S100A4 protein overexpression has been reported in different types of cancer and plays a key role by interacting with the tumor suppressor protein Tp53. Single nucleotide polymorphisms (SNP) in S100A4 could directly influence the biomolecular interaction with the tumor suppressor protein Tp53 due to their aberrant conformations. Hence, the study was designed to predict the deleterious SNP and its effect on the S100A4 protein structure and function. Twenty-one SNP data sets were screened for nonsynonymous mutations and subsequently subjected to deleterious mutation prediction using different computational tools. The screened deleterious mutations were analyzed for their changes in functionality and their interaction with the tumor suppressor protein Tp53 by protein-protein docking analysis. The structural effects were studied using the 3DMissense mutation tool to estimate the solvation energy and torsion angle of the screened mutations on the predicted structures. In our study, 21 deleterious nonsynonymous mutations were screened, including F72V, E74G, L5P, D25E, N65S, A28V, A8D, S20L, L58P, and K26N were found to be remarkably conserved by exhibiting the interaction either with the EF-hand 1 or EF-hand 2 domain. The solvation and torsion values significantly deviated for the mutant-type structures with S20L, N65S, and F72L mutations and showed a marked reduction in their binding affinity with the Tp53 protein. Hence, these deleterious mutations might serve as prospective targets for diagnosing and developing personalized treatments for cancer and other related diseases. S100A4 is a calcium-binding protein belonging to the S100 family of proteins and contributes to the metastasis of different cancer. The increased expression of the S100A4 protein is associated with poor prognosis in patients with various cancer types and is a predictive marker for colorectal and breast cancer .S100A4 exists in intracellular and extracellular forms and possesses no enzymatic activity. However, it has been shown to interact with numerous tumor-related proteins promoting tumor progression through an increase in motility, invasion, apoptosis inhibition, and cancer metastasis through the induction of prometastatic activities such as angiogenesis stimulation –9. The sThe two EF-hand calcium-binding domains (helix-loop-helix motif) are parts of the S100 monomer protein, in which the N-terminal EF-hand comprises 14 amino acids. This part attaches the calcium through weak carbonyl oxygen atoms present in the backbone. At the same time, the C-terminal end is composed of 12 amino acids and binds calcium through the side-chain and carboxylates oxygen with higher affinity [A striking conformational change occurs after calcium binding to the protein, resulting in the disclosure of a hydrophobic binding pocket in each monomer. The interaction of the calcium with the monomeric molecule paves the way for the binding of the other intracellular or extracellular proteins , 12. Upoβ-1 [S100A4 acts as a metastasin, playing a role in tumor progression by interacting with proteins that include p53 tumor suppressor proteins, annexin, nonmuscle myosin, and liprin β-1 –15. The β-1 . Deleterβ-1 .Among the SNPs, 50% of the mutations are consequences of nsSNPs and are reported in autoimmune, genetic, and inflammatory diseases. The changes in the amino acids due to SNPs could alter the protein structures as reflected by the changes in protein dynamics, geometry, charge, hydrophobicity, and finally, the interaction of the protein with other proteins or factors.in silico. The detection of these deleterious SNPs could help propose the development of personalized treatments.Hence, in the present study, the deleterious nonsynonymous SNPs of the S100A4 gene were identified, and their structural and functional effects were analyzed https://www.ncbi.nlm.nih.gov/snp/?term=S100A4), and their respective protein sequences were retrieved from Uniprot  ( Uniprot . The infhttps://sift.jcvi.org/www/SIFT_seq_submit2.html), PANTHER  (https://www.pantherdb.org/tools), PolyPhen-2 (Polymorphism Phenotyping v2) (https://genetics.bwh.harvard.edu/pph2/), PROVEAN (https://provean.jcvi.org/index.php), and Predict SNP (https://loschmidt.chemi.muni.cz/predictsnp) were used to predict the deleterious nsSNPs [The bioinformatics tools SIFT (s nsSNPs –21.https://www.ebi.ac.uk/interpro/) was used to identify nsSNPs locations on protein S100A4 conserved domains. The motif region, domain prediction, and functional characteristics of the proteins were identified by this tool [The software InterPro (his tool .https://folding.biofold.org/i-mutant/i-mutant2.0.html) tool, and the data of nsSNPs protein S100A4 was submitted in FASTA format [The impact of the mutations on the structure and stability of the protein was investigated by the I-Mutant 2-056 (A format .https://consurf.tau.ac.il) tool was employed to identify the evolutionary conservation of amino acids. The analysis was based on the phylogenetic relationships between homologous sequences [The ConSurf  was utilized to predict the SNP's effect on the protein structure. The S100A4 protein with the UniProt Acc IDQ8WWW0 and 24 individual nsSNPs were used as input [https://spdbv.vital-it.ch/) was utilized for the mutated protein model generation with corresponding amino acid substitutions. The 3D Missense mutation tool was used to estimate the solvation energy and torsion angle of the mutations on the predicted structures. It was compared with the wild-type sequence for the deviations [HOPE61 . The chromosome location chr1(-):153516094-153518282 and the SNP rs IDs were submitted to the analysis server.The miRNA seed and target site in UTR regions were detected using the Poly miRTS database web server, and the transcript NM_019554 was used as a query sequence  was used to study the dynamic simulation of the mutant proteins. The simulation was carried out with the default parameters of 50 cycles for 10 ns and a 1.0 fixed global weight for the modeled protein complexes.The CABS-flex 2.0 web server (https://cancer.sanger.ac.uk/cosmic). The gene S100A4 was confirmed by searching for the missense mutation.The identified SNPs of the S100A4 gene were also verified in the COSMIC database to comprehend their effect on different malignancies. The COSMIC database provides comprehensive information on the somatic mutations of human cancer and their distribution (http://www.string-db.org) was used to construct an interactome map of the S100A4 genes screened with the key genes involved in the EMT Pathways . The PPI network was constructed with the key genes involved in this pathway, such as TGF-β, Smad 2, Smad-3, ZEB1, Foxc-2, TWIST, Snail, slug, E-Cadherin, N-Cadherin, β-Cadherin, PTEN, P13K, AKT2, GSK 3β, STAT-3, Cyclin D1, C-myc, Survin, MUC-1, PRR–X1, ZNF488, VGLL4, and Gli 1, were curated and subjected to the interaction analysis. Cytoscape (version 3.6.1) was used to visualize the PPI network, and the pivotal nodes were recognized based on the connectivity degrees.STRING v11  and free energy change values (DDG-Delta Delta G) were predicted by the I-Mutant tool. This helped us to analyze the stability changes represented in The ConSurf Analysis was carried out for 101 amino acid residues of S100A4 identified as SNPs. The highly conserved and exposed residues with the functional characteristics were identified as M1, E6, Y19, S20, G24, L29, E33, E41, E63, N65, D67, E74, N81, and K101. The residues L5, F16, L29, L62, and F72 were highly conserved structural residues buried within the protein structure.Among the mutations, F72V, E74G, L5P, D25E, N65S, A28V, A8D, S20L, L58P, and K26N were highly conserved and exhibited an interaction with the calcium-binding domain, EF-hand 1 or EF-hand 2 domains. The deleterious mutations F72V and E74G were located within the EF-hand 2 protein and were demonstrated to disrupt the calcium ion interaction. In the deleterious mutation, D25E, the amino acid residue of the wild type is smaller than the mutant residue. Distinctively, in the N65S deleterious mutation, the change in the amino acid to serine has made the occupied site significantly smaller and hydrophobic. This mutation highly affects the structure and causes destabilization as it is situated in EF-hand 2. Additionally, it also leads to the loss of the cysteine bond. The mutations K26N and S20L were analyzed as examples presented in Similarly, the mutation in A28V causes disturbance in the core structure of the domain as the mutant residues are buried. In A8D, the mutant residues are bigger and neutral, disturbing the domain core structure and binding properties. The impact of the other deleterious mutations is presented in 3D models were predicted for the 24 deleterious nsSNPs and compared with the wild-type model, which showed the solvation energy of −0.42 and the torsion angle of −1.10. The mutation showed a higher deviation in both the solvation energy and the torsion from the wild type.The phosphorylation sites were predicted at the regions of 15T, 50T, 20S, 60S, 64S, and 80S sites, and only the 60S highly deleterious nsSNP was found. The ubiquitylation sites were located at 100K and 101K predicted by UBpred. SUMOylation sites were not observed in any of the predicted highly deleterious nsSNPs.The PolymiRTS database detected four sites for miRNA binding due to 5 SNPs in the UTR region, and the sites were predicted to be abolished by these SNPs. The results are presented in All the mutant models of S100A4 interacted with a very low binding affinity with the Tp53 protein, as observed in Clus Pro, which ranged from −564.4 to 670. Also, the deviation in the hydrogen bond interaction with the target was observed. The energy minimization through the Swiss PDF viewer revealed a larger scale of variation for all the mutants, but in specific E88G, the wild-type showed −6525 kJ/mol and the mutant exhibited −7625.542 kJ/mol. Also the differences in the energy minimization was observed for the other mutants S20L ; A8V (−6264.361 kJ/mol); D25E (−6578.558 kJ/mol); E74G ; L5P (6319.368 kJ/mol).Root mean square fluctuation (RMSF) was determined by molecular dynamic simulations to understand the atomic level deviation of the mutant proteins in physiological conditions. The RMSF values of the mutant models E88G and D25E were lower than the wild-type model. In the mutant model, E88G, a decrease in the RMSF value was observed (1.0650) compared to the wild-type (1.540). In the mutant model, D25E, the RMSF (3.5130) was very close to the mutant model with a RMSF value of 3.650. The alteration in the RMSF conferred a loss of versatility in the protein's mutant structure, leading to changes in the dynamic behavior. More flexibility was observed between the residues 30 and 70 which significantly affected the stability of the protein .The search in the cosmic database resulted in the identification of 4 hits with the following Ensemble IdsS100A4_ENST00000368714; S100A4_ENST00000354332; S100A4,ENST00000368716.8; S100A4_ENST00000368715 and reported 388 mutations. Further analysis of the missense substitution of the Ensemble IdsS100A4_ENST00000368714 resulted in the report of E88K in the COSMIC database among the screened 21 nonsynonymous SNPs. This E88K mutation has been identified as a somatic mutation reported in large intestine carcinoma and adenocarcinoma. The tissue distribution of sample 1 has been reported for this mutation, with the FATHMM prediction score of 0.82 being pathogenic .p value of less than <1.0e − 16 with an average node degree of 8.12 was detected. S100A4 gene interacted with the TWIST1, SNAI2, CDH2, CDH1, ZEB1, and SMAD2 directly and also with the other interactors such as PTEN, C-MYC, MUCI, CCND1, AKT2, and TGIF-2. Some PPI network connections with the S100A protein, signifying known and predicted genes are indicated in 16 nodes and 65 edges in the PPI network had a local clustering coefficient of 0.734. An enrichment S100A4 interacts with the tumor suppressor protein Tp53 in the various cancer pathways and with other transcription factors involved in the EMT pathways. Thus, the mutations in S100A4 could influence the biomolecular interactions with their target proteins due to their aberrant conformations and possibly affect their downstream functions –31. Therin silico SNP prediction tool. However, among the twenty-one mutations, rs147390231 (T39I), rs199505533 (G92A), and rs368160023 (E88K) were observed to be tolerated in the SIFT algorithm. Polyphen and PANTHER identified 21 nsSNPs as damaging mutations and, hence, these were reconfirmed and analyzed by different tools .Protein structural stability is important to maintain the native structure and function of the proteins. The structural and functional parameters were estimated in this study based on the ΔG value. The energy minimization through the Swiss PDF viewer revealed a larger scale of variation for all the mutants. Still, specifically for E88G, the wild-type showed −6525 kJ/mol and the mutant exhibited −7625.542 kJ/mol. Also the differences in the energy minimization was observed for the other mutants S20L ; A8V (−6264.361 kJ/mol); D25E (−6578 .558 kJ/mol); E74G ; L5P (6319.368 kJ/mol). All the predicted SNPs showed a negative value, indicating the decrease in the stability of the structure and hence its becoming unfavorable due to its unfolded/misfolded state .Furthermore, the mutant structures were subjected to molecular simulations, and the RMSF was estimated to evaluate their flexibility under varying physiological conditions. Limited fluctuations and flexibility represent the stable structural state . The RMSAbout 14 different miRNAs were identified in this study that have altered SNPs. These miRNA were reported to be modulated in different cancers, namely, hsa-miR-505-5p , hsa-miR-1827; hsa-miR-650 , hsa-mir-3612; hsa-miR-940, hsa–miR-4695-3p; hsa-miR-4763-3p; hsa-miR-3128 (ERBB2/Her2 gene) –40. Howein vitro and in vivo studies may lead to possible therapeutic interventions.The search for these deleterious nsSNPs identified that the E88K mutation was associated with the carcinoma of the large intestine and adenocarcinoma tissue samples. This signifies that studying these mutations in clinical samples and analyzing their possible effects on the interaction of S100A4 with the other proteins sought through β-catenin, epidermal growth factor, tumor necrosis factor alpha (TNF-α), and methylation [S100A4 has been demonstrated in the development of an aggressive metastatic phenotype progressing into cancer and metastasis. Also, the poor prognosis of cancer has been correlated with the upregulation of S100A4 in tumor cells, and its expression has been regulated by other factors like hylation , 42. In hylation –45. ExceComprehensive bioinformatics analyses for the identification of the deleterious nonsynonymous mutations were performed for the S100A4 gene, which are reported to play a significant role in cancer and other pathophysiological diseases. In this study, twenty-four deleterious mutations were identified by Provean, SIFT, Polyphen, and PANTHER. The SNPs E88G, S20L, A8D, A8V, D25E, E74G, F72V, and L5P were highly conserved and interacted with the EF-hand domain of the protein, showing significantly higher energy minimization and structural instability, ultimately affecting the functionality of the protein. The E88K mutation identified by our analysis has been reported in the COSMIC database. We conclude that the plethora of mutations identified in this study can be explored in tissue samples of the various cancer types and physiological conditions to facilitate rational drug designing through miRNAs for personalized cancer treatment.


It is developing among infants in more than 80% of cases who exhibit impairments in the anterior chamber angle and the trabecular meshwork. Thus, a comprehensive CYP1B1 missense variants was retrieved from the dbSNP database. Seven different tools, namely: SIFT, PolyPhen-2, PROVEAN, SNAP2, PANTHER, PhD-SNP, and Predict-SNP, were used for functional annotation, and two packages, which were I-Mutant 2.0 and MUpro, were used to predict the effect of the variants on protein stability. A phylogenetic conservation analysis using deleterious variants was performed by the ConSurf server. The 3D structures of the wild-type and mutants were generated using the I-TASSER tool, and a 50 ns molecular dynamic simulation (MDS) was executed using the GROMACS webserver to determine the stability of mutants compared to the native protein. Co-expression, protein-protein interaction (PPI), gene ontology (GO), and pathway analyses were additionally performed for the CYP1B1 in-depth study.All the information for All the retrieved data from the dbSNP database was subjected to functional, structural, and phylogenetic analysis. From the conducted analyses, a total of 19 high-risk variants  were screened out that were considered to be deleterious to the CYP1B1 gene. The phylogenetic analysis revealed that the majority of the variants occurred in highly conserved regions. The MD simulation analysis exhibited that all mutants’ average root mean square deviation (RMSD) values were higher compared to the wild-type protein, which could potentially cause CYP1B1 protein dysfunction, leading to the severity of the disease. Moreover, it has been discovered that CYP1A1, VCAN, HSD17B1, HSD17B2, and AKR1C3 are highly co-expressed and interact with CYP1B1. Besides, the CYP1B1 protein is primarily involved in the metabolism of xenobiotics, chemical carcinogenesis, the retinal metabolic process, and steroid hormone biosynthesis pathways, demonstrating its multifaceted and important roles.This is the first comprehensive study that adds essential information to the ongoing efforts to understand the crucial role of genetic signatures in the development of PCG and will be useful for more targeted gene-disease association studies. Glaucoma is a heterogeneous group of complex diseases that gradually lead to permanent loss of vision. It is estimated that 79.6 million people worldwide were affected in 2020, and the number is predicted to rise to 111.8 million by 2040 . The modCYP1B1  and, LTBP2 (coding for latent transforming growth factor β-binding protein 2) were recently discovered. CYP1B1 was found on GLC3A loci, while LTBP2 was found on GLC3D loci , but pseudodominant transmission has also been shown in some families . ThroughC3D loci . Moleculenotypes .CYP1B1 belongs to the CYP450 superfamily and genomic DNA length of 8.5 kb. This gene is expressed in the eye, kidney, breast, prostate, and brain tissues  are the most common genetic determinants, occurring in one out of every 3000 base pairs in the human genome and serving as potential biomarkers for a variety of life-threatening diseases . Among aogenesis . The ideCYP1B1 gene of Homo sapiens was retrieved from the dbSNP database of the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/projects/SNP/). The encoded protein sequence with reference ID: NP_000095.2 was also downloaded from the same database.The list of missense variations in the in silico method.” These methods use databases, network analysis, and machine learning approaches that are faster, more critical, and more cost-effective than in vitro and in vivo studies  (https://sift.bii.a-star.edu.sg/). It predicts the functional importance of the altered amino acid substitution using evolutionary conservation via sequence homology alignment and the physiochemical characteristics of a protein. The SIFT algorithm predicts substitutions with a probability score ≥ 0.05 as tolerated substitutions, while those with a probability score <0.05 are considered deleterious (http://genetics.bwh.harvard.edu/pph2/) was utilized, acknowledging its structural and functional impact prediction of a single amino acid alteration in a protein. This tool evaluates an amino acid substitution using comparative homology protein models and generates a position specific independent count (PSIC) score. It annotates a substitution as probably damaging, possibly damaging, or benign if the probability score is >0.85, >0.15, and <0.15, respectively (http://provean.jcvi.org/index.php) server was accessed in this functional annotation assay. This server predicts the functional impact of an amino acid alteration in a protein and categorizes them as neutral or deleterious, if the generated score is greater than −2.5 or less than −2.5, respectively (http://www.pantherdb.org/) server. This tool calculates the score of an amino acid variant based on evolutionary preservation (time in millions of years) using the HMM algorithm. It annotates an amino acid alteration as benign: time <200 my, possibly damaging: 450 my to 200 my, or probably damaging: 450 my, respectively (https://rostlab.org/services/snap2web/) tool was employed to determine the pathogenicity of an amino acid alteration in a protein. It is an ensemble of neural network models that predicts both the clinical and molecular effects of a missense variant in the form of strongly effective (score: +100) or strongly neutral (score: −100)  tool that predicts the pathogenicity of an amino acid substitution in a protein based on evolutionary information and a hybrid predictive model. This tool classifies the missense variants as neutral or pathogenic and provides a reliability index (RI) ranging from 0-10, with 10 being the highest reliability (https://loschmidt.chemi.muni.cz/predictsnp/) tool was also approached to predict the disease related variations of the CYP1B1 gene. It uses consensus-based methods to generate its output (https://gnomad.broadinstitute.org/). The gnomAD is the most widely used database that harbors the largest collection of sequencing data for population variations  . Moreoveiability . Additios output . The allriations .https://folding.biofold.org/i-mutant/i-mutant2.0.html) predicts protein stability using an SVM-based algorithm and calculates the amino acid change in the form of Gibbs free energy (DDG) at a given pH and temperature. It categorizes the protein stability as increased or decreased and provides a reliability index (RI) ranging from 0 to 10, with 10 being the highest reliability (http://mupro.proteomics.ics.uci.edu/) was approached to validate the I-Mutant 2.0 outcomes. This server ensembles both SVM and neural network algorithms to predict the effect of amino acid alteration in a protein and classifies the protein stability as increased or decreased if the confidence scores are <0 or >0, respectively . This server works on the empirical Bayesian method and multiple sequence alignment approach to estimate the phylogenetic conservation score, which ranges from 1–9. Residues of conservation scores of 7–9 were considered highly conserved, 4–6 as intermediate, and 1–3 were represented as variable and less conserved. In addition, this tool also predicted the characteristics of residues based on their positions in the protein’s structure: either buried (b), exposed (e), highly conserved and exposed , or highly conserved and buried (s), which further embellished the structural and functional significance of amino acid residues in a protein . To generate the complete structure of the CYP1B1 protein, the I-TASSER (Iterative Threading Assembly Refinement) (https://zhanggroup.org/I-TASSER/) most advanced biological server was approached (https://saves.mbi.ucla.edu/). Subsequently, the resultant structures were further subjected to the TM-Align server  which calculates the RMSD (Root Mean Square Deviation) to analyze the deviation of mutant structures from the wild-type  structure of CYP1B1 protein was downloaded from the Protein Data Bank (PDB) (proached . This isild-type . Graphicild-type .https://simlab.uams.edu (The molecular dynamic simulation (MDS) for the native and mutant CYP1B1 protein structures was carried out using GROMACS simulation software accessed from uams.edu . For theCYP1B1 gene was evaluated through the GeneMANIA server (https://genemania.org/). It is a publically available bioinformatics tool that harbors a large database for gene functional analysis, including co-expression, pathway interaction, physical interaction, co-localization, genetic interaction, and shared protein domains  of CYP1B1 was also determined by utilizing the STRING protein database (https://string-db.org/). The STRING database query terms were cut-off at 0.7 and the maximum additional interaction was 10 (https://cytoscape.org/) was employed to construct the highly co-expressed genes and PPI networks in the present study , a publically available online tool for gene expression profiling, transcriptional factor analysis, PPI networks, pathway analysis, toxicogenomics and pharmacogenomics studies  terms, which were molecular functions (MF), cellular components (CC), and biological processes (BP), and the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway of CYP1B1 were analysed with Enrichr database . In this study, the missense variations were selected in order to investigate their damaging effects and how they influence the phenotypic characteristics of the respective encoded protein. These missense variations are responsible for various complex diseases by affecting the protein’s functional activities and structural stability or folding  and calculated the RI of the variations, while MUpro predicted the effect of protein stability for single site mutation in the form of Gibb’s free energy change (DDG). Of the 19 high-risk missense variations identified, I-Mutant 2.0 revealed that all amino acid variations profoundly affected the CYP1B1 protein by decreasing its stability, except for four variants: G61E, P118L, Y349D, and D430E, which did not influence the protein stability. Apart from I-Mutant 2.0, MUpro identified only P52L had no effect on protein stability and the remaining exhibited a reduction in protein stability. The details of I-Mutant 2.0 and MUpro analysis are provided in The evolutionary conservation characteristic of all the common 19 missense variants was accomplished by the Consurf server. It is essential to determine the magnitude of an amino acid substitution to alter the protein’s structure and function. Results revealed that all amino acid variations occurred in the highly conserved region of the CYP1B1 protein except for one substitution (Y349D) that was observed in the variable region . AdditioFurthermore, of the total 543 amino acids, 255 amino acids (46.96%) were found in the alpha helix, 207 amino acids (38.12%) in the random coils region, 61 (11.23%) in the extended strand, and 20 (3.68%) in the beta turns of the CYP1B1 protein as predicted by SOPMA .Table 3. The results of the TM-Align server elucidated that all 19 mutant structures have profound impacts on the CYP1B1 protein. This suggests that when these variations occur in the CYP1B1 protein, they may interfere with its functional behavior.To predict the structural impact of these 19 high-risk variations, first the 3D structure of the CYP1B1 protein was obtained from the PDB database (PDB ID: 3PM0). The structure had some missing residues; therefore, to generate the complete structure of the CYP1B1 protein, the I-TASSER server was utilized. From the generated models of I-TASSER, the best model with a C-score of 0.16, an estimated TM-score of 0.73 ± 0.11, and an estimated RMSD of 7.0 ± 4.1 A was selected and subjected to further evaluation. The quality of the best generated model structure was validated through the ERRAT and VERIFY3D tools of the SAVES server. The ERRAT tool provided the overall quality score, which was 92.57. The VERIFY3D tool estimates the compatibility of a protein model based on local statistical potentials and predicts that the CYP1B1 protein model passed the criteria with an average score of 89.94% and is worth further investigation. Similarly, the 3D structures of all 19 mutants of CYP1B1 were generated, and their graphical illustration and superimposition with the wild-type structure were performed by UCSF Chimera software version 1.14. The 3D wild-type and some mutant structures are depicted in The molecular dynamic simulation (MDS) was executed to evaluate the protein stability following a missense variation in the CYP1B1 protein. The MDS was run up to 50 ns and the RMSD values were noted for the wild-type and for all the 19 high-risk missense variations. Results demonstrated that the CYP1B1 wild-type structure showed an RMDS in the range of 0.36 nm and structural stability throughout the simulation period . These rCYP1B1 gene was obtained from the GeneMANIA database and then imported into the Cytoscape software to build a network based on the co-expression score  network was also generated via cytoscape software  and pathway analyses. In total, 59 biological processes (BPs) were discovered for CYP1B1 as documented in CYP1B1 and LTBP2) of primary congenital glaucoma (PCG), CYP1B1 is found to be highly mutated and responsible for the etiology of PCG patients worldwide  were found to be common and were considered the high-risk deleterious variants of the CYP1B1 gene . At posiTwo variants (G90R and G365R) where glycine, a neutral amino acid, was mutated by a positively charged arginine, exhibited instability as well as high RMSD , which could affect the proper protein folding . WhereasCYP1B1 gene comprises three exons with 371, 1,044 and 3,707 base pairs in length, respectively, that encode a protein with a length of 543 amino acids. It has been seen that the coding region for the CYP1B1 protein starts from the 5′ end of exon number two and ends within the third exon  through comparative 10.7717/peerj.14132/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.14132/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.14132/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.14132/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.14132/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.14132/supp-6Supplemental Information 6https://www.gromacs.org/).The XVG files can be opened by using Gromacs Software plugins .The XVG files can be opened by using Gromacs Software plugins (Click here for additional data file.


The functional mapping of hotspots has been critical to protein–protein interaction (PPI) inhibitor research, however, the orthogonal approach to stabilize PPIs has not exploited this information. Utilizing the hub protein 14-3-3 as a case study we demonstrate that functional mapping of hotspots provides a triage map for 14-3-3 molecular glue development. Truncation and mutation studies allowed deconvoluting the energetic contributions of sidechain and backbone interactions of a 14-3-3-binding non-natural peptide. Three central 14-3-3 hotspots were identified and their thermodynamic characteristics profiled. In addition to the phospho-binding pocket; (i) Asn226, (ii) Lys122 and (iii) the hydrophobic patch formed by Leu218, Ile219 and Leu222 were critical for protein complex formation. Exploiting this hotspot information allowed a peptide-based molecular glue that elicits high cooperativity ( The functional mapping of a 14-3-3 protein complex, by means of peptide truncations and point mutations, as an approach to identify critical hotspots regions for 14-3-3 molecular glue drug design. Molecular glues were first elucidated from mode-of-action studies of naturally derived macrocycles and tetracyclic compounds; including rapamycin,4,5 FK506,6 cyclosporin6–8 and taxol.9,10Molecular glues represent a breakthrough in drug discovery, enabling non-disruptive control of protein function. The core advancement is the small-molecule strengthening of native protein complexes or induction of non-native protein–protein interactions (PPIs). Stabilization of a protein complex typically results in enhanced cellular or a new cellular function.11–15 The stabilization of native protein–protein interactions has simultaneously gained significant attention, for example with the development of NRX-103094 and Trametiglue that stabilize mutant β-catenin/β-TrCP and KSR/MEK protein complexes, respectively.16,17 Further, stabilization of hub protein 14-3-3 interactions with the p65 domain of NF-κB, chloride channel CFTR, and ChREBP transcription factor have also been demonstrated as promising approaches to molecular glue drug discovery.18–20Medicinal chemistry campaigns have addressed non-native protein complexes, most notably molecular glue degraders such as immunomodulatory drugs (IMiDs) and aryl sulfonylamides, for example targeting cereblon-CUL4 and Cullin ring E3 ligases.These forerunners in the field stand as valuable proofs of concept to the emerging molecular glue drug discovery field. However, systematic drug discovery approaches to target native protein complexes remain to be developed. Three challenges complicate initial molecular glue chemical matter identification: (1) a lack of native ligands and tool compounds, (2) seemingly featureless binding interfaces and (3) the dynamic nature of multi-component protein complexes. To address these challenges a greater thermodynamic understanding of protein complex formation is necessary.21–23 Alanine scanning mutagenesis, for example, shows that not all PPI interfacial amino acids are uniform in their energetic contribution; discrete regions, termed hotspots, disproportionately contribute to the binding energy of protein complex formation.24–26 Hotspot-based drug design has been applied to PPI inhibition, e.g. for the MDM2/p53 interaction to mimic hotspot residues Phe19, Trp23 and Leu26 of p53 using both peptide stapling27 and small molecules such as the nutlin member RG7112  targeting ligands, significant to PROTAC research. There the 3-hydroxyl-l-proline (Hyp) 564 of hypoxia-inducible factor 1α was mimicked.33,34 Surprisingly, given the success of hotspot analysis for PPI inhibitor development, this concept has not received strong attention for molecular glue drug design.Functional mapping of protein interfaces has proven invaluable to the mechanistic understanding of protein complex formation and PPI inhibition drug discovery .21–23 Alr RG7112 .28–32 Fu18,35–37 However optimization of molecular glues in the absence of well-defined hotspot residues is time and labor intensive. Functional mapping of the 14-3-3 binding groove would enable efficient fragment optimization to lead compounds.Previously we have identified initial chemical starting points for 14-3-3 molecular glue design using covalent tethering.Here we disclose how functional mapping of a 14-3-3 protein complex can be used to rapidly develop a peptide-based molecular glue for the 14-3-3/ChREBP protein complex . Hotspot38 Because of its role in regulating many protein partners, 14-3-3 PPIs have become attractive drug discovery targets for various diseases including cancer and neurodegenerative diseases.39,40 Functional mapping of the 14-3-3 binding groove would therefore be valuable in both the design of PPI modulators and in gaining a better understanding of 14-3-3 PPI formation.The hub protein 14-3-3 binds hundreds of different protein partners, typically in phosphorylated and structurally disordered regions of its client proteins.KD value determinations despite affinity losses during point mutation and truncation studies. Previously, Quartararo et al. had identified highly potent non-canonical phosphopeptides with low nanomolar affinity (3–19 nM) from a 108-membered synthetic peptide library. Each library member contained a fixed phosphoserine, flanked by four variable non-canonical amino acids on each side. A C-terminal lysine was added to improve the sequencing results of hit peptides  and known X-ray crystal structural characterization data in complex with 14-3-3 . FurtherKD value of 19.2 ± 4 nM and 45.0 ± 1 nM, respectively  and fluorescence anisotropy (FA) assays . FA and ectively . These r binding .via a phosphorylated serine or threonine residue located in a disordered region of the client protein, for example in unstructured protein domains of the cystic fibrosis transmembrane conductance regulator (CFTR),42 the death-associated protein kinase 2 (DAPK2)43 or the neural precursor cell expressed developmentally down-regulated 4 ligase (Nedd4-2).44 The phosphorylated amino acid binds a small sub-pocket, formed by Arg56, Arg129 and Tyr130, within the amphipathic groove of 14-3-3  of the truncated peptide from the previously truncated peptide, providing the ΔΔG. Furthermore, the cumulative energy contributions of all N- or C-terminal amino acids were also calculated. This provided an overall ΔΔG of the N- and C-terminal amino acids respective to the 9-mer phosphorylated peptide 1. N-terminal amino acids d-Lys, Nva and Nph showed only minor energy contributions to 14-3-3 binding. In contrast, truncations of Thz  elicited a 43-fold change in binding affinity . The β-Ala and β-Ser residues contributed the least, with ΔΔG = 0.4 kcal mol−1 and 0.6 kcal mol−1, respectively. Nph gave a slightly more significant fold change in binding affinity (four-fold) and proved to be more important for 14-3-3 binding. The entire C-terminus affords a cumulative ΔΔG of 4.3 kcal mol−1. Interestingly, this presents an opportunity for 14-3-3 molecular glue design as many native 14-3-3 client proteins are either C-terminally truncated or feature a C-terminus that bends out of the 14-3-3 binding groove  gave a five-fold reduction in binding affinity compared to 1. Removal of Nph resulted in an even stronger change in affinity , Nph(+1) and Nph(+4), which showed to most significantly contribute to 14-3-3 binding . The bacKD values (ESI Tables S4 and S5G) of the side group functionalities , Nph(+1) and Nph(+4) a point mutation study was performed . Specifinalities .KD values from FA and ITC assays aligned well for all mutants   . This re Thz(−1) . This id Thz(−1) .G values of −0.1 ± 0.3 and −0.2 ± 0.2 kcal mol−1 (FA experiments), respectively. The slightly negative values even indicate a minor improvement in the binding affinity of this mutant peptide. Analysis of the thermodynamic parameters showed that removal of the nitro group led to an enhanced entropic contribution relative to 1  relative to the phenylalanine mutant (4b) showed the phenyl ring purely contributes to the enthalpy of binding, whereas the entropy did not change significantly  Tyr) did not restore the binding affinity. Comparing the thermodynamic parameters of peptide 1  to 5b  shows that the removal of the nitro group significantly increases the enthalpy by ∼6 kcal mol−1 (In contrast to Nph(+1), removing the nitro group of the +4 residue (5b) significantly decreased the binding affinity to 14-3-3 ; resultial mol−1 , albeit al mol−1  dominateal mol−1 .47 Previous studies have shown that stabilization of the 14-3-3/ChREBP protein complex, using the natural metabolite AMP or synthetic stabilizers, is valuable to regulate ChREBP binding or activity.20,47Having identified hotspot regions within the 14-3-3 binding groove, we envisioned this information could be used to identify and optimize 14-3-3/ChREBP molecular glues. The binding of 14-3-3 to ChREBP (Carbohydrate-Response Element Binding Protein) has shown to inhibit nuclear localization of ChREBP thereby decreasing its transcriptional activity of glucose responsive genes.20 Since ChREBP does not engage with the phospho-binding pocket, this leaves a critical hotspot region within the 14-3-3 groove unoccupied. In contrast, N-terminally truncated peptide 2d does engage this pocket with a phosphoserine and binds uniquely via an N-terminal motif in the 14-3-3 binding groove  of 1.4 ± 0.5 μM. This increase in anisotropy was not observed when titrating ChREBP to FITC-labelled 2d alone  for each concentration of ChREBP  of ternary 14-3-3/ChREBP/2d complex formation we fitted 2D titration data to a thermodynamic equilibrium model  based on the 2D titrations to be 1.6 μM which corresponds with the affinity of ChREBP for 14-3-3 as found in earlier studies.20To accurately determine the cooperativity factor  and the p65 subdomain of NF-κB. Analysis of the peptide screen showed highly selective ternary complex formation of 14-3-3/peptide 2d/ChREBP. All other partner proteins showed a negative cooperative complex formation, inhibiting the formation of the binnary 14-3-3/2d protein complex formation, at significantly higher client peptide concentrations as compared to the cooperative stabilizing 14-3-3/ChREBP/2d system  of 36. Tool peptide 2d stands as a valuable proof of concept for 14-3-3 molecular glue design based on functional mapping of 14-3-3 hotspots.Utilizing the functional mapping results, we developed a minimum peptide sequence (2d) that was able to selectively stabilize the 14-3-3/ChREBP PPI with an attractive cooperativity factor .Crystal structures described in this manuscript have been deposited to the PDB : L. B. and C. O. are scientific co-founders of Ambagon Therapeutics.SC-013-D2SC04662H-s001


Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typically interact with multiple target proteins to elicit their effects. Various machine learning models have been developed to predict multi-target modulating NCs with desired physiological effects. However, due to deficiencies with existing chemical-protein interaction datasets, which are mostly single-labeled and limited, the existing models struggle to predict new chemical-protein interactions. New techniques are needed to overcome these limitations.We propose a novel NC discovery model called OptNCMiner that offers various advantages. The model is trained via end-to-end learning with a feature extraction step implemented, and it predicts multi-target modulating NCs through multi-label learning. In addition, it offers a few-shot learning approach to predict NC-protein interactions using a small training dataset. OptNCMiner achieved better prediction performance in terms of recall than conventional classification models. It was tested for the prediction of NC-protein interactions using small datasets and for a use case scenario to identify multi-target modulating NCs for type 2 diabetes mellitus complications.OptNCMiner identifies NCs that modulate multiple target proteins, which facilitates the discovery and the understanding of biological activity of novel NCs with desirable health benefits.The online version contains supplementary material available at 10.1186/s12859-022-04752-5. Natural product (NP)s, are defined as substances produced by living organisms. The term NP is often used broadly, covering both natural compound (NC)s and mixtures of NCs derived from natural sources . The divSubject to selective pressure over millions of years, NCs now have diverse bioactive chemotypes, some of which are optimized for particular biological functions, such as endogenous growth and the defense of living organisms . Due to Another interesting characteristic of NCs is their propensity to modulate multiple protein targets. The unique scaffolds and structural motifs of NCs enable interactions with multiple target proteins to elicit diverse biological activities . In the Various experimental methods have been developed over the past few decades to identify NCs that regulate target proteins . HoweverA variety of machine learning methods have been trained on chemical-protein interaction databases to predict compounds with novel target modulating activities –21. Of pSiamese neural network (SNN), first suggested in the early 1990s by Bromley and LeCun, is comprised of two identical networks, with one called ‘heads’, that accepts distinct input pairs and is an activation function called 'body', that concatenates the two heads . SNN is Here, we describe ‘OptNCMiner’, a machine learning model suitable for predicting ‘optimal NCs’ that modulate disease-specific multi-targets. Built on a structure of SNN, OptNCMiner preserves the advantages of DNN to effectively extract essential features of NCs related to chemical-protein interactions. OptNCMiner is validated on its ability with multi-label learning from single positive data on chemical-protein interactions, and is also capable of few-shot learning, enabling multi-class classification on NCs using small datasets. We tested OptNCMiner with the discovery of natural sources containing NCs that regulate target proteins associated with type 2 diabetes mellitus (T2DM)-related complications.OptNCMiner learns structural similarities between compound pairs and is trained to grant high similarity scores to chemical pairs where both compounds are active against a single target protein. Upon successful training, OptNCMiner calculates the activity score of NCs with each target protein in of the context of a similarity score between NCs and known active compounds of target proteins Fig. . OptNCMiChemical-protein interaction data was gathered from ExCAPE-DB and LIT-PCBA. ExCAPE-DB is a database of chemogenomics data curated from two major public databases: PubChem and ChEMBL . CompounThree datasets of different sizes were prepared and named as the 'base dataset', 'transfer learning dataset’, and 'few-shot learning dataset’ in accordance with their decreasing size Table . All datFor each compound, a standard fingerprint of 1024 bits was generated from the canonical SMILES representation using Chemistry Development Kit (CDK) in R software . For eacWe have generated pairs of compounds and their labels, since OptNCMiner is a network that accepts inputs in the form of pairs and computes the similarity between the two. Compound pairs were labeled as ‘positive’ if both were classified as active for the same target protein. Those pairs that did not satisfy the criteria were labeled as ‘negative’. In order to prevent proteins with large interaction data sizes from dominating the training dataset, proteins were randomly sampled from a uniform distribution. For generating positive pairs, active compounds to the protein, which is shown as mentclass2pt{minimmentclass2pt{minimOptNCMiner is built in an SNN structure, where pairs of inputs are fed to identical multi-layer perceptrons called ‘head function’ to generate pairs of embedding vectors. The similarity between two embedding vectors is computed by a distance function referred to as the ‘body function’. The overall structure of the model is represented as follows:entclass1pt{minimad by Eq.  is used Binary Cross Entropy (BCE) was used as the loss function (4). Through the optimization process, the model was trained to minimize BCE between the predicted output and the label. The model hyperparameters were optimized using the validation set during training. We used Adam optimizer  with a lThe training data were utilized in the support set upon completion of training. A support set was constructed by randomly sampling 100 active compounds per protein. Each query in the test set was compared to each compound in the support set. To classify the binding of the compound to the target protein, the highest similarity for each protein was compared to a threshold. Then, for each protein, the maximum similarity score of the query compound is used to classify whether the compound binds to the protein. A threshold of 0.5 is used to determine the binding of the compound to the target protein.For many machine learning problems, better performance can be achieved by applying a transfer learning method, which pre-trains the model on a larger dataset before further training it on the target dataset . We prevFor datasets too small to feasibly train on, few-shot learning can be used. The few-shot learning method uses distance metrics to compute the similarity between a data point in a support set and a data point in a query set . In thisChu and colleagues have provided an updated gold standard dataset of chemical-protein interaction data that accounts for multiple bindings . To testSince OptNCMiner is a classification model, typical classification metrics have been used to evaluate the performance of the model. A recall is a metric that measures the proportion of true positives, The chemical-protein interaction data from three differently-sized datasets are aggregated into a total of 106,317 positive interactions with 25 different target proteins. Since all proteins have different instances of interactions, 1,000 active compounds were randomly selected for each protein in the base dataset, and all active compound data from the transfer learning data and few-shot learning data were used to investigate the target protein-interacting compound space.To explore the physicochemical properties of the sampled compounds, principle component analysis (PCA) was used. The following ten physicochemical properties were calculated using OPERA 2.6  to generAdditionally, the structural similarity distribution of the sampled compounds was investigated using a pairwise Tanimoto similarity matrix. Sampled compounds were represented in a high dimensional space with a 1,024-bit standard fingerprint, containing information for the chemical substructures. All possible pairs of compounds were generated from those sampled and Tanimoto similarity values between 1,024-bit standard fingerprint vectors of chemical pairs were calculated. The resultant similarity matrix was rendered in the form of a heat map  is considered to be the most relevant evaluation metric.The performance of OptNCMiner was compared with five baseline models capable of multi-label classification, which are cosine similarity, NB, LR, RF, and MLP. The baseline models were trained with the same training set and were evaluated using the same test set to that of OptNCMiner, generated from the base and transfer learning datasets. For the cosine similarity method, we calculated the cosine similarity of two standard fingerprint vectors. If the cosine similarity value of a pair exceeded 0.5, two compounds were considered structurally similar and the test compound is predicted to bind to the target protein, and vice versa. The remaining baseline models were trained to classify pairs of compounds with binary labels of 1 or 0. Binary label of 1 indicates that the test chemical is expected to bind to the same target protein because it is similar to its pair. The performance of the baseline models was evaluated with the base dataset and transfer learning dataset. All baseline models were first trained and evaluated with the base dataset, while MLP was further trained in the manner of the transfer learning with the transfer learning dataset. As transfer learning is not applicable to cosine similarity, NB, LR, and RF, the performance of these models was simply evaluated after training on the transfer learning dataset.Table Another strong advantage of OptNCMiner is its ability to predict chemical-protein interactions for proteins with limited training data. The trained and transfer-learned OptNCMiner using the base dataset and transfer learning dataset was used to predict chemical-protein interactions in the few-shot learning dataset using a few-shot learning method. The few-shot learning performance of OptNCMiner was evaluated with 7 proteins with data for less than 100 interacting compounds and is demonstrated in Table OptNCMiner is a model capable of predicting multiple target proteins for NCs through multi-label learning. OptNCMiner predicts not only the known protein targets of NCs, but also unknown target proteins, which results in high false positive rates. To confirm OptNCMiner’s ability to predict unknown chemical-protein interactions, we sought to validate OptNCMiner’s false positive outputs in few-shot learning.OptNCMiner went through few-shot learning using the few-shot learning dataset, where chemical-protein interactions were predicted among 7 proteins and 23 compounds in the test set. To examine whether the false positives were real negatives or newly discovered target proteins, literature searches and in silico docking using GalaxyDockWEB  were undTo investigate the practical application of OptNCMiner in novel NC discovery, the program was trained using the base dataset to identify NCs present in natural sources that modulate target proteins associated with T2DM complications. Diabetic nephropathy, diabetic keratopathy, and cardiomyopathy were chosen as T2DM complications and target proteins for each complication were identified based on previous reports . Among tTo visualize the network of relationships between T2DM complications and NCs of food origin Fig. , a standGinger is a herbaceous plant that has a long history of use in traditional medicines and foods. The root of the plant contains a vast array of NCs which are responsible for a wide range of biological activities, including anti-diabetic effects, gastrointestinal protection, anti-cancer effects, cardiovascular protection, and the prevention of obesity , 48. AmoTLR4 is a receptor protein with multiple physiological functions, and has been implicated in the weakening of ocular surface and corneal nerves, leading to diabetic keratopathy. TLR4 binds to high-mobility group box 1 protein that activates the NF-κB pathway, leading to inflammation in the cornea . InteresOptNCMiner was developed to overcome the limitations faced by existing NC identification methods and to support the effective discovery of novel NCs based on the consideration of multi-target interactions. OptNCMiner was built in an SNN structure to enable learning of hidden structural characteristics that determine chemical-protein interactions. The pair-wise input generation and few-shot learning characteristics of OptNCMiner enable this learning even with small datasets. OptNCMiner generates embedding vectors that place compounds with similar target protein interaction closely in a vector space. Thus, OptNCMiner can be used along with other NC discovery methods such as chemical-chemical interaction prediction , toxicitHowever, there remains room for improvement for better performance of OptNCMiner. One factor limiting the performance of the program is the complexity of NC biological activity in the human body. Slight differences in physicochemical properties can affect the absorption and distribution of NCs, which alters the interaction between NCs and target proteins. For further development, the physicochemical properties of NCs as well as the cellular location of target proteins should be considered in model input and model training. Although in silico docking has validated predicted false positives as true positives, further validation with in vitro and in vivo studies will verify the accuracy of OptNCMiner. This effort of further validation would also clarify the synergistic or unexpected side-effects of multi-target modulating NCs discovered by OptNCMiner.One important recommendation for users of our model is the careful selection of target proteins for NC discovery. In the example of T2DM complications, only proteins ameliorating the three complications were considered target proteins. However, to identify NCs that only regulate the desired target protein, in practice, possible off-target proteins may be affected based on background knowledge. OptNCMiner can also be used in conjunction with other programs to support holistic NC discovery, such as programs that predict the absorption and distribution of NCs after ingestion.In this study, a novel SNN model called OptNCMiner was built to predict multiple target proteins of NCs. Trained to understand similarities between paired fingerprint vectors, OptNCMiner can predict chemical-protein interactions even for proteins with limited and unbalanced training data. We have demonstrated that OptNCMiner can successfully adapt to training data of various sizes and can predict novel chemical-protein interactions. Furthermore, as a use-case scenario, OptNCMiner was used to predict a natural source and its NCs for the potential treatment of T2DM complications. With a careful selection of target protein candidates, OptNCMiner is a powerful tool to predict novel NCs that modulate specific target proteins to elicit the desired bioactivity.Additional file 1: Supplemental tables.Additional file 2: Excel sheet of in silico docking results of false positives.Additional file 3: Excel sheet of predicted interactions between target proteins and NCs and NC containing natural sources.


The interaction between macromolecular proteins and small molecule ligands is an essential component of cellular function. Such ligands may include enzyme substrates, molecules involved in cellular signalling or pharmaceutical drugs. Together with biophysical techniques used to assess the thermodynamic and kinetic properties of ligand binding to proteins, methodology to determine high-resolution structures that enable atomic level interactions between protein and ligand(s) to be directly visualised is required. Whilst such structural approaches are well established with high throughput X-ray crystallography routinely used in the pharmaceutical sector, they provide only a static view of the complex. Recent advances in X-ray structural biology methods offer several new possibilities that can examine protein-ligand complexes at ambient temperature rather than under cryogenic conditions, enable transient binding sites and interactions to be characterised using time-resolved approaches and combine spectroscopic measurements from the same crystal that the structures themselves are determined. This Perspective reviews several recent developments in these areas and discusses new possibilities for applications of these advanced methodologies to transform our understanding of protein-ligand interactions. Often the life-time of the protein-ligand complex is tuned to dictate its function. In this respect, protein ligand interactions can display life-times ranging from microseconds to days. Complex life-times are determined by the ligand off-rate, with a high off-rate commonly associated with transient complexes and resulting in a dissociation equilibrium constant (KD) in the milli to micromolar range, whereas static complexes have a significantly lower off-rate and subsequently KDs into the nanomolar range and beyond. Obtaining high-resolution structural information for protein-ligand complexes provides direct information on the mode of interaction and is particularly powerful when combined with suitable biophysical methods, such as calorimetry or surface plasmon resonance that enable thermodynamic and kinetic parameters of the binding interaction to be quantified. Tremendous effort has been invested into developing methods to obtain structures of protein-ligand complexes, with orders of magnitude improvements in sample throughput and data analysis being achieved, particularly for the application of early fragment-based drug discovery  that are standard in X-ray crystallography may result in binding modes or interactions that are not fully consistent with those occurring under physiological conditions . A fascidentical . In thisAdditionally, the X-rays used to determine structures can themselves alter the structure of the crystalline protein through radiation damage , which iIn this Perspective, we review recent methodological advances relevant to structure determination of validated, high-resolution protein-ligand complexes and provide several examples of how these approaches have the potential to elevate our understanding of ligand binding.Early X-ray crystal structures were almost exclusively obtained at room temperature with crystals typically mounted in capillaries. Radiation damage concerns and increasingly brilliant X-ray synchrotron beamlines led to the development of highly effective cryo cooling methods with almost all structures subsequently determined at 100 K. Such cryogenic structures have led to tremendous insights into protein-ligand binding over many decades. However, the question of physiological relevance on a crystal complex determined at 100 K arises , and thuin situ (VMXi) beamline at Diamond Light Source  of microcrystals  are used for X-ray diffraction in sequence to assemble a complete dataset . The cruWe have shown the utility of serial femtosecond crystallography (SFX) at the SACLA XFEL in Japan to determine high-quality structures of protein-ligand complexes of iron and copper-containing proteins . LigandsUsing SFX to obtain structures of enzyme-substrate or enzyme-product complexes provides the essential starting point to determine time-resolved structures of transient states or enzyme intermediates. Several different methods have been developed to initiate reactivity within microcrystals. The most intuitive approach is to rapidly mix microcrystals with a suitable reagent solution at a defined point prior to X-ray exposure, in a manner analogous to stopped-flow enzyme kinetics. While conceptually simple, there are many technical challenges related to mixing rates and optimal homogeneous reaction initiation. Simple mixing of a protein and ligand can be achieved using a mix-and-inject system . Alternavia a pulse of light of the appropriate wavelength, typically delivered using a laser pulse  simulations of copper nitrite reductase clarified the protonation states of a catalytically important aspartate residue together with that of the substrate, intermediates and products . HoweverContext for the above is provided by further development of ultrahigh throughput structure determination of protein-ligand complexes at 100 K. This field has been pioneered by the XCHEM platform at Diamond Light Source  with a fIn conclusion, recently developed methods tremendously enhance our ability to understand the interactions between proteins and their ligands. Synergies between these methods and envisaged future developments suggest a bright future for time-resolved, ambient conditions structural biology of protein-ligand complexes.


Background: Cross-linking mass spectrometry (XL-MS) is a powerful technology capable of yielding structural insights across the complex cellular protein interaction network. However, up to date most of the studies utilising XL-MS to characterise individual protein complexes’ topology have been carried out on over-expressed or recombinant proteins, which might not accurately represent native cellular conditions.Methods: We performed XL-MS using MS-cleavable crosslinker disuccinimidyl sulfoxide (DSSO) after immunoprecipitation of endogenous BRG/Brahma-associated factors (BAF) complex and co-purifying proteins. Data are available via ProteomeXchange with identifier PXD027611.Results: Although we did not detect the expected enrichment of crosslinks within the BAF complex, we identified numerous crosslinks between three co-purifying proteins, namely Thrap3, Bclaf1 and Erh. Thrap3 and Bclaf1 are mostly disordered proteins for which no 3D structure is available. The XL data allowed us to map interaction surfaces on these proteins, which overlap with the non-disordered portions of both proteins. The identified XLs are in agreement with homology-modelled structures suggesting that the interaction surfaces are globular.Conclusions: Our data shows that MS-cleavable crosslinker DSSO can be used to characterise in detail the topology and interaction surfaces of endogenous protein complexes without the need for overexpression. We demonstrate that Bclaf1, Erh and Thrap3 interact closely with each other, suggesting they might form a novel complex, hereby referred to as TEB complex. This data can be exploited for modelling protein-protein docking to characterise the three-dimensional structure of the complex. Endogenous XL-MS might be challenging due to crosslinker accessibility, protein complex abundance or isolation efficiency, and require further optimisation for some complexes like the BAF complex to detect a substantial number of crosslinks. Intramolecular crosslinks provide distance constraint parameters that can support homology-based tertiary structure modelling or even guidedelling . MoreoveMost XL-MS studies to date have been performed on over-expressed or recombinant proteins or protein complexes . More raHere, we performed XL-MS on affinity-purified (AP) endogenous BRG/Brahma-associated factors (BAF) complex in native conditions to define the interaction surfaces between complex subunits and with associated protein partners. The strategy was based on immunoprecipitation of Arid1a, considered to be a scaffolding/bridging component of the complex  was coupled to crosslinking using MS cleavable crosslinker DSSO and MS3 mass spectrometry. Crosslink peptide identification was performed using the full mouse Uniprot database, with crosslink assignment using XlinkX  with exception of two. We also detected a significant number of XLs between Thrap3, Erh and Bclaf1, due to a strong enrichment of these proteins in Arid1a APs. Specifically, 13% of all XLs identified in the five experiments involved these three proteins. For comparison, 9.6% of total XLs obtained were assigned to BAF, which, in addition to being the bait in the AP, is also a much larger complex with 27 subunits. Given the richness of XL information associated with these proteins, we used this data to gain insight into the structural topology of this protein cluster. Our data suggests that they interact closely and might represent a stable complex. We hereby refer to it as the Bclaf1-Erh-Thrap3 (TEB) complex.Bclaf1 and Thrap3 are highly homologous proteins . They shWe summarised confident crosslinks and corresponding number of CSMs for each of five experiments inPrevious studies identified that both the N-terminus (1-190aa) and the C-terminus (359-951aa) of Thrap3 are important for its function in DNA repair of post double stranded breaks and stalled replication forks, whilst the residues 190-359aa appear to be dispensable for this function  serine/threonine kinase and export of itsmRNA, as well as regulation of other transcripts involved in DDR (ATR) protein (Thrap3 and Bclaf1 have been shown to form part of a high molecular weight complex (the SNARP complex) involved in the regulation of cyclin D1 stability together with SNIP1, SkIP and Pinin  sites with some data suggesting that Thrap3 and Bclaf1a do not localise  were cultured by StemCell Technologies Inc. .Protein G-Dynabeads  were prepared by coupling to antibodies against specific target protein inPeptides generated by trypsin digestion were fractionated with the Pierce High pH Reversed-Phase Peptide Fractionation Kit  according to manufacturer instructions, and eight fractions were collected and dried. Liquid chromatography mass spectrometry (LC-MS) analysis was performed on the Dionex UltiMate 3000 UHPLC system coupled with the Orbitrap Lumos Mass Spectrometer (Thermo Scientific). Each peptide fraction was reconstituted in 15 μL 0.1% formic acid and loaded to the Acclaim PepMap 100, 100 μm × 2 cm C18, 5 μm trapping column at 10 μL/min flow rate of 0.1% formic acid loading buffer. The sample was then subjected to a gradient elution on the EASY-Spray C18 capillary column  at 50°C. Mobile phase A was 0.1% formic acid and mobile phase B was 80% acetonitrile, 0.1% formic acid. The gradient separation method at flow rate 300 nL/min was as follows: for 90 minutes gradient from 5%–38% B, for 10 minutes up to 95% B, for 5 minutes isocratic at 95% B, re-equilibration to 5% B in 5 minutes, for 10 minutes isocratic at 5% B. MS scans were acquired at a mass resolution of 120,000 and precursors between 375–1,600 m/z and charge equal or higher than +3 were isolated for collision-induced dissociation (CID) fragmentation with quadrupole isolation width 1.6 Th in the top speed mode in cycles of 5 seconds. Collision energy was set at 25%. Fragments with targeted mass difference of 31.9721 (DSSO crosslinker) were further subjected to CID fragmentation at the MS3 level with collision energy 35%, iontrap detection and MS2 isolation window 2 Th. Two precursor groups were selected with both ions in the pair. Targeted MS precursors were dynamically excluded for further isolation and activation for 30 seconds with 10 ppm mass tolerance.MS raw data from mESCs IPs was analysed using Proteome Discoverer 2.4 . XLinkX was used to search crosslinked peptides pairs  pLink  .  The manuscript is well written and straightforward to follow. The study sets out to investigate the interactions of the BAF complex, by immunoprecipitating Arid1a, a scaffolding protein of the BAF complex.  Unfortunately, the experiment yielded too few cross-links of the BAF complex to enable detailed topological investigation, however, three proteins, Thrap3, Bclaf1, and Erh were identified with numerous cross-links, suggesting that they may form a previously unidentified complex.  In all, five experiments were carried out yielding a total of 121 cross-links.  Of these, 24 CSMs  were attributed to subunits of this putative complex.The manuscript by Shcherbakova The study highlights an important aspect of structural mass spectrometry, namely that chemical cross-linking enables the structural analysis of intrinsically disordered proteins and disordered regions within a protein.  Thrap3 and Bclaf1 are predicted to have extensively disordered regions, which makes it very difficult if not impossible to study the structures by crystallography or cryo-electron microscopy.  While the data here provide limited intra and inter cross-links, they are starting point for more extensive investigation of this putative new complex. Raw data underlying this work has been deposited in the ProteomeXchange Consortium via the PRIDE repository. Minor comments:The authors suggest naming the putative Bclaf1a-Erh-Thrap3 complex, the BET complex.  This is potentially confusing, as there are already the Bromodomain and Extra-Terminal domain (BET) proteins .  Perhaps an alternative nomenclature would be better.Table 1 should additionally show the confidence score attributed to a CSM by the software used for data analysis (XLinkX). The legend also refers to 4 experiments where five are shown.The authors have employed the Robetta protein modelling tool to predict possible structures for these intrinsically disordered proteins.  It would be useful to compare these structures with those predicted by AlphaFold.Searches were performed against the full UniProt Mouse database.  Potentially an alternative approach would have been to characterise the IP obtained using Arid1a and build a more targeted database against which to search the data.The authors specify how protein concentrations were measured, but don’t say how much protein was used for the IP.  Additionally, a value of 1mM is given for the cross-linking reagent, how does this relate to a protein: cross-linker ratio?  Were several concentration ratios investigated?On page 11, the authors mention that alternative software is available for data analysis.  This list is not extensive and should at least include other applications such as Xi, StavroX, etc.The authors get slightly fixated on the distance restraint of 37Å.  This is a lenient value and may not have any particular importance for these disordered proteins.For the cross-linking data analysis, it may prove useful to include serine and threonine in the reaction specificity for DSSO.  Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Proteomics, structural mass spectrometryI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors suggest naming the putative Bclaf1a-Erh-Thrap3 complex, the BET complex.  This is potentially confusing, as there are already the Bromodomain and Extra-Terminal domain (BET) proteins .  Perhaps an alternative nomenclature would be better.We thank the reviewer for pointing this out. We have now changed the name to TEB complex throughout the text.Table 1 should additionally show the confidence score attributed to a CSM by the software used for data analysis (XLinkX). The legend also refers to 4 experiments where five are shown.The XlinkX Target Decoy PSM Validator was set at 1% FDR as reported in the methods. All crosslinks reported in table 1 fulfilled the 1% FDR cut-off. This has now been added to the legend for clarity and the number of experiments (5) has also been corrected.The authors have employed the Robetta protein modelling tool to predict possible structures for these intrinsically disordered proteins. It would be useful to compare these structures with those predicted by AlphaFold.AlphaFold was released after the manuscript had been submitted, but we agree with the reviewer that it would be interesting to compare the modelled structures with the AlphaFold predicted structures. AlphaFold structures for human THRAP3 and BCLAF1 look more disordered than those presented in the paper for the mouse counterparts but retained a few alpha helixes in the central region of both THRAP3 and BCLAF1, similarly to the structures generated by Robetta. Searches were performed against the full UniProt Mouse database.  Potentially an alternative approach would have been to characterise the IP obtained using Arid1a and build a more targeted database against which to search the data.Originally, since the experiments were designed around the BAF complex, we performed searches using a database restricted to BAF subunits and compared them with the full database search. The restricted database search did not result in a substantial improvement in crosslink assignment for BAF complex subunits and therefore the full mouse database was used to search for additional crosslinks.  The authors specify how protein concentrations were measured, but don’t say how much protein was used for the IP.  Additionally, a value of 1mM is given for the cross-linking reagent, how does this relate to a protein: cross-linker ratio?  Were several concentration ratios investigated?We have now specified that 10-60 mg of nuclear lysate were used for the IP depending on the yield of the protein extraction on the day of the experiment. Protein concentration was not measured after the IP as proteins were crosslinked and digested on the beads. Therefore, we are unable to provide an accurate protein to cross-linker ratio. We also tried 5 mM DSSO concentration . An increase of ~1.8 times in crosslinks was observed. Although it is a substantial improvement, it was not proportional to the 5-fold increase in amount of crosslinker and was therefore deemed to not be cost-effective.On page 11, the authors mention that alternative software is available for data analysis.  This list is not extensive and should at least include other applications such as Xi, StavroX, etc.We have now added other software for XL data analysis: ”As an alternative to Proteome Discoverer 2.4 with XLinkX and Sequest, open-access software such as MaxQuant , pLink , XQuest/xProphet , StravoX , MeroX , Kojak , XiSEARCH  or MaxLinker  could be used for XL identification.”The authors get slightly fixated on the distance restraint of 37Å.  This is a lenient value and may not have any particular importance for these disordered proteins.We agree that 37 Å is a lax value. We have now relaxed the use of the 37 Å threshold throughout the manuscript. Further examination of our results has revealed that most of Thrap3 XLs satisfy a distance constraint of 32 Å is all models, and we have modified the manuscript accordingly.For the cross-linking data analysis, it may prove useful to include serine and threonine in the reaction specificity for DSSO.We thank the reviewer for this suggestion. We did not check for serine and threonine sites as we generally see less than 1% reactivity with these residues when using other reagents with similar reaction chemistry to DSSO such as TMT.References: CHEN, Z.-L., MENG, J.-M., CAO, Y., YIN, J.-L., FANG, R.-Q., FAN, S.-B., LIU, C., ZENG, W.-F., DING, Y.-H., TAN, D., WU, L., ZHOU, W.-J., CHI, H., SUN, R.-X., DONG, M.-Q. & HE, S.-M. 2019. A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides. Nature Communications, 10, 3404. COX, J. & MANN, M. 2008. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 26, 1367-1372. GÖTZE, M., PETTELKAU, J., FRITZSCHE, R., IHLING, C. H., SCHÄFER, M. & SINZ, A. 2015. Automated Assignment of MS/MS Cleavable Cross-Links in Protein 3D-Structure Analysis. Journal of the American Society for Mass Spectrometry, 26, 83-97. GÖTZE, M., PETTELKAU, J., SCHAKS, S., BOSSE, K., IHLING, C. H., KRAUTH, F., FRITZSCHE, R., KÜHN, U. & SINZ, A. 2012. StavroX--a software for analyzing crosslinked products in protein interaction studies. J Am Soc Mass Spectrom, 23, 76-87. HOOPMANN, M. R., ZELTER, A., JOHNSON, R. S., RIFFLE, M., MACCOSS, M. J., DAVIS, T. N. & MORITZ, R. L. 2015. Kojak: Efficient Analysis of Chemically Cross-Linked Protein Complexes. Journal of Proteome Research, 14, 2190-2198. LEITNER, A., WALZTHOENI, T. & AEBERSOLD, R. 2014. Lysine-specific chemical cross-linking of protein complexes and identification of cross-linking sites using LC-MS/MS and the xQuest/xProphet software pipeline. Nat Protoc, 9, 120-37. MENDES, M. L., FISCHER, L., CHEN, Z. A., BARBON, M., O'REILLY, F. J., GIESE, S. H., BOHLKE-SCHNEIDER, M., BELSOM, A., DAU, T., COMBE, C. W., GRAHAM, M., EISELE, M. R., BAUMEISTER, W., SPECK, C. & RAPPSILBER, J. 2019. An integrated workflow for crosslinking mass spectrometry. Mol Syst Biol, 15, e8994. RINNER, O., SEEBACHER, J., WALZTHOENI, T., MUELLER, L. N., BECK, M., SCHMIDT, A., MUELLER, M. & AEBERSOLD, R. 2008. Identification of cross-linked peptides from large sequence databases. Nat Methods, 5, 315-8. WALZTHOENI, T., CLAASSEN, M., LEITNER, A., HERZOG, F., BOHN, S., FÖRSTER, F., BECK, M. & AEBERSOLD, R. 2012. False discovery rate estimation for cross-linked peptides identified by mass spectrometry. Nature Methods, 9, 901-903. YUGANDHAR, K., WANG, T.-Y., LEUNG, A. K.-Y., LANZ, M. C., MOTORYKIN, I., LIANG, J., SHAYHIDIN, E. E., SMOLKA, M. B., ZHANG, S. & YU, H. 2020. MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity*. Molecular & Cellular Proteomics, 19, 554-568.  et al. report the characterization of the ternary interaction between mouse Thrap3, Bclaf1, and Erh by chemical cross-linking (XL) and mass spectrometry. The cross-linking data was obtained from an experiment that initially targeted the BAF complex, which did not yield sufficient cross-links for a detailed structural interpretation. Thrap3, Bclaf1, and Erh, however, were found to be connected through a mutually supporting set of cross-links, suggesting that the observed connections specify the binding interfaces in a ternary complex. The manuscript describes data generation from the IP-XL-MS experiment, for which results from multiple, independent experiments were combined. In total, 24 non-redundant peptide pairs involving at least one of the three proteins were identified repeatedly.In this contribution, Shcherbakova Thrap3 and Bclaf1 are predicted to have large unstructured/intrinsically disordered regions, which makes it difficult to study their interactions by established structural biology methods. The XL data presented here serve as a starting point for further experiments, including integrative modeling approaches to define the organization of the complex. The role of Thrap3 and Bclaf1 in some splicing events may make the complex relevant for diseases such as cancer. The manuscript is clearly written and experiments are described in sufficient detail to enable replication. XL-MS results have been deposited to the PRIDE repository, facilitating reanalysis or reuse of the data.On page 7, the authors discuss the potential ambiguity of XL data when it comes to site pairs on the same protein. While this is true, the case described here - a cross-link that connects partially overlapping sequences - needs to be inter-molecular . I would emphasize this more in the text.Several times throughout the manuscript, the authors mention that they used an upper distance of 37 Å to assess "compatibility" of a cross-link with available PDB structures or structure predictions, citing Liu et al., 2020, as a reference. I would like to point out that 37 Å is a quite lenient threshold, and upper distances of 30-32 Å are much more commonly used. Nevertheless, an interaction between largely unstructured proteins may sample a larger conformational space. Somewhat related to this, the authors discuss that intra-molecular links on Thrap3 were compatible with one of the proposed models (page 7). Since more than one model was predicted, how did the links fit to other models?In the methods section (page 10), an "IPP150" buffer is mentioned that needs to be defined. On the same page, "precursor selection ... with a mass resolution of 120,000" should be rephrased. Precursors were detected at a resolution of 120,000 but their selection for fragmentation is only dependent on the isolation width. "miss cleavages" should read "missed cleavages".The legend in Table 1 mentions four experiments, although the results from five are displayed. Minor comments that could be addressed in a revised version:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?Not applicableAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:cross-linking mass spectrometry; proteomics; structural biology; protein-protein interactions, protein-RNA interactionsI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. On page 7, the authors discuss the potential ambiguity of XL data when it comes to site pairs on the same protein. While this is true, the case described here - a cross-link that connects partially overlapping sequences - needs to be inter-molecular . I would emphasize this more in the text.We have now emphasised that the cross-link that connects partially overlapping sequences corresponds to an inter-molecular connection .Several times throughout the manuscript, the authors mention that they used an upper distance of 37 Å to assess "compatibility" of a cross-link with available PDB structures or structure predictions, citing Liu et al., 2020, as a reference. I would like to point out that 37 Å is a quite lenient threshold, and upper distances of 30-32 Å are much more commonly used. Nevertheless, an interaction between largely unstructured proteins may sample a larger conformational space. Somewhat related to this, the authors discuss that intra-molecular links on Thrap3 were compatible with one of the proposed models (page 7). Since more than one model was predicted, how did the links fit to other models?We thank the reviewer for his expert comment on the cross-link distance upper threshold.The cross-links fit to the other 4 models was similar, with models 2,3 and 5 satisfying all 5/5 crosslinks at a distance of 32 Å and models 1 and 4 satisfying 4/5 crosslinks at 32 Å. We have now modified the text to include this information.In the methods section (page 10), an "IPP150" buffer is mentioned that needs to be defined. On the same page, "precursor selection ... with a mass resolution of 120,000" should be rephrased. Precursors were detected at a resolution of 120,000 but their selection for fragmentation is only dependent on the isolation width. "miss cleavages" should read "missed cleavages".The IPP150 buffer composition  is now defined in the text.We have rephrased the MS mass resolution and precursor selection parameters to: “MS scans were acquired at a mass resolution of 120,000 and precursors between 375-1,600 m/z and charge equal or higher than +3 were isolated for collision-induced dissociation (CID) fragmentation with quadrupole isolation width 1.6 Th in the top speed mode in cycles of 5 seconds.”We have also replaced “miss cleavages” with “missed cleavages”.The legend in Table 1 mentions four experiments, although the results from five are displayedThe legend has been corrected.