STAT3, a pleiotropic transcription factor, plays a critical role in the pathogenesis of autoimmunity, cancer, and many aspects of the immune system, as well as having a link with inflammatory bowel disease. Changes caused by non-synonymous single nucleotide polymorphisms (nsSNPs) have the potential to damage the protein's structure and function.STAT3 and predicted structural changes associated with mutants that disrupt normal protein–protein interactions using different computational algorithms.We identified disease susceptible single nucleotide polymorphisms (SNPs) in in silico tools, such as SIFT, PolyPhen v2, PROVEAN, PhD-SNP, and SNPs&GO, were used to determine nsSNPs of the STAT3. Further, the potentially deleterious SNPs were evaluated using I-Mutant, ConSurf, and other computational tools like DynaMut for structural prediction.Several STAT3 were identified, 6 of which are considered deleterious by in silico SNP prediction algorithms. Amino acid changes in V507F, R335W, E415K, K591M, F561Y, and Q32K were identified as the most deleterious nsSNPs based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein–protein interaction tools.417 nsSNPs of STAT3 may serve as potential targets for different proteomic studies, large population–based studies, diagnoses, and therapeutic interventions.The in silico prediction analysis could be beneficial as a diagnostic tool for both genetic counseling and mutation confirmation. The 6 deleterious nsSNPs of  Signal transducer and activator of transcription factors (STAT) regulate key aspects of cell growth, survival, and differentiation. STAT pathway is linked to the Janus kinases (JAK) family of proteins, i.e., STATs are activated by phosphorylation by JAKs. STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6 are members of the mammalian STAT family [STAT3 include hyper-IgE recurrent infection syndrome 1, autosomal dominant and autoimmune disease, multisystem, and infantile-onset. Genome-wide association studies (GWAS) have revealed the role of STAT3 in diseases like mean corpuscular volume, multiple sclerosis, inflammatory bowel disease, Crohn disease, ankylosing spondylitis, psoriasis, ulcerative colitis, and sclerosing cholangitis. Constitutive activation and regulation of STAT3 induce cellular transformation and can exert various types of tumors including head and neck, prostate, breast, and leukemia [The major disorders associated with STAT3 can enhance cancer stem cell self-renewal and differentiation by altering the gene expression via the epithelial–mesenchymal transition (EMT) [STAT3 induces VEGF expression in the association of hypoxia-inducible factor 1-alpha (HIF1A) to promote tumor angiogenesis [STAT3 has been found to be significantly associated with autoimmune thyroid diseases (AITDs) and further suggests that this may alter the level of thyroid autoantibodies in AITD patients [STAT3 has been associated with a lower risk of gastric cancer in the Chinese population [on (EMT) . STAT3 iogenesis . An exampatients . The rs7Single nucleotide polymorphism (SNP) is considered for over 90% of sequence variations in the human genome  and playSTAT3 gene and to identify their structural and functional impact on the STAT3 protein.In this study, detailed investigations have been carried out using several in silico tools to evaluate the potentially detrimental effects on nsSNPs of the STAT3 gene.Various in silico tools were used to predict the variations in the structure, stability, and function of the STAT3 gene (Accession: NP_003141.2) was collected from the National Center for Biotechnology Information (NCBI) dbSNP database (https://www.ncbi.nlm.nih.gov/projects/SNP) and the protein sequence was retrieved from the UniProt database (UniProtKB – P40763 (STAT3_HUMAN)).The nsSNP distribution of the To estimate the functional repercussions of nsSNPs in the coding area acquired from the dbSNP database, 7 best-performing web tools were employed sequentially.https://sift.bii.a-star.edu.sg/) [Sorting Intolerant From Tolerant   predictshttp://genetics.bwh.harvard.edu/pph2/) [Polymorphism Phenotyping v2   uses thehttp://provean.jcvi.org/index.php) [Based on the alignment score of a protein, Protein Variation Effect Analyzer   analyzeshttps://www.pantherdb.org/tools) [Based on evolutionary relationships, molecular activities, and interactions with other proteins, Protein Analysis through Evolutionary Relationship   predictshttp://snps.biofold.org/snps-and-go/snps-and-go.html) [SNPs&GO (go.html)  used thehttp://snps.biofold.org/phd-snp/phdsnp.html) [Predictor of human Deleterious Single Nucleotide Polymorphisms   is an onhttp://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) [I-Mutant 3.0 (3.0.cgi)  was used3.0.cgi) . A ΔΔG vhttp://consurf.tau.ac.il) [The ConSurf web server (u.ac.il)  analyzesNetsurfP-2.0  predictshttp://www.cmbi.ru.nl/hope/) [HOPE (l/hope/)  was usedhttps://npsa-prabi.ibcp.fr/cgibin/npsa_automat.pl?page=npsa_sopma.html) [SOPMA (ma.html)  was usedhttps://string-db.org/) database predicts the functional linkages between proteins as well as their associations by combining data from physical interactions and curated biological pathway databases.The Search Tool for the Retrieval of Interacting Genes/Proteins  predicts the gene–gene interaction using a large set of functional association data including protein and genetic relationships, pathways, co-expression, co-localization, and protein domain similarity. STAT3 was the input query entered into the software.GeneMANIA   is the most accurate technique for predicting protein structure by neural network–based model. The AlphaFold model includes the coordinates of every heavy atom in a protein as well as “its confidence in the form of a projected LDDT-C score (pLDDT) per residue” [AlphaFold (residue” . The LDDresidue” . The Unihttp://biosig.unimelb.edu.au/dynamut/). DynaMut employs normal mode analysis (NMA) to compare the free energy change (ΔΔG) between the wild-type and mutant structures. DynaMut additionally provides structure-based predictions for mCSM [The impact of a single point mutation on the stability, conformation, flexibility of proteins, and the visualization of protein dynamics, were evaluated using the DynaMut server , 307 synonymous, 3 inframe deletions, and 2 inframe insertions. We exclusively chose nsSNPs for further analysis in this study as they change the encoded amino acid.All the SNP data for the Table 1.Various in silico prediction tools such as SIFT, Polyphen-2, PROVEAN, SNP & GO, PANTHER, and PhD–SNP were used to analyze disease-associated SNPs. Initially, all the 417 nsSNPs were loaded to the SIFT server, which predicted 160 nsSNPs as tolerated or deleterious, whereas the remaining SNPs were not found. Out of this, 67 nsSNPs were classified as deleterious with a SIFT score ≤0.05 and the remaining were tolerated. The nsSNPs were then submitted for PolyPhen2 analysis. To increase the accuracy of the prediction, the combined prediction of both SIFT and PolyPhen, such as the nsSNPs with SIFT score ≤0.05 and PolyPhen score >0.90, was selected and 9 nsSNPs were identified as deleterious. The selected 9 nsSNPs were subjected to other in silico tools, namely PROVEAN, SNP & GO, PANTHER, and PhD–SNP, and the results are given in STAT3 with a RI. Finally, 6 nsSNPs were determined as deleterious by all the above online software. rs145786768, rs193922716, rs193922717, rs193922719, rs1064116, and rs1803125 with a change in amino acid V507F, R335W, E415K, K591M, F561Y, and Q32K were picked as the most deleterious nsSNPs since they showed a mutation in a majority of the in silico prediction tools. In addition, we used the I-Mutant server to analyze the effects of nsSNPs on protein stability. The result showed that 6 nsSNPs have a ΔΔG value < −0.5, which indicates decreasing stability of the STAT3 protein (Table 1).According to PROVEAN results, 6 nsSNPs were predicted as disease-causing and 3 were neutral. Through the PANTHER tool, 6 nsSNPs were predicted as probably damaging, 1 was possibly damaging, and 1 was found to be benign. Moreover, SNP & GO predicted 6 of these 9 nsSNPs as disease-relevant mutations in Figure 1. The result of ConSurf is shown in Table 2.Through the ConSurf web server, the evolutionary conservancy of 6 nsSNPs of STAT3 protein was analyzed, together with the identification of putative, structural, and functional residues. The results showed that all the 6 nsSNPS, namely V507F, R335W, E415K, K591M, F561Y, and Q32K, were highly conserved and the variants are indicated in black boxes in Table 3.The variants with high conservation scores in ConSurf output were assessed for solvent accessibility, stability, and secondary structure prediction by NetsurfP-2.0. The results are displayed in th position the mutant introduced an opposite charge; further, the mutant also introduced a charge at position 32. The variation in charge can cause a loss of interaction with other molecules. Figure 2 illustrates the structural images of 6 deleterious nsSNPs showing both wild-type and mutant residues at the specific protein site.HOPE showed the difference between the wild-type and mutant amino acids in connection with their physical and chemical properties, hydrophobicity, spatial structure, and function. Project HOPE server revealed that the mutant residues V507F, R335W, E415K, F561Y, and Q32K were bigger than the wild-type residues whereas the mutant residue of K591M was smaller than the wild-type. In addition, the mutant residue of R335W, K591M, and F561Y is more hydrophobic than the wild-type. The change in the size and hydrophobicity of the mutant residue can disrupt the H-bonding interaction with neighboring molecules. Moreover, the mutation at 335, 591 positions the wild-type residue charge was lost, whereas at 415Figure 3. SOPMA secondary structure elements calculations showed that 50.39% of sites were in alpha helixes, 33.38% in random coils, 2.86% in beta twists, and 13.38% in extended strands. Out of the 6 high-risk nsSNPs, 2 were found in random coils, 3 in alpha helixes, and 1 in extended strands. The STRING maps were used to depict the protein–protein interaction of STAT3. The STRING results showed the functional interaction pattern of the STAT3 protein with other proteins in a cell, which is useful in interpreting the genotype–phenotype consequences of mutations. Histone acetyltransferase p300 (EP300), E3 SUMO-protein ligase (PIAS3), interleukin-10 receptor (IL10RA), JAK1, JAK2, epidermal growth factor receptor (EGFR), heat shock protein HSP 90-alpha (HSP90AA1), proto-oncogene tyrosine-protein kinase (SRC), and homeobox protein NANOG were all found to have substantial functional connections with STAT3 in the STRING prediction .SOPMA predicted the secondary structure of STAT3, which explained the distributions of alpha-helix, beta-sheet, and random coil. The secondary structure prediction of STAT3 by SOPMA is shown in Figure 5 shows the structure predicted by the RaptorX Binding server.STAT3 ligand binding sites were predicted using the RaptorX Binding server. A pocket multiplicity number greater than 40, according to the RaptorX Binding server, suggests an accurate prediction. In the STAT3 protein, there were only 1 processed domain and 2 predicted pockets, one with a multiplicity of 11 that binds to R382 L430 I431 S465 N466 Q469 and the other with a multiplicity of 3 that binds to M331 H332 I467. The server predicted that 128 locations were disordered. RaptorX also predicted the secondary structure showing 45% helix, 13% sheets, and 41% coil. STAT3, which is shown in Figure 6. According to the predicted results, STAT3 interacts physically and genetically with nuclear factor kappa B Subunit 1 (NFKB1Z) and mitogen-activated protein kinase kinase 5 (MAP2K5), EGFR, and STAT1, and is co-expressed mostly with EGFR, protein disulfide-isomerase A3 (PD1A3), interleukin 6 receptor (IL6R), and STAT5B.GeneMANIA server predicted the functional gene–gene interaction network of Figure 7).The overall confidence for the whole protein chain is shown by the average pLDDT scores across all residues The Alpha-Fold algorithm produces a pLDDT score for each individual residue that ranges from 0 to 100. In STAT3 3D structure, a very high degree of confidence (pLDDT >90) was obtained and majority of the 3D structural area belongs to α-helical domains (Table 4. DynaMut identified the ∆∆G prediction of F561Y as destabilizing; however, NMA-based predictions revealed that all 6 deleterious nsSNPs reduce structural stability when compared to the wild-type protein. Figure 8 depicts the difference in vibrational entropy and interatomic interactions between the wild-type and mutant.DynaMut calculates the effect of point mutations on protein stability and flexibility based on interatomic interactions, as summarized in STAT3 using a variety of computational methods determines the effects of deleterious nsSNPs in STAT3 and their association with various diseases.In mammals, several STAT proteins play an important role in host defense. Despite its importance, STAT3 is linked to the JAK family of proteins and is capable of integrating signals from several signaling pathways. STAT3 that is persistently active has been associated with various malignancies, though it is most commonly associated with head and neck cancer and multiple myelomas . The occIn this study, by merging numerous in silico-based SNP prediction algorithms, we projected 6 deleterious SNPs that are considered high-risk and valid, and they were further examined. The nsSNPsrs145786768, rs193922716, rs193922717, rs193922719, rs1064116, and rs180312 with amino acid changes V507F, R335W, E415K, K591M, F561Y, and Q32K were considered deleterious using 6 in silico SNP prediction tools SIFT, Polyphen-2, PANTHER, SNP&GO, PROVEAN, and PHD -SNP. Based on the conservation profile, structural conformation, relative solvent accessibility, secondary structure prediction, and protein–protein interaction, all of the 6 nsSNPs were identified as the most deleterious nsSNPs. Protein stability is essential for a protein's structural and functional activity. The I-Mutant tool revealed that the predicted 6 nsSNPs on the STAT3 protein had decreased stability. Protein stability determines the conformational structure and function of the protein. Misfolding, degradation, or abnormal protein conglomeration can be affected by changes in protein stability .Conserved residues of the protein were involved in biological system management, including stability, folding, or both of these. Functional amino acids that are present in enzymatic sites interact with the proteins in a significant manner and when compared to other residues in the protein, these residues are found to be more conserved . The evoThe exposed variants were located on the surface of the protein, which might lead to loss of interactions and structural alterations, particularly in the transmembrane domains. Repulsion, misfolding, and loss of interactions may occur from the addition or removal of charge or hydrophobicity. SOPMA secondary structure calculations showed that the deleterious nsSNPs are mainly found in helix and coil regions rather than β turns. RaptorX predicted the high-risk nsSNPs as ligand binding locations.STAT3-targeted genes associated with oncogenesis to minimize therapeutic toxicity [STAT3 gene might influence and disrupt the proper functioning of related genes, which highlights the importance of these linked and co-expressed genes in immune pathways and inflammation-associated tumorigenesis. The need for the acquisition of a higher quality of targeted protein structure and the validation of experimental models in turn necessitate further investigation into analysis of the nsSNPs-induced deteriorative changes in STAT3. In AlphaFold, pLDDT computes the degree of prediction and experimental structure of STAT3. The DynaMut tool provides both the stability change and the difference in entropy energy between wild-type and mutant structures. The 6 nsSNPs were found to affect the molecular flexibility of the STAT3 protein by altering its structural conformation.Protein–protein interaction network is an important factor in understanding biological processes. Using STRING, the functional genomics data and structural assessment, functional, and evolutionary features of STAT3 protein were analyzed . EP300, toxicity . Based oSTAT3 gene are capable of regulating the expression, stimulation, and predisposition in inflammatory and neoplastic diseases [STAT3 activation was detected in various tumors in association with the proliferation, invasion, and angiogenesis of malignant cells and the inhibition of anti-tumor immunity [Deleterious SNPs of the diseases . STAT3 aimmunity . Althougimmunity . The stuSTAT3 exerts important effects on tumorigenesis and tumor-related inflammations. Variations in STAT3 are associated with various human tumors. The present study screened 417 nsSNPs of the STAT3 gene using different computational tools and scrutinized 6 nsSNPs with amino acid changes, namely V507F, R335W, E415K, K591M, F561Y, and Q32K, as most deleterious. The deleterious variants may affect structural and cellular function. STAT3 is involved in different tumors and the identified deleterious nsSNPs from our study could be important candidates and could be used as diagnostic markers. Based on our result, we can conclude that these 6 nsSNPs should be considered as potential biomarkers in causing diseases related to STAT3 variations. However, in silico tools cannot replace conclusive trials, and their conclusions should be double-checked by additional biological evaluation.The transcription factor


Drosophila melanogaster, LRRK1 in Aedes aegypti, and VCPl in Tribolium castaneum. With the possibility of using insect models to investigate neurodegenerative diseases. We conclude from the protein comparative analysis between different insect models and nsSNP analyses that D. melanogaster is the best model for Alzheimer’s representing five nsSNPs of the 21 suggested mutations in the APPl protein. Aedes aegypti is the best model for Parkinson’s representing three nsSNPs in the LRRK1 protein. Tribolium castaneum is the best model for Huntington’s disease representing 13 SNPs of 37 suggested mutations in the VCPl protein. This study aimed to improve human neural health by identifying the best insect to model Alzheimer's, Parkinson’s, and Huntington’s.Alzheimer's, Parkinson’s, and Huntington’s are the most common neurodegenerative diseases that are incurable and affect the elderly population. Discovery of effective treatments for these diseases is often difficult, expensive, and serendipitous. Previous comparative studies on different model organisms have revealed that most animals share similar cellular and molecular characteristics. The meta-SNP tool includes four different integrated tools  was used to identify non synonymous single nucleotide polymorphism (nsSNPs). Prediction of nsSNPs was conducted on three representative proteins for Alzheimer's, Parkinson’s, and Huntington’s diseases; APPl in  They are incurable and mostly affect the elderly population. Their incurability refers to the neural death which is the main cause of these diseases, and the late diagnosis where most symptoms appear in late stages of the diseases. The prevalence of age-related neurodegenerative diseases is increasing with age worldwide. The most recognized NDs were Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD) respectively2  are neurological disorders caused by progressive decline in brain function resulting from gradual neuronal deathly2 Fig. . As they5. The genomes of different model organisms have been sequenced in parallel with the human genomes, starting with Drosophila melanogaster6. The availability of multiple insect genomes creates an outstanding potential for comparative genomics among insects and between insects and humans. These comparative studies provide an effective tool for investigating human gene function compared to model insects8 Table 9. Decision-making centres in the brains of insects and mammals share many similarities in physiology although they have evolved independently10. The central complex in insects and the basal ganglia in vertebrates are similar in the maintenance of behavioural actions11. The hippocampi of vertebrates and mushroom bodies of arthropods were also similar in learning and memory  or SNPs that may fall within coding sequences of genes, non-coding regions of genes, or intergenic regions20. SNPs play a significant role and increase the susceptibility toward many diseases. Synonymous SNPs (sSNPs) in coding regions have no effect on translated proteins21. However, they can also affect mRNA stability and translation rate. Nonsynonymous SNPs (nsSNPs), which cause amino acid substitutions, have a direct impact on protein structure and function. SNPs in non-coding regions may affect gene splicing and other biological processes such as RNA degradation and transcription22.Dysfunctional gene behaviour is commonly caused by mutations that are primarily responsible for the development of illnesses. Many disease-causing mutations have been identified in the genome, around 0.5 million are SNPssequence homology algorithm, computational tools can identify mutations that are significantly pathogenic based on their alignment with known pathogenic mutations as in SIFT, and PANTHER tools24. Other computational tools utilise artificial neural networks, and support vector machines to classify the nsSNVs into diseased or neutral substitutions as SNAP, and PhD-SNP tools26. Consensus-based approaches tool that integrate multiple algorithms to determine the pathogenicity of nsSNPs as Meta-SNP tool, that combine 27.Computational tools are used to predict the effects of mutations on protein function and structure. They are important for the analysis of SNVs and their prioritisation for experimental characterization. Using a Proving a causal link between a gene and disease is expensive and time-consuming. Therefore, the comprehensive prioritisation of candidate SNPs and determination of the best model to simulate the disease before experimental testing drastically reduces the associated costs, saves time, and accelerates the process of drug discovery as shown in Fig. . Our aimThis research paper was approved by the research ethics committee from the Faculty of Science, Ain Shams University (ASU-SCI/ENTO/2023/8/1).https://www.ncbi.nlm.nih.gov/books/)28, KEGG DISEASE Database (https://www.kegg.jp/kegg/disease/)31 , Blastp tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi)32, and genes with similar protein architectures were searched using NCBI's SPARCLE (https://www.ncbi.nlm.nih.gov/protfam/) as a resource of the sequential arrangement of CD domains.Protein sequences were retrieved from NCBI using the Protein database (https://www.ncbi.nlm.nih.gov/snp/) with selection criteria (pathogenic and somatic).Human nsSNPs within the coding region were selected for the APP, LRRK2, and VCP genes . Polymorphism data were retrieved from the dbSNP-SNP database of NCBI  Order Diptera: Drosophila melanogaster (D. melanogaster), Musca domestica (M. domestica), Anopheles gambiae (An. gambiae), and Aedes aegypti (A. aegypti), (2) Order Hymenoptera: Apis mellifera (A. mellifera), (3) Order Coleoptera: Tribolium castaneum (T. castaneum), (4) Order Lepidoptera: Bombyx mori (B. mori), and Galleria mellonella (G. mellonella). The insect models were confirmed based on InsectBase (http://v2.insect-genome.com/Classify/Model%20Organism)34. In addition to the Mus musculus as a transition mammalian model and a distant from insect models.The selection of insect models was based on some criteria; these eight selected insect models were taxonomized in four different insect orders (the four largest insect orders)H. sapiens against its homolog in the selected model organisms using BLASTP  with default parameters from NCBI32, except (GRN and NEP2) alignments were performed against D. melanogaster because they showed no alignment against H. sapiens.Pairwise alignment was performed to detect protein homology and identify query coverage and percentage of protein identity. Alignment was performed between each protein in 36. This tool permits the detection of disease-associated nsSNVs for both well-identified and predicted amino acid sequences (SNPs based on dbSNP of humans) . This approach is characterised by other methods by integrating four existing methods: PANTHER, PhD-SNP, SIFT, and SNAP with defined default threshold parameters PANTHER, PhD-SNP, and Meta-SNP: Between 0 and 1 , SIFT: Positive Value , SNAP: Output normalised between 0 and 1 .A local alignment search was performed between the substituted amino acid in the human protein and its homolog protein in the selected insect model using BLASTP and manual search, depending on finding the best match using 5 aa before and 5 aa after the substituted amino acid to provide a proper short sequence needed to find the accurate position of required aa.The matched amino acids and protein sequence were entered into the meta-SNP analysis tool to determine the probability of causing disease for amino acid substitutions according to the human nsSNPs.In Silico SNP prediction of disease-causing variants was performed using the publicly available tool Meta-SNP (meta-predictor of disease-causing variants)39 .Pairwise alignment using blastp with default parameters was conducted for each selected insect protein against its homolog in humans, except for GRN and NEP2, where pairwise alignment was conducted against the fruit fly Tables , and S6.The results showed that:For Alzheimer’s disease Table A. mellifera shows greater identity to H. sapiens than D. melanogaster for APP protein. Musca domestica has more identity with H. sapiens than D. melanogaster for COL25A1 protein. Aedes aegypti is the nearest in identity to D. melanogaster for the GRN protein. Musca domestica, and A. aegypti show greater identity to H. sapiens than D. melanogaster for HDAC6 protein. Galleria mellonella has the closest Tau/Mapt protein identity to H. sapiens beside D. melanogaster. Aedes aegypti shows greater identity to D. melanogaster for the Nep2 protein. Tribolium castaneum, and B. mori have more identical Psn1 and Psn2 to H. sapiens than D. melanogaster. For RAC1 T. castaneum, M. domestica, and G. mellonella were more identical to H. sapiens than D. melanogaster. In the absence of SORL1 in D. Melanogaster; A. mellifera, T. castaneum, and A. aegypti showed a higher identity with H. sapiens. As shown in .Polymorphism data for APP (NP_001191231.1), LRRK2 (NP_940980.4), and VCP (NP_009057.1) proteins were retrieved from the NCBI dbSNP database as a publicly available database. Accordingly, APP was found to contain four missense SNPs in its coding regions. The LRRK2 gene was found to have one missense SNP in its coding region, and the VCP gene was found to have five missense SNPs in its coding region, but two of them rs779834525, and rs1420316004, were related to the FANCG gene and not VCP.For Alzheimer’s disease, SNP analysis was performed on D. melanogaster App-like protein (NP_001245452.1) as a homolog of H. sapiens APP with 36.27% protein identity, using reference human SNPs rs63750264 , rs63750643 (T > A), rs63750671 (A > G), and rs193922916 .D. melanogaster.In rs63750264, Val680Phe or Val680Leu, or Val680Ile in humans matches Val at positions 94, 863, and 869 in D. melanogaster.In rs63750643, Thr677Ala in humans matches the Thr at position 742 in D. melanogaster.In rs63750671, human Ala655Gly matches Ala at positions 180, 802, and 820 in D. melanogaster.In rs193922916, Ala636Val or Ala636Gly in humans matches Ala at positions 138, 246, 713, and 758 in D. melanogaster model according to PANTHER, Phd-SNP, and Meta-SNP while SIFT and SNAP couldn’t identify the effects of nsSNPs. In spite of the fact that A820G has the highest reliability index.Prediction using the SIFT, PANTHER, SNAP, PhD-SNP, and Meta-SNP tools results from 21 input-suggested mutations. Eleven mutations were predicted, Five of the 11 mutations showed deleterious or diseased points Table For Parkinson’s disease, SNP analysis was performed on A. aegypti LRRK1 protein (XP_021698550.1) as a homolog of H. sapiens LRRK2 with 27.47% protein identity, using the reference human SNP rs33939927 .A. aegypti.In rs33939927, Arg1441Ser or Arg1441Gly, or Arg1441Cys in humans matches Arg at position 1218 in A. aegypti model according to Phd-SNP, and Meta-SNP while PANTHER, SIFT and SNAP couldn’t identify the effects of nsSNPs. In spite of the fact that R1218C has the highest reliability index.Prediction using the SIFT, PANTHER, SNAP, PhD-SNP, and Meta-SNP tools results from three input suggested mutations. Three mutations showed deleterious or diseased points Table For Huntington’s disease, SNP analysis was performed on T. castaneum VCP-like protein (XP_008192481.1) as a homolog of H. sapiens VCP with 43.99% protein identity, using the reference human SNPs rs121909330 , rs121909334 , and rs387906789 .T. castaneum.In rs121909330, Arg155Cys or Arg155Gly, or Arg155Ser in humans matches Arg at positions 268, 282, and 836 in T. castaneum.In rs121909334, Arg191Pro or Arg191Gln in human matches 618, 639, 739, 89, 217, 743, 618, 82, 330, 411, and 462 in T. castaneum.In rs387906789, Arg159Cys or Arg159Gly, or Arg159Ser in humans matches Arg at positions 710, and 750 in T. castaneum model according to PANTHER, Phd-SNP, SIFT, SNAP and Meta-SNP. In spite of the fact that R268C, R268G, R282G, R710C, R750C, R750G have the highest reliability index.Prediction using the SIFT, PANTHER, SNAP, PhD-SNP, and Meta-SNP tools results from 37 input-suggested mutations. Fifteen mutations were predicted, thirteen of the 15 mutations showed deleterious or diseased points Table Prediction; Neutral: Neutral variants. Disease: Disease causing variants.Outputs: Value reported under each prediction.PANTHER, PhD-SNP, and Meta-SNP: between 0 and 1 .SIFT: Positive Value . SNAP: Output normalised between 0 and 1 .RI: A Reliability Index between 0 and 10 provides a means of focusing on the most accurate predictions.H. sapiens. We determined the best insect for studying each protein separately by selecting the highest query coverage with the highest protein identity.Neurodegenerative diseases are devastating diseases which are incurable and mostly result in the death of patients. To accelerate the search for treatments and save money, effort, and time, there is a need to determine the best model that mimics human disease. In turn, this leads to improved human neural health. Pairwise alignment was applied to each protein against humans for all proteins except (GRN and NEP2) against the fruit fly because they showed no alignment against In this study, a total of eight insect models were used to find out which of them is the best to model each of AD, PD and HD.D. melanogaster then A. gambiae. Drosophila melanogaster is believed to have nearly 75% of human disease-causing genes functional homologs41. The fruitfly showed a high protein identity to human with reasonable query coverage in GRN, COL25A1, MAPT and RAC1. They can express different phenotypes of induced AD15. From the 10 proteins, APP was selected as a representative of AD related proteins in human. The analysis of nsSNPs related to APPl protein in the fruit fly showed predicted pathogenic nsSNPs  that could be used for further studies on the induction of familial forms of early-onset Alzheimer's disease and cerebral amyloid angiopathy, and study the factors that increase total Aβ levels43. Anopheles gambiae is known to become an important model organism for the study of insect-parasite interactions and innate immune responses to protozoan parasites44. Anopheles gambiae shows better protein identity to H. sapiens than D. melanogaster for DJ-1, VCP and PLA2G6 proteins. Moreover, A. gambiae infection with Toxoplasma gondii promotes the accumulation of glutamate. Glutamate is a neurotransmitter in the brain that triggers neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease in individuals predisposed to such conditions45. Thus in turn makes A. gambiae a potential model to study the pathology of these AD.For Alzheimer’s, the best overall two models according to the average protein identity percentage for the 10 selected proteins were A. aegypti then A. mellifera. A. aegypti has an advanced nervous system, with sensory organs used to locate their hosts in their environment46. On applying a sublethal dose of spinosyn insecticides to A. aegypti. Parkinson's disease-related genes were significantly enriched in spinetoram-exposed mosquitoes compared with controls47. Through our studies, it showed a high protein identity to human with reasonable query coverage for PARK6, VPS35, ATP13A2 and PLA2G6. From the 13 proteins, LRRK2 was selected as a representative of PD related proteins in human. The analysis of nsSNPs related to LRRK1 protein in the yellow fever mosquito showed predicted pathogenic nsSNPs  that could be used for induction of PD through mutations in the catalytic domains that may result in hyperactivation of the kinase domain, and show Lewy Body pathology48. Apis mellifera is more similar to vertebrates in terms of RNA (Ribonucleic acid) interference, DNA (Deoxyribonucleic Acid) methylation, and circadian rhythm49. It showed a high protein identity to human with reasonable query coverage in PARK2, VPS35 and ATP13A2. Honey bees’ ethanol exposure causes changes in their body and wing kinematics50. Mechanisms identified in the cellular stress response to ethanol, such as the oxidative stress response, are also involved in Parkinson’s disease51. Apis mellifera is a key social behavioural model that displays sophisticated cognitive abilities52. This makes it possible to analyse the changes occurring in honeybee brains during learning and remembering and increases the opportunity to be used also as a model for AD, along with the ability to identify new genome-based single-nucleotide polymorphisms (SNPs)53.For Parkinson’s, the best two models according to the average protein identity percentage for the 13 selected proteins were T. castaneum then B. mori were the best models according to the average protein identity percentage. Tribolium castaneum has more olfactory receptors and detoxification genes than D. melanogaster and other insects and may be better adapted to its environment45. It shows a higher genetic homology to humans when compared to other invertebrate models, such as D. melanogaster54. Therefore, T. castaneum is one of the most suitable genetic models for post-genomic studies such as proteomics and functional genomics. It showed a high protein identity to human with reasonable query coverage in GRIK2, VPS13A and UBQLN2. From the 10 proteins, VCP was selected as a representative of HD related proteins in human. The analysis of SNPs related to VCPl protein in the Red flour beetle revealed predicted pathogenic nsSNPs  that could be used for further studies on the gene role in cell division, the cell apoptosis, repairing damaged DNA, and formation of abnormal proteins build up in muscle, bone and brain cells that lead to induction of HD. These protein aggregations interfere with the normal functions of the brain cells56. The PINK1 protein from the T. castaneum beetle (TcPINK1) exhibits catalytic activity toward ubiquitin, parkin, and generic substrates and provides a basis for further studies on human Parkinson’s disease57. Bombyx mori shares 58% of diseased human homologs genes, which are related to neurodegenerative diseases such as HD, oxidative stress, and protein degradation-associated genes58. Bombyx mori has higher identical VPS35, and UCHL1 to H. sapiens than D. melanogaster. Downregulation of the DJ-1 gene causes p-translucent silkworm as a result of increased oxidative stress response of the body, which leads to oxidative damage to the nerves and tissues18.For Huntington’s, Galleria mellonella didn’t represent the best model for any of the three studied NDs, although it has a similar innate immune response to that of mammals, regardless of whether it evolved separately from mammals several thousand years ago31. Comparative studies of genomes have shown that it has numerous homologues of human genes encoding proteins involved in pathogen recognition or signal transduction60. According to our study, it showed a high protein identity to human with reasonable query coverage in MAPT, ATP13A2, GIGYF2 and RAC1. In addition, its larvae can cultivate Bacteria such as Borrelia burgdorferi61, Enterococcus faecalis62, and Staphylococcus aureus63, which are believed to play a role in neuroinflammation and may contribute to AD.Muscadomestica has a strong immune system and has been used as a model to investigate the presence of enhanced detoxification64. Applying its larval extract on an AD diseased mouse has therapeutic effects against memory impairment, structural damage, and oxidative stress65. According to our study, it showed a high protein identity to human with reasonable query coverage in RAC1, COL25A1, HDAC6, DJ-1, GRIK2, VPS13A, VCP and UBQLN2.These findings will assist in the selection of the best model for further studies in simulation diseases, deep understanding for mutations and their effects and how to fix them genetically or through improving drug discovery. The average percentage of protein identity between the different insect models and the selected proteins is provided in the supplementary data, as shown in Figs. The increasing prevalence of neurodegenerative diseases such as Alzheimer's, Parkinson’s and Huntington's necessitates improvement in our understanding of these diseases. The research strategy for NDs is two-armed; one of them focuses on finding actual treatments that work on delaying symptoms or preventing disease development, whereas the other depends on searching for tools that can be used to detect the earliest and indirect signs of the disease and this is our point. Thus, it is crucial to simulate the disease, identify the counterparts of human diseased genes, test and apply their findings to easily handled model organisms. Comparative analysis has the potential to improve research and drug development for human diseases.D. melanogaster, A. aegypti and T. castaneum respectively by five prediction tools; 21 out of 29 SNPs showed a deleterious effect and 8 of the 21 showed high reliability index. For the 21 deleterious nsSNPs, most of them are located on the functional domains of the proteins.In this study, a total of 61 SNPs were checked in APPl, LRRK1 and VCPl proteins of Although mammalian models are more similar to humans, insects are often preferred because of their shorter lifespan and fewer ethical constraints. Human insect disease models provide new tools for drug discovery to overcome current limitations by using them at different stages as models that show a significant response to many drugs that act on the mammalian central nervous system (CNS) instead of differences in their brains, which allows researchers to find new therapeutic strategies.A. mellifera, T. castaneum, B. mori, A. aegypti besides D. melanogaster have promising future in the field of medical research and provide valuable insights into common neurodegenerative diseases as AD and PD and rare diseases as HD. This study provides comprehensive information on the available insect models on the protein-level resources and analysis of the predicted functional nsSNPs to improve human neural health by finding the best insect model to study Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease, and to find answers to complex biological questions as the functional impacts of these variants. This will happen by using the findings of the predicted nsSNPs for example to enhance wet-labs experiments and detect the proper position to be knocked down and find out the pathological effects of it and on determining the possible affected genes or proteins on induction of one of the NDs in its proper models.In conclusion To maximise the benefits, we recommend the provision of stock centres of different insect models, mutant and transgenic strains, microarrays, or RNA interference libraries, and working on updating annotations, providing more genome sequencing and assembly of sequenced insects. Additionally, we recommend the development of tools specific to insect model organisms.Supplementary Information.


Protein interaction networks underlie countless biological mechanisms. However, most protein interaction predictions are based on biological evidence that are biased to well-known protein interaction or physical evidence that exhibits low accuracy for weak interactions and requires high computational power. In this study, a novel method has been suggested to predict protein interaction partners by investigating narrow funnel-like interaction energy distribution. In this study, it was demonstrated that various protein interactions including kinases and E3 ubiquitin ligases have narrow funnel-like interaction energy distribution. To analyze protein interaction distribution, modified scores of iRMS and TM-score are introduced. Then, using these scores, algorithm and deep learning model for prediction of protein interaction partner and substrate of kinase and E3 ubiquitin ligase were developed. The prediction accuracy was similar to or even better than that of yeast two-hybrid screening. Ultimately, this knowledge-free protein interaction prediction method will broaden our understanding of protein interaction networks.  •Proteins that interact show narrow funnel-like interaction energy distribution•Protein interaction prediction using energy distribution is accurate across species•This method can predict substrates of kinase and EUL•Deep learning model can predict protein interaction using energy distribution Artificial intelligence; Biochemistry; Biochemistry methods; Machine learning; Protein After the genome sequencing of several tens of thousands of organisms, understanding biomolecule networks beyond the individual genes is becoming increasingly important. In particular, protein-protein interactions (PPI) are among the most diverse biomolecular networks and the center of system biology. Several experimental techniques, such as yeast two-hybrid screening, coimmunoprecipitation, affinity-chromatography, tandem affinity purification-mass spectroscopy, etc., have been developed to identify PPI.In silico PPI prediction methods utilize biological, structural, and physical results. Biological evidence includes gene or domain fusion,,,,,,,,,,,in silico protein docking.,Arabidopsis encode kinases and EULs.,,,,,,,,,,,,,One of the most difficult PPIs to predict is that of enzyme-specific interaction partners, such as substrates, inhibitors, and regulators, or signaling pathway interactions. Their diversity of complicated signaling pathways,,In this study, I suggest a new, interaction energy distribution-based PPI prediction strategy. PPIs with a specific interaction partner, such as PPIs of kinase and EULs, exhibit the specificity to maintain sophisticated signaling pathways. For instance, in humans, kinase phosphorylates specific phosphorylation sites ranging from one to hundreds among approximately 700,000 potential phosphorylation sites.Although there are considerable differences of interaction energy distributions between interacting and non-interacting protein complexes, setting a classification criterion with high accuracy is difficult. Recent advances in deep learning have made it possible to extract hidden features from complicated data and classify them.,,To analyze the interaction energy landscape of kinases/EULs and their interaction partners, I retrieved 135 human kinase-interaction partners, including regulator, inhibitor, and substrate, and 189 human EUL-substrate pairs randomly choosen from known databases.Using Phyre2, a server for template-based structure modeling,To analyze the interaction energy landscape A, I calcAs every theoretical protein energy exhibits differences compared with the experimental data, I ignored energy change because of structure change and considered only the affinity to minimize error. To indicate interacting positions, I used interface root-mean-square deviation (iRMS).As To illustrate the structural meaning of the narrow funnel-like interaction energy distribution, For the quantitative analysis of the interaction energy distribution, I devised a strategy to distinguish narrow funnel-like interaction energy distribution A. DockinAs per my hypothesis, if a protein pair with specific interacting partners showed narrow funnel-like interaction energy distribution, specific partners could be predicted with interaction energy distribution analysis by taking specific criteria. Therefore, I calculated the difference between the true-positive (identified pair with narrow funnel-like interaction energy distribution) and false-positive (random pair with narrow funnel-like interaction energy distribution) ratios for each criterion. As per the hypothesis, the narrow funnel distribution of the functionally interacting protein complex was higher than that of random protein pairs in the case of most criteria.For whole protein pairs, docking structures generated by RosettaDock, HDOCKlite showed up to 11.9%–20.3% difference of true- and false-positives . InteracAs protein global docking works well for small proteins,As kinase and EUL PPIs represent only a small part of the whole PPIs, I investigated the interaction energy distribution in other protein complexes. I retrieved 183 curated experimentally determined protein complex structures randomly choosen from the IntAct database,t test, t test). These processes are well-known to display molecular cascades with specific PPIs. Among those, cellular process-, developmental process-, and response to stimuli-related proteins pairs showed higher differences (99% confidence using Student’s t test). In particular, developmental process-related proteins pairs showed the biggest differences of iRMSs between the interacting complexes and non-interacting pairs . This result might be related to developmental processes controlled by complex molecular cascades and networks with specific PPIs.To confirm whether other protein complexes showed narrow funnel-like interaction energy distribution, I calculated the iRMS averages of simulated docking structures. As per the hypothesis A, the avAs I have heretofore demonstrated, there are significant differences in the interaction energy distribution between interacting and non-interacting protein complexes. Thus, I developed deep learning models to predict protein interactions based on complex interaction energy distributions. As described in ,To develop the prediction model, protein interaction energy distributions of 183 experimentally determined interacting protein complex from the IntAct databaseTo apply this deep learning method to predict substrates of EULs and kinases, I retrieved EULs and kinases from various species along with information about their substrates from the Ubibrowser 2.0For substrate prediction of EULs, the optimal prediction model showed a 77.7% accuracy using an Adam optimizer containing a 1024-2-layer structure and 61 epochs. For substrate prediction of kinases, the optimal model for prediction showed a 61.4% of accuracy with an Adam optimizer containing a 1024-64-2-layer structure and 9 epochs  and S15.iRMS is a very useful indicator for analyzing protein interaction positions. However, as the positional information necessary for protein interaction analysis  is condensed into a single indicator, this approach has several limitations. Therefore, it is necessary to introduce various indicators. Especially, structural analysis using root-mean-square deviation, including iRMS, is sensitive to local error.Reciprocal TM-score (rTM-score) is the harmonic average of the TM-scores of the components of the protein complex. Therefore, TM-score of one protein cannot dominate rTM-score of protein complexMoreover, TM-score has shape dependence when used for protein interaction analysis. A slight change in interaction angle can lead to large structural changes depending on the shape. In the case of a long protein, when it interacts to a position similar to the template, it has a smaller TM-score than when a round protein interacts. Therefore, I introduced interface TM-score  in the ROC curve analysis  force field.,,,,,In this study, the development of EUL substrate prediction using deep learning enabled the proposal of new application. Innovative methods such as CRISPR/Cas9, siRNA, or miRNA, which inhibit DNA and RNA levels in the central dogma, have limitations as potential medicines for regulating the expression of a specific gene. Therefore, recently, studies on degrading undrugable targets at the protein level using small molecules called proteolysis targeting chimera (PROTAC) that help interaction to EUL have been actively conducted.,Another possible interaction energy distribution analysis application is that narrow funnel-like interaction energy distribution can be a specificity indicator. Finding specific interactions is important in drug discovery. Nonspecific interactions can result in side effects.Still, this study has several limitations. Although over 1,000 PPIs were analyzed in this study, this number is far less than the number of discovered PPIs. As demonstrated in this study, different types of protein interactions exhibit distinct patterns of interaction energy distribution. Therefore, limited protein interaction analysis leads to restricted data on the distribution patterns of interaction energy. Especially, Artificial Intelligence (AI) model for predicting PPIs trained on limited data can exhibit low accuracy for diverse proteins. Therefore, further research involving large-scale analysis utilizing effective protein docking program and high-performance computing device such as supercomputer will improve PPI prediction.,Moreover, indicators for PPI used in this study  require strongest interacting docking structure as a model structure. Therefore, these indicators may be not appropriate for analyzing PPIs with multi-binding sites or allosteric regulation. Furthermore, because of small dataset and curse of dimensionality,This study only presents an initial model that demonstrates the possibility of predicting protein interactions using the distribution of interaction energy. Deep learning model applied in this study is simple recurrent neural network. Diverse AI structure including convolutional neural networkrgdfs@sogang.ac.kr)Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Juyoung Choi  -docking:partners A_B -dock_pert 3 8 -randomize1 -randomize2 -spin -out:path:all (Output directory)Next, I generated 1,000 docking structures using the following command../bin/docking_protocol.static.linuxgccrelease -in:file:s (prepacked files) -docking:partners A_B -dock_pert 3 8 -randomize1 -randomize2 -spin -use_ellipsoidal_randomization true -nstruct 1000 -out:path:all (output directory)To analyze interacting proteins complexes and non-interacting protein pairs, I optimized their side-chain conformations (prepacking) using the following command../bin/docking_protocol.static.linuxgccrelease -in:file:s (protein complex structure file) -docking:partners (chain ID of proteins which participating in interaction)_(chain ID of proteins in opposite side of interaction) -dock_pert 3 8 -randomize1 -randomize2 -spin -out:path:all (output directory)Then, I generated 1,000 docking structures using the following command../bin/docking_protocol.static.linuxgccrelease -in:file:s (protein complex structure file) -docking:partners (chain ID of proteins which participating in interaction)_(chain ID of proteins in opposite side of interaction) -dock_pert 3 8 -randomize1 -randomize2 -spin -use_ellipsoidal_randomization true -nstruct 1000 -out:path:all (output directory)To analyze kinase and E3 ubiquitin ligase interactions using HDOCKlite,./hdock (kinase or E3 ubiquitin ligase) (Interaction partner) -out (docking file)./createpl (docking file) top1000.pdb -nmax 1000 -complex -modelsAll commands were written in a Linux shell file and executed.For further analysis, kinase or E3 ubiquitin ligase chain IDs were designated as “A” and those of interacting partners as “B” using PyMOL.To calculate interaction energy, I calculated the Rosetta energy of each protein or complex using the following command:./bin/score_jd2.linuxgccrelease -in:file:s (protein or complex) -out:file:scorefile (score file)Then, I calculated the interaction energy using the following equation:Then, iRMS were calculated using DockQ./DockQ.py (Docking structure) (Strongest interacting docking structure) -short ≫>>(iRMS file)For interacting protein complexes and non-interacting protein pairs, iRMS were calculated using the following command:./DockQ.py (Docking structure) (Strongest interacting docking structure) -short -native_chain1 (chain ID of proteins which participating in interaction) -model_chain1 (chain ID of proteins which participating in interaction) -native_chain2 (chain ID of proteins in opposite side of interaction) -model_chain2 (chain ID of proteins in opposite side of interaction) -perm1 -perm2 >>(iRMS file)All commands were written in a Linux shell file and executed.,Moreover, in this study, rTM-score, iTM-score and riTM-score are applied to interaction energy distribution analysis.  To calcuInterface in iTM-score and riTM-score is defined as every residue pairs from two proteins within 10 Å of each other. Interface are selected using interface selection module to calculate iRMS in DockQ program.The bioinformatics analysis was performed as described previously,To construct training dataset for general PPI prediction using deep learning, iRMS and interaction energy of 183 protein complex structures, retrieved from IntAct databasehttps://github.com/cjy318/protein_interaction).Deep learning models were designed as a simple recurrent neural network model with one block using Keras library.


However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties.Deep learning-based molecule generation becomes a new paradigm of de novo molecule design.To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It’s anticipated that this work can boost https://github.com/lijianing0902/CProMG.The code and data underlying this article are freely available at  Typically, ligand-based methods leverage three types of generative models, including Recurrent Neural Networks (RNN) , GeneratSince RNN naturally processes variable-length sequences, it can generate novel molecules by representing molecules in SMILES strings. Furthermore, aiming to refine generated molecules having desired drug-like properties, RNN-based methods always employ diverse optimization strategies. For example, ChemTS directly applies an RNN to generate novel molecules, which are further optimized by a tree search to find molecules having specific drug-like properties . Based oGAN and VAE are two typical distribution-based generative models, which can characterize the small molecule space. GAN contains a generator and a discriminator, contesting with each other by a zero-sum  game. They are trained together in an adversarial manner which enables the generation of novel molecules. ORGAN  utilizesVAEs are generative encoder–decoder models under explicit normal distribution assumptions. Since the latent distribution space is analogous to the chemical space, the designated sampling in it enables the generation of novel molecules owning specific properties. There are various approaches to generate novel molecules with desired properties, such as an extra property predictor  and a coLigand-based methods can generate novel compounds with favorable physicochemical properties. However, since they consider no or less protein information when generating molecules, they cannot guarantee that generated molecules have desired binding affinity to new protein targets.(2) Protein-based methodsIn contrast, recent protein-based methods ensure that generated molecules bind to specific protein targets with high-binding affinities. Some methods turn such a generation into a machine translation problem, which translates protein sequences (amino acid sequences) into molecule sequences (SMILE strings). In this context, Transformer can be directly applied for protein-based molecule generation . AlphaDrSome methods attempt to utilize protein 3D structures when generating molecules. For example, based on the voxelization of 3D protein pockets and 3D molecule structures, To enhance the protein structure representation, recent works characterize the binding interface between a protein and a molecule. By considering the 3D coordinates of atoms in given binding sites, However, the representation of 3D structures is still challenging so far. In terms of binding affinity, the molecules they generated are surprisingly lower than those generated by 1D sequences . More imTo address the above issues, we elaborate a protein-oriented generative framework (CProMG), which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Overall, the main contributions of our CProMG are as follows.It serves in a controllable learning framework to generate novel small molecules having high-binding affinities to specific protein targets while owning desired drug-like properties.It provides a better representation of 3D protein structure (pocket) by integrating a fine-grained atom view with a coarse-grained amino acid view based on an interactive attention block in the encoder.It leverages the protein-interactive multi-head attention block in the decoder to calculate the proximity of molecule tokens to protein residues and atoms, such that crucial interactions between protein pockets and molecules can be captured.m proteins n small molecules Suppose that hesized) . We consGiven a new protein nsformer , we treansformer .Inspired by the hierarchy of protein structure , we char(a) Protein graph constructionTechnically, given a protein k nearest neighbor nodes k = 48), where In the residue-based graph K = 30) as recommended by In the atom-based graph Once the protein graphs are built, our task is to generate the embeddings of nodes and edges. In common, suppose that each node  network , we obtaWhen we attempt to encode each node in a protein graph to obtain a unique positional representation of each node, however, it’s hard to directly define the positions of nodes in a graph. In other words, we cannot apply the positional coding in the vanilla Transformer to the protein graphs. To cope with this issue, we borrow the idea of the Laplacian position encoding in graph neural networks to obtain unique positional representations of nodes in the following.(b) Laplacian positional encodingBecause any signal can be represented as a combination of sine/cosine functions with varying frequencies, Transformer regards the positional coding as a Fourier Transform on a signal. As a result, each entity in turn is coded into a position-unique vector. However, such positional coding cannot be directly applied to graphs because it’s hard to define the positions of nodes in a graph. To cope with this issue, we leverage the Laplacian position encoding in graph neural networks  to assigk-th element k-th node and Given a weighted graph  follows :where Iunctions , can be he graph .k = 8  . Accordik = 8 ] as theik elements in the i-th row of For each node t tandem encoding units, of which each contains an edge-augmented encoding block The embeddings of the amino acid graph Remarkably, (1) Graph encodersTechnically, suppose that node r-th attention layer and defined asSuppose that multi-head attention block Then, inspired by the encoder in the Transformer, the updated node representation (2) Dual-view fusionDuring the parallel encoding process, we designed a one-way cross-fusion block between the W matrices represent linear layers. Thus, the fusion block updates amino acid representations by the concatenation r-th attention layer and is defined asThe fusion block is also a multi-head attention block. Let Similarly, the concatenation is mapped further by another linear layer as To reduce the information redundancy, we only build two cross-fusion blocks for a middle encoding unit (e.g. the 3rd unit) and the last encoding unit (e.g. the 6th unit), respectively.The molecule embedding module encodes molecule sequences based on a pre-built vocabulary and encodes their drug-like properties as well as binding affinities w.r.t. proteins simultaneously. The molecule decoder can generate novel molecules owning desired properties in a controllable manner.We utilize the tokenization proposed by n-length SMILES sequence of a small molecule Formally, given the Let Moreover, we add a property tag “d is an even number or r·2π, where r = 10 000 as suggested by Furthermore, we consider the positional relationship among tokens. Inspired by the Transformer , we use c is defined as:Finally, the whole embedding of small molecule t tandem decoding units. Each unit is composed of a masked multi-head attention block, a protein-interactive multi-head attention block, and an ordinary multilayer perceptron. The masked module prevents the decoder from information leakage when predicting the next token. The interactive module calculates the proximity of molecule tokens to protein residues and atoms by regarding the former as queries and the latter as keys and values in an attention layer. See also We directly adopt the same architecture of the decoder as that of the original Transformer, which contains The molecule generation is completed by an autoregressive decoding process, which begins with the sequence of two tokens As remarked in The training of the model aims to maximize the following negative log-likelihood:where To evaluate the performance of molecule generation models, we follow the conventional settings in recent works , which uVS measures the average binding affinity between generated molecules and proteins of interest. We use Autodock Vina  to calcuSA reflects the average difficulty of synthesizing a given molecule by its synthesizable fragments . A drug-HAR indicates the percentage of generated molecules having higher binding scores than those of reference molecules or equal to them. The greater, the better.QED measures the average similarity between generated molecules and existing drugs by multiple chemical attributes . Its valDiversity evaluates the diversity within a group of generated molecules logP, the water-octanol partition coefficient, is a ratio of a chemical’s concentration in the octanol phase to its concentration in the aqueous phase of a two-phase octanol/water system. According to the Rule of 5(RO5) proposed by Lipinski , logP shTPSA refers to the total surface area of all polar atoms. It measures the drug’s ability to permeate cell membranes. Molecules with a TPSA > 140 A detailed discussion of property-controllable generation can be found in Section 3.3.We adopted the dataset popularly used in previous works . Built bock Vina . Followiock Vina , such thWe used the training set to tune the learnable model parameters while determining the hyperparameters by empirical suggestions in other works.k = 30 as recommended by k) was empirically assigned as 48. We used 64 Gaussian RBFs equidistantly spaced from 0 to 15 Å and set the width parameter to 15/64. As a result, each edge in atom graphs was also represented as a 64-dimensional vector. Finally, as Specifically, when constructing amino acid graphs in the protein embedding module, each node was initially represented as a 20-dimensional one-hot feature vector accounting for amino acid types, and the number of its nearest neighbors In the dual-view encoder, each of the encoders contains 6 tandem encoding units, of which each unit is composed of 4 heads of attention layers. The hidden dimensions of both nodes and edges were set as 256. The dimensions of Query and Key in both the encoder and the cross-fusion module were set as 32, while the dimension of Value was set as 64. In addition, the feedforward network contains 1024 neurons.In the molecule embedding module, each token (including tags and properties) in SMILES strings was initially represented as a 112-dimensional binary vector, including the beginning tag(1-d), the ending tag(1-d), the non-overlapping tokens w.r.t. SMILES strings (110-d) (Section 2.4). In the decoder, we set the length of token strings by the maximum length of SMILES sequences (i.e. 200). In addition, the parameters in the attention module in the decoder adopt the same values as those in the encoder.When training our model, we set the batch size as 4, the initial learning rate We assessed the performance of our CProMG by comparison with five state-of-the-art (SOTA) protein-oriented generative approaches, which including LiGANN , 3D-SBDDSince those approaches adopt the same dataset, to make a fair comparison, we used the default values of parameters as those in the original papers in the comparison. For each protein in the independent testing set, top-10 molecules were generated for comparison.It is the prime requirement that generated molecules bind to specific proteins with high affinities. Thus, we principally set the expected binding affinity as P-values achieved by two-tailed t-tests are in parenthesis). In addition, since both HAR and Diversity are global metrics, the calculation of P-value is inappropriate for them. Especially, it reveals that our CProMG controlling VS, QED, and SA achieves the lowest VS, the lowest QED, and the highest SA as expected. In contrast, since these SOTA approaches cannot control the generation of molecules in terms of drug-like properties, they achieve sharply worse VS, QED, and SA. Therefore, the comparison demonstrates the superiority of our CProMG.The results show that our CProMG significantly outperforms the Reference and other generative methods over all the metrics , while the second ignores the atom view (denoted as w/o Atom). The third removes the Laplacian position encoding (denoted as w/o LPE). The last (denoted as w/o C) removes the conditional control of binding affinity (i.e. docking score).P-values are in parenthesis). The results demonstrate that both the amino acid view and the atom view contribute to protein-oriented molecule generation because they provide coarse-grained representations and fine-grained representations of protein binding pockets respectively. Also, the Laplacian positional encoding has a untrivial contribution to protein-oriented molecule generation because it can extract the unique positional representations of protein binding pockets. Last, the results reveal again that the conditionally control of binding affinity is crucial to generating molecules with high-binding affinity to specific proteins.The comparison shows that CProMG significantly outperforms all the variants on the VS and HAR . The results validate that the molecules generated are novel.In summary, the case study demonstrates that novel molecules generated by our CProMG can not only bind to given specific proteins in high affinity but also own desired drug-like properties.In this article, we have proposed a protein-oriented generative framework for molecule generation (CProMG) under the control of high-binding affinity and desired drug-like properties. CProMG contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. This end-to-end framework can address two existing issues, including inadequate protein representation and incontrollable generation in properties.The comparison with recently published deep generative methods demonstrates the superiority of CProMG. Moreover, the progressive Property-control, the ablation studies as well as the case study validate its contributions. First, CProMG provides a comprehensive framework to generate novel molecules for given proteins with high-binding affinities and desired drug-like properties. Secondly, by fusing the hierarchical views of proteins, it significantly enhances the characterization of protein binding pockets by associating amino acid residues with their comprising atoms. Thirdly, the protein-interactive multi-head attention block in the decoder calculates the proximity of molecule tokens to protein residues and atoms, such that crucial interactions between protein pockets and molecules can be captured.de novo drug design.In summary, we believe that our study provides new insights into molecule generation for


Protein-protein interactions (PPIs) are crucial for biological functions and have applications ranging from drug design to synthetic cell circuits. Coiled-coils have been used as a model to study the sequence determinants of specificity. However, building well-behaved sets of orthogonal pairs of coiled-coils remains challenging due to inaccurate predictions of orthogonality and difficulties in testing at scale. To address this, we develop the next-generation bacterial two-hybrid (NGB2H) method, which allows for the rapid exploration of interactions of programmed protein libraries in a quantitative and scalable way using next-generation sequencing readout. We design, build, and test large sets of orthogonal synthetic coiled-coils, assayed over 8,000 PPIs, and used the dataset to train a more accurate coiled-coil scoring algorithm (iCipa). After characterizing nearly 18,000 new PPIs, we identify to the best of our knowledge the largest set of orthogonal coiled-coils to date, with fifteen on-target interactions. Our approach provides a powerful tool for the design of orthogonal PPIs. Protein-protein interactions (PPIs) are crucial for biological functions and have applications ranging from drug design to synthetic cell circuits. Here the authors develop an assay and computational methods to identify more orthogonal coiled-coil pairs, critical for biological processes and drug design. Our ability to design and create functions and structures as complex as those found in nature, though still in its infancy, is progressing with advances in both protein design algorithms and gene synthesis.Protein–protein interactions (PPIs) are integral to most biological functions and are required for such diverse processes as cell division, signalling, metabolism, transcription and translation2. Supramolecular protein designs can be created using simple, natural protein families such as coiled-coils, which have been used to build numerous designed protein assemblies5. However, identifying orthogonal natural proteins is difficult because evolutionarily related proteins often display significant cross-interactions. Another method is to computationally design de novo proteins; in particular, Rosetta-based designs have produced homodimers7 and heterodimers8. In a state-of-the-art example, Chen et al.8 performed a single-pot experiment mixing fifteen designed heterodimer pairs, which resulted in a set of twelve orthogonal heterodimers. However, the Rosetta energy function did not successfully predict orthogonality, and predicting orthogonal binding and designing large orthogonal sets remain beyond current de novo design methods3.For example, designed orthogonal sets of interacting proteins, in which each pair of proteins interacts only with its intended on-target pair and none of the other, off-target proteins present in the set, can be used to build nanoscale superstructures for applications in biology, biological engineering and materials science9 and Crick10, coiled-coils are defined by their heptad repeat HPPHPPP , represented as positions abcdefg. This relatively simple structure has given rise to many computational models describing coiled-coil interactions, from the parametric Crick equations of 195311 to contemporary linear models15. However, because of their shared similar structure, building large sets of orthogonally interacting coiled-coils, in which all on-target interactions are favoured over all off-target interactions, remains difficult. Though numerous groups have attempted to create orthogonal sets of coiled-coils, these sets have been limited in size and displayed significant off-target interactions16. Increasing our ability to build and characterise large sets of interacting proteins could help solve this problem by providing empirical data with which to improve computational models of PPIs. Simultaneously, this would vastly increase the number of available orthogonal building blocks for nanoscale structural design, allowing for the creation of previously unbuildable structures.Coiled-coils, in particular, have many useful characteristics in terms of creating atomically precise designs for macromolecular structures. They are small and precisely oriented, and numerous sequence-based and parametric models exist with which to describe their properties. First identified at the dawn of molecular biology by both PaulingHere, we combine gene synthesis, an assay that allows for multiplexed bimolecular interaction screening, and a computational pipeline to design large libraries of orthogonally interacting coiled-coils. We first built and validated the next-generation bacterial two-hybrid (NGB2H) system, which has several unique advantages over other methodologies in terms of characterising protein libraries. In particular, the NGB2H system allows for the screening of bimolecular interactions without having to test all-against-all libraries; direct large-scale synthesis using oligonucleotide arrays to explore the design space; quantitative readouts on an entire library, including negative interactions, and the characterisation of low-affinity interactions inside the crowded cellular context. We did this iteratively, with synthetically designed libraries increasing in size from 256 interactions to more than 18,000 interactions. From this, we identified to the best of our knowledge the largest sets of orthogonal proteins to date and developed an improved coiled-coil scoring algorithm (iCipa) for use in future investigations of this versatile protein domain.B. pertussis adenylate cyclase two-hybrid17  titratable and inducible control of hybrid protein expression and optimised reporter response on a single plasmid, (2) a background strain with linear cAMP accumulation, (3) a green fluorescence protein (GFP) reporter instead of beta-galactosidase for more rapid individual characterisation, (4) the use of multiple barcodes per construct to achieve statistically robust results and (5) a scarless cloning scheme that allows for library creation with any designed sequence , e-position and g-position (Lys/Glu)  Fig. . We desilu) Fig.  and thenlu) Fig. , 4. After > 0.98, p < 10−15), with a dynamic range of more than 100-fold , which we named the CCNG1 Library. After testing a subset of the CCNG1 Library to validate our in-house designs, which we call the CC1 Library, , is several orders of magnitude larger than what could be screened experimentally (~25 k), so the design process is crucial in identifying feasible orthogonal sets that can be experimentally tested.orthogonal subset as the largest experimentally characterised group of orthogonal interactions among what was computationally predicted to be an orthogonal set. To identify the orthogonal subset of each designed orthogonal set, we used a similar approach to that described above and reduced the problem to the maximum independent set problem using Interaction scores from the NGB2H assay.Although we designed our coiled-coils to form orthogonal sets, the current state-of-the-art coiled coil scoring functions are not sufficiently accurate to do so reliably, and nearly all sets contained off-target interactions stronger than some of the on-target pairs. The proteins involved in strong off-target interactions can be removed from the set, leaving only those interactions that are experimentally verified to be orthogonal. Thus, we refer to an To make our results robust to experimental noise from the NGB2H assay, we needed to find an appropriate orthogonality gap, that is larger than the uncertainty of the interactions score. We have performed a thorough analysis of both the CC0 internal control , external controls  or our iCipa candidate algorithms (CCmax Library). Calculating orthogonality is a challenging problem that scales in exponential time with the number of possible binding partners. We used a maximal clique algorithm to identify sets of orthogonal coiled-coils where all on-target interactions have a higher score than all off-target interactions and it runs in under a minute on a standard laptop. Python v3.7, Pandas 0.25.1, numpy 1.15.4, jupyter lab 4.4.0 were used for the development.To predict orthogonal coiled-coils, we generated all 4,096 possible four heptad coiled-coil with asparagine or isoleucine at the Each library was cloned in a similar manner, with slight differences in methods to attach a random DNA barcode to the OLS pools. After the 20 bp of random DNA was attached with PCR to the 3’ end of the X and Y construct , with custom primers spiked into the Illumina primers. Sequences were demultiplexed, and mapped with a BBtools pipeline and consensus building custom script. Full descriptions of how each library was mapped can be found in the Supplementary Information Section 39 carrying pSK34. TK310 is a previously published MG1655 derivative with deletions in cpdA, lacY and cyaA, which give it a large linear response range to cAMP. pSK34 contains repressors for both the phlF and Tet promoters to maintain repression of the two-hybrid proteins. CB216 is a NEB5ɑ derivative with pSK34 integrated genomically and only used for cloning. All plasmids used for basic cloning are listed in Supplementary Data All NGB2H experiments were run in TK310Glycerol stocks of each library were thawed, and 100uL were grown up overnight in 100 mL MOPS EZ Rich Defined Media (Teknova M2105) with kanamycin (Teknova K2125) and carbenicillin (Teknova C2130). For time course studies, a glycerol stock containing a library of constitutive GFP constructs was also thawed, and 100uL was inoculated into 10 mL of MOPS EZ Rich Defined Media with kanamycin and carbenicillin and grown overnight. The next morning 1 mL of the GFP library was added to the 100 mL of library culture. After mixing GFP and experimental libraries, 1 mL of overnight culture was added to a fresh culture of 100 mL MOPS EZ Rich Defined Media with carbenicillin and kanamycin and the inducers for two hybrid expression: 5 ng/mL anhydrotetracycline, 1.5 uM 2,4-Diacylphlorolglucinol and 100 uM IPTG, done twice for biological replicates, except where indicated  according to manufacturer’s instructions, with on-column DNase digestion (Qiagen 79254) and concentrated with RNeasy MinElute Cleanup kit (Qiagen 74204). RNA was reverse transcribed with Superscript IV (ThermoFisher 18090050) with a modified protocol such that 25 ug of input RNA was used, the extension step ran for 1 h at 55 C, and 1 uL of RNase A was added in the RNA removal step. Each sample was transcribed with a specific primer, often oSK193 or oSK194, that attached the i7 index and P7 sequencing primer. Samples of DNA were prepared with Qiagen Plasmid Plus Maxi kits (Qiagen 12963) according to the manufacturer’s instructions. RNA samples were verified to contain very low levels of DNA (<1:1000) by qPCR (Kapa Biotechnology KK4601) with oSK199 and oSK200, which was repeated with a high-fidelity PCR for a low number of cycles to keep samples in the exponential amplification phase. DNA samples were similarly quantified with qPCR and amplified for low cycles to attach P5 and P7 and multiplexing indices. Amplified samples were then quantified on an Agilent Tapestation 2200 with D1000 screentape (Agilent 5067-5582), verified to be monodispersed and mixed in equimolar quantities. Complete details for RNA and DNA preparation can be found in the Supplementary Information Section Pooled RNA and DNA barcodes from each experiment were sequenced with various cores and startups at UCLA. The CC1 and CCNG1 Libraries were sequenced on a Hiseq 2500 while the CCmax and CC0 libraries were sequenced on a Nextseq 550. Samples were diluted and mixed with 5–20% phiX control v3 (Illumina FC-110-3001) and sequenced with oSK326 for read 1 and oSK324 for the index read.We used a custom bash script to count DNA barcodes from barcode sequencing. After demultiplexing into reads from RNA or DNA samples, reads were truncated to the 20 bp containing the barcode and unique sequences counted. Barcode counts were then processed with Starcode (v1.3), to condense barcodes within a levenshtein distance of one to remove sequencing errors and tallied again.Barcode count files were imported into R where they were merged with the mapping file to provide the protein pair identified with each barcode. Barcodes corresponding to the same construct were summarized (dplyr 0.7.4) and total counts of RNA barcodes and DNA barcodes per protein pair were obtained. For our analysis we used Interactions scores calculated as Orthogonal sets were identified for the CCNG1 and CCmax libraries. Briefly, we wrote a script, find_orthogonal_sets_w_MIS.py that took the Interaction scores for each set and built a graph with interactions forming the edges between proteins. Finding the maximum independent set of the line graph of this graph gave us the largest orthogonal set of interactions, listed in Supplementary Data We have verified that technical repeats are very well correlated Fig.  and so sFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesReporting SummaryPeer Review FileSupplementary Dataset 1 OLS oligonucleotides for gene synthesis of librariesSupplementary Dataset 2 Sequences of proteins used in librariesSupplementary Dataset 3 Oligonucleotides for PCRSupplementary Dataset 4 Plasmids referred to by nameSupplementary Dataset 5 Interaction Scores from all librariesSupplementary Dataset 6 Largest orthogonal subset for each set in the CCNG1 and CCMax librarySupplementary Dataset 7 Largest orthogonality gaps by number of on-target interactionsSupplementary Dataset 8 Comparisons between librariesSupplementary Dataset 9 Uncertainty analysis


The identification and characterization of interfaces in protein complexes is crucial for understanding the mechanisms of molecular recognition. These interfaces are also attractive targets for protein inhibition. However, targeting protein interfaces can be challenging for large interfaces that consist of multiple interacting regions. We present PICKLUSTER [Protein Interface C(K)luster]—a program for identifying “sub-interfaces” in protein–protein complexes using distance clustering. The division of the interface into smaller “sub-interfaces” offers a more focused approach for targeting protein–protein interfaces.https://gitlab.com/topf-lab/pickluster.git.PICKLUSTER is implemented as a plug-in for the molecular visualization program UCSF ChimeraX 1.4 and subsequent versions. It is freely available for download in the ChimeraX Toolshed and  Protein complexes are key components of the majority of biological processes within cells such as the activation of receptor molecules, DNA replication, signal transduction, protein export and transport, recombination, and repair luster], a UCSF ChimeraX 1.4 . In case of an AlphaFold model, the JSON or Pickle file is also required as an input. PICKLUSTER colors the protein complex according to protein interface clusters in the input chains and reports the list of the residues at the interface in the UCSF ChimeraX Log.PICKLUSTER is written in Python 3.9 and implemented as a UCSF ChimeraX 1.4 plug-in. ChimeraX as the improved successor of Chimera, enables a user-friendly visualization of biological structures. To calculate the interface, PICKLUSTER requires as input the PDB/mmCIF file of a protein complex, the chain identifiers for the pair of chains, and the model classification  of user-specified chains are calculated using a KDTree data structure [provided by the To identify geometrically separated entities of the entire interface, referred to as “sub-interfaces,” we subsequently cluster the interface area based on distance. The center of mass of each residue exhibiting an “interaction partner” in the protein interface is computed. If the plug-in’s clustering option is selected, the division of the interface into sub-interfaces is performed by calculating the distance of these centers of masses in the interface. If the distance is below the distance cut-off (default: 5.0 Å) the residues are considered to be in the same cluster. Furthermore, PICKLUSTER offers the alternative of displaying the interface without clustering.Epstein–Barr virus (EBV). The output provides various types of structural information, including the detection of three clusters  (btad629_Supplementary_DataClick here for additional data file.