlights why AI protein structure prediction tools should be used with caution for drug targets comprising two or more structural domains, especially when the drug binding site is located at the interfaces of subunits. To help overcome difficulties, MD simulations can be applied to finetune the details of models in complex systems. In the future, we will evaluate the performance of AlphaFold in small-molecule drug discovery when applied to further drug target systems.