CDK4 is one of the genes most frequently affected by somatic genetic variations that are associated with various forms of cancer. Thus, because the abnormal function of the CDK4-cyclin D1 protein complex might play a vital role in causing cancer, CDK4 can be considered a genetically validated therapeutic target. In this study, we used a systematic, integrated computational approach to identify deleterious nsSNPs and predict their effects on protein-protein (CDK4-cyclin D1) and protein-ligand (CDK4-flavopiridol) interactions. This analysis resulted in the identification of possible inhibitors of mutant CDK4 proteins that bind the conformations induced by deleterious nsSNPs. Using computational prediction methods, we identified five nsSNPs as highly deleterious: R24C, Y180H, A205T, R210P, and R246C. From molecular docking and molecular dynamic studies, we observed that these deleterious nsSNPs affected CDK4-cyclin D1 and CDK4-flavopiridol interactions. Furthermore, in a virtual screening approach, the drug 5_7_DIHYDROXY_ 2_ (3_4_5_TRI HYDROXYPHENYL) _4H_CHROMEN_ 4_ONE displayed good binding affinity for proteins with the mutations R24C or R246C, the drug diosmin displayed good binding affinity for the protein with the mutation Y180H, and the drug rutin displayed good binding affinity for proteins with the mutations A205T and R210P. Overall, this computational investigation of the CDK4 gene highlights the link between genetic variation and biological phenomena in human cancer and aids in the discovery of molecularly targeted therapies for personalized treatment.The cyclin-dependent kinase 4 (CDK4)-cyclin D1 complex plays a crucial role in the transition from the G1 phase to S phase of the cell cycle. Among the CDKs,  Cyclin-dependent kinases (CDKs) drive cell cycle progression, control transcriptional regulation processes and maintain cell proliferation. Irregular entry into the cell cycle and uncontrolled cell proliferation are hallmarks of cancer . Hence, In more than 90% of melanoma cases, genomic variations associated with CDK4 pathway activation are present, as determined in human and mouse models of melanoma , 7. A coProtein-protein interacting interfaces are usually referred to as binding hot spots of proteins. These regions are charged, structurally conserved and highly polar and are surrounded by hydrophobic residues, which are the residues that are mostly involved in the binding . DeleterCDK4 gene. We then evaluated the effects of deleterious CDK4 variants on CDK4-Cyclin D1 protein interactions and drug binding. Protein structure-based virtual screening analysis was performed to identify suitable inhibitors of mutant CDK4 proteins. Furthermore, atomic level studies were performed by molecular dynamics (MD) simulations to better understand the effects of deleterious variants on CDK4-Cyclin D1 complex formation and to check the binding efficacies of selected inhibitors for the mutant proteins.In this study, we employed efficient computational prediction methods to identify deleterious nsSNPs in the CDK4 gene were extracted from the dbSNP [The nsSNPs in the he dbSNP  and UniPhe dbSNP  databasehe dbSNP . For virhe dbSNP .Different computational methods have been previously developed for the prediction of phenotypic effects of nsSNPs. In this study, three variation tolerance prediction methods, SIFT , PolyPheAutoDock is one of the most widely accepted docking software programs available and requires a set of preparation steps for general screening . IncludeMolecular dynamics simulations of native and mutant protein-protein and protein-ligand complexes were performed using Gromacs 5.0 software . The formax. The g_sas computes total solvent accessible surface area, hydrophilic and hydrophobic interaction of protein molecules. To check whether the systems follow constant NVT or NPT ensembles, differences in total, potential, and kinetic energies, pressure and temperature were calculated as a function of simulation time throughout the simulation period. The numbers of H-bond formation and the minimum distance between protein-protein and protein-ligand complexes were calculated to explain the stability of the complexes. SASA analysis was performed to identify the solvent traceable area of a molecule, and all graphs were generated using the XM grace tool [The trajectory potentials obtained from each MD simulation were thoroughly investigated by using GROMACS utilities . The utiace tool .Essential Dynamics (ED) method was used to calculate the eigenvectors and eigenvalues, and their projection along the first two principal components . ED or tOut of 20 nsSNPs submitted to SIFT, 11 nsSNPs  were predicted to be deleterious, with SIFT scores <0.05 . Of thesin vitro kinase activity of the CDK4 protein [in vitro studies.e., the inhibitor flavopiridol binds exactly at the ATP binding site of the native CDK4 protein  and mutant  complexes.The root mean square deviations (RMSDs) of protein backbone atoms of native and mutant CDK4-Cyclin D1 complexes were analysed. For all of the simulations, the energy minimised protein complexes were taken as a starting reference. To obtain all-atom levels in detail, MD simulations were repeated twice for native and mutant complexes for a time period of 50 ns. No significant changes were observed from the repeated MD simulation trajectories of native and mutant CDK4-Cyclin D1 complex structures. The simulated CDK4-Cyclin D1 complex backbone atoms were aligned with RMSDs of less than 3.5 A°.After 35000 ps, all of the native and mutant protein complexes attained an equilibrated state. Native complexes obtained an RMSD ~0.3 nm in the equilibrated state, but the mutant complex Y180H displayed a different pattern with an RMSD value of ~0.5 to ~0.6 nm until the end of the simulation period. Mutant complexes R24C, A205T, R210P and R246C obtained an RMSD of~0.4 to ~0.5 nm in the equilibrated state . AlthougThe C-alpha root means square fluctuation (RMSF) values for each amino acid of CDK4 proteins were calculated. Graphs were plotted, for native and mutant proteins, from the values from the last 10ns of trajectories to obtain the amino acid mobility of each protein . We founHydrogen bonds arise when a hydrogen atom covalently bound to a molecule interacts with an electronegative atom within the same molecule or in a different molecule. As they are responsible for maintaining the stability of protein structure, determining hydrogen bonds significantly reveals the stability of a protein or the stability between the proteins . TherefoFurthermore, the minimum distance between CDK4 and the Cyclin D1 protein was computed for both the native and mutant complexes . The min2 to ~ 21.5 nm2 in the last 10ns simulation period but that the mutant structures R24C, Y180H, A205T, R210P, and R246Chad different SASAs, of ~19 nm2to ~21.5 nm2, ~19 nm2 to ~21.5 nm2, ~19 nm2 to ~22 nm2, ~18.5 nm2 to ~22 nm2and ~18.5 nm2 to ~21.5 nm2, respectively. Compared with the native protein, all five mutant proteins had different SASAs. The differences in the SASAs of the mutant proteins indicated that there might be a shift in the amino acid residues from the buried region to the accessible area or vice versa, and this may be the reason for the reduced affinity between the mutant CDK4 and Cyclin D1 proteins.The protein surface that is traced by a solvent molecule is referred to as the solvent-accessible surface area (SASA). The solvation effect plays a significant role in maintaining protein stability and folding. Likewise, the solvation effect accompanies protein-protein interaction processes and protein structure modifications. This solvation effect can be measured using explicit solvent models; MD simulations use a sphere of water molecules . SASA wa2, ~26.786 nm2, ~32.264 nm2, ~36.569 nm2, ~27.987 nm2, and ~26.018 nm2 respectively  protein-ligand complexes CDK4-flavopiridol, R24C-5_7_DIHYDROXY_ 2_ (3_4_5_TRIHYDROXYPHENYL) _4H_CHROMEN_ 4_ONE, Y180H-Diosmin, A205T-Rutin, R210P-Rutin, and R246C-5_7_DIHYDROXY_2_ (3_4_5_TRIHYDROXYPHENYL) _4H_CHROMEN_ 4_ONE.The atomic RMSD of the backbone atoms of the protein-ligand complexes obtained from the trajectories and the initial structures was monitored during the simulation. A sharpFurthermore, to provide insight into the binding affinity of the protein and ligand, hydrogen bond contributions were analysed in the well equilibrated simulation period of the last 10 ns. The total number of hydrogen bonds between CDK4-flavopiridol and mutant CDK4-virtually screened compounds were analysed . CDK4-flThe minimum distance between CDK4-flavopiridol and mutant CDK4-virtually screened compounds was analysed . The minin silico methods, which has significant implications for specific target identification. Each computational pathogenicity prediction method follows a different strategy to estimate the effects of nsSNPs; hence, the prediction results are sometimes dissimilar. However, the positive predictions that overlap in the prediction methods should provide the greatest reliability to behave similarly. The variation in the identification of deleterious nsSNPs might be because of the differences in the adopted methodologies or the training datasets.The endpoint of deleterious nsSNP identification should be protein structural and functional analysis. Proper understanding of protein conformational changes elucidates the mechanisms underlying disease phenotypes and aids in the identification of suitable drugs for structurally modified proteins. Deleterious nsSNPs cause protein structural and functional changes and are associated with diverse responses in drug efficacy in the human population. Therefore, these nsSNPs could occur in drug-binding target proteins and influence treatment outcomes. Rapid growth in the identification of SNPs makes it more difficult for experimental biologists to evaluate the biological significance of each SNP. However, computational, theoretical approaches can be used to identify and investigate the effects of deleterious polymorphisms that affect protein structure and function before undertaking further validation by experimental methods. Computational prediction methods such as SIFT, PolyPhen2 and I-Mutant 3.0 can screen for deleterious nsSNPs potentially involved in disease conditions. Drug targets for various diseases that are influenced by nsSNPs can be investigated using An in-depth knowledge of protein function could reveal the molecular mechanisms of the process that causes the disease condition. Deleterious nsSNPs in protein binding hot spots or the core regions of proteins can evidently disrupt protein interactions. The mapping of nsSNPs onto three-dimensional protein structures and analysing these changes at the structural level will help to find the exact point where they alter interactions with proteins. Three-dimensional models of mutant proteins are constructed by substituting the deleterious nsSNPs in the appropriate three-dimensional protein structures. Three-dimensional models are the simplest way to detect the types of harmful effects that deleterious nsSNPs can have on protein-protein and protein-ligand interactions. To obtain in-depth knowledge of protein structure and understand the adverse changes that these deleterious nsSNPs cause in CDK4 proteins, we extended our study by analysing the native and proposed mutant (protein-protein and protein-drug) structures at the atomic level by using a molecular dynamics simulation approach. Basic parameters such as RMSD, RMSF, hydrogen bond numbers, minimum distances, and SASA were analysed from the simulation trajectory values. Molecular stability and flexibility changes were observed from RMSD and RMSF. Stability is the fundamental property enhancing biomolecular function, activity, and regulation. Structural mutations affected buried residues in the protein core, causing changes in amino acid size, amino acid charge and hydrogen bond numbers . HydrogeCDK4 gene to identify potentially deleterious nsSNPs and their structural and functional significance, through molecular docking and molecular dynamics approaches. To predict the significant effect of nsSNPs in theCDK4 gene, we applied three widely used computational methods, SIFT, PloyPhen2 and I-Mutant3, and each tool predicted 11, 9 and 15 nsSNPs as deleterious, respectively. Comparing the predictions made by all three methods, five amino acid variants  of the CDK4protein were identified as highly deleterious, and all five variants were investigated structurally and functionally.For the structural analysis, we modelled the mutant structure with Spdbv software, after which energy minimization was performed for the native and mutant CDK4-cyclin D1 complexes using a steepest descent force field to check and repair the geometry of the modelled complex. Next, docking analysis was performed between the native and mutant structures of CDK4 with flavopiridol. Several factors enhancing the protein-ligand interactions were analysed. Binding energies between the mutant protein and ligand revealed a lower binding capability of the mutant structures to the drug flavopiridol. Notably, flavopiridol interacting residues differed in the mutant proteins compared with native CDK4 protein. Although the mutant structures R24C, A205T, R210P, and R246C obtained a similar number of hydrogen bonds with flavopiridol, the ligand failed to bind to the ATP-binding site of the mutant proteins. These results indicate that the structural changes in the CDK4protein occurred because of the substitution of deleterious amino acids. Another functionally significant property of the protein was explored through the docking of flavopiridol with CDK4, demonstrating the effect of fluctuation in protein-ligand complex formation. A change in the pattern of flexibility of the functional residues might alter the functional region and activity of mutant proteins. A decrease in overall flexibility and an increase in rigidity due to mutations may affect the binding properties of proteins. To identify a suitable inhibitor for mutant proteins, structure-based virtual screening and docking analysis were performed. A total of 19 compounds that were structurally similar to flavopiridol were retrieved from the DrugBank database. All of the compounds were individually docked with theR24C, Y180H, A205T, R210P and R246C mutant structures of the CDK4 protein. The inhibitor 5_7_DIHYDROXY_ 2_ (3_4_5_TRI HYDRO XYPHENYL) _4H_CHROMEN_ 4_ONE displayed good binding affinity and inhibition at the ATP-binding site of R24C and R246C.Diosmin displayed good binding affinity and interaction with the ATP-binding residue of Y180H. Rutin displayed good binding affinity and interaction with the A205T and R210P mutant proteins.We performed a systematic computational analysis of theFurthermore, we performed 50ns MD simulation analysis on the native and mutant  protein-protein and protein-ligand complexes. MD simulation analysis provides in-depth knowledge regarding the protein-protein and protein-ligand interactions at the atomic level. Five fundamental parameters  were examined during the last 10 ns of the CDK4-Cyclin D1 simulation trajectories for the native and mutant complexes. Molecular stability and flexibility changes were observed through RMSD and RMSF analysis. Stability is the primary property that enhances biomolecular function, activity, and regulation. The results obtained from the CDK4-Cyclin D1complex stability analysis concluded that all five mutant complexes had different RMSD values from the native complex. A larger deviation increases the stability of molecules and vice versa. A higher stability increases the rigidity of the protein, and a lower stability increases the flexibility of the protein. Henceforth, from the stability analyses of native and mutant CDK4-CyclinD1protein complexes, it was observed that the mutant complexes had different stabilities because of the substitution of deleterious amino acids. From the RMSF analyses, we observed changes in flexibility for all five mutant complexes. Based on the results of the RMSD and RMSF analyses, we confirmed that the substitution of amino acids adversely affected the stability and flexibility of CDK4-CyclinD1 mutant complexes. In addition to the different electrostatic forces, the hydrogen bonds across the protein-protein interacting interface act as the main contributor in maintaining the stability of the protein. Furthermore, the presence of deleterious polymorphisms might change the hydrogen bond formation between the molecules. Consequently, in all five mutant complexes less number of hydrogen bonds was observed between the CDK4-Cyclin D1 proteins. A decrease in the number of hydrogen bonds revealed that the binding stability of the CDK4-Cyclin D1 mutant complexes might be affected. The minimum distance between CDK4 and CyclinD1 in the protein-protein complexes was analysed for both the native and mutant CDK4-Cyclin D1 complexes. The distance between the CDK4 and Cyclin-D1 proteins increased in all five mutant complexes compared with the native complex. An increase in the distance might reduce the binding affinity between the CDK4-CyclinD1 mutant complexes. Furthermore, in the SASA analysis, with respect to the native protein, different areas of solvent accessible surface were observed in all five mutant CDK4 proteins. The differences in the accessible areas in mutant proteins might alter the probability of an interaction between CDK4 in Cyclin D1 protein complexes. Subsequent SASA analysis revealed that the presence of deleterious polymorphisms in the CDK4 protein might change the hydrophilic and hydrophobic areas of the mutant CDK4 proteins.For CDK4-flavopiridol, R24C-5_7_DIHYDROXY_ 2_ (3_4_5_TRI HYDRO XYPHENYL) _4H_CHROMEN_ 4_ONE, Y180H-Diosmin, A205T-Rutin, R210P-Rutin and R246C-5_7_DIHYDROXY_2_ (3_4_ 5_ TRIHYDROXYPHENYL) _4H_CHROMEN_ 4_ONE, docked protein-ligand complexes were subjected to MD simulation for 50 ns to analyse RMSD, H-bonds, and minimum distances. In the molecular stability change analysis, all five protein-ligand complexes averaged lower RMSD values than those of the native protein-ligand complex. Hydrogen bond interactions between the protein and ligand served as the main contributor in maintaining the stable contact between the molecules. The substitution of deleterious nsSNPs might change the electrostatic charge distribution in proteins and affect the normal protein-ligand interactions. However, the virtually selected inhibitors exhibited good binding affinity for the mutant proteins, having the potential to maintain a stable number of hydrogen bonds during the simulation period. In all five mutant complexes , a similar number of H-bonds was observed between the proteins and their respective virtually selected ligand molecules. The distances between the mutant proteins and their respective virtually screened potential ligands were consistently lower than that measured for the native CDK4-flavopiridol complex. A reduction in this distance may increase the affinity of the mutant CDK4 proteins for their respective virtually screened compounds.CDK4 gene. Furthermore, this study highlights successful drug repositioning in mutant CDK4 proteins in the event of conformational changes attributable to deleterious nsSNPs. This information on drug repositioning can accelerate the development of cancer drugs, and this study of new cancer drug targets can potentially be used for future target identification. The identification of new drug targets and new uses for existing drugs hold promise for the future use of computational methods in cancer research. Overall, the methodology adopted in this study is extremely useful in the drug discovery process and will improve drug efficacy and safety profiles in cancer treatment.Alterations in protein structure are mainly caused by deleterious nsSNPs in the nucleotide sequence. The occurrence of deleterious nsSNPs in the genome is relatively rare but has a significant effect on protein structure and function. A number of computational methods have been designed to predict the functional effects of nsSNPs whether a particular nsSNP is a driver of carcinogenesis. In this study, by following an integrated computational approach, we identified and investigated the effects of harmful polymorphisms at both the structural and functional level for the S1 Fig(A) Native complex is showing high number of residues interacting with the drug flavopiridol. (B) Ligplot showing the interaction between mutant model R24C and flavopiridol. (C) Ligplot showing the interaction between mutant type Y180H and flavopiridol. (D) Ligplot showing the interaction between mutant type A205T and flavopiridol. (E) Ligplot showing the interaction between mutant model R210P and flavopiridol. (F) Ligplot showing the interaction between mutant type R246C and flavopiridol.(TIF)Click here for additional data file.S2 FigThe ordinate is RMSD (nm), and the abscissa is the time (ps). Black, Red, Green, Blue, Orange, and Pink lines indicate native, R24C, Y180H, A205T, R210P, and R246C protein complexes respectively.(TIF)Click here for additional data file.S3 FigThe ordinate is RMSF (nm) and the abscissa amino acid residues. Black, Red, Green, Blue, Orange, and Pink lines indicate native, R24C, Y180H, A205T, R210P and R246C protein complexes respectively.(TIF)Click here for additional data file.S4 FigBlack, Red, Green, Blue, Orange and pink lines indicate native, R24C, Y180H, A205T, R210P, and R246C CDK4-Cyclin D1 protein complexes.(TIF)Click here for additional data file.S5 FigBlack, Red, Green, Blue, Orange and pink lines indicate native, R24C, Y180H, A205T, R210P, and R246C CDK4-Cyclin D1 protein complexes.(TIF)Click here for additional data file.S6 FigThe ordinate is RMSD (nm), and the abscissa is the time (ps). Black, Red, Green, Blue, Orange and Pink lines indicate native, R24C, Y180H, A205T, R210P, and R246C protein-ligand complexes respectively.(TIF)Click here for additional data file.


Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at  The number of protein structures in the Protein Data Bank (PDB) is increasing rapidly  patterns by comparing the query protein with all the protein chains in the nr-PDB using ProBiS algorithm  in the 3D space using a fast density clustering algorithm with a distance cutoff of 5 Å for proteins and nucleic acids, and 3 Å for small molecules and ion ligands . PredictLigand 3D Viewer and on the right side the Ligand Tabs  to structurally conserved (red), as in the ProBiS web server  and the predicted ligands (CPK colored sticks) are visualized in an integrated JSmol molecular viewer . This vib server . The useSmall Molecules, Proteins, Nucleic Acids or the Ions tab opens the corresponding interactive table, one for each ligand type; a table for small-molecule ligands is shown in Figure View 3D link in the Ligand column shows the predicted ligand in the Ligand 3D viewer, and zooms in on the ligand; the View 3D link in the BSite column shows invariant binding site residues for the corresponding ligand. The Name column has the names of the ligands, and Structure column shows a small picture of the 2D ligand structures that is enlarged when a mouse cursor is over it.On the right side of the output page, clicking on the Glyoxalase family protein shown in panel A has no sequence homologues in the PDB; however, ProBiS-ligands predicts three ion clusters based on the detected invariant residues in various distantly related glyoxalases. In panel B, the query protein (3tkz) is an SH2 domain and the predicted ligand is a protein transposed from the source PDB (1r1s) which shares ∼30% sequence identity with the query protein. This protein–protein complex is not seen in 3tkz and similar protein structures, and the possibility of its existence probably has been overlooked previously. Panel C shows a binding site in bacterial enzyme D-alanine:D-alanine ligase with an endogenous ATP ligand and transposed inhibitor of biotin carboxylase (<30% sequence identity). This enables one to find different fragments of inhibitors bind to the same location in a query binding site, which could be used to design new compounds as a combination of existing ligands, i.e. ligand homology modeling. Finally, in panel D, a predicted DNA ligand bound to endonuclease IV query protein is shown. In ProBiS-ligands, we currently do not remove steric clashes between the predicted ligands and the query proteins; we expect to address this in the future.ProBiS-ligands can be used to construct protein–ligand complexes Figure  by transZ-scored predicted specific ligand from the first small molecule or ion clusters with the actual known ligands of the query proteins using an in-house developed 2D molecular graph matching algorithm. Ligand similarities, expressed as Tanimoto coefficients, were averaged over the predictions, and range between 0 and 1, where 1 is the highest similarity.We assessed the performance of the ProBiS-ligands web server on 500 protein models and their corresponding experimental structures Table . This tehttp://probis.cmm.ki.si/ligands/benchmark.ProBiS-ligands predictions are better for experimental protein structures than for protein models, which suggests that the ProBiS algorithm is relatively sensitive to the structural accuracy of query proteins. To simulate the lack of similar templates, a situation that occurs frequently in protein structures from structural genomics projects, we consecutively excluded template proteins sharing >30%, >20% and >10% sequence identity with the query proteins from the test set. The performance dropped when similar templates were unavailable; however, for experimental query structures, reasonable predictions with MCC of 0.28 and ligand similarity of 0.40 were possible even when templates with <10% sequence identity were available; for protein models, templates with at least 20–30% sequence identity were required for similar prediction accuracy. Our results show that ProBiS-ligands predicts ligand clusters that correlate well with actual ligand binding sites even when only evolutionary unrelated templates are available. The benchmark results can be found at ProBiS-ligands is a web server for prediction of ligands based on detected local structural similarities in proteins. One of the major advantages of ProBiS-ligands is that it allows transposition of ligands between protein structures irrespective of protein folding and with no prior knowledge of binding sites. This allows an established ligand, e.g. a drug, to be seen in a new perspective in which binding to other proteins, not hitherto recognized as targets, can be recognized. Repurposing of established drugs follows directly from this. We envision that the construction of accurate models of known ligands in binding sites will enable design of more specific ligands.


The development of high-speed analytical techniques such as next-generation sequencing and microarrays allows high-throughput analysis of biological information at a low cost. These techniques contribute to medical and bioscience advancements and provide new avenues for scientific research. Here, we outline a variety of new innovative techniques and discuss their use in omics research . We also discuss the possible applications of these methods, including an interactome sequencing technology that we developed, in future medical and life science research. More than a decade has passed since the human genome sequence was decoded. Subsequent advancements in and integration of personal genome analysis, post-genome functional analysis, and multiomics analyses have facilitated the development of personalized medicine, which is emerging as the optimal therapeutic direction for the future of medical science . The advNGS has contributed substantially to recent advances in omics research. In NGS, a technology used in genome sequencing, sequences containing millions of DNA fragments are read by performing numerous reactions in parallel . The useNGS can be used for performing genomic and epigenomic analyses . In geno BRAF mutations in papillary craniopharyngiomas .–35.32–35Metabolomics is divided roughly into two categories based on the experimental methods used: nontargeted and target-defined metabolomics. Nontargeted metabolome analysis is extremely attractive because this method can be used to identify an unknown metabolite and to concurrently determine its relative amount; thus, this method is suitable for nonbiased metabolite fingerprinting and diagnostic-marker exploration. However, nontargeted analysis performed using a single routine method remains challenging. This is because the metabolome includes compounds that differ considerably in molecular weight, electric charge, and concentration. Furthermore, although reference mass-spectrum databases –43 have  in vivo and in vitro techniques ( in vitro virus (IVV) and yeast two-hybrid (Y2H) tests allow the application of interactome sequencing  and MS . Howeverchniques . Among tquencing . in vivo approach used to detect PPIs [ in vivo techniques contain extremely high levels of false positives and false negatives. For example, although the exact rate of false-positive results in Y2H experiments is unknown, early estimates were that these are as high as 70% [The Y2H assay is anect PPIs . The assect PPIs , 50. Theh as 70% . False-ph as 70% . in vitro have been detected using the tandem affinity purification (TAP) method [ in vivo PPIs without the requirement of prior knowledge [ in vivo) through the identification of a wide variety of protein complexes [Traditionally, PPIs) method , 54. In ) method , 54. TAPnowledge . The TAPnowledge . The metnowledge . Proteinnowledge  and sodiomplexes .The protein microarray method is currently being established as a powerful tool for detecting proteins, observing their expression levels, and probing their interactions and functions. A protein microarray is a glass plate on which single proteins are bound at distinct positions according to a defined procedure . ProteinAnother method that allows high-throughput identification of PPIs in original extracts is shotgun proteomics . In thisThe development of the techniques described thus far has facilitated the high-throughput identification of protein interactions. However, the number of proteins that cannot be detected or identified even using these methods, such as the proteins that are expressed at extremely low levels or are highly insoluble, is considerably greater than the number of proteins that can be detected or identified . Therefore, new technologies are required for performing highly efficient high-throughput analysis of numerous proteins.12 molecules, which is beyond the capacity of conventional high-throughput protein-selection methods [ in silico analysis reduces the number of false-positive interactions contained in the initial raw data [ in silico analyses, were positive. IVV-HiTSeq can potentially be applied to several cell-free display technologies, such as mRNA display, DNA display, and ribosome display. Moreover, the IVV test can be applied not only to in vitro selection of PPIs but also to the detection of protein-DNA, protein-RNA, and protein-chemical compound interactions [The IVV-high-throughput sequencing  that can be analyzed. This large size of the library increases the probability of selecting extremely rare sequences, and it also enhances the diversity of the selected sequences.To further our understanding of living cells, we must collect data by using multiomics analysis. Multiomics includes gene-, transcription-, metabolite-, and protein-specific information. By contrast, the interactome includes network data that are obtained based on direct interactions between molecules. Therefore, an integrated approach, which includes both interactome and multiomics data, is required for comparing the identities of individual cells. This approach is referred to as “integrome” analysis . The int6 reads, at a rate of approximately 1000 bp per run. This capacity is sufficient for covering both of the linked variable regions. Although 106 reads are not adequate for covering the entire selected IVV library, this advance in NGS might make it possible to obtain unique, high-affinity binders.The use of NGS will allow the potential of IVV to be maximized. The latest Roche 454 Sequencer can sequence approximately 10Improvements in NGS might also facilitate the development of additional applications, and particularly notable is the speed with which NGS technology is being improved. When we have the capability to sequence the entire selected IVV library, we should be able to select low-affinity ligands that are commonly lost in a typical selection of repetitious rounds. IVV libraries will be subjected to high-throughput sequencing performed using NGS in order to generate interactome information, which will facilitate the archiving of the interactome map of a whole-cell library at a low cost. The use of IVV systems could make key contributions to our understanding of the interactome networks in cells and thus help in the development of pharmaceutical agents for treating currently intractable diseases .


Several challenges exist. First, intrinsically sticky proteins, such as chaperones, are often falsely assigned to many functionally unrelated complexes. Second, the reported collections of proteins may not be true “complexes” in the sense that they bind together and perform a joint cellular function. Third, due to imperfect sensitivity of protein detection methods, both false positive and false negative assignments of a protein to complexes may occur. We mitigate the first issue by down-weighting sticky proteins by their occurrence frequencies. We approach the other two problems by merging nearly identical complexes and by constructing a directed acyclic graph (DAG) based on the relationship of partial inclusion. The constructed DAG, within which smaller complexes form parts of the larger, can reveal how different complexes are joined. By merging almost identical complexes one can deemphasize the influence of false positives, while allowing false negatives to be rescued by other nearly identical association data. We investigate several protein weighting schemes and compare their corresponding DAGs using yeast and human complexes. We find that the scheme incorporating weights based on information flow in the network of direct protein–protein interactions produces biologically most meaningful DAGs. In either yeast or human, isolated nodes form a large proportion of the final hierarchy. While most connected components encompass very few nodes, the largest one for each species contains a sizable portion of all nodes. By considering examples of subgraphs composed of nodes containing a specified protein, we illustrate that the graphs' topological features can correctly suggest the biological roles of protein complexes. The input data, final results and the source code are available at  The second class of methods isolate groups of proteins from cellular mixtures and therefore report protein associations, without necessarily providing the information about which proteins directly interact within the mixture. These techniques may be based on fractionations of cellular extracts using liquid chromatography A living cell functions through coordination of its molecular components, within which proteins play a key role. In many cases proteins do not act individually but by joining with other proteins to form functional complexes consisting of several identical or different subunits. The last decade has seen a surge of interest in generating networks of interactions between cellular proteins in a variety of organisms The results of more than a decade of intensive investigations of protein interactions in model organisms can be easily retrieved. Numerous existing public databases, such as BIND unrelated parent nodes (larger complexes). This is in contrast to previous tree-resulting approaches, such as In this paper we present a novel way to organize protein association data, aiming to elucidate structure of protein complexes and modularity of subcomplexes (which may be interchangeable). It is well known that proteins or complexes can be multifunctional intrinsically sticky proteins may be falsely assigned to many groups of functionally organized but unrelated complexes. Second, the reported collections of proteins may not be true ‘complexes’ in the sense that they bind together and perform a joint cellular function. For example, it is possible that the prey proteins retrieved using one bait may belong to several independent complexes. Hence, the assemblies derived from interaction databases should be properly called ‘associations’ . Third, due to imperfect sensitivity of protein detection methods, it is possible to obtain both false positive and false negative assignments of a protein within a particular association. We mitigate the first issue by down-weighting these sticky proteins according to their numbers of associated complexes. To avoid accidentally up-weighting infrequent proteins that only appear in a very small number of associations, we set a lower bound on the number of complexes each protein participates in. We tame the other two problems by constructing a DAG and by merging nearly identical complexes. The constructed DAG can show how different complexes are joined by a common bait protein. By merging almost identical complexes one can down-weight false positives while at the same time allow false negatives in one association data to be rescued by other nearly identical association data.There are several challenges associated with building a hierarchy of protein complexes based on experimental findings. The first challenge is what to do with the sticky proteins? Due to their inherent trend to bind other proteins, We construct DAGs for protein complexes in yeast and human, the two species with the most abundant association and interaction data. Each node in the hierarchy represents a protein complex and each edge a sub-part relationship. A directed edge links a complex Saccharomyces cerevisiae) and human (Homo sapiens) by running the ppiTrim et al.et al. and mapped their protein identifiers to Entrez Gene IDs using the algorithm employed by ppiTrim. We excluded the interactions from the Collins et al. paper We obtained the evidences for protein complexes (associations) and protein-protein interactions in yeast , a protein complex nodemembers (or supports) of Let The support weight function for a complex One can see that The adjusted weight function for a complex Information flow weights generalize adjusted weights by taking into account the direct protein-protein interactions within complexes. We represent the PPI set For each protein Information flow weights can be interpreted in the context of the channel model of network information flow from Each walker visits at least one member of Since One can easily show that Here we describe the procedure for constructing a hierarchy that treats a protein weight function as an input parameter. Each protein weighting scheme results in a different final hierarchy.For a complex The rationale for In general, the weights of proteins can vary significantly, both within and between complexes. Hence, it is more robust to compare the weight of each protein relative to the total weights of complexes (self-similarities), rather than absolutely. Define the relative distance measure Clearly, Although the partial order The graph Proposition 1.Let  be a directed graph where for ,  if and only if  for some  Suppose that for any  implies  Then,  contains no cycles.Proof. Let non-redundant setFor small participation ratioSince this investigation focuses on the large heteromeric complexes, the initial PC set includes only the evidences for complexes containing at least four proteins. However, using adjusted and information flow weights allows us to identify nominally large complexes that contain many of the common proteins and hence have a small effective size. For example, a four-subunit complex may contain three very common chaperones and one other protein and hence behave like a single-member complex with respect to the distance All nodes with the participation ratio (effective size) smaller than The hierarchies obtained using our algorithm above contain different nodes and edges depending on the type of the weight function used and the value of We ascribed biological interpretation to the individual complexes and connected components of the constructed hierarchies through functional enrichment analysis based on the Gene Ontology (GO) Homo sapiens) and yeast (Saccharomyces cerevisiae). These two species account for the overwhelming majority of available evidences of multimeric protein complexes. The summary of initial data sets is shown in Using the procedure described above, we first extracted from a recent edition of iRefIndex the datasets of protein complexes and direct interactions in human . The parameter For both investigated species, we constructed three hierarchy DAGs using each of the three protein weighting schemes: support (code P), adjusted (code A), and information flow  provide the most extensive and biologically most sensible hierarchies, we will henceforth consider only sceN4 and hsaN4 as the representative hierarchies for yeast and human, respectively. Here, we broadly examine some topological features of these DAGs and their relation to biology.et al.et al.et al.As shown in et al.et al.Isolated nodes comprise 29% (2308/8023) of the entire hierarchy in yeast and 55% (3198/5801) in human. While some isolated nodes comprise merged evidences , the majTo investigate whether a node's isolation can be explained by plausible simple criteria, we compared the distributions of participation ratios (effective complex sizes), proportions of infrequent proteins and numbers of directed interactions per protein for isolated and connected nodes in yeast and human . While tWe found 4364/8023 connected nodes in yeast, and 2446/5801 in human. If the hierarchies are treated as undirected graphs, where two complex nodes are linked if either one is approximately a part of the other, they decompose into 420 connected components in yeast, and 360 in human. The distribution of component sizes is very nonuniform. The largest component contains 2807 nodes in yeast and 1031 in human, while the remaining ones mostly encompass very few nodes .A summary of the components with ten or more nodes in yeast and human are shown in The connectivity pattern of the final hierarchy provides a way to describe the cellular compartmentalization of proteins from the complex data. Every protein generates a subgraph of the DAG, consisting of all nodes that contain the said protein. If the generated subgraph mostly consists of isolated nodes, it is very likely that the associations of the selected protein to these isolated nodes are due to noise. On the other hand, if the generated subgraph contains a relatively large set of related evidences, the selected protein may be viewed as an integral member of a persistent complex. This is often reflected by a relatively large set of connected nodes.As an example, consider the subgraph of the yeast DAG induced by ELP3, a member protein of the transcriptional elongator complex . It consThe subgraph generated by human RPTOR protein  offers aIn contrast to the above two examples, the subgraph of the yeast GSY2 protein is almost completely disconnected. Given that the enzyme GSY2 is a glycogen synthase, which has an important metabolic role, this almost disconnected topological pattern is not surprising. In fact, we could not find any documented stable associations of GSY2.et al. in their high-throughput study concentrating on chaperone-protein interactions An important step in our methodology is the filtering of complexes of small sizes that are most susceptible to accidental associations or false negatives. By restricting to complexes of size four or more , we are dropping some possibly noisy connections but do not lose the essential information. Coincidentally, this heuristic largely removes in yeast a large number of complexes, each made of chaperones and few other proteins, reported by Gong resonance effect which amplifies weights of tightly connected sets of members. By considering only the walkers that both start and end at a complex A DAG naturally emerges as a consequence of merging and embedding based on our similarity scoring. In this DAG construction, however, there are other important ingredients: downweighting frequently seen proteins, limiting the influence of proteins having few associations, and use of protein-protein interactions to emphasize proteins with documented binary interactions. Downweighting frequently binding proteins is not new Although PPIs play an important role in our DAG construction, it is worth pointing out that using PPI data alone may miss important information that only get revealed via association data. In We also note that, in the finally constructed DAG, there are many disconnected components. There are several possible reasons for this. Since we are pooling information from published data, the final collection is susceptible to sparsity of data, is dependent on different experimental/biological conditions, and may be biased towards tissue/disease-specific studies. Ideally, such less uniform samplings of complexes (protein associations) might be remedied by unbiased high-throughput approaches. Another way to mitigate the sparsity and bias in the human dataset is to use information obtained from mammalian models such as rat and mouse. This, however, requires appropriate functional mappings of orthologs, which is beyond the scope of the current study. As for the case of yeast DAG where many high-throughput data are employed, we still see many disconnected components. While this may simply suggest functional modularity, the largest components in both human and yeast DAGs, containing respectively more than 1000 and 2000 interconnected complexes, do not fit this picture. As shown in The constructed DAGs can serve as a starting point (or a scaffold) for other algorithms for other computational analyses. Basing on published association data, these DAGs also form a knowledge database of complexes. Our DAG construction also offers a natural and well-balanced integration of complex data and binary data. By combining the DAGs with binary interaction data to form the underlying dataset for a network analysis tool, one can arrive at a stronger hypothesis-forming and exploration tool than that using only complexes data or binary data.Figure S1Two procedures used by ppiTrim for complex deflation.(PDF)Click here for additional data file.Table S1Top ten publications according to the number of reported complexes.(PDF)Click here for additional data file.Table S2Top 25 most frequent Yeast protein members of complexes.(PDF)Click here for additional data file.Table S3Top 25 most abundant Human protein members of complexes.(PDF)Click here for additional data file.Table S4Yeast components containing 10 or more nodes.(PDF)Click here for additional data file.Table S5Human components containing 10 or more nodes.(PDF)Click here for additional data file.Text S1iRefIndex and ppiTrim.(PDF)Click here for additional data file.


Altogether, our results suggest that some observed comorbidities between phenotypically different diseases could be due to a shared protein involved in unrelated biological processes.Moonlighting proteins are a subset of multifunctional proteins characterized by their multiple, independent, and unrelated biological functions. We recently set up a large-scale identification of moonlighting proteins using a protein-protein interaction (PPI) network approach. We established that 3% of the current human interactome is composed of predicted moonlighting proteins. We found that disease-related genes are over-represented among those candidates. Here, by comparing moonlighting candidates to non-candidates as groups, we further show that  ACO1 gene, is an enzyme of the tricarboxylic acid cycle (TCA cycle) which turns into a translation regulator upon a conformational change, when the iron concentration is low  from a protein-protein interaction (PPI) network  . We still observed this enrichment when considering MF-CAN and MCNC separately   are enriched in proteins involved in diseases   Figure . This, t) Figure  suggestsDifferent OMIM diseases caused by the dysregulation of a same gene can affect the same body organs or have similar phenotypic manifestations  and MONO . This result shows that MF-CAN proteins are involved in disease affecting distinct body parts. Therefore, this led us to evaluate the phenotypic similarity between disease pairs by computing the Sorensen-Dice distance based on their HPO annotation terms (see Methods for details). For each pair of diseases in which a same gene is involved, we have compared their phenotypic descriptions to assess their similarity aiming at grasping possible differences. We indeed found that those pairs associated with MF-CAN are significantly different . We obtained similar results using semantic similarity measures . These results could be due to the fact that multifunctional candidates (MF-CAN and MCNC) are acting in several biological processes, and are therefore involved in different diseases and phenotypes.To do so, we mapped OMIM diseases to the Human Phenotype Ontology (HPO) , whereas MONO are significantly depleted in such pairs (P = 1.1 × 10−5).In order to estimate whether moonlighting candidates could explain some comorbidity patterns, we used the list of comorbid diseases established by Park et al. , based oOur results therefore indicate that EMF proteins involved in several diseases can contribute to comorbidity, probably through the unrelated biological processes in which they participate.in vivo studies on mice harboring humanized AR alleles showed that long polyQ may be involved in late-onset PC and influence hormone sensitivity  is one of the MF-CAN proteins associated to a disease pair showing a comorbidity pattern  the fact that diseases in which an EMF is involved can occur in a same individual and (ii) our hypothesis that comorbidity can be due their involvement.Moreover, we found that a significant fraction of these EMF candidates represents a possible molecular link between comorbid diseases. Nevertheless, although a gene/protein may be involved in different diseases, these diseases are not necessarily co-occurring within a single individual. Our finding that comorbid diseases identified from Medicare records are enriched among EMF candidates therefore reinforces (i) we previously showed that EMFs are enriched in SLiMs compared to MCNC  a recent report showed that disease mutations occur more likely within SLiMs than neutral missense mutations in intrinsically disordered regions (Uyar et al., Diseases associated to EMF proteins display different phenotypes according to HPO, independently of the location of the mutations in the protein sequence, within or outside known protein domains. This raises several possibilities. First, these mutations may occur in existing domains not yet discovered. Indeed, current tools may not have detected some domains, since they are below the detection thresholds, or differing from the model used by the prediction tools. Second, this lack of difference may also be explained by the fact that disease mutations occur in SLiMs, short stretches of amino acids sequences that represent potential regulatory sites and are located mostly in intrinsically disordered regions (van Roey et al., In an early study on co-occurring metabolic diseases, Barabasi and colleagues (Lee et al., It should be noted that our observations cross and link several integrated functional levels (Brun et al., Finally, grounded on our computational identification of moonlighting candidates, this work represents, to our knowledge, the first effort toward the elucidation of the convoluted relationships between moonlighting protein functions, disease phenotypes and comorbidities. Overall, although much remains to be learned, our analysis underlined the necessity of identifying moonlighting proteins and to better understand their role in human diseases, particularly in the context of drug development to avoid side effects. Indeed, knowing that the different diseases in which a protein is involved, are due to the dysregulation of different processes may help choosing specific interactions to inhibit in order to act on one disease and not the other. In addition, a drug inhibiting an unsuspected moonlighting protein may cause side effects if it also impairs the secondary function of the protein. Therefore, the identification of candidate moonlighting proteins can be useful to gain a deeper understanding of disease molecular details and to design more effective therapeutic strategies.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Recent advances in high-throughput laboratory techniques captured large-scale protein–protein interaction (PPI) data, making it possible to create a detailed map of protein interaction networks, and thus enable us to detect protein complexes from these PPI networks. However, most of the current state-of-the-art studies still have some problems, for instance, incapability of identifying overlapping clusters, without considering the inherent organization within protein complexes, and overlooking the biological meaning of complexes. Therefore, we present a novel overlapping protein complexes prediction method based on core–attachment structure and function annotations (CFOCM), which performs in two stages: first, it detects protein complex cores with the maximum value of our defined cluster closeness function, in which the proteins are also closely related to at least one common function. Then it appends attach proteins into these detected cores to form the returned complexes. For performance evaluation, CFOCM and six classical methods have been used to identify protein complexes on three different yeast PPI networks, and three sets of real complexes including the Munich Information Center for Protein Sequences (MIPS), the Saccharomyces Genome Database (SGD) and the Catalogues of Yeast protein Complexes (CYC2008) are selected as benchmark sets, and the results show that CFOCM is indeed effective and robust for achieving the highest F-measure values in all tests. Most proteins in living organisms, performing their biological functions or involving with cellular processes, barely serve as single isolated entities, but rather via molecular interactions with other partners to form complexes . In factUp to now, significant progress in high-throughput laboratory techniques involving Tandem Affinity Purification (TAP)  and MassSince some proteins have multiple functions, in other words, they may belong to more than one protein complex, so the ideal approaches need to be able to detect overlapping complexes. However, several types of graph clustering methods don’t allow overlaps between detected protein complexes due to the confinements of the rationales behind them. For example, the partition-based clustering methods such as the Restricted Neighborhood Search Clustering algorithm (RNSC) , the BayFurthermore, the aforementioned methods still have a common fatal weakness—ignorance of the inherent organization of the complexes—but actually experimental analysis has already reported that a protein complex generally consists of a core, in which proteins share similar functions and tend to be highly co-expressed, and other attach proteins surrounding to the core . Based oIn addition, to precisely predict more biological explainable complexes, some methods of fusing various types of prior knowledge including functional annotations ,19,20, gIn summary, we may come to the conclusion that a comparatively well-designed protein complexes identification method may need to meet the following conditions: capable of detecting overlapping complexes, fewer parameters, being easy to be determine, consideration of the inherent organization of protein complexes, particularly finding topological and biological meaningful cores, properly incorporating prior information as much as possible into the predicting model, and robust to PPI networks with false positives and false negatives. Unfortunately, even though many effective techniques have been proposed, as far as we know, few of them satisfy most of the above-mentioned requirements, which results in impeding further practical applications, and thus there is still urgent need for new approaches.In this manuscript, we introduced a novel core–attachment based method to predict protein complexes, and the proteins in our detected cores are closely linked, share high similar topology that is highly connected to internal vertexes and relatively sparsely connected to outsides, and are more biologically significant, namely more likely to participate in one or more biological processes with the appliance of GO functional annotation. Furthermore, the detected complexes can be overlapping. We applied our algorithm to two PPI networks of yeast, and validated our predicted complexes using benchmark complexes collected from several public databases. The experimental results indicated that our algorithm is efficient and outperforms other existing classical methods.t affecting the performance of CFOCM. Next, we perform comprehensive comparisons with various existing classical methods and analyse the results in detail. Finally, we explore the functional definition of the complex-core as a whole, contributing to the biological significance of the detected complexes.We have applied our CFOCM method on the Database of Interacting Proteins (DIP) data and Gavin data. In this section, we will first discuss parameter p and a real complex r, and if p and r are considered to be matching. On the one hand, the greater setting value of The neighborhood affinity score tures do ,13,15,28P and R represent the set of predicted complexes and the real complexes in benchmarks, respectively. Let t calculated by t; nevertheless, this may lead to compromise on the quality of the predicted complexes, and thus how to choose a relatively appropriate t to achieve a balance between the predicted complexes’ quality and quantity needs to be probed. Here, varying t from 0.2 to 0.6 with the interval 0.01, the F-measure values of each predicted complex set are computed, and help us to intuitively observe that the variation of t affects the performance of our CFOCM method and selects the relatively suitable t as well  are conducted. As the results listed in This section illustrates two predicted protein complexes, namely the Glycine decarboxylase complex and the RNA polymerase I complex as V and G if v’s direct interacting neighbors in graph G is denoted as v’s neighbors in subgraph A PPI Network typically can be represented as an undirected graph t is a predefined threshold), we may declare cluster A neighborhood affinity score metric , denotedG connecting one protein annotated with GO item i and the other annotated with item j, i and the other annotated with item j in G, hence it equals (Number of edges in G with one protein annotated with i)*(Number of edges in G with one protein annotated with j to the others)/i and j are functionally interdependent if As is well known, GO is composed of three orthogonal ontologies capturing knowledge about biological process, molecular function and cellular component, and each ontology consists of controlled and structured biological terms that can be used to annotate genes and proteins. Some GO item pairs are highly functionally related—for example, sharing a common parent node, or one is just a near ancestor of the other, while other GO item pairs may possess much weaker relationships or even be functionally independent. Therefore, the urgent need is to design a metric to quantify the functional interdependence between two GO items. Fortunately, Ref.  has doneet. Ref.  also indG, the proteins in which have dense intra-connections and are sparely connected to the rest of the network, thus we introduce a new and effective closeness function to quantify the probability that v’s direct neighbors located within v are in v should be a member of v has little chance to be a member of A protein complex is pervasively modeled as an induced subgraph of PPI network Most of the protein complexes contain core–attachment structure, and the proteins in the core share similar topology and are highly functionally related, while the attach proteins are usually located in the periphery of the core . As the Protein-complex core plays a key role for complex performing biological function, and determines the cellular role and significance of the complex in the context to a large extent . The resAssumption 1.A subgraph 1.The topology of 2.If 3.Different from traditional methods exploring each core protein separately, our above complex-core assumption is more plausible for considering all proteins in the core as a whole. Benefiting from this renovation ensures that each protein in the core owns similar topology and contributes to the enforcement of core’s biological functions. Conditions 1, 2, and 3 guarantees the maximizes closeness function value of core, the nearest distance can be retained between different cores, and participation of at least one common biological functions, respectively. Specifically, most traditional literature is mainly focused on the assurance of highly functional similarity between each protein pair in the core, which will result in neglecting that the core as a whole should perform some common functions, while this flaw is certainly renovated by our integrated global view of the core.t. For each certain candidate-core, its family set is obtained in lines 8–13, and a more reasonable combined candidate-core comes into being through algorithm Merge_Similar_Cores in line 14. The details of Merge_Similar_Cores algorithm are described in Algorithm 2. Still, in lines 15–17, if the current generated candidate-core already exists in the generated candidate-core set, we simply discard it; otherwise, we add it to the candidate-core set. After these steps, though, there unavoidably exist some incorrect manipulations, excessive overlapping and biological meaningless candidate cores are substantially removed, and the overwhelming majority of the vertexes in retained cores are densely connected internally, possess similar topology and attend to share at least one common GO annotated function.Algorithm 1 illustrates that the overall framework to detect protein-complex cores, and, without question, the discovered cores comply with definitions in Assumption 1. We first compute the functional interdependence between each GO items pair by the definition n method , based oAlgorithm 1: Complex cores detection algorithm.Require: The PPI network   Neighborhood affinity score threshold t.Ensure: The detected complex cores set 1:calculate each GO item pair functional interdependence 2:G;find all the maximum cliques 3:4:repeat5: 6: 7:fordo 8:  9:fordo  10:ifthen   11:    12:end if   13:end for  14:  15:ifc is not exists in then  16:   17:end if  18:end for 19:until not exists any two elements 20:returnA crucial artifice, not described in Algorithm 1, is applied in the process of detecting cores. First, for each maximal cliques set with the same number of vertexes, we generate their corresponding new candidate cores by executing steps in lines 4–19, and then form the final detected cores via the same steps on these different-sized generated cores. Without using this, the smaller cliques may be annexed by the larger similar cliques so that they barely contribute to the generation of the new candidate core. Actually, this artifice is proved to be an effective means of improving the predicting performance.Given the family m disappeared in the GO annotations of maximal proteins in line 2. Afterwards, in lines 3–7, we remove proteins that are neither annotated by the common item m nor have a GO item functional interdependent with item m, and this procedure ensures that the returned candidate-core has a high probability of owning at least one common GO function because the proteins in the returned candidate-core either have the common GO item m or a GO item j exists that is functionally interdependent with m. Finally, in lines 8–10, we iteratively delete a protein p from the p exists, satisfying Our Merge_Similar_Cores algorithm works as follows. To begin with, we extract the proteins Algorithm 2: 1:get all proteins 2:m which annotating maximum number of proteins in find the GO item 3:for each p in do4:ifp is not annotated by m and exists no GO item j annotating p satisfying: then 5:  6:end if 7:end for8:while exists do9: 10:end while11:returnEach input candidate-core family goes through these steps, and a newer candidate-core has been formed. In addition, c, for each external boundary protein p of current core c, the following Assumption 2 presents whether p should be an attachment to the core c or not.After the foregoing phase of our CFOCM method, the protein-complex cores have already been mined from PPI network Assumption A2.A external boundary protein p is affirmed as an attachment to the complex core c if satisfying p improves the closeness function From the above assumption, the external boundary protein Algorithm 3: Attach-proteins screening algorithm.Require: Protein complex cores Ensure: The predicted complexes 1:2:for each c in do3:while exists do 4:  5:end while 6: 7:end for8:returnThree publicly available yeast PPI networks, namely the Database of Interacting Proteins (DIP) data , Gavin dTo validate our predicted complexes, three reference sets of real complexes, denoted as the Munich Information Center for Protein Sequence (MIPS) , SaccharThe yeast GO annotation dataset is downloaded from the SGD database, and the submission data is February 2014.In this paper, we have proposed a novel algorithm CFOCM for protein complex identification from the protein–protein interaction network. According to the fact that there some proteins involved in more than one biological function or cellular processes, CFOCM implements allowing overlaps between detected complexes. Meanwhile, CFOCM also takes into account the inherent core–attachment structure in the protein complexes. Moreover, CFOCM ensures topological similarity and functional interdependence between each pair of proteins within detected cores.Comparison experiments between CFOCM and the other six state-of-the-art methods are carried out in DIP networks, Gavin networks and Srihari data, and the results of all tests show that CFOCM significantly outperforms the others. Moreover, CFOCM has been demonstrated to be capable of filtering the low-confidence or biological insignificant protein complexes via comparing with unCFOCM without consideration that the proteins in a complex core should occupy some common functions. In a word, CFOCM is efficient, robust, and it is applicable for helping biologists search for new biological meaningful protein complexes.The follow-up works are ongoing. For instance, since some proteins still have not been functionally annotated, and we intend to find a more suitable strategy to handle this data problem, and design a parallel version of CFOCM to accelerate the operating speed. In addition, how to extend CFOCM to detect protein complexes and functional modules in dynamic PPI networks, which can be constructed by incorporating gene expression data, is also a promising direction.


Caenorhabditis elegans and Drosophila melanogaster, whose genomes are well-curated. In this paper, we describe a bioinformatic strategy for the curation of gene families and subsequent annotation of encoded proteins. This strategy relies on pairwise gene curation between at least two closely related species using genomic and transcriptomic data sets, and is built on recent work on kinase complements of parasitic worms. Here, we discuss salient technical aspects of this strategy and its implications for the curation of protein families more generally.Despite the substantial amount of genomic and transcriptomic data available for a wide range of eukaryotic organisms, most genomes are still in a draft state and can have inaccurate gene predictions. To gain a sound understanding of the biology of an organism, it is crucial that inferred protein sequences are accurately identified and annotated. However, this can be challenging to achieve, particularly for organisms such as parasitic worms (helminths), as most gene prediction approaches do not account for substantial phylogenetic divergence from model organisms, such as  For these data to be useful biologically, they need to be represented by high-quality genomes and transcriptomes, which need to be annotated. Accurate annotation requires a reliable and, preferably, automated procedure for the identification of genes and classification of inferred protein sequences. Although many software packages and tools have been developed for this purpose6, accurate annotation remains a challenge, particularly for most eukaryotes7. Nevertheless, algorithms allow automatic searches and comparisons of biological sequences, as well as the identification of conserved patterns and/or domains8. Such algorithms have substantially increased the ability to infer the relatedness of sequences and predict their biological functions10.In the last decade, the number of available nucleotide data sets has increased substantially, enabled by major advances in sequencing technologies12. In this context, sequences are often compared with those of model organisms (such as Caenorhabditis elegans and Drosophila melanogaster), for which experimental evidence of protein function is available. However, for organisms that are genetically very distinct from model organisms, such comparisons often result in limited sequence similarity and inaccurate annotations14.Historically, the prediction of protein function and/or the classification of proteins have relied on sequence similarity searches against protein sequence databases using the Basic Local Alignment Search Tool (BLAST)17. These models allow sensitive and specific searches for conserved functional domains and have been implemented in many tools, including those employed to identify protein kinases19. HMMs are statistical descriptors of sequence similarity inferred from multiple sequence alignments (MSAs), and have been shown to be more sensitive and specific than local alignment-based methods for sequence searches and/or comparisons, such as BLAST20. However, most HMMs have been constructed using ‘seed’ MSAs that poorly represent non-model organisms and, thus, might not be sensitive enough to detect divergent but related sequences.To address this limitation, stochastic models, such as hidden Markov models (HMMs) and position-specific scoring matrices (PSSMs), have been developed21 or genes being located close to each other in the genome23. Incorrect predictions can have a substantial impact on subsequent analyses. For example, the identification of a protein that is represented by an incorrect combination of two adjacent gene models  of a range of parasitic worms 29 for which genomes and transcriptomes are available. Despite the expansion of such data sets for parasitic worms30 and the importance of protein kinases in a plethora of key biochemical signalling pathways33, very little is known about protein kinases encoded in these genomes and their functions. Fortunately, protein kinases contain a well-conserved catalytic domain whose sequence and biochemical mechanisms have been studied extensively in most model organisms, such as Saccharomyces cerevisiae34, Homo sapiens35, C. elegans and D. melanogaster31. The wealth of curated literature and data on kinases can be harnessed to curate respective genes, and to characterise kinomes of parasites.To assess the accuracies of gene predictions and functional annotation of protein sequences achieved by ‘best-practice’ bioinformatic pipelines38, providing promising avenues for the control of parasitic worms41. Taken together, there is a need to establish an approach for the accurate annotation of protein kinases of parasitic worms.Elucidating kinomes of parasites will provide new insights into the biology of these unique organisms. In this context, the biological and phylogenetic differences between parasitic eukaryotic organisms and well-studied free-living model eukaryotes can provide clues regarding the evolution of parasitism. In addition, protein kinases have also been extensively investigated as drug targets for human diseases, such as cancers, and are amenable to targeting by small molecule drugs18 and Kinannote19. These tools rely on a set of kinase-specific HMMs, PSSMs and/or BLAST searches against curated kinase databases. For instance, Kinannote takes approximately five minutes to run on a protein data set of 10,000–15,000 sequences and is fully automated. Furthermore, it produces a draft kinome and comparative statistics of the analysed kinome, and is the only tool that automatically classifies protein kinases  using the currently recognised classification scheme for these enzymes42.Several tools and resources for the identification and classification of protein kinases are available, including the programs Kinomerin silico gene predictions from draft genomes will preclude the accurate identification and classification of protein kinase sequences.However, there are challenges associated with the use of such tools. Firstly, automated protein identification and classification rely on the use of evidence from either protein domain annotation or sequence comparison; decisions based on such evidence are sometimes made in a ‘black box’, without the user being able to assess intermediate results or adapt the workflow to individual requirements . Secondly, the models employed to identify and classify sequences often do not include or consider divergent sequences , thus hampering reliable detection. Additionally, most protein identification/classification tools accept protein sequence data as input, but do not permit the use of RNA-sequencing (RNA-Seq) evidence or genomic data, which could aid in the curation process. Inaccurate C. elegans43 and other, distantly related worms - this represents a challenge, both for the identification and the classification of protein kinase sequences in these genomes. Moreover, none of the currently available tools provides outputs that allow for the detailed, semi- or fully automated curation of nucleotide/amino acid sequences inferred from draft genomes. These limitations show that these tools are not universally applicable to kinomes of eukaryotes.Thus, none of presently available tools achieves a comprehensive curation of a kinome. Furthermore, current tools do not adequately consider the fragmentation of draft genomes and the substantial kinome diversity between, for instance, the well-curated model organism Here, we describe a strategy for the curation of gene models and subsequent annotation of inferred protein sequences for protein families of non-model organisms, based on recent studies of kinase genes of parasitic organisms. This strategy is continually being improved to tackle some of the inherent challenges in analysing different genomic and transcriptomic data sets.The gene curation strategy integrates draft genomic and/or transcriptomic data from two or more species, and is divided into five distinct steps Fig. :Figure 220), PANTHER v.9.0 (ref.44) and SUPERFAMILY v.1.75 (ref.45) within the program InterProScan v.5.15.54 (ref.46). As this first step identifies candidate sequences, stringent cut-off values  are not applied to not miss related but divergent sequences. Importantly, accessory domains that are commonly found in this protein family are also considered as candidate sequences. For example, in the case of protein kinases, sequences that lack a conserved kinase catalytic domain but contain one or more SH2 domains (accessory domain/s commonly present in tyrosine kinases) might represent fragments of a protein kinase sequence. Such fragments can be highly informative and essential for curating a final gene model. Accordingly, every sequence identified that contains at least one characteristic conserved domain represents a ‘seed’ that can be extended N- and C-terminally in subsequent steps, thus complementing the underlying draft gene model. Additionally, to provide a reference data set, sequences in databases representing the investigated protein family are interrogated in the same way. For studies of kinases, we use all domain combinations present in curated protein kinase complements  and new domain architectures as they become available. We also include subsets of the identified domains as candidates, reasoning that they might represent sequence fragments that can be joined into a complete gene model during the curation process.First, all amino acid sequences (encoded within a draft genome) that contain one or more conserved functional domains characteristic of a particular protein (super-)family , are identified employing information from domain matches against the databases Pfam v.27.0  are available, all transcripts are mapped to genomic scaffolds using the program BLAT v.34 × 12 (ref.47). Then, all mapped transcripts overlapping with a region to which at least one candidate ‘seed’ transcript has been mapped are reassembled using CAP3 (build 15 Oct 2007)48. The longest, reassembled transcript sequence containing an open reading frame (ORF) is then selected for the subsequent re-prediction of its gene model.If 49) or an all-against-all pairwise global alignment 50.Both transcripts identified from existing gene sets and extended, reassembled transcripts are mapped to a genome sequence of a phylogenetically closely related species. In this context, it is important to carefully select the related species, and ensure that high-quality genomic and transcriptomic data are available. On one hand, the selection of two, phylogenetically divergent species might lead to a lack of comparative genomic and transcriptomic evidence needed for curation. On the other hand, two closely related species might not add any complementary information to the re-prediction process. Based on this mapping step, the most closely related ortholog in the related species can be identified, forming pairs of sequences for subsequent curation. The pairwise orthology is then confirmed using tools such as OrthoMCL v.2.0.4 (ref.51) is used to define gene structures (i.e. intron-exon boundaries) based on mapped transcripts encoding candidate proteins. This step is then reciprocally iterated between the two genomes, being compared until an optimal gene prediction is achieved for pairs of orthologs in both genomes (i.e. the prediction converges).The “coding2genome” model in the program Exonerate v.2.2.0 −5; sequence similarity of ≥80%) to cluster sequences from the species being studied, C. elegans and H. sapiens, and subsequently infer classification based on the annotation of the curated sequences in the latter two species28. Sequences that cannot be categorised using these two approaches are classified based on the presence and order of functional domains and/or signatures (“domain architecture”) as determined by InterProScan. However, the family/subfamily-specific HMMs within the Pfam, PANTHER and/or SUPERFAMILY databases employed for this task are not always representative of the diversity across the Tree of Life52, being built based on a relatively small number of well-characterised sequences. Thus, we employ specialised models built specifically for the classification of particular protein families54, which are more sensitive for selected taxa than related models in established databases.For protein kinase classification, we employ a sequence similarity (BLAST) search against KinBase within Kinannote. Additionally, for sequences not (fully) classified by Kinannote, we use OrthoMCL , and by employing the program I-TASSER v.4.4 (ref.55) for de novo predictions. Stringent cut-offs are applied  to ensure high confidence structural predictions and associated classifications. For example, for kinase classification, we only consider the structurally most similar entry in the PDB and apply cut-offs for template modelling (TM) scores (≥0.75), confidence (C) scores (>0) and root-mean-square deviation (RMSD) values (<3 Å).In addition to sequence-based classification, we employ three-dimensional structural modelling of unclassified sequences using solved crystal structures deposited in the Protein Data Bank  to sequences in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database56. The predicted subcellular localisation of proteins is inferred using the program MultiLoc2 (ref.57), applying a conservative cut-off of ≥0.8 for the confidence score. Additionally, transcription analysis in particular developmental stages or different tissues can aid functional annotation. However, as the present article focuses on sequence- and structure-based annotation, we refer the reader to two recent publications29 for more information on transcription analysis as a tool for annotation.Following classification, functional annotation is achieved by assigning domain identifiers and gene ontology (GO) terms to sequences and by linking them to biochemical pathways based on similarity . In this context, it is important to carefully select the part of the sequences that should be used for the construction of MSAs underlying the phylogenetic trees. For example, to achieve robust trees , in kinase studies, we only use the catalytic domains of protein kinases and analyse all nine recognised kinase groups independently.The inferred classification and pairwise orthology of sequences from different species are confirmed by phylogenetic analysisHaemonchus, Trichinella, Trichuris and Schistosoma), and classified and functionally annotated the kinomes of these worms29.The applicability, robustness and usefulness of the established curation strategy described here is exemplified by several recent studies that have curated the gene models encoding protein kinases in the genomes of some key parasitic worms  and subsequent inference of ORFs accelerated the detection and correction of erroneous gene predictions, and the confirmation of correct ones. However, this approach relied on the availability of extensive RNA-Seq data. In cases where such data were not available or were of insufficient quality/quantity, the re-prediction of gene models was achieved by transferring re-assembled, high-confidence gene models from one species to a closely related one27.We have introduced several additional steps to achieve improved gene prediction. For example, the use of extensive transcriptomic data sets allowed for the application of an improved gene re-prediction approach. Additionally, the automated re-assembly of In addition to re-predicting gene models based on re-assembly of transcriptomic data, the location of genes on genomic scaffolds allowed the inference of a range of other key features that were used in the curation and/or annotation process. For example, information about intergenic distances and the completeness of protein domains encoded by adjacent genes was employed to complement fragmented genes and curate incorrect splice sites that were introduced during gene prediction and/or scaffold assembly. Furthermore, analysing predicted functional domains and comparing them with known architectures of functional domains in multiple organisms helped to identify and curate fragmented and/or incorrectly assembled genes.Employing this approach for the curation of worm kinomes, the considered selection of two species for pairwise curation and the use of additional, independent data sets  allowed ‘consensus’ gene models to be created from all data sets. Using this strategy, we have overcome some of the challenges inherent to draft genome assemblies and inferred high-confidence gene models.65 was an indispensable tool for the simultaneous display of gene models, genomic locations and complementary data, including functional domains, RNA-Seq support and de novo-assembled transcripts pts Fig. .Figure 466) tended to splice two or more genes with short intergenic distances to each other, to produce a single gene model, when, in fact, they represented separate genes  intronic regions. In contrast, most other gene models were automatically confirmed either using transcriptomic evidence for the same species or, employing our automated curation strategy, based on transcripts assembled for closely related species.In our experience, manual curation helped identify ‘hotspots’ of potentially incorrect predictions, such as mis-assembled genes. For example, the gene prediction software originally employed in the genome projects  of domains that do not occur together in other kinase sequences within the InterPro database28. Such ‘fusions’ were indicative of an erroneously assembled gene sequence, and required critical assessment at the genomic and transcriptomic sequence level to either support (based on RNA-Seq evidence) or refute the predicted model. This proved useful for the correction and complementation of erroneous gene predictions. Taken together, the integration of the different data types resulted in consensus gene predictions that enabled the inference and classification of protein sequences with high confidence.The pairwise comparison approach applied for gene-level (i.e. protein-coding sequence) curation allowed an iterative refinement of protein classification and functional annotation through cross-validation of genomic and transcriptomic data with the inferred protein sequences. For every prediction, a consensus was based on the evidence from RNA-Seq data, 26. However, such an approach might ‘overfit’ the stochastic model to a particular organism, thus losing general specificity for a wide range of protein kinases from other species. Accordingly, curated kinase sequences of worms should be added to the protein kinase HMMs in Pfam, thus building improved models that are more sensitive to phylogenetic distant kinase sequences, while retaining specificity. In this context, it might also be useful to integrate a recently established BLAST-based taxon sampling approach to identify more distantly related (‘hidden’) orthologs52.For kinase sequences from species that were not accurately represented by the HMMs available in public databases, we built sensitive, taxon-specific kinase HMMs to account for sequence diversity among species28, we identified kinase sequences that were phylogenetically distinct from those in recognised families or subfamilies by grouping the predicted catalytic domains of unclassified kinases into orthologous clusters and subsequently constructing phylogenetic trees. Such analyses elucidated the phylogenetic relationship between recognised families and novel, species-specific families that were not detected by tools such as Kinannote. A comparison between the annotation by Kinannote and that of the orthology/phylogeny-based approach revealed kinase sequences that were specific to a phylogenetic branch or even a single genus/species. This approach provided important information about the evolution of protein kinase families/subfamilies and about the potential function of novel kinases. By using a combination of a controlled vocabulary and an orthology-/phylogeny-based approach, most sequences that have diverged from their non-helminth orthologs were automatically assigned to the recognised groups, families and subfamilies.In previous studiesIn addition, the classification of sequences based on functional domain architectures enabled (sub-)grouping of kinases based on the presence and order of functional domains. To date, this approach has been applied solely to the classification of protein kinases. To extend the use of this approach to any protein family, the entire InterPro database would have to be processed in a similar manner. The online version of InterProScan IDA tool allows searches for single architectures but does not permit batch queries for entire proteomes. Furthermore, a locally installed, stand-alone version of the IDA tool is currently not available or planned . Such a tool would allow searching for sequences with the same or similar domain architecture in a high-throughput manner, thus facilitating the exploration of gains or losses of functional domains in sequences across different phylogenetic taxa.28, this approach facilitated the reliable annotation and classification of a previously uncharacterised kinase in four species of roundworm (phylum Nematoda) within the class Enoplea.Lastly, the integration of three-dimensional modelling data enriched the annotation of protein sequences, beyond what would have been possible using primary sequence- and sequence domain-based approaches. For example, in two recent kinome studies18 and Kinannote19). Importantly, although applied to kinases, our practical and transparent strategy should be broadly applicable to the comprehensive annotation of most other protein families.Taken together, the combination of a pairwise reciprocal curation approach, Kinannote, an orthology-/phylogeny-based classification, a domain-based curation and annotation strategy and three-dimensional modelling underpins our improved strategy for the characterisation of kinases and addresses salient limitations of other tools presently available  for de novo gene predictions  in genome projects of related species. This issue emphasises the need for improved gene prediction software , for the comprehensive curation of erroneous gene predictions  and importantly, for the integration of curated data into existing repositories.The improvement or correction of gene predictions represents an important component of any genome project, and has a substantial impact on subsequent analyses of genomic and transcriptomic data sets. Without iterative cycles of curation of existing genomic and transcriptomic resources, incomplete or incorrect gene predictions for a genome deposited in public repositories will most likely be perpetuated, as data will be utilised as protein evidence . This systematic evaluation was aided by manual confirmation of representative gene/transcript features (‘spot checking’) 51 was established in an earlier investigation28 and then automated and further improved in a subsequent study27. This improvement included an automated, global analysis of donor and acceptor sites of introns, which was then integrated into the gene prediction process in Exonerate, thus improving the accuracy of intron-exon boundary predictions. Although we employed Exonerate to re-predict gene models initially26, we carried out multiple individual runs of single genes that needed to be complemented based on manual inspection. These examples show how the curation and annotation strategy was successively adapted to tackle the technical challenges of multiple, distinct data sets, particularly with respect to the quality and amount of data available. The continuous growth of publicly available data sets and data repositories should enable the integration of additional data in the future, and highlights the need for reliable and reproducible gene prediction/annotation workflows that are able to process and analyse such data sets.For example, the prediction/correction of gene models based on transcript data employing CAP3 and Exonerateversus the time spent implementing the automation, and the accuracy and speed of the automated method versus that achieved by manual curation. It is also important to consider the size of the data set that is to be curated. For example, protein kinases represent a relatively small and well-defined data set (~200–500 sequences) compared with other assemblages of proteins , which allowed for the application of additional, manual curation efforts. Generally, automated steps should only be included in the next iteration of a tool or pipeline if they can be achieved without a loss of accuracy. Importantly, our focus has been to automate steps that, although being algorithmically less challenging , often represent steps in a workflow that are most prone to human error. In contrast, other steps proved harder to automate and were more reliable if implemented in a semi-automated manner, with guidance/intervention by an expert. This approach did not affect the ability to process the input in a computer-readable format and allowed decisions to be made and stored for future analyses, achieving a near-optimal or optimal solution after multiple iterations.Taken together, manual curation and systematic assessment of gene models informed the automation process. In this context, it is important to carefully consider the benefit gained from automating a particular step 74 should resolve genomic regions that could not be assembled previously using short-read technologies, thus significantly reducing the need for sequence curation. More contiguous assemblies would allow for improved identification, classification and functional annotation of inferred protein sequences.The integration of new types of data sets created using new technologies should allow improvements and expansions to the curation and annotation approach established. For example, recent advances in long-read sequencing technologiesOther improvements in functional annotation could also be achieved by employing a systems biology approach, integrating transcriptomic, proteomic and metabolomic data into the annotation process. For instance, information on pathways, essentiality and excretory/secretory (ES) proteins inferred from proteomic and metabolomic data sets could be integrated into the workflow, to add further experimental evidence to the computational predictions. However, the new, diverse and large data sets produced would need to be integrated into the existing data. Thus, experimental investigations should be followed by technical improvements and extensions. The establishment of a flexible, user-friendly and expandable platform that would facilitate the integration of such data sets would be desirable. Clearly, the present strategy provides a solid framework for further technical developments in this direction.28  search tool, which has been established and employed in recent studiesversus HMM” alignments (as implemented in the program HHblits75). However, such an expansion would require an adaptation of all third-party tools and the construction of customised databases of query sequence data sets  for use with HHblits. Given that “sequence versus HMM” searches perform very well for protein kinase catalytic domains, an upgrade to a “HMM versus HMM” alignment-based method might be considered when applying the present strategy to other protein families in the future.Furthermore, for all steps that employ HMM searches against sequence databases , there is also potential to increase search speed, sensitivity and accuracy by replacing the current search strategy with “HMM https://www.melbournebioinformatics.org.au/capabilities/), which led to a restriction of this approach to a subset of kinases28.In contrast, the structural prediction using I-TASSER could be readily automated and scaled to allow high-throughput analyses. The challenge here would be the requirement for substantial computing infrastructure and time. For example, computations of inferred kinase structures were run on an IBM iDataplex x86 system containing 67 nodes with 256 GB RAM and 16 cores per node  that must be run in succession by the user, which requires relatively advanced bioinformatic skills. The major advantage of a workflow that is separated into multiple well-defined steps, is that it provides flexibility that could not easily be achieved using a more restrictive , stand-alone version. Given that genomic and transcriptomic data sets of parasitic organisms are often highly diverse and that each project requires a careful assessment of data and subsequent selection of analysis strategies, an interactive, stepwise workflow represents a considerable advantage. To retain this flexibility and simultaneously create a more user-friendly application, a framework that allows the modularisation of individual steps into compatible and interchangeable components could be developed in the future. For instance, the module for kinase annotation could then simply be replaced with a module designed for a different protein group or family.Taken together, our current strategy employs third-party tools including InterProScan, OrthoMCL, BLAT, CAP3, Exonerate, hmmalignSER Fig.  and scri77; other less well-understood and studied classes might be more challenging. For instance, proteins of the cysteine-rich secretory proteins/antigen 5/pathogenesis-related 1 (CAP) superfamily 79 form an assembly of very diverse proteins with different domain architectures, which bears challenges regarding the functional annotation and classification of members of this superfamily79. For such a complex group of proteins, a library of subfamily-specific HMMs could be built based on functional domain architectures, as employed previously for atypical protein kinase families and subfamilies28. A module representing such a library could then be integrated into the established strategy. Taken together, we believe that the approach that we have established should be broadly applicable to a wide range of protein families.This extension might readily be achieved for well-defined enzyme classes such as phosphatasesThe bioinformatic strategy described and discussed here achieves comprehensive curation of kinase gene families and reliable annotation of the proteins that they encode, providing a powerful platform to explore the fundamental kinase biology of eukaryotic organisms and evolutionary relationships. Establishing a modular, robust and reproducible workflow that is generally applicable to the analysis of a wide range of protein families, from any organism, will likely be a useful tool to support systems biological investigations.https://github.com/vetscience/kinase-gene-curation.All computer code and data associated with this work is available from