Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions.We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer’s method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of https://rubygems.org/gems/DomFun. Code maintained at https://github.com/ElenaRojano/DomFun. Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project.DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer’s method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from The online version contains supplementary material available at 10.1186/s12859-022-04565-6. Determining protein function is one of the major goals of bioinformatics. A key factor influencing the role of a given protein is its domain composition , 2. AlthVarious features are important for function prediction, including sequence homology and conserved structure, which are used to classify protein domains by resources such CATH-Gene3D , 8, whicWithin the homologous superfamilies, domains can be further sub-divided into functional families (FunFams), based on shared patterns of sequence conservation . Such doIn recent years, we have developed tools to analyse and extract information from network-based data structures , 13. We Here, we present DomFun, a framework that uses associations between protein domains and functional annotation to predict protein function Fig. .These associations are calculated by exploiting tripartite networks connecting domains and functional groups via proteins , using GWe validated our method using the prediction benchmark of the third version of the Critical Assessment of Functional Annotation challenge (CAFA 3) . This evhttps://rubygems.org/gems/DomFun. Ruby code is available from https://github.com/ElenaRojano/DomFun and the workflow from https://github.com/ElenaRojano/DomFun_project. Technical information to install and use DomFun is provided in these repositories.DomFun can be used to predict protein function for multiple organisms for which protein domain and functional annotation information is available. Although we used CATH-Gene3D annotation in this work, other features can be used. It can be downloaded from We have developed and implemented DomFun, a tool to predict function for a given protein based on associations between its constituent domains and functions, obtained from various annotation-databases. Associations are calculated using a tripartite network comprised of domains, proteins and functional annotation. The DomFun algorithm works in the following manner: Based on a training dataset, protein-domain and protein-function annotation data are combined to produce a tripartite network of 3 layers: domains-proteins-functions. This tripartite network is analysed using the NetAnalyzer software . NetAnalThen, given a testing protein, DomFun predicts function for this protein by obtaining all of its annotated domains and searching for their associated functions in the list generated by NetAnalyzer. The scores for these associated functions are then combined using the methods whose mathematical formulae are described below, to obtain overall scores, which represent the predicted functions for the protein.An overview of the architecture of the software implementation is described in Fig. To build the domain-protein-function tripartite networks used to calculate the domain-function associations with DomFun, two types of relations were combined: protein-function and protein-domain.https://github.com/ElenaRojano/DomFun_workflow.The workflow followed to perform the different steps explained next and data download links are included in a GitHub repository at We used two datasets to establish protein-function relations.https://www.biofunctionprediction.org/cafa-targets/CAFA3_training_data.tgz.The first dataset was based on the functional annotation included in the third Critical Assessment of Functional Annotation challenge (CAFA 3) . We usedfragments and protein fusions. We used this information to construct the protein-annotation layer for ten different tripartite networks. After these filters, we were left with 4,283,876 annotated proteins. Please note that the same protein may have annotation in any of the GO sub-ontologies, KEGG or Reactome.The second dataset was downloaded from UniProt (release 2021_02). We obtained 23,391,902 proteins both manually curated (Swiss-Prot) and computationally inferred (TrEMBL) for multiple species. We also downloaded their annotations in GOMF, GOBP, GOCC, KEGG and Reactome. We discarded proteins with GO annotation tagged as Inferred from Electronic Annotation (IEA) to ensure high-quality annotations for our study. We also discarded all protein tagged as To establish protein-domain relations, we used protein domains classified into homologous superfamilies and FunFams from the protein structure classification database CATH-Gene3D . SuperfaWe obtained multiple sets of protein-function and protein-domain relations. In total we built 16 networks connecting domains to functional annotation via shared proteins, one for each combination of protein-domain and protein-function datasets.These domain-protein-function networks were analysed with NetAnalyzer, a tool that calculates associations in multipartite networks . We emplThe domain-function associations calculated using the above methods were used to predict protein function using DomFun. First, for a given protein, associated with a specific UniProt identifier, DomFun searches for its constituent domains within CATH-Gene3D. If domains are found, it then searches for any functions associated with them. If a protein contains multiple domains associated with the same functional annotation Fig.  , DomFun By calculating this score for all functions associated with at least one domain for a given protein, we obtain a vector of scores, which represents the predicted functions for the protein.DomFun outputs a table of predicted functions for each protein, containing the UniProt identifier, the domains for that protein classified according to CATH-Gene3D superfamilies or FunFams, the predicted functions  and the combined score for each putative protein-function association.P-values by calculating their antilogarithm (base=10), which represents the probability of having an equal or greater number of interactions between a pair of nodes  than would be expected by chance [P-values we use the Fisher’s combined probability test . We also developed our own validation procedure to evaluate DomFun in terms of predicting function for KEGG and Reactome pathways. We refer to this procedure as Pathway Prediction Performance (PPP). We also used PPP for GOMF, GOBP and GOCC annotations — as similar evaluation values for the three GO sub-ontologies to those found with CAFA 3 would suggest that our benchmark procedure is reliable and lend confidence to the interpretation of the PPP results for KEGG and Reactome.We evaluated the ability of DomFun to predict protein function using the CAFA 3 prediction benchmark for GOBP, GOMF and GOCC, using the methodology described in  with datFor this validation, we looked at the maximum value of the harmonic mean , we initially compared the eight sets to each other, to see which performed best across all testing scenarios. The top performing method was then compared against the best performing method from CAFA 3 for each scenario.We used data from CAFA 3, a challenge aimed at comparing various methods for predicting protein function, to evaluate the predictive capability of DomFun . In brieThe CAFA 3 dataset includes various sub-divisions to evaluate the prediction methods, as explained in , 26, incWith respect to the different proteins in the benchmark testing set, no knowledge proteins are those with no experimentally verified annotation in any of the three GO sub-ontologies at time tcument}0 , 26. We We used the CAFA protein-centric evaluation mode. It calculates maximum F-measure ribed in .The CAFA 3 benchmark files include 3089 proteins. We consider this set as our testing set. We predicted GOMF, GOBP and GOCC for 2483 proteins from this set. As occurred with the training proteins set, we lost 606 testing proteins as they had no CATH domains. This loss affects prediction performance in terms of coverage.We compared DomFun performance against the two baseline models, Naïve and BLAST. We generated both models following instructions provided by CAFA authors . We alsoZ-scores and any value with absolute value greater than 2 was set to 2. Finally, the distribution was normalised to the  and adding 0.5. In the case of scores calculated using Fisher’s method, i.e. the combined HyI derived P-values, we transformed them into the  .In the pathway prediction performance (PPP) validation procedure, we predicted protein function  for all proteins that were used to build the domain-protein-function tripartite networks. We compared these predicted associations to the original protein-function associations, and built precision-recall (PR) curves. For this, all predicted annotations that matched the original annotation were considered true positives; all predictions that did not match the original annotation were considered false positives. This additional validation procedure was necessary to evaluate the results obtained using KEGG and Reactome. As we had already validated the GO annotation using the CAFA 3 benchmarking procedure, we were able to use the PPP procedure to compare the results using KEGG and Reactome to the GO results and put them in the context of the CAFA 3 predictions. PR curves were generated using the ROCR package .We associated protein domains with functions by applying various metrics to tripartite networks formed by combining protein-domain and protein-function annotation via shared proteins. Functional annotation was obtained from the Gene Ontology molecular function (GOMF), biological process (GOBP) and cellular component (GOCC) sub-ontologies and KEGG and Reactome pathways.The domain-function associations were then used by DomFun for protein function prediction. Two validation procedures were used, one using the training and testing proteins from the CAFA 3 benchmark dataset , and anoThe CAFA 3 training set contains 98,567 proteins from multiple organisms, of which 50,813 had annotations in GOBP, 35,086 in GOMF and 49,328 in GOCC . We usedWe used the domain-function associations calculated for the CAFA 3 training proteins at We analysed the results to evaluate which combination of domain family, association and integration methods led to the most accurate predictions. This was performed separately for the FunFam and homologous superfamily classifications. We initially compared the results of each method to each other and to the two CAFA 3 baseline methods (BLAST and Naïve) in terms of maximum F-measure entclass1pt{minima methods .Fig. 2DoSimpson index with Stouffer’s method applied to FunFams ranked highest on average amongst all association indices implemented here using NetAnalyzer, for all evaluation procedures across all sub-ontologies, according to both entclass1pt{minimaentclass1pt{minimaWe compared the results obtained by DomFun to the top results from CAFA 3 for all four evaluation procedure combinations and all three GO sub-ontologies  curves for are shown in the Additional files We compared the PP Table  against mentclass2pt{minimWe observed similar The highest With respect to the predictions performed with superfamilies, the We have presented DomFun, a novel approach to predict protein function based on associations between domains and functions. The method is based on the same protein domains classification system used by the FunFHMMer method , evaluatAlthough both methods are based on similar underlying data, DomFun differs fundamentally from Orengo-FunFams in terms of how it assigns functions to the test proteins. Orengo-FunFams first assigns FunFam domains to a test protein then, for each of these FunFams, obtains corresponding GO term annotations, which are scored based on their frequency among the seed sequences for the given FunFam. Parental terms of these GO terms are also obtained. Finally, the set of all domain-GO terms annotations for the test protein IS considered. This differs markedly from our approach, which first obtains GO association values for all domains in a given protein based on tripartite network analysis, and then combines these values to produce a single score for each predicted protein-annotation association using data-fusion methods.Like the Orengo-FunFams method, DomFun performs particularly well for the no-knowledge partial evaluation and for GOMF. However, it performs less well in full evaluation procedures. The loss of training proteins due to the lack of CATH domains could explain this. As they will not form part of the association network, their information will not be available to make predictions and this could consequently decrease More concretely, we obtained Fmax values of 0.624 (GOMF) and 0.492 (GOBP) for the no knowledge partial evaluation procedure  into an automated system that is able to extract testing and training data from the benchmark dataset, build the tripartite networks to calculate associations and combine them to predict function for the testing proteins such that we could perform validation using CAFA3 in a high-throughput manner. These scripts are available from https://github.com/ElenaRojano/DomFun_project and can be used by others to validate their own method, or to validate adaptations of our methods, for example by adding additional protein features to the tripartite network.To validate our results using the CAFA 3 data, we incorporated the original Matlab scripts , depth (maximum degree of specificity) and branching factor  can affect the results .As mentioned above, the PPP benchmark showed better results for KEGG than GO. These resources include well-curated representations of specific metabolic pathways with shared catalytic activities performed by multiple proteins, which may explain these findings. Further work could investigate methodology to select the more specific GO terms using semantic similarity measures .Results for both CAFA 3 and PPP validation show that, of the 8 prediction frameworks implemented here, combining the four association metrics with the two protein domain annotation types (superfamilies and FunFam), the best combination for making domain-function predictions with DomFun is the Simpson index with Stouffer’s method using FunFams.We have shown that protein domain associations based on network analysis can be useful for predicting protein function for multiple species, showing comparable performance when predicting GO annotation to other methods based on structural domain-based in the CAFA 3 challenge. We have also presented a novel validation system for protein function prediction that shows similar results to the CAFA 3 benchmark, but can also be extended to use KEGG and Reactome annotation.DomFun has been implemented in such a way that other protein features, such as different domain annotation classifications can also be integrated. Future work of DomFun should focus on improving domain-function associations using additional protein-features for protein annotation, more domain annotations and predicting for a greater range of functional systems.https://github.com/ElenaRojano/DomFun_project Operating system(s): Unix-like systems Programming language: Ruby, Matlab Other requirements: CAFA 3 benchmarking system used to compare methods License: GNU GPL Any restrictions to use by non-academics: None.Project name: DomFun Project home page: Additional file 1. Table S1: Fmax values for all DomFun methods and all combinations of evaluation scenarios and ontologies, compared to the highest equivalent value from CAFA 3 and the baseline methods. Type 1: no knowledge, type 2: limited knowledge. Mode 1: Full, mode 2: partial. FF: FunFams, SF: superfamilies. Jac: Jaccard, Sim: Simpson, PCC: Pearson correlation coe_cient, HyI: hypergeometric. Sto: Stou_er, Fis: Fisher.Additional file 2. Table S2: Additional file 3. Table S3: DomFun Smin Top vs. CAFA Smin Top (top means lowest score). Type 1: no knowledge, type 2: limited knowledge. Mode 1: Full, mode 2: partial.Additional file 4. Table S4: Coverage values for all DomFun methods and all combinations of evaluation scenarios and ontologies, compared to the highest equivalent value from CAFA 3 and the baseline methods. Type 1: no knowledge, type 2: limited knowledge. Mode 1: Full, mode 2: partial. FF: FunFams, SF: superfamilies. Jac: Jaccard, Sim: Simpson, PCC: Pearson correlation coefficient, HyI: hypergeometric. Sto: Stouffer, Fis: Fisher.Additional file 5. Table S5: Smin values for all DomFun methods and all combinations of evaluation scenarios and ontologies, compared to the highest equivalent value from CAFA 3 and the baseline methods. Type 1: no knowledge, type 2: limited knowledge. Mode 1: Full, mode 2: partial. FF: FunFams, SF: superfamilies. Jac: Jaccard, Sim: Simpson, PCC: Pearson correlation coefficient, HyI: hypergeometric. Sto: Stouffer, Fis: Fisher.Additional file 6. Fig. S1: Precision and recall curves to ascertain DomFun accuracy using domain-function associations with FunFams (PPP). funfamsPPP.pdf Prediction results for Gene Ontology (GO) molecular functions , biological process , GOCC (green curves), KEGG (orange curves) and Reactome pathways (black curves) are shown. These curves compare DomFun results using associations between FunFam domains and functions calculated with (a) Jaccard index, (b) Pearson Correlation Coefficient (PCC), (c) hypergeometric index (HyI) and (d) Simpson index. The area under the precision-recall curve (AUC-PR) for each comparison is also shown.Additional file 7. Fig. S2: Precision and recall curves to ascertain DomFun accuracy using domain-function associations with superfamilies (PPP). superfamilyPPP.pdf Prediction results for Gene Ontology (GO) molecular functions , biological process , GOCC (green curves), KEGG (orange curves) and Reactome pathways (black curves) are shown. These curves compare DomFun results using associations between superfamily domains and functions calculated with (a) Jaccard index, (b) Pearson Correlation Coefficient (PCC), (c) hypergeometric index (HyI) and (d) Simpson index. The area under the precision-recall curve (AUC-PR) for each comparison is also shown.


TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs . Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the  TP63 gene, which is 4944 bp (4.94 kb) long and located in chromosome 3 at the 3q28 locus. It has 12 isoforms listed in the UniProt database. TP63 isoform 1 is 680 amino acids long and acts as a transcription factor (TF) that regulates gene expressions in multiple pathways, notably in tumorigenesis and development [Human tumor protein 63 is encoded by the TP63 gene can assist in identifying the altered gene expression together with gene regulation.Several genome-wide association studies (GWAS) and case-control studies have shown the association of single nucleotide polymorphisms (SNPs) in the human TP63 locus with various cancers, i.e., lung cancer, head and neck cancer, colon cancer, urinary bladder cancer, and other diseases ,8,9,10. CHK2 gene using computational tools to predict the structural and functional impacts on the protein [STK11 gene [hTERT gene were analyzed for their structural and functional significance through in silico tools [TP63 gene on the TP63 protein using different in silico tools.Determine the consequences of various nsSNPs in the human Evaluate the effect of nsSNPs on the binding affinity of the TP63 protein with its ligands (DNA) by molecular docking to confirm the consequences.Simulate interactions of DNA and TP63 protein with molecular dynamics simulations to validate the effect on protein function caused by high impact nsSNPs.Different computational tools and approaches to predict deleterious SNPs and their roles in protein stability, function, and structure are extensively used ,17,18,19 protein . In anotK11 gene . Similarco tools . Overallco tools ,24 and tco tools ,26. HoweTP63 gene through RegulomeDB, PolymiRTS, and GTEx portal to investigate the functionally important non-coding SNPs in this locus.Furthermore, the study also aimed to analyze non-coding SNPs in the We retrieved the SNP data from the ENSEMBLE genome database and obtained 64,144 SNPs in both the coding and non-coding categories. Then, we filtered missense SNPs, which are described as nsSNPs of the coding region in the TP63 protein. Intron, 5′ UTR, and 3′ UTR SNPs were also selected for non-coding SNPs (836) of the TP63 protein. The functional consequence analysis was carried out using nine different in silico tools. First, 28 nsSNPs were retrieved through analysis with SIFT, Polyphen2, and CADD. Later, 17 nsSNPs were obtained after analysis with six in silico tools . The structural impact analysis was accomplished by implementing ConSurf, HOPE, I-Mutant, and Mutation 3D, and nine nsSNPs were found with significant deleterious effects. Further, molecular docking analysis was performed. Docking analyses were chosen to perform MD simulations to verify the outcome of the predictions. In the case of non-coding SNPs, Regulome DB analysis and GTEx portal were utilized along with PolymiRTS analysis. A flowchart showing the different steps of this study is presented in TP63 gene was investigated in the ENSEMBL genome browser [In this study, the human  browser . The tra browser . Further browser . Sorting Intolerant From Tolerant (SIFT) was used to detect the deleterious nsSNPs. SIFT can differentiate functionally neutral amino acid changes from functionally deleterious ones . This soPolymorphism Phenotyping v2 (PolyPhen2) uses Naive Bayes to classify and predict the functional impacts of allele modifications . For eacCombined Annotation-Dependent Depletion (CADD)  is an SNProtein Variation Effect Analyzer (PROVEAN) is a server that assesses the functional impact of a substituted amino acid or insertion-deletion mutation on a protein across organisms. PROVEAN can provide high-throughput analysis at both the genomic and protein levels for human and mouse variants . For eacSNPs&GO uses functional annotations of proteins to assess the impact of single amino acid substitutions . SNPs&GOClinVar helps to analyze human genetic variations along with annotation of variant disease associations. ClinVar aggregates the known variant interpretations and makes them publicly available on the ClinVar database . The CliMutPred2 enhances pathogenic amino acid substitution prioritization, predicts potentially disease-causing molecular pathways, and returns interpretable distributions of the pathogenicity score on individual genomes . Input iInterPro and NCBI domain prediction tools employ protein families to predict domains of protein by evaluating them functionally. The Conserved Domain Search tools in NCBI  and InteI-Mutant 2.0 is an algorithm that uses a support vector machine to provide an assessment of protein stability change for the variations in single nucleotides . I-MutanAs an algorithm and web server, Mutation3D detects changes in amino acids within protein three-dimensional structures. Mutation3D can distinguish functional and nonfunctional variations . The mutConSurf web server was utilized to recognize evolutionarily conserved amino acid residues (native) and to classify nsSNPs in each position . AccordiHOPE analyzes functional and structural impacts of point mutations. HOPE incorporates data from multiple information sources including measurements of 3D protein coordinates using services of WHAT IF Web, sequence annotations at UniProt database, and predictions of DAS service . Data stPhyre2  was usedRASSF5 gene on protein structure and function were predicted by employing in silico analysis [Molecular docking was performed to assess the impacts of nsSNPs on the interactions between DNA and TP63. The DNA is a direct ligand of the TP63 protein. Binding affinity is a measurement of the interactions between protein molecules and ligands, protein, peptide, or DNA. In this study, molecular docking was performed on the AutoDock Vina platform using virtual screening tools -PyRx . 3QYN  simulation was performed to observe the binding interactions of wild-type and mutant TP63 protein with DNA to obtain insight into the structural dynamics and stability for 250 ns. The YASARA Dynamics suite  was usedd method  was usedd method . FinallyRegulomeDB offers an annotation of regulatory SNPs and integrates the information from experimental data sets, computational predictions, and manual annotations from ENCODE. This tool assigns scores to variants to distinguish the functional SNPs from a wide pool . rsIDs oPolymorphism in microRNAs and their Target Sites (PolymiRTS) database 3.0 incorporates data from ligation, hybrids sequence, and crosslink experiments to analyze miRNA–mRNA interactions . The PolGenotype-Tissue Expression (GTEx) finds the association between genetic alterations and gene expression in human tissues. GTEx relates these regulatory mechanisms to traits and diseases . rsIDs oTP63 gene contained a total of 64,144 SNPs with transcript ID: ENST00000264731.8. Among all, different SNPs were as follows: (1) 455 missense or nonsynonymous SNPs (nsSNPs), (2) 242 synonymous SNPs, (3) 680 UTR SNPs, (4) 62,727 intron SNPs, (5) 11 frameshift SNPs, (6) 6 inframe SNPs, (7) 11 stop-gained SNPs, and (8) 12 other SNPs. Different categories of SNPs are presented in The SNP data for the A variety of tools were selected for computational analysis of the nsSNPs of TP63. Initially, SIFT, PolyPhen2, and CADD were used for the analysis. Out of 455 nsSNPs, 194 SNPs were predicted to be deleterious by SIFT. Probably damaging criteria were selected using PolyPhen2, and 121 nsSNPs were found as deleterious. Later, CADD was used to predict the nsSNPs. Likely deleterious criteria were chosen, and 28 nsSNPs were predicted to be deleterious . FurtherSNPs&GO simultaneously performed the analysis using three different tools: PhD-SNP, PANTHER, and SNPs&GO, and provided results separately along with probability scores . The numDifferent types of gain/loss of structure and function/altered function for protein TP63 were observed after analysis with MutPred2. The impacts in MutPred2, such as loss of strand, gain of strand/intrinsic disorder, altered ordered interface, altered DNA binding, altered stability, altered metal binding, etc., were found with significant p-value and high MutPred2 scores . MutPredThe NCBI conserved domain search tool provided three domains of TP63 protein as follows; (1) P53 DNA binding domain (177–358 amino acid residues) usually binds to the DNA, (2) P53_tetramer domain (392–428 amino acid residues), which is described as P53 tetramerization motif according to the Pfam protein family, and (3) SAM_tumor_63 (543–607 amino acid residues), which is defined as SAM domain of tumor-p63 proteins, where SAM stands for sterile alpha motif . InterPrSIFT, PolyPhen-2, CADD, PROVEAN, ClinVar, MutPred2, SNPs&GO, PhD-SNP, and PANTHER were evaluated as pathogenic/deleterious 17 nsSNPs that are positioned in three principal domains . Among tThe selected 17 nsSNPs of the TP63 protein were then chosen to check for protein stability through analysis with I-Mutant 2.0. All 17 nsSNPs were predicted to decrease the stability of the TP63 protein by I-Mutant 2.0 , as DDG/Seventeen nsSNPs were submitted for ConSurf analysis. 11 wild-type residues  were observed as highly conserved. Among them, R319 is a structural and buried residue, and the others are functional and exposed A. The stSeventeen nsSNPs were selected to perform 3D analysis using the Mutation 3D tool. This analysis showed the three-dimensional structure of 153–388 residue length (available in PDB with 100% sequence similarity) TP63 protein with 12 nsSNPs. Nine SNPs are present in the DNA binding domain of TP63. The other SNPs are in two different domains: one is the p53 tetramer domain, and the other is the SAM-2 domain .Seventeen SNPs were chosen for evaluation using the HOPE web server tool. For each nsSNP, there was a change in size, charge, and hydrophobicity of the amino acid residues in the respective positions of the TP63 protein. The size of amino acid residues was changed to large/small and the charge was observed to lose/gain. Increased/decreased hydrophobicity was also observed in the protein through the loss of hydrogen bonds due to nsSNPs. These changes disrupt the correct folding of the protein. Results from HOPE analysis are presented in C347F showed loss of di-sulfide bridge that affects the stability of the TP63 protein structure. R343Q, R266Q, R319H, R337Q, and R318H were observed to cause (1) smaller sizes of amino acid residue, (2) loss of positive charge, and (3) increased hydrophobicity in the protein. Glycine (G) is very flexible and possesses a particular conformation for the TP63 protein; D351G or G349E disrupts the protein structure. HOPE created images for the nine SNPs using a partial 3D structure of the TP63 protein (PDB ID: 3QYN). These SNPs are present in the DNA binding domain showing the wild-type and mutated residues at one position with two different colors . TherefoThe DNA binding domain structure of TP63 protein is an available PDB structure, and the DNA is a direct ligand for the TP63 protein. Thus, DNA binding domain nsSNPs were selected for molecular docking analysis. Furthermore, these nsSNPs were evaluated as pathogenic by all in silico tools, and HOPE created the image for these SNPs. The results of molecular docking showed the binding affinity of mutated and native TP63 peptides (ligands) towards DNA and determined the consequences of nsSNPs on TP63–DNA interactions. Five nsSNPs  were shown to decrease in binding affinity compared to wild-type peptides while interacting with DNA . The intA 250 ns molecular dynamics simulation was conducted to study the deviation of binding interactions of mutant TP63 proteins  with DNA from native TP63 proteins  with DNA. MD simulations were performed in terms of root mean square deviation (RMSD), Rg, SASA, and H-bond analysis to observe the deviations of structural dynamics between native and mutant(s)in normal physiological conditions. RMSD values were estimated for native and mutant TP63–DNA complexes to assess the alteration effects.While G349 and E349 showed similar RMSD values for DNA–peptide structure, H319 showed increased RMSD values compared to the R319–DNA complex and no significant change in RMSD for native 347 and mutant 347 complexes. Increased RMSD values in R319–DNA were observed and reached an apex of 23.6 Å at 33.2 ns. Similarly, an increased RMSD value was observed between F347–DNA (24Å) and C347–DNA (20Å), and the average value was decreased for the mutant structure. The average RMSD values for G349–DNA, R319–DNA, and C347–DNA were 12.06 Å, 12 Å, and 9.82 Å respectively, whereas the average RMSD values for E349–DNA, H319–DNA, and F347–DNA were 11.03 Å, 13.09 Å, and 8.50 Å respectively . SimulatFurthermore, Rg analysis was performed to determine the firmness and rigidity of the wild-type of DNA and mutant protein–DNA. Rg analysis showed a significant deviation in mutant H319–DNA structure, and increased Rg values were observed compared to those of the wild-type complex B1. The a2 and 5295 Å2, respectively. However, average SASA values for wild-type and mutant 349 were 5426 Å2 and 5355 Å2, respectively  was performed for all the wild-type and mutant structures . Wild-tyThe gene variants of TP63 with transcript ID ENST00000264731.8 were retrieved from the Ensemble database (Ensemble genome browser). The SNP source was the NCBI dbSNP database. The total number of noncoding SNPs  was 836, with a global MAF range of 0.05–0.5.A total of 836 SNPs were selected for RegulomeDB analysis. The filtering process led to 21 SNPs based on the proposed ranking criteria described on the RegulomeDB site . Out of The filtered 23 SNPs from Regulome DB analysis were further analyzed with the GTEX portal to identify the single tissue eQTLs. Among 23 noncoding SNPs, five SNPs  were obtained for seven eQTLs . In addiA total of 836 SNPs were analyzed with the PolymiRTS tool. Only 44 SNPs with miRbase IDs and affecting miRNA sites were identified primarily. Those SNPs had ancestral alleles, changed alleles, and conservation scores that reflected the occurrence of miRNA sites in other vertebrates with a significant context + score change. These SNPs were classified into four functional groups: (1) D describes the disruption of a conserved miRNA site; (2) N describes the disruption of a non-conserved miRNA site; (3) C denotes the creation of a new miRNA site; and (4) O denotes without determination of an ancestral allele . The resTP63 gene were found to be associated with different types of disorders along with cancers [TP63 gene using various in silico tools and approaches to obtain higher precision in the predictions of the functional impacts of the nsSNPs on the TP63 protein. Most of the in silico algorithms work based on either sequence or structural features. For example, Polyphen 2.0 uses both 3D structure and sequence to give accurate predictions [TP63 is a tumor protein and acts as a transcription activator suppressor. nsSNPs of the  cancers . In rece cancers ,59,60,61 cancers . We perfdictions ,65 as suIn this study, three nsSNPs  were predicted as significantly deleterious or detrimental based on their respective prediction scores. Conserved residues are generally used in proteins to regulate the biological system, such as in folding or protein stability. Enzymatic sites in proteins contain functional amino acids involving insignificant interactions with other proteins or molecules. These residues are more conserved than the rest of the protein. From ConSurf analysis, it was observed that R319, G439, and C347 are highly conserved regions in the protein with a conservation score of 9 . MoreoveWe executed molecular docking of the TP63 protein and DNA complex to substantiate the outcomes obtained with all the in silico tools. In a few studies, researchers showed a reduced binding affinity score for mutant protein compared to the wild-type ,46. The We executed MD simulations for three nsSNPs . Because these three SNPs showed significantly low BA scores  and wereSimulations from 150–250 ns clearly showed the deviation in terms of RMSD, Rg, and SASA values of mutant structures compared to those of native TP63–DNA complexes. These analyses determined that the replacement of cysteine, arginine, and glycine with phenylalanine, histidine, and glutamate at respective positions in the TP63 protein significantly altered its structure from the native shape and configuration. As a result, the DNA binding affinity of TP63 protein potentially can be critically affected. Therefore, the normal functionality of the TP63–DNA complex may not be accomplished. Variations in the Rg value of mutant TP63–DNA complexes compared to native complexes may cause the TP63–DNA complex to be less compact than wild-type by enhancing the flexibility of the compound. This outcome can affect the binding of TP63 protein to DNA B1,B2.The SASA findings C1,C2 shoPrior studies on mutant protein–ligand complexes have produced similar results with the analysis of RMSD, H-bonds, and SASA ,70,71,72The deleterious effects of R319H, G349E, and C347F are evident from the in silico analyses, molecular docking, and MD simulation. More specifically, the primary conformation of the pathogenicity of R319H was observed due to reduced DNA binding, and the outcome was obtained through molecular docking. Subsequently, RMSD, Rg, and SASA values were increased significantly compared to those of the wild-type complexes. Hence, MD simulations confirmed all the previous predictions for R319H. The outcome was corroborated by another study where research showed the association of R319H with ectrodactyly, ectodermal dysplasia, and cleft lip/palate (EEC) syndrome, and non-syndromic split hand-split foot malformation (SHFM) . Hence, TP63 gene generate or interrupt target sites in mRNA, changing miRNA–mRNA interaction and perhaps resulting in aberrant TP63 suppression [TP63 gene (TP63 gene with a specific SNP. TP63 gene affecting the regulation of protein products that cause diseases. Therefore, noncoding SNPs affect the normal regulation of TP63 gene expression and protein production. These effects can result in tumor progression.PolymiRTS analysis showed five non-coding SNPs that can alter the miRNA binding sites, hampering the regulation of the TP63 protein. miRNA binds to TP63 mRNA to regulate the production of TP63 protein by inducing translation inhibition through mRNA degradation. The 3′ UTR SNPs in the pression . The GTEpression . QTLs plpression . Our stuP63 gene  with varTP63 gene in different tissue types. To confirm the association of three nsSNPs, five non-coding PolymiRTS analyzed SNPs, and five non-coding GTEx analyzed SNPs with different diseases or cancers, experimental analysis needs to be carried out in the future, i.e., performing functional analysis in cell lines. This study will serve as a benchmark for further validation of the association of TP63 SNPs with cancers.In this study, we initially harnessed a combination of in silico tools to collect a total of 17 nsSNPs that can alter the structure and function of the TP63 protein. Among them, 11 previously unreported nsSNPs, which were predicted to be pathogenic in our study, are crucial due to their impact on the structure and function of TP63. Nine DNA binding domain nsSNPs were finally predicted to be deleterious by all in silico tools. Molecular docking analysis of those SNPs indicated the deleterious effects by measuring the binding affinity with DNA. MD simulations revealed that R319H, G349E, and C347F are likely to make the TP63 protein structure less stable and may hinder binding with the DNA that possesses a significant effect on the function of the protein. Specifically, R319H was found to be a significantly deleterious nsSNP of TP63 protein compared to the other two i.e., G349E and C347F. The noncoding SNP analysis revealed miRNA binding site disturbance and dysregulation of the expression of the


Nuclear magneticresonance (NMR) is an effective, commonly usedexperimental approach to screen small organic molecules against aprotein target. A very popular method consists of monitoring the changesof the NMR chemical shifts of the protein nuclei upon addition ofthe small molecule to the free protein. Multidimensional NMR experimentsallow the interacting residues to be mapped along the protein sequence.A significant amount of human effort goes into manually tracking thechemical shift variations, especially when many signals exhibit chemicalshift changes and when many ligands are tested. Some computationalapproaches to automate the procedure are available, but none of themas a web server. Furthermore, some methods require the adoption ofa fairly specific experimental setup, such as recording a series ofspectra at increasing small molecule:protein ratios. In this work,we developed a tool requesting a minimal amount of experimental datafrom the user, implemented it as an open-source program, and madeit available as a web application. Our tool compares two spectra,one of the free protein and one of the small molecule:protein mixture,based on the corresponding peak lists. The performance of the toolin terms of correct identification of the protein-binding regionshas been evaluated on different protein targets, using experimentaldata from interaction studies already available in the literature.For a total of 16 systems, our tool achieved between 79% and 100%correct assignments, properly identifying the protein regions involvedin the interaction. This approach aims to provide information about the residuesin the protein that are interacting with the ligand, either directlyor through a modification of their environment induced by the bindingevent. The protein-observed method focuses on changes in the chemicalshifts of the protein residues upon binding of the ligand. This istypically called the chemical shift perturbation (CSP) or chemicalshift mapping (CSM) method. When the 3D structure of the protein isavailable, CSP data allow the identification of the interaction regionon the protein surface and thus can drive docking calculations toderive a structural model of the protein:ligand adduct.12Nuclear magnetic resonance (NMR) spectroscopy is a very effectivetechnique to get information about protein–ligand interactionsat atomic resolution.13 In the case of fast  exchange, the positions of the peaks vary according tothe population-weighted average of the free and bound chemical shifts.Consequently, in the case of two-site exchange, each peak moves linearlyfrom the position of the spectrum of the free protein to that of theadduct. In the case of slow exchange, separate peaks correspond tothe free and bound states, with intensities proportional to the correspondingpopulations. In the case of intermediate exchange, line-broadeningeffects occur during the titration potentially leading to the disappearanceof peaks from the spectrum. In all cases, tracking these chemicalshift perturbations manually is tedious, especially when many peaksexhibit chemical shift changes and when many ligands should be tested.Automation of the tracking procedure via a variety of computationalapproaches has been described.21 These methods differ also in the type and quantity of experimentaldata required, from hand-picked peak lists of pairs of spectra toraw NMR data for all experiments in a titration series. None of thesetools is available as a web server.Depending on the exchange rate between the free and boundspecies,NMR peaks change in position and/or shape during the titration ofthe target protein with the ligand in various manners.In this work, we developeda tool (PICASSO) requesting a minimalamount of experimental data from the user, implemented it as an open-sourceprogram, and made it available as a web application. PICASSO usesonly two spectra, namely, those of the free protein and of the protein:ligandcomplex, represented by the corresponding peak lists. This avoidsthe need to upload the full spectrum but leaves the user with theburden to perform the peak picking procedure. Our approach minimizesthe experimental information needed; when testing a series of ligandsagainst a given protein target, it is sufficient to record one spectrumper protein:ligand adduct, plus the single reference of the free protein.The performance of PICASSO in terms of the correct identificationof the protein-binding regions has been evaluated on different proteintargets, using experimental data from published protein–ligandinteraction studies.i) is the weighted distance of itsresonance peaks in the two 1H–15N HSQCspectra, defined as22H and ΔδN are the chemical shift variations(in ppm) experienced by the 1H and 15N proteinnuclei, respectively. HetScale is a scaling factor used to weightthe heteronuclear shifts, due to the different origins of 1H vs 15N or 13C shifts.22 The HetScale factor has a default value of 5.0 in the PICASSOweb server, which can be modified through the interface. Althoughsome authors have suggested that a different weighting factor shouldbe used for Gly residues,24 this is a relativelyuncommon practice in the literature. Thus, for the sake of simplicity,we decided to use the same factor for all amino acids. 1H–13C HSQC spectra by replacing 15N with 13C chemical shift variations , as well as other multidimensionalspectra.22 In the following, we use theterms CSP and “peak distance” synonymously.The CSP (in ppm) ofa given residue . In thiscase, we can thus propagate the assignments from P to P′; unassigned peaks in P are neglected.Inall algorithms, we take as input two lists pi, pj′), which most likelycorrespond to the same amino acid, then progressively assign all peaksbased on proximity between the two sets, thus leaving the residuesexperiencing the largest CSPs as the last assignments. For this, wecalculate the distances among all peaks in P to peaksin P′, resulting in a distance matrix D correspondsto the first  pair that is assigned.Then, row i and column j are removedfrom D and the procedure repeated until all peaksin the shortest list between P and P′ have been matched.In this approach,we tried toemulate the human approach to the task, namely, first identify theclosest pairs  pair is similar to that of the neighborhoodof residue i. The latter is defined through the avg_csp function, which calculates the average CSP around residue i, over a window range of size k. Consequently,here we want that pj′is such that |CSP – avg_csp| is the smallest possible. The average CSPs are recalculatedafter each new assignment. The SDS assignment procedure is iterateduntil convergence, starting from the average CSP values of the previousiteration; for the first iteration, all CSPs are set to zero.Inthis improved versionof SD, we take into account the distribution of CSPs along the sequenceof the protein, based on the empirical observation that neighboringresidues in the sequence tend to have CSPs of similar magnitude. Wethus modified the algorithm in order to favor a situation where nearbyresidues have similar CSPs. For a peak P as the tasks, the peaks in P′as the workers, and the CSP as the cost of assigning p′j to pi. In this scenario, solving the RA problem consistsof finding the solution entailing the minimum CSPs. We implementeda well-known algorithm based on the documentation available at https://developers.google.com/optimization/assignment/overview.A completely different approachis that of looking at the CSP identification problem as a resourceallocation (RA) problem. Given a set of tasks to be solved, and aset of available workers with a certain cost, RA approaches find task-workerallocation solutions that minimize the total cost. For the currentapplication, a possible set up is considering the peaks in pi ∈ P, the averageCSP of its neighbors is calculated with the aforementioned avg_csp function.Subsequently, the distances between pi and all p′j are updated as dij = dij /avg_csp. TheRA algorithm is then applied again using this updated distance matrix D, generating a new solution. This refinement procedureis repeated for a predefined number of times or until convergenceis reached.In this approach, weadded a refinement process to the RA algorithm. Given the assignmentsfound by the RA algorithm and the corresponding set of CSPs, the goalis to refine the assignments in order to penalize configurations thatfeature high deviations from the local CSP average, as done in theSDS approach. On the basis of the RA results, for each 25 scripts. The protocol implementedwas as follows:(1)Compute the average and standard deviation(SD) of the CSP values for the whole protein.(2)Remove residues having CSP valuesexceeding the whole protein average by at least four times the SD,as likely outliers.(3)Define highly perturbed residues asthose having CSP values exceeding the whole protein average by twicethe SD.(4)Define moderatelyperturbed residuesas those having CSP values exceeding the whole protein average byat least one SD but not more than twice the SD.(5)All heavy atoms of highly perturbedresidues are displayed as red spheres of radius 1.0.(6)All heavy atoms of moderately perturbedresidues are displayed as pink spheres of radius 0.7.(7)The protein backbone is displayedas a gray cartoon, whereas the ligand is shown as green sticks forsmall-molecule ligands or as a green cartoon for oligopeptide ligands.To mapthe experimental and automated CSP values onto X-ray structures ina reproducible manner, we used a Python program to automatically buildPymolExperimental and automatically generatedCSP valueswere mapped independently. Note that the above protocol is only aimedat creating a reproducible graphical representation of the mapping.The data themselves are not affected, and the user is provided withthe full unfiltered list of assignments and CSPs.1H–15N HSQC spectra. If an extensive assignmentof all resonances is available, typically for the free protein, thenPICASSO infers the assignments for the second peak list and hencederives the CSPs, using the SDS or RAS algorithms. In the absenceof such assignments, PICASSO uses the SD and RA algorithms to identitypeak displacements based on the minimization of the overall CSP. Consequently,peaks from the first list are matched to peaks in the second listbut not to the protein sequence.We tested two differentapproaches to automatically transfer peak assignments from the freeprotein spectrum to the spectrum of the protein:ligand mixture. Theinput to our protocol is a pair of peak lists, usually from 14 Wetherefore introduced a correction, based on the empirical observationthat in well-behaved systems residues having significant CSPs tendto cluster along the sequence. We thus replaced the proximity metricswith a function that depends on the deviation of the CSP of each residuefrom the average CSP of its sequence neighbors, calculated over awindow of seven residues. This refined approach (SDS) penalizes isolatedresidues featuring high CSP values within sequence stretches withsmall CSPs and vice versa. The SDS method can be applied only if theresidue assignments of the free protein are available, in order tobe able to identify the sequence neighbors of each residue.In the SD method, we triedto replicate the human approach to the task, namely, assigning peakswith no or very little shift first and then trying to resolve by proximitythe assignment of more shifted peaks. This approach tends to minimizethe sum of all CSPs, which is known to have some shortcomings, forexample, when the signal(s) from one (or more) residue(s) is missingin either spectrum or when two nearby peaks experience very differentshifts upon addition of the ligand.For the design of our second approach (RA), we formulated the algorithmas a resource allocation problem, in which a number of agents (thepeaks from the first spectrum) are assigned to a number of tasks (thepeaks from the second spectrum), incurring a cost that depends onthe agent–task assignment (the corresponding CSP). The goalis to perform as many tasks as possible by assigning at most one agentto each task and at most one task to each agent, at a minimum totalcost. To go beyond the simple approach of minimizing the sum of allCSPs, we also implemented a “smart” version of algorithmRAS, incorporating the concept of local similarity along the sequencedescribed above.Supporting Information), withan average performance of 94%.We ran our algorithmson 16 experimental systems, from five different proteins 1. For thHowever, https://picasso.cerm.unifi.it/, maintained at the University of Florence. The web server is basedon a combination of React (https://reactjs.org) for the frontend and Spring Boot (https://spring.io/projects/spring-boot) for the backend technologies. The input should be provided as csv files, with the organization of the columnsshown in Table S1 and on the web serveritself.We provide access to the tool (PICASSO)via a web server at The web server provides the user with the predictionscomputed by either the RAS algorithm (method: Smart) or the RA algorithm(method: Proximity). The Smart method requires residue assignmentsto be included in the peak list of the free protein. The Proximitymethod permits the use of PICASSO in the absence of assignments. Regardlessof the method chosen, the input value of the HetScale parameter  is used according to 1H–15N HSQC, of a free proteinsample (reference spectrum) to the corresponding spectrum acquiredon a protein:ligand mixture (target spectrum). This is done by selectingthe Smart method of the web server . PICASSO is less versatile than tools such as CSP Analyzer,21 which leverages machine learning to analyzeextensive series of peak lists against a reference peak list to automaticallyidentify binding events based only on the changes in the appearanceof each list . Anotherrelevant approach was the Automatic PEak Tracking (APET) algorithm,14 which allowed mapping reference and ligand-containingspectra with sophisticated mathematical tools. This is only availableas a module in the FELIX suite, distributed by Felix NMR, Inc. A relatedalgorithm is PROPET, to analyze titration series.14 Similarly, the NvMap tool16 isa module of the NMRViewJ package33 usinga greedy algorithm to sequentially match peak couples from the referenceand target peak lists, generated using NMRViewJ, based on their distance.There are also tools available as stand-alone programs, such as PeakWalker18 or GAPT.17 PeakWalkeris a Java program available from the authors that allows a peak tobe followed through a series of spectra. GAPT is a Visual Basic programthat also enables the automated tracking of peak trajectories alonga titration series, through graph search methods.17 A more recent tool is Trace in Track (TinT),19 which also deals with a series of NMR spectrawhere a stepwise perturbation is applied .Finally, a quite recent platform that incorporates, among many othertools, the capability of determining CSPs and mapping them onto 3Dprotein structures is the POKY suite,34 the successor of NMRFAM-SPARKY.35 Theseare very articulated program suites, encompassing tasks from the interactiveanalysis of spectra to resonance assignments and automated 3D structuredetermination, written over several years in different computer languages.35 In summary, the discussion above highlights how the currently availabletools are not of immediate availability to users; some require thelocal installation of a program, upon request to the authors, whereasothers are embedded in complex, sometimes commercial, suites. Thesource codes are typically not readily available. Furthermore, sometools focus exclusively on the reconstruction of peak trajectoriesalong a titration series. This is a different problem than the onetackled by PICASSO, i.e., transferring assignments from the referenceto the target spectrum. Besides the additional amount of experimentalinformation needed, the analysis of peak trajectories is not suitableto address systems in the slow exchange regime.18Our application exploits an algorithmic approach thatis simplerthan other tools previously described in the literature. Our aim indeveloping the PICASSO tool was mainly to provide a user-friendlytool that did not require any local installation nor explicitly designedexperimental schemes. Hence, PICASSO uses a pair of simple HSQC experimentsand is available as a web server (https://picasso.cerm.unifi.it/. There are two main lines for future development: (i) integrationwith automated procedures for HSQC spectra processing and peak picking,for example, using NMRpipe36 as the mainsoftware package and Nextflow37 as a lightweightworkflow management system and (ii) incorporating 3D structural information.Our tool is entirely written in Python, which permits easy integrationwith other packages in the same language, such as nmrglue,38 a Python-based module for working with NMR data.Currently, users must produce the input peak lists for PICASSO independently;to facilitate this task, our tool requires input in the csv format,which can be produced by most software used to handle NMR data. Thus,there is no need for a specific software upstream. Peak picking canbe performed through the software interface of the spectrometer, theNMR processing package adopted in the lab, or a separate peak pickingtool.40PICASSO is available open access and without registrationat https://github.com/cerm-cirmmp/picasso. Test data were taken from the cited publications or are availableas Supporting Information.Thesource code of PICASSO is freely available at


The vast majority of known proteins have not been experimentally tested even at the level of measuring their expression, and the function of many proteins remains unknown. In order to decipher protein function and examine functional associations, we developed "Cliquely", a software tool based on the exploration of co-occurrence patterns.Bron–Kerbosch algorithm to detect maximal cliques in this graph, fully-connected subgraphs that represent meaningful biological networks from different functional categories.Using a set of more than 23 million proteins divided into 404,947 orthologous clusters, we explored the co-occurrence graph of 4,742 fully sequenced genomes from the three domains of life. Edge weights in this graph represent co-occurrence probabilities. We use the We demonstrate that Cliquely can successfully identify known networks from various pathways, including nitrogen fixation, glycolysis, methanogenesis, mevalonate and ribosome proteins. Identifying the virulence-associated type III secretion system (T3SS) network, Cliquely also added 13 previously uncharacterized novel proteins to the T3SS network, demonstrating the strength of this approach. Cliquely is freely available and open source. Users can employ the tool to explore co-occurrence networks using a protein of interest and a customizable level of stringency, either for the entire dataset or for a one of the three domains—Archaea, Bacteria, or Eukarya. However, in spite of the progress in utilizing structural approaches [The next-generation sequencing revolution has resulted in exponentially-growing databases of tens-of-thousands of protein sequences from thousands of organisms. The vast majority of these proteins have not been experimentally tested , and expproaches , 12, theproaches . Howeverguilt by association’. The assumption is that if proteins are inherited or lost from a large number of genomes co-dependently, despite multiple evolutionary events of gene loss, speciation and lateral transfer, then they are likely to interact functionally or physically [phylogenetic profiling, a method which is based on the assumption that genes that are functionally related will be characterized by similar presence-absence patterns across different organisms [Functional links can also be identified computationally based on the principle of ’ysically . One of rganisms , 16. In rganisms –19. Exisrganisms , 20 generganisms , the pairganisms , and therganisms . HoweverHere we present Cliquely, a new phylogenetic profiling tool for protein function prediction. Cliquely uses highly-efficient algorithms, incorporating a dataset of protein sequences obtained from 4,340 prokaryotic, 266 archaeal, and 166 eukaryotic, fully sequenced non-draft genomes. Building on an existing orthology inference from the Microbial Genome Database (MBGD), Cliquely avoids the difficult computational challenge of identifying orthology within thousands of genomes, focusing instead on efficiently utilizing orthology information for the exploration of protein function and conservation, using a network approach. Unlike similar tools , 25, Clihttp://mbgd.genome.ad.jp) on 2018. The data contained about 23 million proteins from 4,772 fully sequenced genomes, clustered to 404,947 orthologous groups. Raw data was obtained as a cluster-based, extended-tab archive format, with orthologous clusters generally reflecting protein families. The data associates orthology information, organism identification and functional parameters. Preprocessing consisted of reducing sequence data to a single sequence per cluster chosen at random, and further eliminating unnecessary information as previously described [Fully-sequenced genomes were obtained from the Microbial Genome Database  is the conditional probability for the presence of a protein from cluster u in an organism, given that the organism has a protein from cluster v. Pco is guaranteed to lie between 0 to 1 and is symmetric with respect to u and v. It reflects the probability of co-occurrence, with high scores indicating high probability for co-occurrence. An example for Pco calculation in a case of two protein clusters and 10 organisms is given in In this equation, Pco scores are used as weights. We expect clusters of proteins that share a biological function or pathway to have similar phylogenetic profiles, and a Pco which is thus close to 1. Similarly, we expect proteins that lie within the same biochemical network to have a Pco close to 1. Given the weighted graph, Cliquely identifies sub-graphs within this graph that are fully connected . Such sub-graphs are also known as cliques, and some cliques are defined as maximal, i.e., cliques that cannot be further extended. Thus, Cliquely identifies maximal cliques in the graph that contain a protein of interest, or its closest homolog in our dataset. The stringency of the association is determined by a single parameter given by the user, which represents a Pco (i.e. edge-weight) threshold for the inclusion of edges in the graph. Edges whose weights are smaller that this threshold will not be included in the graph. Cliquley treats orthologous clusters as nodes in a fully connected weighted graph, G = . Vertices in set V represent clusters or protein families, and n/3), implying that running times for the full graph could be impractical on a personal computer. Tighter bounds on worst-case running time can be obtained for sparse graphs, depending on the degeneracy of the graph.The identification of maximal cliques is performed using the Bron–Kerbosch algorithm  with pivPco). To make the process more efficient, the full graph encompassing the entire dataset is first pruned, removing nodes and edges that are of no relevance to the entry (input) protein, and edges with weights that are smaller than the chosen threshold (Pco). Graph pruning is performed as a four-step process; Ignoring non-relevant organisms, Cliquely first aggregates protein families (i.e. clusters) from organisms that have at least one member in the protein family (i.e. cluster) associated with the entry protein. Secondly, using the user-specified Pco threshold, protein families (nodes) appearing in a fraction of the organisms that is smaller than Pco are removed. The reduced set of protein families are then used as nodes in a preliminary graph. At the final stage of graph construction, the preliminary graph is further pruned, removing edges whose weights are below the given Pco threshold. Following graph pruning, the resulting graph is then passed to the Bron–Kerbosch algorithm to identify maximal cliques. The results are presented to the user both on the screen and as a text file. Users can then further explore the output cliques, which may correspond to know or novel protein pathways and networks.Cliquely allows users to examine the properties of co-occurrence graphs, using the user’s protein of interest and a specified co-occurrence probability threshold , and define the maximal size and maximal number of cliques to be presented (https://github.com/NoamAndRoy/Cliquely), along with a user manual and running examples.Cliquely is designed to operate on the weighted graph described above, which is based on our dataset. Construction of the full graph is computationally demanding, and is thus restricted to incremental updates. Assuming a pre-constructed graph, network exploration is based on single-protein queries, and allows for the identification and exploration of cliques that may represent pathways or molecular complexes, and can thus reveal the functionality of the protein. Users are offered a Windows-compatible downloadable software. Cliquely’s user interface is user-friendly and straightforward . The graresented . In addiresented , listingPco = 1. The list of all cliques for Pco = 1 can be downloaded from https://github.com/Cliquely/Cliquely. The median maximal clique size was 3 for Archaea, 4 for Bacteria, and 3 for Eukarya. The means vary considerably between the three groups, 5.85±10.05, 56.21±133.56, and 155.5±557.10 for Archaea, Bacteria, and Eukarya, respectively. Due to the differences stemming from larger  cliques, we also examined maximal clique size distributions specifically for maximal cliques of size 2 to 10 . To control for differences that stem from differential genome size, we also examined the number of cliques normalized to genome size . As can be seen in Cliquely was validated by examining known networks from various pathways, such as nitrogen fixation, nitrification, methanogenesis and the mevalonate pathways, and ribosome proteins. Using query proteins from different organisms , we ran Pco thresholds (ranging from 0.6 to 0.85) efficiently  for the entire graph . As demonstrated in the previous section, many of the Cliquely’s identified cliques correspond to known networks and pathways , including two structural proteins, FliF and FliH, forming parts of the injectisome [Chlamydia avium) as query, we additionally compared Cliquely to two more tools: another phylogenetic profiling software called ProtPhylo [https://effectors.csb.univie.ac.at) found that different Chlamydia strains contain a total of between 100–300 T3SS effector proteins in their genomes. Additional validation of Cliquely is presented in One of the identified cliques for ectisome , 34. Of rotPhylo , and therotPhylo . ProtPhyThe vast amount of protein sequence data accumulating in public databases has the potential to improve our understanding on non-model organisms, protein functions and interactions, and more. However, such insights usually are limited by what we already know about protein function. Computational methods such as Cliquely can facilitate this understanding by predicting protein functions for previously unannotated sequences. Cliquely was developed to simplify the task of elucidating protein function, via a straight-forward identification and exploration of co-occurrence patterns and motifs in a co-occurrence graph. Cliquely treats the problem of identifying co-occurrence patterns as a maximal clique problem, and provides users with a practical method for investigating protein function based on co-occurrence. Phylogenetic profiles capture functional constraints, and can be used to explore the functional properties of protein sequences. While other tools are available for investigating protein function, the user-friendly Cliquely revealed protein function for proteins misidentified by other tools, was found to be computationally efficient on a large dataset, and offers a friendly interface. The code of Cliquely is open source, and users have the option of altering the tool to better suit their needs.Our analysis with Cliquely found that eukaryotic genomes have significantly more cliques than prokaryotic ones, and that the size of these cliques is both higher and more diverse. This can be attributed to the generally larger and more complex genomes of Eukarya, as well as their abundance of pseudogenes. Within the prokaryotes, however, the differences are harder to explain. Archaea and Bacteria share a common gene-dense genome architecture, with about 1000 genes per 1 million basepairs; yet for an unknown reason, Bacteria seem to be much more diverse in terms of rDNA phylogeny and genome size . The ranUsing a large dataset of more than 23 million protein sequences, we tested the computational efficiency of Cliquely, and validated the obtained biological results. Cliquely identified known networks from various pathways, and revealed the biological function and modality for previously unannotated proteins belonging to the T3SS system, identifying 33 previously uncharacterized proteins as part of this network. This achievement demonstrates one important application of Cliquely: finding new target proteins for antibiotic treatment of infectious diseases. The dramatic rise of antibiotic resistance among bacterial pathogens, which has evolved against nearly every clinically used antibiotic, means that standard antibiotic drugs become less effective. Found in dozens of Gram-negative pathogens, the T3SS virulence factor is an attractive target for novel antimicrobial drugs which will inhibit the pathogen from injecting effector proteins into the cytosol of host cells, thus preventing their ability to cause disease and harm the host . DespiteS1 FigThe plot presents the relationship between the number of protein clusters (i.e. gene families) and the number of identified cliques in the genome.(PDF)Click here for additional data file.S1 TablePco) using different single proteins as queries. Inputs and full outputs are presented. The identified cliques include known proteins from these pathways. NifD—the nitrogen fixation pathway. MvaD—the mevalonate pathway. AmoB—the nitrification pathway. McrA—the methanogenesis pathway. L10—the Ribosome pathway.We ran Cliquely with a range of possible threshold values of co-occurrence ((XLSX)Click here for additional data file.S2 TableProbability (Pco) is Cliquely’s probability cutoff; Number of proteins is the clique size; the columns Che, MCP, Fli and Mot give the number of proteins from each of these groups appearing in the identified clique; True positive is the percentage of proteins in Cliquely’s clique that are part of the KEGG pathway. False positive is the percentage of proteins in Cliquely’s clique that do not appear in the KEGG pathway. The pathway can be viewed at https://www.genome.jp/pathway/map02030+K03407. The table demonstrate that as Pco decreases, Cliquely identifies more cliques, and the number of proteins per cliques increases . True positive percentage decreases with Pco , and false positive percentage increases . A false positive here does necessarily imply an erroneous identification, but rather that the protein identified by Cliquely as connected to CheA does not appear in the small KEGG pathway 02030.Compared to KEGG’s bacterial chemotaxis pathway (pathway 02030). (PDF)Click here for additional data file.S3 TableChe proteins are marked in bold. CheA was used as query for the phylogenetic profiling software ProtPhylo (http://ido.helmholtz-muenchen.de/protphylo/Phylogenetic.php), using default parameters. 121 proteins were determined to accompany CheA, with increasing Hamming Distance (HD) between the phylogenetic profile of CheA and that of a second protein. This distance is in fact the number of organisms (out of 2048 in the ProtPhylo database) for which the presence/absence of the two proteins does not agree. Smaller Hamming Distance implies a more similar evolutionary history. As an example, in 108 out of 2048 organisms CheW does not share the presence/absence pattern of CheA. Notably, the two tools operate very differently–while Cliquely identifies groups of proteins (or protein families) that operate together (many-to-many relationship), ProtPhylo examines only one-on-one relationships between the query protein and a second protein. Thus, it is not surprising that Cliquely retrieves the main protein groups in the relevant pathway  within the identified cliques, in accordance KEGG map 02030 (https://www.genome.jp/pathway/map02030+K03407)–while ProtPhylo identifies many more proteins that have a similar phylogenetic profile, but are not necessarily part of the same pathway.(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.


Taking advantage of the last cryogenic electron microscopy structure of human huntingtin, we explored with computational methods its physicochemical properties, focusing on the solvent accessible surface of the protein and highlighting a quite interesting mix of hydrophobic and hydrophilic patterns, with the prevalence of the latter ones. We then evaluated the probability of exposed residues to be in contact with other proteins, discovering that they tend to cluster in specific regions of the protein. We then found that the remaining portions of the protein surface can contain calcium-binding sites that we propose here as putative mediators for the protein to interact with membranes. Our findings are justified in relation to the present knowledge of huntingtin functional annotation. HTT, a gene whose major characteristic, when mutated, is to be associated to the Huntington disease (HD) . Huntine P42858 . It is me P42858 .In the past 30 years, huntingtin has been the focus of many studies given its association to HD. As a result, several different functions for the wild-type protein emerged (for review see ). Among HTT gene is strongly associated with the Huntington disease, generating a protein variant with an increasing number of glutamine residues  at the N-terminal domain. It is believed that the disease is caused by the gain of function of the mutant [HTT (mHTT), with the propensity of mHTT to form abnormal aggregates and its indirect effects on cellular proteostasis. Indeed, huntingtin interacts with a huge number of proteins, although molecular details of these interactions are still lacking [The human huntingtin comprises 3142 residues: the CAG repetition at the 5′ end of the coding sequence of the e mutant ; other se mutant , summing lacking . It is d lacking .Huntingtin interacts with membranes . Lipid oHTT-associated protein 40 (HAP40) ) [Recently, cryogenic electron microscopy (cryoEM) helped to obtain and release a high-resolution and high-coverage three-dimensional (3D) structure of huntingtin in complex with r 2020)) ,19. The Indeed, thanks to advanced technologies, including machine and deep learning-based methods, it is possible to infer properties at the level of a single residue to highlight unexplored molecular features of the protein and seek for the tendency of the protein to participate in possible protein–protein or protein–membrane interactions .Here, we focus on the protein solvent accessible surface for a better understanding of the huntingtin interactions with other proteins and with membranes by adopting ISPRED4  for deteHAP40, we suggest that polar regions where calcium ion binding occurs are likely to interact with membranes, possibly in a calcium ion-regulated mode.Our results indicate that a large fraction of the protein solvent accessible surface is polar and that at least 192 residues are involved in protein–protein interactions. Furthermore, we found that some 81 residues are likely to be involved in calcium ion binding, and that they are mainly clustered in the regions where protein–protein interactions do not occur. Considering that we are possibly working on a specific conformation of the protein due to the stabilizing interaction with HTT-associated protein 40 (HAP40), clamping one of the many different conformations that the protein may assume [https://www.rcsb.org/structure/6EZ8 (accessed on 28 December 2020)), [https://www.ebi.ac.uk/interpro/entry/InterPro/IPR000357 (accessed on 28 December 2020)) [The first structure of human huntingtin obtained with cryoEM has been released in 2018. Huntingtin is in complex with y assume . This cr 2020)), ,25. Thisr 2020)) . Apparenr 2020)) .HAP40 protein has been released. It is characterized by a higher resolution (2.6 Å) and a higher coverage (77%) ) [Recently, a new cryoEM structure of huntingtin in complex with the same r 2020)) ,19. In sThe superimposition of 6EZ8 and 6X9O gives a root mean square deviation (RMSD) of 1.70 Å at the level of the protein backbone, computed over 2343 equivalent positions sharing 97.54% identity. N-HEAT, C-HEAT and BRIDGE domains and the large membrane-interacting region are conserved.HTT–HAP40 complex [In both structures, no density was observed for the huntingtin fragment encoded by exon 1 . Region 1–64 of huntingtin was separately crystallized: it consists of an amino-terminal alpha helix, poly17Q region, adopting multiple conformations , and a polyproline helix formed by the proline-rich region  ,27 and t complex .Unfortunately, the available structures for huntingtin do not cover the gap between the first fragment 1–64 and the largest structures starting at residue 90; a full-coverage structure is therefore still missing. For this study, we adopted 6X9O as the reference structure, considering that this folding with the highest resolution and largest coverage is presently the only available for inspecting a large fraction of the protein solvent accessible surface.First, we analyzed the huntingtin protein surface with the Define Secondary Structure Program (DSSP) ,29, whicWe computed the hydrophilicity/hydrophobicity of each exposed residue adopting the Kyte and Doolittle scale (KD)  and averIn https://ispred4.biocomp.unibo.it/ispred/ (accessed on 28 December 2020)), which is suited for computing the likelihood of a surface residue to be or not to be an interaction site. The tool is based on machine-learning approaches and it performs at the state-of-the-art level [ISPRED4  is a toort level . We discThe high number of putative interaction sites well agrees with the high number of possible interactors that can be retrieved from such databases as IntAct ,34 and BWe can take into consideration the notion of “interaction patch” introduced previously , which gOur analysis supports the notion that huntingtin serves as a protein–protein interaction hub ,37 and tGiven the high percentage of hydrophilic regions on its surface, huntingtin can indeed interact with membrane lipids, as previously suggested . In liteWhen considering a neighborhood within 3.5 Å centered at the putative Ca ions, 81 residues shown in We can conclude that the protein sides containing residues with very low tendency to be involved in interaction sites C, in turIn HTT-associated protein 40 (HAP40). The structure is solved for sequence region 97–3139, including 3042 residues. However, only 2425 residues are described in our 6X9O reference PDB file due to 18 gaps, the longest being in position 407–665. In spite of this, we can observe that the protein in this stabilized conformation, due to the presence of HAP40, is mainly polar . Polar and non-polar regions are intermixed on the protein surface and exposed residues can be labeled taking advantage of their likelihood of being in interacting sites participating in protein–protein interactions. This is possible thanks to the development of machine learning-based tools such as ISPRED4 [In this paper, we characterize the solvent accessible surface of a recently released high-resolution and high-coverage huntingtin structure in complex with huntingtin-binding  ISPRED4  that havhttps://edgar.biocomp.unibo.it (accessed on 28 December 2020)) the complex space of gene–disease relations and taking advantage of the 98 gene interactors, we were able to associate huntingtin to some 43 more diseases different from HD (data not shown).It is known and determined in experiments of interactomics that huntingtin can physically interact with a large number of different proteins. This is mainly due to the fact that the protein is ubiquitous and that it can participate in relevant biological processes such as vesicular transport, cell division, ciliogenesis, endocytosis, endosomal trafficking, autophagy, calcium homeostasis in mitochondria and proteostasis . We founOur effort here was mainly devoted to exploring the protein surface (of possibly one of the many conformations that the protein may have in a solution) to understand whether predicted interaction sites are consistent with the huge number of interactors that the protein seems to have in interactomics experiments. It is possible that this number may change depending on the different conformations that the protein most likely acquires in a solution. Unfortunately, these data are not yet available. Neither is the complete structure of the protein with the polyQ tail, and this hampers any speculation on the specific role of the tail in affecting protein–protein interactions. Interestingly, when we explored the protein surface with a predictor suited to locate protein–protein interaction sites, we found that there was a large fraction of residues that were left behind.Prompted by previous observations on huntingtin–membrane interaction, we found that in the surface patches not containing interacting residues, we were able to allocate calcium ion-binding sites with a precision higher than 95%  Figure . Some ofHTT-associated protein 40 (HAP40).We used two huntingtin structures obtained via cryogenic electron microscopy: PDB ID 6EZ8 ,25 and PWe computed the superimposition of the two structures, 6EZ8 and 6X9O, using the online version of FATCAT 2.0 ,45, obtaWe defined the protein surface computing the relative solvent accessibility (RSA) of protein residues using the DSSP program ,29, consHTT-associated protein 40 (HAP40) [PyMOL is a molecular visualization tool on an open-source foundation, . We use  (HAP40) ,19. The  (HAP40)  and the ISPRED4  is a macFEATURE  is a fraBION-2  is a rec


As the body of scientific research focusing on the severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 continues to grow, several mutations have been reported as very common across the globe. In this study, we analysed the SARS-CoV-2 nucleocapsid protein (N protein) with respect to the widely observed 28881-28883 GGG to AAC variant. One of the major functions of the SARS-CoV-2 nucleocapsid protein is virion packaging through its interactions with the membrane protein (M protein). Our goal was to investigate, using in silico studies, the interaction between the mutant nucleocapsid protein and the M protein and how it differed from that of wild type N-M protein interaction. The results showed significant differences in interactions between the two. The mutant protein was predicted to form 3 salt bridges with the M protein, while the wild type only formed 2. The mutant protein was also predicted to display less temperature sensitivity than its wild type counterpart.The online version contains supplementary material available at 10.1186/s43141-021-00233-z. The 204 G>R and 203 R>K amino acid substitutions are two of the most commonly observed mutations in the SARS-CoV-2 proteome. Owing to its global distribution, we raise the question whether this is an advantageous mutation that became a stable change over time, or vice versa. To narrow our focus toward a more specific goal, we analysed the effect of these substitutions on the nucleocapsid protein’s interactions with the membrane protein, a process vital for virion packaging.The severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, caused COVID-19 pandemic continues to affect the world . As of AA lot of the research has focussed on the presence and distribution of genomic variants. Many of these have been found in isolates from all parts of the world. Genome characterization of SARS-CoV-2 isolates has revealed well over 100 globally distributed mutations . VariantThe two variants in focus occur within the intrinsically disordered region (IDR) of the N protein. This region has been shown to be involved in liquid-liquid phase separation (LLPS), a process that plays a part in RNA packaging. Furthermore, the disordered regions are all known to take part in protein-protein interactions , 28, 29.In this study, we wished to explore if this common variant can have a potential impact on the interactions between the N protein and the membrane protein (M protein), something that has not been widely investigated yet. These interactions are vital for RNA packaging and virion assembly; hence, any factor that has a significant influence on them should be investigated to the utmost, as it may be associated with changes in stability of virion structure and consequently its transmissibility and survival rates.Here, we explored these interactions by predicting the 3D structures of both the mutant (28881-28883 variant containing) and wild type (accession ID: YP_009724393.1) (Wuhan RefSeq) N-proteins using amino acid sequences. Subsequently, we carried out an in silico docking with M protein (accession ID: YP_009724393.1). This was followed by analysis and comparison of 2 N-M complexes. Both the N and M proteins are key structural components of the virus [https://robetta.bakerlab.org/) [http://sysbio.rnet.missouri.edu/3Drefine/) [http://galaxy.seoklab.org/). Structures with the lowest Molprobity, RMSD values and Clash scores were chosen and then further validated using Ramachandran plot analysis. Protein-protein docking was carried out using Cluspro server (https://cluspro.bu.edu/login.php) [https://pymol.org/2/). For confirmation, the protein-protein interactions between the 2 docked complexes were visualized using separate tools so as to ensure all possible interactions were properly identified. In addition to PyMol version 1.2, PDBSum (http://www.ebi.ac.uk/thorntonsrv/databases/cgibin/pdbsum) [https://www.molnac.unisa.it/BioTools/cocomaps/) [https://discover.3ds.com/discovery-studio-visualizer-download) were used. Temperature sensitivity of both the mutant and wild type protein complexes were also checked using the Prodigy tool [Protein structure prediction for the M and both the N proteins were carried out using Robetta server (ab.org/) . The strrefine/) . The pdbrefine/)  (http://gin.php) . Each of/pdbsum) , CoCoMapcomaps/)  and Disoigy tool .The structures of M and both N-proteins were predicted, refined and validated. The final predicted structure of M protein has a Molprobity score of 1.736, clash score of 11.7 and RMSD value of 0.290. Validity of the structures analysed by Ramachandran plot shows 94% residues residing in most favoured regions, 1% in disallowed and 4% in additional acceptable regions. The mutant N-protein has a MolProbity score of 1.660 RMSD value of 0.264 and Clash score of 10.4. Ramachandran plot analysis carried out on the Mutant isolate N-protein showed 92.8% residues residing in highly favoured regions, 0.6% in moderately allowed and 0.3% in disfavoured regions. Wild type N-protein has a MolProbity score of 1.451 RMSD value of 0.236 and Clash score of 8.3. Ramachandran plot analysis carried out on the wild type N protein showed 91.6% residues residing in highly favoured regions, 0.0% in moderately allowed and 0.6% in disfavoured regions  were both lower for the mutant protein. There were also differences in the key residue interactions. For the wild type, there were two salt bridges predicted between the M and N proteins (LYS162-ASP341 and LYS162-ASP348). The number of salt bridges observed for the mutant protein was 3, and they involved different residues . The number of hydrogen bonds was also higher for the mutant at 15. The wild type shared 14 hydrogen bonds with the M protein. Lastly, the number of non-bonded contacts  was also higher for the mutant (162) than the wild type (134). Other important residues include the Serine at position 318, which was shown to be an interacting residue for both the complexes. The list of major interacting residues is shown in Tables Table Another interesting observation here was the fact that increasing the temperature value affected the two complexes differently. For the mutant protein, a simulation that increased temperature from 25 to 35 °C resulted in a weaker affinity with the M protein. For the wild type, however, there was no discernible difference. This could suggest that the mutant protein is more sensitive to higher temperatures than the wild type. Table In order to observe whether the mutated residues formed any strong hydrogen bonds, pymol was used to determine whether the altered amino acid residues (K replacing an R and an R replacing a G) formed polar contacts. A cutoff value 2.5 was used, with all possible atomic contacts occurring within this range being taken into consideration Fig. A. FigureOur primary research question was whether the two N protein variants in focus alter the protein’s interactions with the M protein. To that end, we highlighted two major observations that can hold potential significance. The first is the supposed temperature sensitivity of the mutant N protein. This would seem to indicate that the mutations may be of a disadvantageous nature. An N protein that is adversely affected by increased temperatures may be more vulnerable to having its bonds with the M proteins disrupted by increased heat. The end result could be a loss of viral transmission and survival chances in hotter climates. This is especially important given that this variant is found across the globe, including warmer regions . SecondlOther studies have also highlighted this variant and the two resultant amino acid substitutions as potentially destabilizing the protein structure . These hHow these additional interactions and the resulting stronger nucleocapsid structure could impact virion transmission and pathogenesis is an area that should be targeted by future research. These findings do make for difficult interpretation in terms of the original question we posed. The potential for the 28881-28883 variant to enhance pathogenic potential of the virus is still very much a possibility. Moreover, this MNV is now distributed globally, suggesting this may have been an advantageous variant that became stably integrated into the genome over time.Regardless of how severe the symptoms of infection are, a more stable virus will be able to survive for longer and infect more people. The real effect of the changes in salt bridge bonds is of course something that needs to be confirmed and determined by experimental evidence. One major factor behind throwing that into doubt is the fact that while the number of bridges was more for the mutant protein, the participating atoms were further apart compared to the two salt bridges for the wild type.As the battle against SARS-CoV-2 continues to complicate, it becomes important for the research community to once more pay close attention to the fundamental aspects of the virus. The more practical consequences of this variant, such as vaccine effectiveness, remains difficult to ascertain. The ones that do not rely on detection of N protein components, such as the BNT162b1 or ChAdOx1 nCoV-19 vaccines (both Spike protein reliant), should not theoretically be affected. However, if future vaccines choose to use the N protein for eliciting an immune reaction, then this could change. The nature of the impact demands future investigation .We believe there is evidence to suggest that the SARS-CoV-2 isolates harbouring the 28881-28883 mutation have the potential to increase stability of the viral nucleocapsid. This interpretation is derived from the gain of certain amino acid interactions that may strengthen its complex with the viral M protein, potentially leading to a more stable virus structure. Though causality cannot yet be established, this does nonetheless deserve further investigation with regards to its potential impact on viral survival and transmission.Additional file 1:Supplementary Table 1A. – Complete list of closely situated residues from mutant N protein and M protein in the docked complex.Additional file 2:Supplementary Table 1B. – Complete list of closely situated residues from wild type N protein and M protein in the docked complex.Additional file 3:Supplementary File 1. – Docked complex between mutant N protein and M protein.Additional file 4:Supplementary File 2. – Docked complex between wild type N protein and M protein.