TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As  Among these, the nonsynonymous SNPs (nsSNPs) change the residues of amino acids of the protein sequences and may have damaging or neutral effects on the protein functions or structures3. Damaging nsSNPs may affect the function or structure of a protein by modifying the protein charge, geometry, hydrophobicity4, stability, dynamics, translation, and protein interactions6. These are probably the significant factors that contribute to the functional diversity of encoded proteins in the human population7. Therefore, many human diseases could be due to these damaging nsSNPs.Single nucleotide polymorphisms (SNPs) are the major type of genetic variation in humans (~ 90%). Thus far, around 500,000 SNPs have been reported on the coding regions of the human genome10. With the massive human genome sequence data now available and we are yet to know the functional effects of some of the SNPs, a more cost-effective approach is required to unravel the functions of the unknown SNPs effects. Many studies have used bioinformatics tools to predict the deleterious effects of nsSNPs on the functions of proteins that result in diseases before expensive in vitro or in vivo experiments are conducted. Two nsSNPs on the ABCB1 gene had been associated with breast cancer, and these SNPs were predicted for their deleterious effects, which caused the change in protein conformation using comprehensive bioinformatics analysis11. A similar study using functional and structural bioinformatics tools had identified three damaging nsSNPs that alter the functions and structures of the RNASEL gene. These nsSNPs are most likely pathogenic and associated with the increase of prostate cancer susceptibility12. nsSNPs in the KRAS gene have been found to be associated with lung cancer due to their damaging effects on the structural features of the protein. The structure and function of the native proteins were found to be altered due to the nsSNPs using a pipeline comprised of several bioinformatics tools13. A recent study had identified the deleterious nsSNPs on the hOGG1 gene that altered the secondary structure of the expressed protein and destabilized its local conformation, which increases the risks for lung cancer14. Furthermore, in-silico modeling has been widely used to assess the functional impacts of nsSNPs and their possible roles in cancers16. in-silico modeling has the advantage of being able to make rapid predictions for the mechanisms of actions of a wide range of compounds in a high-throughput mode. Another advantage is that prediction can be made based on the structure of a compound before it issynthesized17.Previous studies have shown that nsSNPs cause numerous genetic disorders such as inflammatory and autoimmune disorders and cancers18 in achieving personalized medicine19. The genome-wide association study (GWAS) database (https://www.ebi.ac.uk/gwas/) is widely used to associate SNPs with diseases. Although there are other existing human variant databases such as ClinVar, COSMIC, SwissVar, and Humsavar, GWAS is the only database that gives a world of information or catalogs on disease mutations in different populations. This database also provides information on the statistically significant variants and the increase/decrease associated risks for each phenotype20.Databases of human variants have been developed with different scopes and contents used to predict diseases22. Various tools have been used to predict the functional effects of nonsynonymous coding variants using basic sequence homology25; empirically derived rules26; structural and functional features29; a weighted average of the normalized scores30; decision trees32; support vector machines36; and Bayesian classifiers27. A comprehensive systematic evaluation study on the performances of these widely used prediction methods to identify the pathogenicity of the SNPs is required37. While new and more algorithms are being developed, the accuracy of prediction using a combination of the different algorithms should be validated. It is recommended that different computational methods are used to determine the impact of different SNPs during the screening step, and further validation should be incorporated in studying the impacts of nsSNPs on specific proteins38. In addition, complementary methods could be combined in a meta-server to yield more reliable predictions39. Several recent studies had reported on the use of a combination of various methods to uncover the potential impact of the nsSNPs in understanding the molecular mechanisms of various diseases, which includes cancers44. The combination of these tools allows more accurate prediction using the multiple conservation, structural, or combined methods . Therefore, combined methods and meta-prediction methods (predictors that integrate multi-predictor results) are important for biomedical applications. This is because they can be applied to a much greater number of single nucleotide variants, considering that many human proteins do not currently have an experimentally defined structure or a close homolog to construct a model. Thus, combined and meta-prediction methods have a wide range of potential applications using the combinations of features yet to be explored45. As GWAS is usually conducted on a large population size using a high throughput detection method and is costly, some world populations were not studied. Therefore, their disease risks are not available. The Orang Asli are still practicing traditional healing methods, therefore the record on the incidence of cancers among the Orang Asli is lacking. This has posed challenges to the authorities to strategize health programs to ensure the sustainability of the Orang Asli. Due to the lack of phenotypic data on cancers, mining the genomes of the Orang Asli to predict their susceptibility for the different types of cancers would provide important data that allows the scientists to strategize research focus areas and for the authorities to provide relevant funding. In this study, we aimed to develop and validate a bioinformatics pipeline to detect and annotate the cancer-associated nsSNPs of a genome database and predict the structural and functional impacts of these nsSNPs that might increase the risks of cancers among the Orang Asli. Using the same pipeline, we also investigate the cancer risks of the Malays, which constitute the biggest population in Malaysia. The database of the Malay genomes was provided by Wong et al.46 and lacks information on the phenotypic traits, therefore it is interesting to predict the cancer susceptibility risks for this cohort using the established pipeline. The pipeline is developed using multiple bioinformatic tools in order to analyze the most deleterious and damaging nsSNPs associated with cancers. It includes the steps used for mining and annotating the genotypes and in silico modeling to predict the structural and functional impacts of the genetic variants with unknown functions. The new variants with potential impacts would be subsequently investigated in our laboratory using zebrafish models, and genotyping methods targeting the nsSNP would be developed for population study. In this study, three-dimensional (3D) protein models of the native and their variants (or mutant) were prepared. This is the first report which covers a comprehensive in silico analysis of three (3) nsSNPs, rs1126809, rs10936600, and rs757978 for TYR, LRRC34, and FARP2 proteins, respectively. This study is a part of our initiatives to enhance precision health in our country. The bioinformatics pipeline developed in this study will be used in the future to predict genomic variations associated with different diseases.The application of genomics, bioinformatics, and the availability of data generated from high-throughput technologies are the fundamental tools for implementing precision medicine not only for cancer diseases but also for other common and rare diseasesThe whole-genome sequences of ninety-eight (98) healthy and unrelated Orang Asli from six different sub-tribes were retrieved from the Whole-Genome-Sequence Database at Integrative Pharmacogenomics Institute (iPROMISE) in the form of a bam file. The Orang Asli were recruited from sub-tribes that are located in the (i) northern region of the Peninsular Malaysia  and the Department of Orang Asli Development  Research Ethics Committee [JAKOA.PP.30.052 Jld 5(62)].High-risk nsSNPs associated with cancer were classified using the GWAS-Catalog as the source of the dataset, and various bioinformatics tools were employed in the workflow Fig. .Figure 137. Datasets of different types of cancer and a larger sample size may also be used to achieve the same objective.The sensitivity, specificity, and accuracy of the functional effect prediction were determined using a combination of five different algorithms , with and without conservation (Consurf) and protein stability (I-Mutant). The standard dataset used comprised of nsSNPs associated with breast cancer from ClinVar. The ClinVar dataset includes a total of 100 clinically tested nsSNPs in which 50 nsSNPs were reported as pathogenic while the other 50 nsSNPs were reported as benign (Table S1). The 100 nsSNPs training dataset were randomly chosen out of 1020 clinically tested nsSNPs associated with breast cancer reported in the ClinVar as it is one of the most commonly studied cancer dataset. Although the dataset is primarily associated with breast cancer, the main purpose of using the training dataset is to test the ability of the pipeline to detect all the deleterious nsSNPs. Additionally, the sample size chosen also is sufficient as concluded by Thusberg et al., that the analysis result of using a small dataset (100SNPs) is comparable to a larger size (1000 SNPs) for a training dataset99 and Glantz100.Analytical parameters of studied tools were calculated using Eqs. , 2), an, an2), aSensitivity (Se) is a proportion of the true-positive results (correct identification of pathogenic variants), according to Eq. .1\documeSpecificity (Sp) is a proportion of the true negative results (correct identification of benign variants), according to Eq. .2\documeAccuracy (Ac) is the ratio of complete, correct predictions to the total number of predictions, according to the following Eq. .3\documewww.ebi.ac.uk/gwas). Residue change, risk allele frequency, phenotype, and protein accession number were retrieved from The NHGRI GWAS Catalog20. The dataset was built after 179,365 genetic variants were filtered based on the keywords' cancer', 'carcinoma', 'glioma', 'leukemia', 'lymphoma', 'melanoma', and 'sarcoma' (Table S2).Information on the genetic variants associated with cancers (SNP ID) was retrieved from the GWAS-Catalog database (https:// 102 based on the dataset (Table S2). The variants were then annotated to identify the associated genes, allele frequency (AF), location of the SNPs in the genome sequences, the position of amino acid change in protein sequences, and codon changes using Variant Effect Predictor103. hg19/GRCh37 was used as the reference genome for the analyses.The SNPs that are associated with cancer risks were identified using VCFtoolshttp://sift.jcvi.org/www/SIFT_seq_submit2.html]25, Polymorphism Phenotyping v2 (PolyPhen-2) [http://genetics.bwh.harvard.edu/pph2/]27, Consensus Deleterious (Condel) [http://bbglab.irbbarcelona.org/fannsdb/query/condel]50, Protein Variation Effect Analyzer (PROVEAN) [http://provean.jcvi.org/index.php]104, and Protein Analysis Through Evolutionary Relationships (Panther v14.1) [http://www.pantherdb.org/tools/csnpScore.do]52. SIFT predicts the effects of an amino acid substitution on protein functions. The sequence homology and the physiochemical characteristics were computed using a normalized probability score (SIFT score) for each substitution25. PolyPhen-2 predicts the potential effect of an amino acid substitution on both protein structure and function using a combination of multiple homolog sequence alignment-based methods and protein 3D structure. The prediction is provided as benign, possibly damaging, and probably damaging according to the scores differences of the position-specific independent count (PSIC) between 2 variants (native amino acid and mutant amino acid)27. Condel predicts the effect of coding variants on protein function based on the ensemble score of multiple prediction tools 50. PROVEAN predicts the functional effects of protein sequence variations, including single or multiple amino acid substitutions and in-frame insertions and deletions104. PANTHER estimates the likelihood of a particular nsSNP to cause a functional effect on the protein using position-specific evolutionary preservation52. The description of the tools used is presented in Table The functional effects of identified nsSNPS were predicted by using five different bioinformatics tools. These algorithmic programs included Sorting Intolerant From Tolerant (SIFT) .Those three selected mutant protein models were then superimposed on the native protein models to show the location of observed mutations Fig. . The detThree (3) nsSNPs were shortlisted and submitted to the MutPred2 server. MutPred2 predicts the modification of structural and functional protein structures, including the altered order or disordered interface, transmembrane protein, metal binding, DNA binding, loss of allosteric site, and gain of allosteric site. Based on Table HOPE was further used to explore the structural effects of these three amino acid substitutions. It was shown that the substitution of L286, T260, and R402 were highly conserved. Based on Fig. ModPred tools predict possible post-translational modification (PTM) sites to investigate the effects of PTMs on the three substitutions of amino acid L286I, T260N, and R402Q in LRRC34, FARP, and TYR proteins, respectively. Post-translational modifications (PTMs) play a crucial role in regulating many biological processes, such as protein–protein interaction network, protein stability and enzymatic activity, and others. ModPred tool had predicted proteolytic cleavage sites of the substituted amino acids L286I, T260N, and R402Q in LRRC34, FARP, and TYR proteins, respectively Table . ProteolThe STRING server was used to investigate the interaction of HLA-G with various proteins. The interaction analysis revealed that LRCC34 is related to Leucine-rich repeat-containing 32 (LRRC32), Leucine-rich repeat containing 31 (LRRC31), Leucine-rich repeats and IQ motif containing 4 (LRRIQ4), Actin related protein T3 (ACTRT3), Myoneurin (MYNN), Protein FAM196B (FAM196B), Transmembrane protein 174 (TMEM174), Ly6/PLAUR domain-containing protein 6 (LYPD6), Aspartyl aminopeptidase (DNPEP) and DAZ-associated protein 1 (DAZAP1) as shown in Fig. While FARP2 is related to cell division control protein 42 homolog (CDC42), Proto-oncogene tyrosine-protein kinase Src (SRC), Tyrosine-protein kinase Fyn (FYN), Neuropilin-1 (NRP1), Plexin-A1 (PLXNA1), Plexin-A2 (PLXNA2), Plexin-A3 (PLXNA3), Plexin-A4 (PLXNA4), Semaphorin-3A (SEMA3A), and Tyrosine-protein kinase Fes/Fps (FES) as shown in Fig. The interaction analysis also revealed that TYR is related to Short transient receptor potential channel 1 (TRPC1), Tyrosine 3-monooxygenase(TH), Phenylalanine hydroxylase (PAH), Aromatic-L-amino-acid decarboxylase (DDC), Thyroid peroxidase (TPO), L-dopachrome tautomerase (DCT), Melanocyte protein PMEL (PMEL), Melanoma antigen recognized by T-cells 1 (MLANA), P protein (OCA), and Microphthalmia-associated transcription factor (MITF) as shown in Fig. ). Moreover, interaction analysis of SRC, DCT, and MYNN with the native and mutant FARP2 TYR and LRRC34 proteins showed a reduction in the number of hydrogen bonds with residues in mutant proteins  to classify the nsSNPs. It provides insight into the impact of the mutation on the biological activities of the proteins affected50. PROVEAN algorithm is capable of predicting the functional impacts of the amino acid substitution on a protein sequence with commensurable performance and accuracy. It utilizes alignment-based scores to measure the change in sequence variation correlated with the biological function of a protein51. Additionally, SIFT, PolyPhen-2, Condel, and PROVEAN, are easy and quick to employ, which allows direct batch queries. Other tools include PANTHER, a powerful and unique method with a curated database of protein families, trees, subfamilies and functions, and evolutionary relationships. It uses phylogenetic trees, multiple sequence alignments, and statistical technique to evaluate the deleterious effects of nsSNPs, making it a viable platform for SNP characterization53. Consurf is another widely used tool that can pinpoint critically important sites (nsSNPs) within the functional regions. It is a statistically robust approach that estimates the evolutionary rates due to amino acids substitutions and maps them onto the homologous sequence and/or structures47. I Mutant 3.0 tool measures the change in protein-free energy caused by a specific mutation54. It helps to detect the changes in protein 3D conformation stability. The tools used in this study cover a wide range of prediction techniques . The sensitivity, specificity, and accuracy of four models  in predicting the clinical significance were determined. Model D represents at least four tools that predicted nsSNPs as deleterious or benign, and it showed the highest percentages of specificity (94%), and accuracy  followed by Model C , Model B  and Model A . While Model A has the highest sensitivity (100%) followed by Model B (92%), Model C (90%), and Model D (84%). The highest sensitivity scores mean that fewer potentially deleterious nsSNPs were missed. Thus, we concluded that Model D using at least four out of five tools had the best performance in predicting the most deleterious nsSNPs.Further analyses using the combination of five functional effect tools with conservation and stability tools showed that Model D3 had the highest specificity (96%), but the lowest sensitivity (76%) in identifying deleterious and benign nsSNPs. Despite not having the highest accuracy, Model D3 was able to classify both pathogenic and benign SNVs accurately (86%). The validated workflow is adequate with good sensitivity, specificity, and accuracy to classify the deleterious and neutral nsSNPs in ClinVar using a combination of SIFT, PolyPhen-2, Condel, PROVEAN, PANTHER, Consurf, and I-Mutant.20. We only focused on nsSNPs as they are capable of altering protein function, structure, conformation, and interaction which cause the increased risk of cancer58. Out of the 80 nsSNPs associated with cancer risks from the GWAS dataset, a total of 52 nsSNPs were identified among the Orang Asli and Malays . They were subjected for further analysis.The GWAS database was used to identify nsSNPs associated with cancer risks as it is the most extensive SNPs databaseCACFD1), rs9379084 (RREB1), rs10936600 (ETFA) rs1801591 (LRRC34), rs117744081 (CPVL), rs2277283 (INCENP), rs757978 , (FARP2) and rs1126809. (TYR). In terms of the useability of these five tools for prediction, different algorithms for evolutionary conservation, protein function or structure, alignment, and measurement of similarity between variant sequences and protein sequence homologs were analyzed. Hasan et al.,59 had reported that the combination of the best individual tools, FATHMM, iFish, and Mutation Assessor, in one classifier called Meta (Combined Scores through J48 "CSTJ48") enhances the predictive power of these tools. However, no specific classifier outperforms overall datasets in pathogenic predictability. Additionally, these tools have proven performance in identifying deleterious nsSNPs61, and these make them useful for our study. Thus, these eight (8) nsSNPs identified were further investigated.Hence, we conducted the concordance analysis with SIFT, PolyPhen-2, Condel, PROVEAN, PANTHER, Consurf, I-Mutant, ModPred, and MutPred tools to predict the most deleterious nsSNPs among the Orang Asli and Malays Table . From th26. The evolution of amino acids influence their properties such as size, shape, hydrophobicity, and charge of amino acids at the molecular level62. For example, 53 missense mutations that caused cystic fibrosis were found within highly conserved positions. These regions were significant for conserving the structural and functional integrity of the CFTR protein63. Besides, functional sites of proteins like DNA interaction sites, protein–protein interaction sites, and enzymatic sites are essential for biological functions65. This may suggest that the nsSNPs found in these conserved regions have higher deleterious effects than other non-conservative nsSNPs and may significantly affected the biological functions66. The findings further indicated that these eight (8) high-risk nsSNPs were indeed deleterious to the protein functions and structures.The Consurf server had predicted the eight (8) variations, D1171N, I58M, L286, T171I, Y168H, M506T, R402Q, and T260N, were highly conserved  and reliability index (RI). I-Mutant predicted 6 out of 8 variants  to have decreased stability. Protein stability is important for the protein structural and functional behaviorFor structural analysis, the six native and mutant protein structures  were successfully generated using I-TASSER as there are no available close homologous templates. I-TASSER generates full-length models by the iterative structural fragment reassembly method, which consistently drives the threading alignment relative to the native state. They were then verified by ERRAT and Ramachandran Plot Server, which proved the stability, reliability, and consistency of the tertiary structures of the proteins. The three-dimensional structures for the native and mutant proteins predicted by I-TASSER clearly revealed the structural changes resulting from amino acids substitutions , D1171N (RREB1), and M506T(INCENP) with TM-scores below 0.5, were excluded. TM-scores below 0.5 correspond to randomly chosen unrelated proteins, meaning that those models were generated from random proteins and had different folding compared to the native protein49. Hence, we finally selected only three mutants, L286I (LRRC34), T260N (FARP2), and R402Q (TYR), those with a score higher than 0.5 and which generally assumed the same fold in SCOP/CATH  sites to investigate the effects of PTMs further. ModPred tool had predicted proteolytic cleavage sites of the substituted amino acids L286I, T260N, and R402Q in LRRC34, FARP, and TYR proteins, respectively  and Nucleolin (NCL), in ribosome biogenesis of pluripotent stem cells81.The structural analysis was performed by using molecular docking. The study aims to identify the correct poses of ligands in the binding pocket of a protein and to predict the affinity between the ligand and the protein, which may enhance or inhibit its biological function, respectively. SRC proto-oncogene plays an essential role in development, growth, progression, and metastasis of some human cancers, including those of the colon, breast, pancreas, and brain85. FARP2 were identified as guanine nucleotide exchange factors (GEFs) for RhoGTPases that play regulatory roles in neuronal development, and several studies have revealed the genetic alterations in Ras homologous RhoGEFs in several human cancers88. Thus, the deviation observed in the bound SRC molecule with mutant FARP2 protein might disrupt the protein interaction, leading to cancers. A previous study had reported that mutations of melanogenic enzyme tyrosinase (TYR) result in hypopigmentation of the hair, skin and eyes74. Besides, DCT is one of the related enzymes that catalyzes different post-TYR reactions in melanin biosynthesis. TYR and DCT also have been proposed to interact with and stabilize each other in multi-enzyme complexes80. Thus, the deviation observed in the bound DCT molecule can reduce the catalytic efficiency of TYR. LRRC34 is a member of the leucine-rich repeat-containing protein family that has been suggested to be implicated in the maintenance and regulation of pluripotency. MYNN protein is a member of the BTB/POZ and zinc finger containing family involved in transcriptional regulation. It has also been shown to interact with a few other proteins, including LRRC34, which are part of the transcription factors that participate in DNA repair89. A study showed that disruption of LRRC34 protein function could result in reduced expression of some pluripotency genes. Its altered expression impacts the pluripotency-regulating genes and interacts with other proteins known to be involved in ribosome biogenesis78. This molecular docking analysis further evaluates our hypothesis as to whether T260N, R402Q, and L238I mutants have deleterious effects on FARP2, TYR, and LRRC34 proteins, respectively. The most prominent change was noticed in T260N, R402Q, and L238I, where a significant loss of H-bond interactions within the binding pocket residues can be observed compared to that in the native protein. These H-bonds were disrupted when the amino acid in mutants was replaced with other amino acids, which altered the binding affinity. The change in the number of hydrogen bonds indicates the deleterious effect of amino-acid substitution. Therefore, an increase or decrease of hydrogen bonds of the native form could destabilize the protein and affect protein functions93. As a result, genetic mutation which alters the protein structure, therefore influences how the protein interacts with its ligands, potentially leading to a disease condition. This method has previously been used to discover functionally significant variants that may play a role in disease mechanisms95. Molecular docking analysis conducted in this study revealed that T260N, R402Q, and L238I mutants could significantly affect the functional activity of FARP2, TYR, and LRRC34 proteins, respectively.The molecular docking analysis of SRC, DCT, and MYNN with native and mutant FARP2, TYR, and LRRC34 modeled structures showed a difference in binding affinity, reduction in the number of hydrogen bonds with residues in mutant proteins Table , and a sWith the advancement of genomics, predicting and preventing diseases that are preventable will definitely bring a new facet to medical practice. We had illustrated that with the availability of a local genome database, we could predict disease risks in our population using a validated bioinformatics pipeline and the established GWAS and ClinVar database. The pipeline will help strategize experimental research to prioritize studies on the SNPs with predicted functional impact as thousands and millions of SNPs with unknown functions are detected using whole-genome sequencing technologies.In this study, a bioinformatics pipeline was developed and validated to predict the effects of nsSNPs, rs1126809, rs757978, and rs10936600 on the functional and structural changes on TYR, FARP2, and LRRC34 proteins, respectively. The analysis also provides significant insight into the deleterious effects of these nsSNPs on the protein structures.These three (3) nsSNPs were predicted to confer high risks of multiple myeloma, chronic lymphocytic leukemia, and basal cell carcinoma or squamous cell carcinoma in the Orang Asli and Malays population. The prediction pipeline developed in this study helps to reduce the number of extensive investigations and wet lab experiments which are required to explain the impacts of these nsSNPs on the structures and functions of these proteins. We intend to analyze further the risks conferred by these SNPs in the cancer patients in the local population.We believed that a similar approach could be used to develop and validate bioinformatics pipelines in annotating and predicting the functional effects of SNPs related to other diseases. This study also allows us to establish a database of predicted phenotypes based on the new SNPs identified in our population.Supplementary Information 1. Table S1. The functional, conservation and protein stability prediction results for 100 nsSNPs associated with breast cancer from ClinVar as a gold standard dataset.Supplementary Information 2. Table S2. The datasets of nsSNPs associated with cancer, including their ID, gene symbol, protein ID, amino acid change and position, and risk allele frequencies.Supplementary Information 3. Table S3. The details of native and mutant three-dimensional protein structure models.


About 10% of human proteins have no annotated function in protein knowledge bases. A workflow to generate hypotheses for the function of these uncharacterized proteins has been developed, based on predicted and experimental information on protein properties, interactions, tissular expression, subcellular localization, conservation in other organisms, as well as phenotypic data in mutant model organisms. This workflow has been applied to seven uncharacterized human proteins  in the frame of a course-based undergraduate research experience named Functionathon organized at the University of Geneva to teach undergraduate students how to use biological databases and bioinformatics tools and interpret the results. C6orf118, CXorf58 and TMEM232 were proposed to be involved in cilia-related functions; TMEM53 and SMLR1 were proposed to be involved in lipid metabolism and C7orf25 and RSRP1 were proposed to be involved in RNA metabolism and gene expression. Experimental strategies to test these hypotheses were also discussed. The results of this manual data mining study may contribute to the project recently launched by the Human Proteome Organization (HUPO) Human Proteome Project aiming to fill gaps in the functional annotation of human proteins.http://www.nextprot.orgDatabase URL:  Biomedical research is an area generating a prodigious amount of information obtained from massive datasets. Databases organizing, standardizing and distributing this information, as well as bioinformatics tools allowing to mine these data, so as to translate it into knowledge useful for discovery are thus crucial to modern biological research. neXtProt is a knowledge platform that provides advanced query tools to explore the universe of human proteins . In neXtThe HUPO Human Proteome Project (HPP) has recently launched a project aiming to characterize the remaining 10% of human proteins that have no annotated function . While lSaccharomyces cerevisiae . In CUREs, students learn the scientific method  by addressing a research question that is of interest to the scientific community . CUREs orevisiae . Our couInitial literature searches were performed by the tutors in December 2019. The first draft of the identity cards of the candidates was built using UniProtKB  release For all bioinformatics tools, default parameters were used unless specified otherwise. For neXtProt, only gold annotations were considered. The URLs for the databases and tools are recapitulated in The presence of homologs for the selected proteins was checked in TreeFam , EggNOG BLASTp analysis was done at both NCBI and UniProt against all nonredundant sequences at NCBI (nr) and UniProtKB reference proteome + SwissProt at UniProtKB and set to retrieve the maximum of hits. Orthologs were inferred by reciprocal best hit BLASTp.psiBLAST was run against all nonredundant sequences at NCBI (nr) with a maximum of six iterations.tBLASTn at NCBI against nucleotide collection (nr/nt) limited to a specific taxon was used to search for sequences when not found by BLASTp or psiBLAST.For HHPRED , we searMultiple sequence alignments were done at EBI via the European Bioinformatics Institute servers with Clustalw Omega , coloredFor the human protein, subcellular location annotations were retrieved from neXtProt. For the orthologs in model organisms, predicted transmembrane domains and signal sequences were retrieved from UniProtKB.As general subcellular location prediction programs, we used WoLF PSORT   and DeepTransmembrane domains were predicted using TMHMM2.0  and PhobMitoProt  was usedhttp://www.enyopharma.com/). Only interactions confirmed with at least two experiments were considered.Protein–protein interaction information was retrieved from neXtProt that integrates human data from UniProtKB/Swiss-Prot, IntAct  and EnyoHuman expression data were first explored using the Human Protein Atlas  . For antex vivo cultures, such as mesenchymal stem cell–derived adipocytes, were ignored. mRNA-seq gene expression in Genevestigator is measured in transcripts per million. For humans, high and medium expressions correspond to the log (2) of the average of the mean value higher than 3 and 0, respectively; for mouse, high and medium expressions correspond to the log (2) of the average of the mean value higher than 1.25 and 0, respectively. For microarrays, gene expression is expressed as log (2) relative mean values. For human and mouse, high and medium expressions correspond to values higher than 11.5 and 8.5, respectively.Human and mouse expression data were then explored using Genevestigator version 7.4.0  . Part ofP-value 0.05). Then, using the ‘coexpression’ tool option anatomy, the list of the top 100 genes coexpressing with the uncharacterized gene was retrieved. For SEEK analysis, the top 100 genes were analyzed. Both Genevestigator and SEEK gene names were mapped to Swiss-Prot accession numbers using the ID mapping tool at the UniProtKB website, and the Swiss-Prot accession numbers were then used in the following step.Coexpression analysis for the human protein was performed using microarray data in Genevestigator version 7.4.0 and a combination of microarray and RNA-seq data in SEEK . For GenP < 0.05). All the positive overrepresented GO biological process (BP) terms were considered, and the more precise GO terms selected using the hierarchical ontology view of PANTHER. The more specific GO terms were manually grouped into categories in order to summarize the data.The list of genes obtained with Genevestigator (releases 10 August 2020 and 08 October 2019) and SEEK (release 24 February 2021) were analyzed with the statistic GO term overrepresentation analysis at PANTHER  using thPhenotype information was retrieved from UniProtKB and the model organism databases for the organisms in which the protein is conserved: MGI , Zfin 3, XenbaseDisease information was retrieved from neXtProt. Results from genome-wide association studies associating single-nucleotide polymorphisms with human diseases or traits were gathered from the literature.The GO  was browAs shown in The function of newly characterized proteins, or their association with diseases, may not yet be annotated in databases. In addition, proteins that have been characterized but for which authors did not use the official gene name or for which the official gene name changed after publication are difficult to identify by curators. For these reasons, the official gene name and all the synonyms retrieved from neXtProt, UniProtKB and model organism databases were used to search in PubMed, PMC and Google to spot functional or clinically relevant data hidden in the literature. When a characterization study was spotted, it was sent to UniProtKB/Swiss-Prot to update the function of the protein.For proteins for which no characterization study has been found by extensive literature search, we explored the data available in public databases and ran bioinformatics analysis tools to predict function(s) and help in the design of experiments to validate these predictions. We used primary databases that produce their own data (e.g. HPA) and knowledge databases that manually integrate data from primary sources . Building the identity card of the protein of interest helps to organize the information relevant for formulating hypotheses and contains the following information: (i) official gene name, identifiers and synonyms; (ii) conservation; (iii) domains, PTMs and three-dimensional structure; (iv) subcellular location(s); (v) protein–protein interactions; (vi) expression and coexpression and (vii) phenotypes and diseases. In some cases, articles covering these aspects were found during the previous literature mining step. The information was noted in the corresponding section with its respective reference.For a given topic, the available resources might have some degree of overlap in their content. As algorithms based on different properties and using different training sets may give different results, the same datasets can be annotated differently. In principle, all the data retrieved should be combined to maximize the information coverage, but in some cases, extracting the consensus from a selection of resources is required to strengthen its reliability. As the different databases share contents and sometimes independently annotate the same experiments, keeping track of the references for each annotation avoids reporting data as independent observations when they are based on the same observation.Databases and tools are constantly evolving, and different versions may lead to different results. Hence, regularly updating the information using the most recent versions of resources is important.Study of the conservation of the protein allows potential paralogs and orthologs to be detected. When a paralog is found, the results of the subsequent analyses are compared with what is known about the paralog.In addition, it enables the evolutionary history of the gene of interest to be constructed. The absence of the protein in certain branches or its presence in others can be indicators of specialized functions. For example, phylogenetic profiling has been successfully used to identify genes involved in ciliated functions .Mus musculus, Gallus gallus, Xenopus tropicalis, Xenopus laevis, Danio rerio, Drosophila melanogaster, Caenorhabditis elegans, S. cerevisiae and Arabidopsis thaliana (listed in increasing evolutionary distance from man). We considered all the orthologs proposed by the different phylogenomic databases. Performing protein BLAST (BLASTp) and psiBLAST allows sequences that are absent in phylogenomic databases to be identified. Nucleotide BLAST (tBLASTn) at NCBI is used to systematically look for orthologs in the genomes of model organisms when they have not been found in databases or by BLASTp and psiBLAST. HHPRED is a highly sensitive method based on HMM–HMM alignments that allow to do structure prediction and to detect remote protein homologies where BLAST search fails.Phylogenomic databases, BLAST, psiBLAST and HHPRED were used to look for homologs, with a focus on the model organisms The multiple alignment of homologous sequences covering different phylogenetic lineages—and at least the selected model organisms when they exist, helps to evaluate the quality of the selected sequences. For example, sequences in which the N-terminal or C-terminal regions are too long or too short may be indicators of wrong gene predictions and should be removed from the alignment and from further analysis. The multiple sequence alignment also allows conserved regions and sites to be detected.Domains, binding sites and PTMs are important for protein function because they may be directly involved in function or regulate the activity of the protein or its interactions with other molecules. UniProtKB/Swiss-Prot contains experimental and predicted information about sites, domains and PTM for human and orthologs. neXtProt contains additional PTM information for the human proteins from Glyconnect, PeptideAtlas and its own annotations. InterPro contains additional information about domains integrated from various databases. SWISS-MODEL and HHPRED can help identify additional active sites or structural domains based on structural similarity. The available experimental or modeled three-dimensional structure of the protein can be used to check that predicted domains, PTMs and active sites are in agreement with the folding constraints and accessibility.Proteins must be targeted to the appropriate compartments to perform their functions. The different subcellular locations determine if the protein has access to metabolites and interacting partners.Subcellular location annotations were first retrieved from UniProtKB and neXtProt. UniProtKB uses TMHMM, Phobius and SignalP to automatically annotate transmembrane domains and signal sequences. In UniProtKB/Swiss-Prot, these annotations are manually reviewed and enriched with other predictions, such as mitochondrial target peptides, and with experimental observations. For human proteins, neXtProt adds experimental information from GO and HPA. Wolf PSORT and DeepLoc, which are general subcellular location prediction programs based on different algorithms, were run for the human and model organism ortholog sequences for proteins without experimental subcellular localization in order to define their most probable localization(s) or to compare and predict the localization of orthologs. The cutoff to consider a prediction as significant or not is not clearly documented. For WolfPSort, we followed the same approach as other databases , and forin vivo. Human protein–protein interactions were retrieved from neXtProt, together with information on the study in which these interactions were identified (PMID) and the experimental methods used. For each interactor, the function \, subcellular localizations and disease information annotated in neXtProt were analyzed and summarized.In cells, proteins rarely act in isolation but rely on a network of interactions with other proteins to perform their functions. Physical interactions found in low- or high-throughput experiments can be used to infer protein function. The known or predicted subcellular locations of the interacting partners indicate if these interactions may occur The RNA-seq data that were mined were obtained on polyadenylated messenger RNA (mRNA) and not on total RNA, which increases the probability that genes with positive expression are protein coding. However, one cannot exclude that these transcripts may correspond to noncoding polyadenylated mRNAs . The defFunctional clues can be inferred if the expression of the gene is specific to a functional system, tissue, organ or cell type or enriched in a group of tissues or organs sharing a similar function. Whereas the mouse gene expression at the RNA level was only explored using Genevestigator, the human gene expression was explored using both Genevestigator and HPA, which are complementary in terms of contents and functionalities. These databases have manually curated microarrays (Genevestigator) and RNA-seq (HPA and Genevestigator) data from various tissues and cell types. Data are quantitatively displayed and organized in functional systems allowing the comparison between different anatomical structures.Because protein and transcript abundances are not always well correlated, it is important to examine the expression profile of the gene at the protein level. HPA provides tissue expression profiles obtained by immunohistochemistry for human genes with validation scores. Expression profiles with an ‘enhanced’ score have been validated orthogonally with RNA-seq data or with a combination of antibodies.Comparison of expression data in human and mouse indicates if the expression profile is conserved in these species and strengthens functional clues derived from expression data. Expression comparison is not a trivial analysis because the apparent lack of conservation can be the result of incomplete experimental data, similar functions performed by different organs in the different species or an indication of divergence in protein function.Gene coexpression analysis is used to associate genes of unknown function with BPs. The results of coexpression are very sensitive to the methods and the parameters used; it is thus important to analyze coexpression with different programs that use different approaches. Regardless of the method, it is important to keep in mind that coexpression is the result of a correlation of expression at the mRNA level and does not necessarily reflect regulation at the protein level and does not provide information on causation or distinguish between regulated and regulatory genes. The output of a coexpression analysis is a list of genes that can be analyzed with an overrepresentation test to investigate if there is an enrichment in particular processes. This analysis is highly dependent on the degree of annotation of the genes under consideration. The use of PANTHER to analyze the list of coexpressed genes not only ensures that the annotations are up to date but also that the GO terms are displayed hierarchically, enabling the GO terms to be grouped by shared parent terms and the results to be summarized.Mutant phenotypes in model organisms may provide hints as to the function of the human protein. IMPC aims to create knockout strains for all mouse genes and characterize their phenotype through standardized protocols. It is particularly important to check the conditions in which the phenotypes were obtained , as subtle phenotypes may be revealed only under specific conditions.All the data gathered should be checked for inconsistencies, and potential conflicts should be carefully examined. This manual step is critical as it enables the coherence of the data compiled to be determined. At this stage, we do not advise to systematically prioritize experimental data versus predictions: predictions with good confidence scores may be more relevant than unreliable experimental data.Functionally important sites, such as active sites or binding sites and domains, are expected to be conserved among a wide range of orthologs. PTMs, which generally have a regulatory role, might be less conserved, as the modification can be made in other nearby residues in the three-dimensional structure.PTMs can influence the subcellular location of the protein. Conversely, a protein can only be post-translationally modified if it comes in contact with the enzyme catalyzing the modification and if the physicochemical conditions of the environment are appropriate. Therefore, any predicted or experimental PTM is checked for compatibility with the predicted or experimental subcellular location of the protein.in vivo.Similarly, the subcellular location of each interactor retrieved from databases is compared with the subcellular location of the protein of interest to check that the two proteins can interact In addition, it is interesting to know if the interacting partners coexpress with the uncharacterized gene. If the interacting proteins coexpress, this may indicate a close dependence for function or regulation.Once the collected data have been checked, functional hypotheses can potentially be inferred. We define a functional hypothesis as an educated guess of the protein’s function based upon the elements of the identity cards combined with previous knowledge in the field. The predicted functions can be MFs (e.g. enzyme activity) or processes taking place at the level of the cell, system or organism but should be specific enough to allow scientists to design experiments to test them.General rules for functional inference based on the collected data are difficult to establish. In general, sequence- or structure-based data, such as the presence of functional domains, might aid in building MF-based hypotheses. Phylogenomic profiling and data that reflect the spatiotemporal context of the protein, such as expression profile, protein–protein interactions or coexpression, are expected to generate BP-based hypotheses.The hypotheses are refined until the predicted function(s) is/are compatible with the information on the protein and what is known in the field. For example, if a protein is proposed to play a key role in a system, then this system should be impaired in mutant animals. In these hypothesis generation and refinement tasks, it should be kept in mind that proteins can have several MFs and multiple roles in different BPs, depending on the tissue or the organism in which they are expressed, their interactors and subcellular location. If some of the data do not support the predicted functions, it should ideally trigger the generation of an additional prediction. Data that were not rejected but do not support any testable hypothesis was stored in the section ‘Additional comments’.GO describes genes and their products in terms of MF, BP and cellular components and is the standard ontology to analyze and share data. Accordingly, proposed functions at the molecular level and at the level of a BP were expressed using MF and BP GO terms, respectively. These terms were searched by browsing the GO.The evidence used to build these hypotheses, originating from experiments, computational methods or literature curation, were described using terms from the ECO.In both GO and ECO ontologies, the most specific terms were chosen. The selection was based on the definition of the term, rather than the term name, to ensure that the concept is correctly attributed.Because scientific hypotheses must be testable, the workflow includes looking for possible experimental approaches to test the predicted functions.In order to provide the basis for further functional characterization, the key data used to build the functional hypotheses should be confirmed, as they often result from high-throughput experiments. Antibody-based techniques such as immunofluorescence, immunohistochemistry or co-immunoprecipitation can be used to validate expression, subcellular location and interaction data. HPA is an invaluable source of antibodies that are provided with a detailed description of their suitability to different techniques.The strategy to validate a functional prediction will depend on the function(s) to be tested and will preferably be based on a combination of orthogonal methods.Recombinant proteins can be used to test MF predictions. Subcellular and PTM analysis may guide the choice of the system to express the protein. For example, secreted proteins should be expressed in eukaryotic systems and collected in the extracellular medium in order to respect their folding (disulfide bonds) or PTMs (glycosylation or cleavage).in vivo models.Human cell lines are routinely used for preliminary characterization studies as they are easy to manipulate, inexpensive and minimize ethical issues. HPA contains expression data at mRNA and protein levels on a broad panel of 63 cell lines that can be used to identify which cell line expresses the gene or protein of interest. The main drawback is that most cell lines are cancer cell lines that might not completely reproduce the physiology of normal cells. Functions that are specific to differentiated cells are difficult to study in cell lines and should instead be studied in primary cells or induced stem cells. Biological processes that involve a dialog between different cell types, such as immune response, development or reproduction, need more sophisticated systems such as organoid cultures or The choice of the animal model will not only depend on the conservation profile of the protein but also on the adequation between its anatomical, physiological or behavioral properties and the function to be studied. As a general principle, the closer the system resembles that in humans, the better because results can be extrapolated to humans with more confidence, but for ethical reasons, it is better to use evolutionary distant organisms. Mouse is by far the most common mammalian model. Available mouse embryonic stem (ES) cells and mutant animals are available from the International Mouse Strain Resource. In many cases, available ES cell clones have been targeted using the ‘knockout-first, conditional-ready’ approach . This apRegardless of the cellular or animal model chosen, the strategy usually consists in downregulating the expression of the considered gene by genome editing and measuring phenotypes at the cellular or organism level. This strategy can fail when another gene can substitute for the targeted gene, preventing the observation of any phenotype. In this case, comparison between single and double mutants and complementation analysis may help to elucidate their function.This approach can also be difficult if the targeted gene has a function that cannot be easily studied in the usual standardized settings of a laboratory . In those cases, the phenotyping needs to be done in optimized conditions.The workflow, which has been designed for scientists without experience or competencies in bioinformatics, has been tested with a class of undergraduate students in biomedical sciences.The tutors selected seven proteins for which no experimental characterization had been published in the literature, and evidence of expression at the transcript level was available. They ensured that these proteins were conserved outside primates to increase the probability of finding experimental data in model organisms and checked that high confidence data were available for at least two of these sections of the ID cards: domains and/or PTM, interactions, subcellular location, phenotypes and/or diseases. This was done in order to guarantee that the students would be able to explore and integrate these different aspects together with conservation, expression and coexpression data.The 20 students were randomly distributed into seven groups. Each group was randomly assigned one of the seven selected uncharacterized human proteins. During six practical sessions of 3 hours each, the students followed the workflow described above in order to gather, critically analyze and synthesize the information. Sessions started with a brief introduction on how to use the relevant tools and interpret the data, and the rest of the time was dedicated to the collection of data. In order to guide the students, a list of the objectives and mandatory tasks to achieve were provided at each session. Each group was assigned to one tutor, who was available to answer questions and discuss the work shared using online documents.By comparing the collected data to their own data mining results, tutors ensured that students were able to find all the relevant information, organize their findings and discuss conflicting data.The data integration and hypothesis formulation require time to study or review the BPs that are the output of the coexpression analysis, that describe the function of the interacting proteins and that can be associated with the subcellular location(s) of the protein and their interactors. These steps largely depend on the amount of the gathered data and the biological background of the students. Tutors helped the students by answering their questions and providing informative resources on specific biological concepts.The definition of an experimental strategy to validate the hypothesis requires some experience with laboratory techniques. Although the students had a good theoretical biological background, they obviously lacked such expertise. Tutors helped them to design and refine the experimental validation strategies. At the end of the course, the students reported their research results in the form of an oral presentation in which each member of the group participated. In addition to the tutor, four senior scientists independently evaluated the presentations and provided some feedback about the proposed experimental strategies. The consensus among the evaluators was that all the presentations were of high quality and that students had not only acquired bioinformatics knowledge but also communication skills and scientific maturity. During the final discussions, some students suggested drafting a publication with the data mining, the functional hypotheses proposed and ideas for experimental verification. This suggestion confirmed that the students projected themselves as real researchers in this project. As this coincided with the end of the academic year and their undergraduate curriculum, we undertook the writing of this manuscript.The standardization of functional hypotheses using GO/ECO terms was performed by the tutors after the course, as assigning GO and ECO terms requires special training that was out of scope for the course.The identity cards obtained for the seven uncharacterized proteins are shown in In contrast to function prediction methods based solely on sequence or structure similarity search which are more prone to predict MF than BP GO terms , our worBased on the predicted GO terms, the seven proteins can be associated with three high-level functional categories: C6orf118, CXorf58 and TMEM232 with cilia-related functions, C7orf25 and RSRP1 with RNA metabolism and gene expression and TMEM53 and SMLR1 with lipid metabolism.The experimental strategies proposed to validate our hypotheses in each functional category are based on the available tools for each protein (antibodies and knockout mice), recapitulated in In contrast to C6orf118 and TMEM232, CXorf58 awaits validation at the protein level, according to neXtProt. Antibody- or mass-spectrometry-based experiments should be planned to ensure that this gene is protein coding.Multiciliated cells generated from human-induced pluripotent stem cells  or humanKnockout mice are available for C6orf118 and Tmem232. Non-motile (sensory) cilia-associated phenotypes that could be measured are defects in hearing, vision and limb patterning. Motile cilia-associated phenotypes that could be measured are defects in lung clearance, establishment of left–right patterning during development, male and female fertility and cerebrospinal fluid circulation . In addiThe potential role of CXorf58 in flagellar motility and spermatogenesis can be analyzed by generating inducible or spermatocyte-specific knockout mice , 58. As Xenopus and D. rerio after morpholino-based knockdown, CRISPR knockout or mRNA-induced overexpression. In Xenopus embryos, cilium movement can be measured in skin multiciliated cells  could be expressed in HepG2 cells are liver cancer cells that express SMLR1 and TMEM53 at high levels. These cells could be used to analyze the difference in content or quality of lipids after downregulation of these genes, by a metabolomic approach. It would be informative to analyze if TMEM53 mutants of the proposed active site are able to rescue a wild-type phenotype in TMEM53 knockout cell lines. The interaction between SMLR1 and BSCL2 and the colocalization of the two proteins in lipid droplets and ER could be tested in those cells using the available antibodies from HPA, HPA066060 for SMLR1 and HPA042394 for BSCL2.D. rerio, X. laevis and/or M. musculus SMLR1 mutants could be designed to analyze lipid levels and body mass.Available TMEM53 knockout mice  or genera priori how much time to spend on each article.Extracting the relevant data from publications at the beginning of the workflow was found to be time-consuming. First, dozens of publications may be available for a single protein, even if it is uncharacterized. Then, the data often appear both in the main text and in An important aspect of the data gathering step is to properly document the evidence of the information found in databases. Two main issues were identified when the students tried to report evidence for annotations. Firstly, some annotations were redundant. For example, neXtProt sometimes integrates the same subcellular localization data from HPA or UniProtKB and from GOA, as GOA also annotates subcellular location based on HPA and UniProtKB information. Secondly, annotations based on similarity with other proteins are difficult to trace as an explicit link to the similar sequence considered, its annotation and associated evidence is often missing. For example, in neXtProt and UniProtKB, a number of GO annotations inferred from the biological aspect of ancestor evidence with source GO_central are only associated with a publication describing how GO uses phylogenetic context to infer protein function , and theThe tutors identified a mistake in mouse genome databases (fam90a1b wrongly annotated as a CXorf58 ortholog) and one in the literature (TMEM232 wrongly described as a tetraspan protein) that would have been difficult for the students to spot. While MGI was notified and the mistake will be corrected, mistakes in the literature are generally not corrected.et al., who discuss that there is not a unique valid way to analyze data and highlight the importance of analyzing the information taking into account the context and the methods used to produce it. In addition, the authors of this study provide some clues for the proper interpretation of statistical tests . These difficulties are encountered also by experienced researchers as mentioned by Rafi al tests .The analysis of the coexpression results was not straightforward. Indeed, the output of the overrepresentation test obtained using PANTHER is usually many different BPs that are difficult to summarize in one or few pathways. In order to improve the precision of coexpression results, analyses based on RNA-seq data could be included. Comparison with mouse or other model organisms could also be useful. Microarray and RNA-seq data are available for mouse at Genevestigator.The first experiments the students proposed were experiments to validate the experimental or predicted data collected, which is a good practice. Concerning the functional validation, the details to which students proposed experiments greatly depended on the knowledge the students had on the different subjects. Experiments to validate general BPs, such as transcription or translation, were more easily described than those concerning processes that are less discussed during their studies, such as ciliogenesis.For the seven examples in this study, our workflow retrieved sufficient information for each of the human proteins to build testable hypotheses. However, in some cases, data may be scarce and additional resources or tools will need to be used to compile sufficient data to come up with a hypothesis. Additional high confidence genetic or physical interactions can be retrieved from databases such as BioGRID. STRING also provides predicted associations deriving from genomic context (gene fusions and neighborhood information). These associations can be used to infer function, as fused genes or neighboring genes may function in the same pathways. STRING also allows the user to perform a COG (clusters of orthologous groups of proteins) centric analysis to search for genes with the same or similar phylogenetic profiles. This tool could be useful for gene function prediction particularly if combined with the other options offered. Finally, manually curated GWAS databases such as GWAS Catalog  and GWASThe primary output of this study is a data mining workflow that uses a combination of databases and tools to predict the function of uncharacterized human proteins. The final output of the workflow is predictions expressed as combinations of GO MF/BP and ECO terms and complemented by suggestions for experimental validation.The workflow has been tested with undergraduate students on seven preselected proteins. Compared to the CACAO project, which is conceived as a competition, the Functionathon course is a collaborative project. Tutors give feedback all along the course and build functional predictions together with the students, which is closer to the situation in which master’s or PhD students work. While CACAO instructors can evaluate if the experimental data presented in the literature are correctly interpreted and translated into GO/ECO terms, tutors of CUREs like the Functionathon or the Yeast ORFan Gene Project  cannot tThe Functionathon course can be adapted to master’s and PhD students that are able to work in a more autonomous manner. At that stage, students have a deeper and broader biological knowledge, may already have acquired some expertise at the laboratory  and experience in research projects. This background would allow them to formulate more precise hypotheses and experimental validation strategies. We hope that this study will trigger the creation of courses at different levels that will use our workflow and propose functions on a higher number of proteins. The workflow could potentially be applied to any randomly selected human uncharacterized protein; however, it should be kept in mind that it might be impossible to formulate a testable hypothesis if there are very little data or data that the researcher is not able to integrate. In such cases, we suggest that the researcher should try using complementary resources. Feedback and suggestions for improvements from students, researchers and teachers who will use the workflow are welcome.For teaching purposes, we estimate that the workflow is appropriate because it involves interacting with the databases and tools in a thorough manner. The workflow is entirely manual and hence time-consuming. Automating some tasks could improve efficiency. The first task that could be automated is literature mining. Even if their function is still unknown, some proteins are associated with dozens of publications. Text-mining tools that allow the sentence in which the name of the protein appears together with the title of the study to be automatically extracted would accelerate the first step of the workflow. Database querying could also be automated. For example, all the gold data for one or several uncharacterized proteins and their interactors can be automatically retrieved from neXtProt in different formats using a combination of SPARQL is a recursive acronym for SPARQL Protocol and RDF Query Language, a semantic query language able to retrieve and manipulate data stored in Resource Description Framework (RDF) format SPARQL queries. Phylogeny data from OMA and UniProtKB annotations for ortholog proteins can also be extracted using SPARQL queries on their respective end points. Writing SPARQL queries requires some training, and tutorials have been recently published by neXtProt  and OMA Many function prediction tools based on machine learning and sequence alignment are under assessment by the Critical Assessment of Functional Annotation . The prehttps://www.nextprot.org/entry/NX_Q9BPX7/function-predictions) and in a previous publication (neXtProt has just launched new community pages to host predicted functions. They have already been populated with the predictions generated in the present study (lication . They wilication , 5 and flication , and we We hope that the current study will stimulate experimental studies on C6orf118, CXorf58 and TMEM232 in cilia-related functions and on TMEM53 and SMLR1 in lipid metabolism by the HPP teams who have demonstrated their expertise in characterizing human proteins involved in male reproduction , ciliogebaab046_SuppClick here for additional data file.


However, predictions for some species tend to be lower confidence than model organisms. Problematic species include Trypanosoma cruzi and Leishmania infantum: important unicellular eukaryotic human parasites in an early-branching eukaryotic lineage. The cause appears to be due to poor sampling of this branch of life (Discoba) in the protein sequences databases used for the AlphaFold database and ColabFold. Here, by comprehensively gathering openly available protein sequence data for Discoba species, significant improvements to AlphaFold2 protein structure prediction over the AlphaFold database and ColabFold are demonstrated. This is made available as an easy-to-use tool for the parasitology community in the form of Colaboratory notebooks for generating multiple sequence alignments and AlphaFold2 predictions of protein structure for Trypanosoma, Leishmania and related species.AlphaFold2 and RoseTTAfold represent a transformative advance for predicting protein structure. They are able to make very high-quality predictions given a high-quality alignment of the protein sequence with related proteins. These predictions are now readily available  Machine learning approaches to protein structure prediction have crossed a critical success threshold. While predicting the three-dimensional structure of a protein from sequence alone is still unsolved problem, a multiple sequence alignment (MSA) of the target protein sequence with related proteins provides key additional information. Cutting edge approaches using such MSAs now have the potential to reach very high accuracy. MSAs are the input for AlphaFold2  and RoseTrypanosoma cruzi, Trypanosoma brucei and many human-infective Leishmania species. T. cruzi and Leishmania infantum are the most deadly of these species and were included in the initial 21 AlphaFold whole proteome predictions [Naegleria fowleri. Other speciose Discoba lineages are Euglena and Diplonema, unicellular aquatic organisms and important and abundant auto- and heterotrophic plankton respectively. An initial inspection of the AlphaFold database  suggested protein structure prediction accuracy for T. cruzi and L. infantum is often low–particularly for kinetoplastid specific proteins–based on self-reported prediction quality scores. Many of these proteins are vital, like the unconventional kinetochore proteins [Trypanosomatids pose a challenge because of their large evolutionary distance from common model eukaryotes , 8. Thisdictions . Trypanoproteins .Discoba diversity is less well sampled by genomes and transcriptomes than lineages like plants or metazoa, making construction of deep MSAs more difficult. This is important as MSAs encode additional structural information beyond the primary protein sequence alone: They capture evidence for co-evolution of different regions of the primary sequence which may correspond to proximity or interaction in the three-dimensional structure. AlphaFold2 and RoseTTAFold prediction of protein structure is greatly improved by high MSA quality and depth, with high MSA coverage critical for high confidence predictions , 2. WhilTrypanosoma, Leishmania and other Discoba species.Currently, the input databases for the AlphaFold database and the ColabFold notebook are of UniRef  plus envL. infantum proteins, MSA coverage was always improved, leading to increased AlphaFold2 prediction accuracy in 2/3 of cases. Improvements were greatest for kinetoplastids-specific proteins, with dramatic improvements often possible. The necessary tools to make similar protein structure predictions have been made openly available: The Discoba protein sequence database (for custom searches and MSA generation), Colaboratory notebooks for generating MSAs by HMMER or MMSeqs2 (for use in AlphaFold2 or RoseTTAFold implementations), and a standalone Colaboratory notebook for AlphaFold2 structure predictions based on ColabFold incorporating a search of the Discoba database. These tools are available at github.com/zephyris/discoba_alphafold.Here, protein sequence data was gathered into a comprehensive Discoba database and the ColabFold MMSeqs2-based pipeline , 20 was Predicted protein sequences were gathered from 243 Discoba transcriptomes or genomes : 160 traPredicted proteins from genome sequencing were gathered from two sources: TriTrypDB: All 53 trypanosomatid species available in TriTrypDB ,16 releaSequencing read archive (SRA): 17 whole genome sequencing (WG-seq) datasets from axenic cultures of Discoba species and 32 single cell WG-seq datasets. For each, assembly was carried out using Velvet , 24 (seePredicted proteins from transcriptome sequencing were gathered from three sources: Transcriptome shotgun assembly (TSA) database: 11 transcriptomes for Discoba species, using protein sequence predicted by TransDecoder . Marine Transcriptome assembly from RNA-seq data used a standardised pipeline, with the same approach used for axenic culture, mixed culture and single cell transcriptomic data. Reads were first error corrected using Rcorrector v1.0.4 , 30 , which were used as the basis for identifying kinetoplastid specific proteins.Protein orthogroups were identified using OrthoFinder v2.5.4 –45  Trypanosoma cruzi (UP000002296) and Mus musculus (UP000000589) were taken from alphafold.ebi.ac.uk [Existing AlphaFold predictions of protein structures for  UP00000853 TrypanColabFold predictions were made using an unmodified version of ColabFold , 20, witAlphaFold2 predictions incorporating the new Discoba protein sequence database were carried out using a modified version of ColabFold , 20. TheL. infantum proteins were selected, using a random number generator, from the proteins meeting the criteria for each group, see The test set of Leishmania infantum and Trypanosoma cruzi  was selected as, unlike human proteins, predictions were carried out without special treatment. Overall, L. infantum and T. cruzi protein structure predictions are skewed to lower pLDDT  , a per rbi.ac.uk , 4) had er pLDDT .L. infantum and T. cruzi proteins do not have a markedly different predicted degree of disorder to M. musculus , and lesser known, unpublished or very recent data available via nucleotide sequencing databases, gathered using the NCBI taxonomy browser  and 10 random proteins which appeared ‘promising’ and likely to have globular domains but with a low pLDDT in the AlphaFold database. The latter were selected based on size , lack of low complexity or repetitive regions , orthologs in few species (≲10), without numerous paralogs, and low average pLDDT (≲60).To benchmark any improvements over the AlphaFold database predictions a set of 30 To carry out AlphaFold2 protein structure predictions ColabFold was selected as a fast but high accuracy and accessible AlphaFold2 implementation , 20. As Improvement was most marked among the test proteins not conserved outside of the kinetoplastids, especially the ones selected as ‘promising’ . InspectTrypanosoma and Leishmania proteins, relative to the publicly available AlphaFold database at alphafold.ebi.ac.uk [github.com/zephyris/discoba_alphafold. Even in these early-branching eukaryotes, the huge advance AlphaFold (and RoseTTAfold) represent can therefore, to a great extent, translate protein structure determination into a genome and transcriptome sequencing problem. Although, experimental protein structure determination will continue to be vital to confirm predictions, explore dynamics and complexes, etc.This work shows that significant improvement in the pLDDT and pAE of AlphaFold2 structure predictions is possible for bi.ac.uk , 4 and tbi.ac.uk , 20 Click here for additional data file.


Tomato leaf curl New Delhi virus (ToLCNDV) and Cotton leaf curl Kokhran Virus-Burewala Strain (CLCuKoV-Bu) in CLCuD infected samples, demand that the interactions taking between the two viruses be properly evaluated. This study is designed to assess virus-virus interactions at the molecular level and determine the type of co-infection taking place.Cotton leaf curl disease (CLCuD) is a disease of cotton caused by begomoviruses, leading to a drastic loss in the annual yield of the crop. Pakistan has suffered two epidemics of this disease leading to the loss of billions in annual exports. The speculation that a third epidemic of CLCuD may result as consequence of the frequent occurrence of i.e., MODELLER, I-TASSER, QUARKS, LOMETS and RAPTORX. A consensus model for each protein was selected after model quality assessment using ERRAT, QMEANDisCo, PROCHECK Z-Score and Ramachandran plot analysis. The active and passive residues in the protein structures were identified using the CPORT server. Protein–Protein Docking was done using the HADDOCK webserver, and 169 Protein–Protein Interaction (PPIs) were performed between the proteins of the two viruses. The docked complexes were submitted to the PRODIGY server to identify the interacting residues between the complexes. The strongest interactions were determined based on the HADDOCK Score, Desolvation energy, Van der Waals Energy, Restraint Violation Energy, Electrostatic Energy, Buried Surface Area and Restraint Violation Energy, Binding Affinity and Dissociation constant (Kd). A total of 50 ns Molecular Dynamic simulations were performed on complexes that exhibited the strongest affinity in order to validate the stability of the complexes, and to remove any steric hindrances that may exist within the structures.Based on the amino acid sequences of the gene products of both CLCuKoV-Bu and ToLCNDV, protein structures were generated using different software, Our results indicate significant interactions taking place between the proteins of the two viruses. Out of all the interactions, the strongest were observed between the Replication Initiation protein (Rep) of CLCuKoV-Bu with the Movement protein (MP), Nuclear Shuttle Protein (NSP) of ToLCNDV (DNA-B), while the weakest were seen between the Replication Enhancer protein (REn) of CLCuKoV-Bu with the REn protein of ToLCNDV. The residues identified to be taking a part in interaction belonged to domains having a pivotal role in the viral life cycle and pathogenicity. It maybe deduced that the two viruses exhibit antagonistic behavior towards each other, and the type of infection may be categorised as a type of Super Infection Exclusion (SIE) or homologous interference. However, further experimentation, in the form of transient expression analysis, is needed to confirm the nature of these interactions and increase our understanding of the direct interactions taking place between two viruses. Geminiviridae, and genus Begomovirus (Bemisia tabaci)  genomes that are packed into icosahedral twinned particles, a feature unique to this family. Based on their host range, the vector of transmission, and genome arrangement these viruses have been arranged into nine genera . These satellites are typically 1,350 bp in size, incorporating an adenine-rich region, the satellite conserved region (SCR) of a predicted hairpin structure that contains a loop region along with a single ORF that is named βC1, coded by the complementary strand (Cotton leaf curl disease (CLCuD) is a viral disease and notable menace to cotton productivity. CLCuD has been a serious threat to cotton production in Pakistan and India, leading to severe economic losses . Vein thomovirus . These v tabaci) . Geminive genera . Among t diverse . The gen diverse . The gen diverse . Some bey strand . In the y strand ; it perfy strand . It is ty strand . TrAP cry strand , and cauy strand . REn is y strand . REn undy strand . C4 proty strand . CP encay strand . V2 proty strand . MP carry strand . DetailsCotton leaf curl Kokhran virus-Burewala strain (CLCuKoV-Bu) was observed near the town of Burewala in Punjab, Pakistan . The unique feature of this new recombinant virus was that it possessed a truncated TrAP protein, consisting of only 35 amino acids (Cotton leaf curl Multan beta-satellite (CLCuMuB) (Tomato leaf curl New Delhi virus (ToLCNDV) in the CLCuD affected samples of the Cotton plant.   has beenCLCuMuB) . Until tn plant.  has raisn plant. . During n plant. . Virus-vn plant. . The inthttp://www.uniprot.org). For CLCuKoV-Bu, the sequences of Rep (A0A0S2MSV0), TrAP (A0A0S2MST9), Ren (A0A0S2MSU6), C4 (A0A2K8HNC7), CP (A0A0S2MSU9), and V2 (A0A0S2MSV4) were retrieved. Along with the sequence for ßC1 (A0A0K2SU38) protein from the associating beta-satellite, CLCuMuB. For ToLCNDV, the sequences for Rep (A0A565D4R1), TrAP (A7WPF3), Ren (A6PYE1), AC4 (A7U6D0), CP (A0A2Z2GPB3), and AV2 (A0A3G2KQ59) proteins from DNA-A, and MP (E6N191) and NSP (A0A2P2CKV4) from DNA-B were obtained. These sequences were submitted to NCBI provided PSI-BLAST (https://www.ncbi.nlm.nih.gov/BLAST/) to identify homologous protein structures present in Protein Data Bank (PDB) (http://www.rcsb.org/pdb).Gene sequences for CLCuKoV-Bu and ToLCNDV were retrieved from Uniprot (ab initio approaches were used for protein structure prediction. Homology modeling based structure prediction was performed using MODELLER 9.24 (http://zhanglab.ccmb.med.umich.edu/I-TASSER/), LOMETS version 3 webserver  (https://zhanglab.ccmb.med.umich.edu/LOMETS/), and RaptorX webserver (http://raptorx.uchicago.edu). I-TASSER is one of the most widely used online servers that is used for protein structure prediction and annotation of the structure-based functions. The server generates 3D models by matching the biological insights of the target proteins with the structures of known proteins present in biological databases (Ab initio approach was performed by submitting gene sequences comprising of 200 or fewer amino acids to the QUARK webserver (https://zhanglab.ccmb.med.umich.edu/QUARK/).Three different approaches were used for predicting the structure of the proteins of the two viruses. If the sequence identity for the viral gene products was greater than 75% then a homology-based approach was adopted, if it was less than 75% then threading-based and LER 9.24  where thLER 9.24  (http:// Server)  (https:/ebserver  (http://atabases . LOMETS atabases . RaptorXatabases . Ab initebserver  (https:/https://servicesn.mbi.ucla. edu/ERRAT/), global model quality assessment was done through QMEANDisCo   feature evaluates the pairwise distance that exists within the residues in the model and assesses the multitude of constraints that may exist within the structure, based on the information from experimentally determined protein structures that are homologous to the protein model being assessed. It is calculated as the average score per residue and occurs in the range of 0 to 1 (https://prosa.services.came.sbg.ac.at/prosa.php) is a diagnostic tool that is based on the statistical analysis of all the available protein structures. This program relates to the overall quality score of the protein structure in the form of a plot comprising experimentally determined protein structures that are present in the PDB. The overall quality of the protein models was estimated using global checks on the model’s stereochemistry, geometry, and other structural properties. This was done utilizing Ramachandran Plot, obtained through PROCHECK   (https:/f 0 to 1 . ProSA-Wf 0 to 1  (https:/PROCHECK .https://milou.science.uu.nl/services/HADDOCK2.2/). HADDOCK is a widely used docking tool that is based on information-driven docking protocol. It requires the generation of an Ambiguous Interaction Restraints (AIRs) file for a manual run. The prerequisite to the AIRs file is to define the active and passive residues present at the interface of each molecule. These residues are defined using experimental data or through bioinformatic predictors like CPORT (Consensus Prediction Of Residues in Transient complexes) (http://haddock.science.uu.nl/services/CPORT/). The HADDOCK server distinguishes between two types of residues. The active residues must be present at the interface between the two molecules and maybe in contact with the active or passive residues of the other molecule, if not, an energy penalty would be paid. The passive residues, on the contrary, may be present at the interface but are not penalized if found otherwise  and Inter-viral PPI (taking place between the proteins of two different viruses) were performed to evaluate whether a stronger interaction is observed within the proteins of the same virus, or between the proteins of different viruses. PPIs of the proteins of CLCuKoV-Bu and ToLCNDV encoded proteins were studied using the High Ambiguity Driven bio-molecular DOCKing (HADDOCK2.2) web portal  (https:/mplexes)  . Interatomic contacts (ICs) at a distance threshold of about 5.5 Å within the protein–protein complex are counted and classified based on the Polar/Non-Polar/Charged nature of the interacting amino acids. The server also provides the number of residues in the Non-Interacting Surface (NIS) of the protein (https://pymol.org/). By performing sequence analysis through Jalview software  the interacting residues determined through PRODIGY server were highlighted, and the domains these residues belonged to, were identified.The protein–protein complexes were selected based on their HADDOCK scores and associative energies. The binding energies and the intermolecular contacts present within the complexes were determined by submitting the top scoring complex, for each interaction, to PRODIGY (PROtein binDIng enerGY predict) webserver . It is a protein . Both IC protein . The str protein  , and a constant number of particles pressure, temperature (NPT). Following the equilibrations, 50 ns simulation was performed for the protein complexes.Molecular dynamics (MD) simulations provide identification of the conformational changes and the evaluation of the molecular interactions that are present within the complex assembly . MD simuSequences for the genes of CLCuKoV-Bu and ToLCNDV were retrieved from Uniprot, and PSI-BLAST analysis were performed to find homologous protein sequences. The information is provided in ab initio-based approaches, by using five different software. Homology modeling was performed using MODELLER. Through this approach, the structures of Rep and CP of CLCuKoV-Bu and ToLCNDV, which showed percentage sequence identity greater than 75% after PSI-BLAST analysis, were produced. In the case of ab initio modeling, done through QUARK, only the proteins comprising of less than 200 amino acids were modeled. These included the structures of TrAP, REn, C4, V2 proteins of CLCuKoV-Bu, βC1 protein of CLCuMuB, and TrAP, REn, C4, and V2 proteins of ToLCNDV. By using software like I-TASSER, LOMETS, and RAPTORX, structures for all the proteins of the two viruses were produced, by the threading-based approach.3D structures of the proteins were generated through homology, threading and Ab initio and threading based approaches proved to be the most stable. QUARK and I-TASSER were the most efficient programs. The overall quality of the structures assessed by ERRAT was found to be greater than 80% for the models of all proteins of both the viruses, except for βC1 of CLCuMuB, which had a value of 74%. The QMEANDisCo score, of all the protein structures, exhibited structure stability with their scores occurring within the given range. The Ramachandran plot exhibited that the maximum number of residues in the favorable and allowed regions. The Z-score by ProSA-web exhibited the structures were reliable, with the energy conformation of the models lying within a range of scores that are usually found for similar-sized proteins. Detailed results of structure evaluation are presented in The protein structures generated through different approaches were evaluated using ERRAT, QMEAN, ProSA Z-Scores, and Ramachandran plot, and a consensus structure for each protein was selected based on the results of these assessment tools. The structures produced by Homology modeling were the least stable, while those generated by PPIs were performed to access the intermolecular interactions that may be taking place between the proteins of the two viruses. This is a crucial step in accessing the domains taking part in the interaction and identifying the proteins showing the most affinity for the proteins of the other virus. CPORT analysis was performed to identify the active and passive residues of the proteins, provided in The PPIs performed using HADDOCK2.2 webserver showed affinity among the proteins of the two viruses. In total 169 interactions were performed, with 56 interactions taking place between the proteins of CLCuKoV-Bu and ToLCNDV, 49 between the proteins of CLCuKoV-Bu and CLCuKoV-Bu, and 64 between the proteins of ToLCNDV and ToLCNDV. The inter-viral PPIs, between CLCuKoV-Bu and ToLCNDV were the strongest based on the HADDOCK scores and corresponding energy values. Rep and CP of CLCuKoV-Bu and MP and NSP of ToLCNDV were most actively involved in the interactions. Among all the interactions, the strongest that were observed involved the Rep (CLCuKoV-Bu) protein. As was seen in the interaction of Rep (CLCuKoV-Bu) – MP (ToLCNDV), Rep (CLCuKoV-Bu) – NSP (ToLCNDV) and Rep (CLCuKoV-Bu) - Rep (ToLCNDV). Another significantly strong interaction was observed between REn (CLCuKoV-Bu) - NSP (ToLCNDV). The docked structures were evaluated based on their RMSD values and energies like, desolvation energy, Van der Waals energy, electrostatic energy, restraint violation energy, buried surface area and the Z- score, that is based on the probability of the results among the various clusters. The details of these interactions, HADDOCK scores and energy values of the complexes are provided in d) values of the docked structures, calculated by the PRODIGY webserver are provided in Overall, all the observed interactions exhibited strong binding affinity, based on the recorded energy values, only some interactions with the REn protein of CLCuKoV-Bu seemed to be weak, exhibiting low dissociation energy and low affinity values. Details of the energy values for the observed interactions are provided in The amino acids residues taking part in the interaction were determined by submitting the docked complexes to PRODIGY server. The maximum number of interacting residues among all the strong PPIs belonged to Polar-Apolar ICs, while the minimum number belonged to Charged-Charged ICs. Details of charges of the interacting residues are given in Based on the knowledge of these interacting residues, the domains involved in the interaction were identified using Jalview software. For the two begomoviruses, similar domains were identified to take part in the interactions. This was deduced after taking into account all 169 interactions and identifying the interacting amino acid residues. For the Rep (CLCuKoV-Bu) and Rep (ToLCNDV), the DNA Binding and the Helicase/ATPase domains were involved in interactions. The majority of the interacting residues belong to the helicase domain, while only one amino acid residue from the DNA binding domain seemed to be involved, these residues are presented in The REn protein of CLCuKoV-Bu and ToLCNDV, exhibited that both N and C terminals were involved in interactions. The interacting residues occurred in the Rep binding domain, PCNA binding domain, SINAC1 binding domain, and the retinoblastoma related protein binding regions: V2 (CLCuKoV-Bu) showed participation of the Putative Protein Kinase C (PKC) domain and the WCCH domain in interaction, while for the AV2 (ToLCNDV), interacting residues were only present in the latter domain, and no participation was shown for the former, The βC1 protein of the CLCuMuB had several residues involved in the interactions, ranging from the center to the C-terminal of this protein. Interacting residues also occurred in the myristoylation domain of this protein, MP (ToLCNDV-DNA B) had the strongest interaction with the proteins of CLCuKoV-Bu and it exhibited interacting residues from the Pilot, anchor and oligomerization domains. with the majority of the interacting residues belonging to the latter. As the NSP interacting domain also overlaps with the anchor and oligomerization domains, it may also be involved in the interactions, The NSP (ToLCNDV- DNA B) had the interacting residues present in the NLS-A region while the residues from NLS-B did not take part in interactions. The residues from the initial parts of the DNA binding domain, the MP interacting domain and the AtNSI interacting domains were involved in the interactions, To understand key structural details, in terms of stability, structural integrity, and compactness of the complexes of the interacting proteins, MD simulations were carried out. The HADDOCK server also conducts water-based simulations, therefore, to remove steric hindrances present within the structures of the complexes 50 ns simulations were performed. The Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) analyses provide information about structural stability and flexibility of the protein–protein complexes.g) was calculated to determine the compactness of the complexes. Rg provides the overall dimensions of the protein structure as it shows the mass-weighted root mean square distance of a collection of atoms from their common center of mass. Results show the average Rg value of 2.66 for complex 1 and 2.825 for complex 2 for a 50 ns simulation at 300 K. Hydrogen bonds are considered to play a vital role in the structural stability and molecular interaction of the proteins. The greater number of hydrogen bonds helps to maintain the stability of the structures. The hydrogen bond analysis was carried out through the VMD 1.9.4 program , suggesting an association between the betasatellite and the bipartite begomovirus (−1), indicated that the protein–protein complex was stable, with the amino acids from the Myristoylation-like motif domain of βC1 and pilot domain, the anchor domain and oligomerization domains of MP were involved in the interaction.The MP and NSP of DNA-B of ToLCNDV are involved in the cell to cell transport of the viral products and in the export of the Viral DNA from the nucleus , these fomovirus . A signiin-silico research and serve as the first step towards understanding the mechanism in which virus-virus interactions could be taking place. It is important to carryout in planta experimentation and evaluate these findings.The outcome of interactions between dual viral infections may range from synergism to neutralism to antagonism, with each having a direct impact on the health of the plant. In case of no change in the viral accumulation as compared to single virus infection, the state is referred to as neutralism, whereas if the accumulation of either one or both of the viral agent is enhanced it is called synergism and if the population of either one or both viral agents is reduced it is termed as antagonism . The dirin vivo analysis of protein complexes within the host plant The residues and domains highlighted in this study may provide a valuable insight into virus-virus interactions and can serve a lead in understanding the elusive VVIs. Strong interactions among the key domains of the proteins of the two co-infecting viruses suggest the interaction maybe antagonistic or a type of cross-protection or homologous interference, however such claims can only be proved by carrying out further experimentation.The present study aims to develop an understanding of the nature of coinfection taking place between the proteins of CLCuKoV-Bu and ToLCNDV on the basis of the speculations that this coinfection can lead to a third epidemic of the CLCuD. VVIs were determined by taking into account the PPI between the proteins of the two viruses. The findings reveal domains of viral proteins, responsible for performing crucial roles in the replication and translocation in the viral life cycle interacting with each other. This interaction shows that the proteins of the two co-infecting viruses may have an affinity, and bind easily to one another, altering the course of the disease in the host. The effect such interactions may have on the host can only be confirmed by 10.7717/peerj.12018/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.12018/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.12018/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.12018/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.12018/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.12018/supp-6Supplemental Information 6Click here for additional data file.