Prediction of protein complexes from protein–protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction.To address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P: (i) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (ii) outperforms the existing solutions with respect to a composite score of five performance measures on 75% and 100% of the analyzed PPI networks and gold standards in yeast and human, respectively, and  does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks.https://github.com/SaraOmranian/PC2P.Bioinformatics online. Escherichia coli (Saccharomyces cerevisiae (yeast) (Homo sapiens (human)   to Homo The increase in size and quality of PPI networks is paralleled by the development of algorithms for mining of these networks. In particular, there has been a considerable interest in design of algorithms for prediction of protein complexes based on PPI networks , largely due to the assumption that protein complexes correspond to dense subnetworks . MoreoveThis brief summary of the state-of-the-art approaches for protein complex prediction based on PPI networks points at the need to formalize the problem of identifying protein complexes so that both sparse and dense subnetworks can be considered. Moreover, the existing network clustering approaches depend on multiple parameters, and it is challenging to optimize their values on different network topologies and gold standards. Further, the optimal values of different parameters may result in drastically different predictions of protein complexes. Therefore, it is critical that the formalization of the problem of protein complex prediction is parameter-free. It is also important to ensure that the novel formalization extracts clusterings of local structures with large cluster quality measures. This will allow us to determine the effect of the network structure on the prediction of protein complexes.Our contribution is fourfold: (i) we formalize the concept of a protein complex as a biclique spanned subgraph, that addresses the density issue of the existing approaches; (ii) we propose a parameter-free approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), that solves the network partitioning into biclique spanned subgraphs by removing the minimum number of edges in a given PPI network; (iii) we demonstrate that the resulting clusterings are of high modularity, thus reflecting the local structures in the network and (iv) we provide a thorough comparison with nine seminal unsupervised approaches for protein complex prediction and show that our biclique spanned subgraph partition outperforms them with respect to twelve established performance measures and composite score combining five of these measures.https://www.comp.nus.edu.sg/∼wongls/projects/complexprediction/CMC-26may09/, http://cfinder.org/ and https://faculty.uaeu.ac.ae/nzaki/Research.htm. Similarly, we used the implementation of Core and Peel and IMHRC which are available at http://bioalgo.iit.cnr.it/ppin/index.php?page=app&id=572664770 and http://www.eslahchilab.ir/softwares/cdap, respectively. Since all of these approaches depend on multiple parameters, we have used their default values. Optimizing the parameters is challenging since they depend on both the networks and gold standards used as well as on the objective to be optimized. Finally, optimization of different performance measures yields different clusterings (i.e. predicted protein complexes), rendering it impossible to do meaningful interpretation and combination of the findings.We compared the performance of our approximation algorithm with nine contenders, including: Markov Clustering (MCL) , CFinderTo test our proposed approach and compare its performance with contending algorithms, we used PPI networks and gold standards of protein complexes from yeast and human. For yeast, we used four PPI networks, including: Collins (≥ 0.101) , and com+, recall+ and F-measure+ from The predicted complexes based on the clusterings obtained from the computational approaches, above, were compared with the complexes in the gold standard based on twelve performance measures: sensitivity, positive predictive value, accuracy and separation from SN The sensitivity  . Thus, tA coherent network partition in a graph aph in G . A netwoGiven a graph  NP-hard . Thus, iGiven a graph nts in G .To obtain a biclique spanned subgraph in ed score , we firsproblems . To speem degree , to rendprocedure Find_Coherent_Partition 6:       edge_cut7:    Update connected_components8:    Update return (edge_cut)9: procedure CNP (cmp)1: cmp2: complement_3: if complement_disconnected then4: cut_ratio cmp5:   return 6:   else7: cut_node complement_8:   remove cut_node from cmp9:   cut_ratio cmp sub-graph10:  return 11:  The complexity of finding a complement of a graph on There are two other algorithms for graph clustering based on iterative determination of min cuts, but they do not operate in the complement of a given graph and have not yet been applied to the problem of protein complex prediction. The algorithm of of nodes . In contWe inspected the empirical running time on the four PPI networks of yeast and the two PPI networks of human, as specified in the methods see . We founTo inspect the performance of PC2P with respect to structural, functional quality and biological relevance of the resulting clusters, we determined the modularity, the GO semantic similarity and standard enrichment score of the clusterings in four PPI networks for yeast, i.e. Collins, Gavin, KroganCore and KroganExt, and two for human, i.e. STRING and PIPS. We compared the performance of nine other approaches, i.e. MCL, MCODE, CFinder, AP, CMC, ClusterOne, ProRank+, Core&Peel and IMHRC (see Section 2.1) with respect to these three performance measures (see Section 2.3).or the yeast PPI networks, we found that PC2P results in the largest modularity with respect to nine contenders for three out of four networks irrespective of whether or not cluster overlap was considered . For theWe also calculated the modularity of the clusterings obtained when only the proteins in two yeast gold standard datasets of protein complexes, CYC2008 and SGD, were considered . Our finWith respect to the functional quality of the clusterings, we determined the minimum and mean semantic similarity for every pair of proteins in each predicted complex (i.e. clusters) and compared the distribution of these values over all clusters for the nine contenders. For the minimum GO semantic similarity, in the four yeast PPI networks as well as in the two human PPI networks, we found that all of the approaches resulted in largely overlapping distributions of GO semantic similarity over the respective clusters for the three GO categories, i.e. biological process (BP), cellular component (CC) and molecular function (MF) . NeverthInterestingly, the same conclusions can be drawn when analysing the distributions of mean GO semantic similarity over all clusters: all approaches, except for AP, exhibited comparable distributions, with the medians of Core&Peel and PC2P ranked highest . These findings hold for both yeast and human PPI networks, demonstrating that PC2P performs as well as the existing approaches in capturing functional quality of the resulting clusters.We also conducted ANOVA analyses to test if PC2P results in smaller average GO semantic similarity in comparison to the contending algorithms. To summarize the GO semantic similarity of a cluster we considered the minimum and the mean semantic similarities. For the ANOVA with the minimum semantic similarity of each cluster, from 6 comparisons in each of the 4 yeast and 2 human PPI networks for the MF GO category PC2P has either the same or greater average in comparison to the other approaches. For the BP and CC GO categories, PC2P exhibits a smaller average in 1.85% and 0.37%of the comparisons, respectively, i.e. BP semantic similarity of Core&Peel and ProRank+ on Gavin and KroganExt and MCODE on the KroganCore PPI network as well as CC semantic similarity of Core&peel on KroganCore PPI networks . For theP-value adjustment method with a significant level of 0.05. We compared the average of predicted complexes with at least one enriched annotation over all clusters for the nine approaches across all datasets . We used the enrichGo function in the clusterProfiler package and selected Benjamini-Hochberg as a datasets . The GO datasets .+, recall+ and F-measure+ (see Section 2.2). In addition, we calculated a composite score that combines MMR, FMR, SEP, ACC and F-measure for each of the ten compared approaches. Furthermore, we calculated the MMR and F-measure+ over different Next, we employed the two gold standards for yeast and the one for human with the respective PPI networks to compare the performance of PC2P with respect to its ability to accurately predict protein complexes. To this end, we determined twelve performance measures, including: maximum matching ratio (MMR), fraction match (FRM), separation (SEP), positive predictive value (PPV), Sensitivity (SN), accuracy (ACC), precision, recall, F-measure, precision+ in 7 combinations. Moreover, PC2P resulted in the highest recall in all 8 combinations, as high as 71% and 69% in Collins and KroganCore PPI networks with CYC2008 gold standard  in majority of examined datasets, demonstrating that PC2P offers a parameter-free means to overcome the key shortcoming of the existing approaches. Finally, we also observed that the distribution of cluster sizes resulting from PC2P does not differ with respect to the compared approaches: For all algorithms, except ClusterOne, Core&Peel and IMHRC, the cluster size distributions are monotonically decreasing . This finding indicates that the improvements of performance in predicting protein complexes by PC2P are due to the formalization of the protein complex as a biclique spanned subgraph rather than the resulting distribution of cluster sizes.Future studies will aim on one hand to speed up the running time of the implementation of the algorithm, by exploring the properties of the second neighborhood of a node; on the other, additional research efforts will be aimed to identify the connection between single gene and whole genome duplication in driving the formation of biclique spanned subgraphs underlying the predicted protein complexes. Finally, we note that the current formulation of PC2P is not applicable for detection of overlapping clusters and weighted networks, and future work will address these shortcomings to improve the versatility of the presented formulation of protein complexes in terms of bicliques.btaa1089_Supplementary_DataClick here for additional data file.


These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein–ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein–ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of 0.6 million mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein–ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is open source and freely available at  Over the last 50 years, pharmacogenomics has studied the genetic basis for inter-individual drug response variability . Many faMutations that occur in the binding site of a target protein may change the protein–ligand binding affinity, which can lead to a substantially different phenotype resembling lower efficiency of the drug or higher off-target binding affinity that could lead to side effects . NeverthSeveral studies –23 tacklhttps://psnpbind.org) to address the lack of large datasets about the binding affinity of binding site mutated protein-ligand complexes and to provide a base for new studies in related fields like drug discovery, pharmacogenomics, and structural bioinformatics.For those reasons, we decided to develop the PSnpBind database , ligands structures and protein-ligand complexes structures and relevant information. Figure General set: all protein–ligand complexes from PDB.Refined set: a standard data set for docking and scoring studies.Core set: includes high-resolution crystal structures and reliable binding constants.Building the PDBbind database included collecting all the complexes from PDB without performing structural optimization or any type of transformation on the coordinates. Binding affinity data (dissociation constant (Kd), inhibition constant (Ki), and concentration at 50% inhibition (IC50)) were collected from the primary references of the deposited entries. The authors applied a priority order of Kd > Ki > IC50 when more than one value appeared in the references, and they only recorded the data with the highest priority. Also, in case of binding affinities measured under different conditions (temperature and pH) for any complex, only the results measured at neutral pH and room temperature or in assay conditions close to that were recorded.PDBbind  providesPDBbind also provides a residue-level annotation for the amino acids involved in the binding pockets of the protein-ligand complexes. It is a crucial piece of information for our project to map the missense SNPs onto the binding pocket residues.We chose to use the PDBbind core set version 2016  in this work mainly because it is a concise version of the refined set where all redundancy is eliminated. Besides, the size of the dataset is smaller and more practical to work with. CASF 2016 was downloaded on October 6th, 2019, from the PDBbind website, and contained 285 high-quality protein–ligand complexes, out of which 123 complexes are for human proteins.UniProt  was usedSEQRES record in the PDB entry: the complete sequence of the protein used in the experiment.ATOM record in the PDB entry: the observed residues in the crystal structure.This dataset is a vital resource in this project in order to be able to map the protein-altering variants in the protein sequence to the binding pocket residues in the crystal structure.SIFTS  is a project in the PDBe-KB resource for residue-level mapping between UniProt and PDB entries . It is aChEMBL is an open database that contains functional, binding, and ADMET information for many drug-like bio-active compounds (about two million compounds) . It is mThe created library was used to perform molecular docking against the proteins selected from PDBbind with their mutated version to obtain a reference dataset large enough to train a machine learning model on it. ChEMBL version 25, released on February 1st, 2019, was used in this work.To build the dataset of mutated protein-ligand complexes, we needed to identify the natural mutations occurring in binding pockets of human proteins selected from CASF. The next sections describe the process in detail.First, the PDBbind dataset was downloaded and filtered to include only high-resolution structures obtained with X-ray crystallography . Therefore, entries were aggregated by UniProt ID, and the proteins with the highest resolution were selected from each group. Each ligand that shares a complex with those proteins was also stored.Next, the variant data from UniProt was downloaded and filtered to only include missense variants. Then, by doing an “inner join” over the UniProt ID column between the UniProt variants and the PDBbind entries selected from the previous step, only those missense variants occurring in the proteins of the PDBbind entries list were selected. All the duplicates were removed, and the joining resulted in 11749 missense variants belonging to 26 proteins (26 unique UniProt IDs). Since the variants dataset only contains human variants, the resulting dataset only contained human proteins with their variants. Table After that, the SIFTS mappings for the 26 human proteins were downloaded from the PDB website. Next, the BioJava  library The residues where the mutations occurred were further analyzed to understand their nature by assigning the wildtype and the mutation amino acids in each residue location to one of the seven functional categories . Next, an UpSet plot was generated to quantify the frequency of the mutation types (transition from one group to another) across the 26 proteins in the dataset. The UpSet plot was generated using the ComplexHeatmap R package  as shownFigure After finding the missense variants in the binding pockets of the selected proteins, mutations were introduced to the protein structures using FoldX version 5.0 , one of LA92F;FA93V;IA96V;GA101C;GA101S;RA137C;RA137G;RA137H;GA140A;The FoldX “BuildModel” command takes the PDB structure file as input besides a text file named “individual_list.txt” containing the mutations wanted to be introduced to the structure separated by a comma. The syntax of representing the mutation in FoldX is . In our case, we only need to introduce a single mutation at a time, so we do not have comma-separated values. For example, using the variant information from Table The next step is energy minimization (EM) on the mutated protein structures. Gromacs version 2019.3  with CHAThe protein structures were solvated in a cubic box of TIP3P water molecules at a distance of 1.2 nm (12 Å) from the solvent. The system’s net charge was neutralized by adding enough ions in correspondence to the type and amount of the protein charge. A cutoff of 1.2 nm for both short-range van der Waals and electrostatic interactions was used, and PME  was used for long-range interactions in all minimizations.2+) can be coordinated in a small protein structural motif, called “zinc finger,” which can be found in protein binding pockets in order to stabilize the fold.For structures that contained ions in the binding site, those ions were added and position restraints were applied to the protein structure. Metal ions play important roles in biological processes like respiration and the structural stability of protein folds . For exaEnergy minimization was performed using the steepest descent algorithm. A maximum force of 100 kJ/mol/nm on any atom of the system was set as the end goal for convergence. A maximum number of 50000 steps of minimization was used. After EM, a potential energy plot was generated for each simulation to examine how the minimization went.http://manual.gromacs.org/documentation/2019/manual-2019.pdf), and several settings and configurations were examined from the literature [Lastly, simulation trajectories were exported to the final PDB files using the “trjconv” program in Gromacs. Figure terature –48.Fig. A library of chemical compounds needs to be created to carry out a docking experiment against the mutated proteins. This library needs to be large enough to build a reference dataset of mutated protein–ligand binding affinities. Since the set of ligands binding to the selected proteins from PDBbind is too small to train a robust machine learning model that covers a wide range of ligands, such a library is needed.ChEMBL  was chosNext, for each group of ligands belonging to a selected PDBbind entry, a similarity search was performed against ChEMBL to select similar compounds to each one of them. A similarity threshold (Tanimoto index) of 0.6 was chosen. The rationale behind choosing a low threshold is the need for compounds with a low similarity that will probably result in a low binding affinity. Including low binding affinities helps to cover a wider distribution of binding affinities which can be used to train machine learning models for binding affinity prediction applications. The Tanimoto index is a popular similarity coefficient used to measure the similarity between pairs of molecules . The TanNext, the similar molecules collected as a single file were split into one file per molecule, and converted to mol2 format needed for molecular docking. After that, the resulting molecules were energy minimized with OpenBabel using the MMFF94 force field . The eneModulating the function of proteins by small molecules has been an active research area with applications in drug design and development. To quantify the binding of a ligand to its target protein, a commonly used measure is their binding affinity, which describes how strongly the ligand binds to its biological counterpart. Binding strength can be measured experimentally by Microscale thermophoresis  , 53, NucPredicting interactions between ligands and proteins is a crucial element in the drug discovery process , 58. In The molecular docking was performed using AutoDock Vina software . AutoDocm SMILES   to ensure better coverage of the search space and the binding poses. The docking experiment’s implementation used both internal and external parallelization by utilizing multiple cores (12 cores) for the single docking experiment provided by the “cpu” configuration parameter, and running multiple dockings in parallel at the time as recommended by an extensive study on AutoDock Vina in [The configuration file contains coordinates and dimensions of the grid box where the docking will take place. The grid box, when the binding pocket is known, should encapsulate the binding pocket and cover the entire cavity to where the ligand may bind. Defining the grid box was implemented programmatically by reading the coordinates of the binding pocket residues (since the pocket is annotated in PDBbind). Next, the coordinates of the grid box corners were defined to include all the atoms of all pocket residues. The center of the search space was set to be the center of the grid box. None of the grid boxes exceeded 27000 Å Vina in .https://maastrichtu-ids.github.io/dsri-documentation/) (128 CPUs each). The time required for each docking was recorded along with CPU usage. Table Since Vina uses a stochastic search method, random numbers are used in the process. Therefore, a seed was used to allow reproducibility. Also, the same seed was used for all performed docking experiments. The same parameters were also unified to ensure maximum reproducibility on the computing platform used for conducting the experiment. To test if the seed has an effect on the docking results, 10 random seeds were tested by performing the docking on the 26 selected protein–ligand CASF entries and calculating the correlation with the experimental values for each docking set. The evaluation didn’t result a considerable change in the docking behaviour. The dockings were performed using ten nodes in the data science research infrastructure provided by our institution .The PSnpBind front-end Fig.  is impleThe search function aims at enabling the user to find or filter the ligands bound to one of the PSnpBind database protein structures based on an input string. The users can search using criteria like CHEMBL ID (of the ligand). String search uses native MySQL regex matching functionality, allowing flexible search in all the columns corresponding to the search criteria. The input string is first wrapped in a regular expression, then the DB is queried against the appropriate fields, and the results, if exists, are sent back to the front-end.https://psnpbind.org/swagger-ui.html). The PSnpBind supports the following endpoints:A RESTful API is also provided to the users of PSnpBind web application to obtain information about proteins, mutations, ligands and binding complexes (dockings) that are hosted in the database. The API returns JSON objects with a structure corresponding to the entity in question. The API follows the OpenAPI/api/v1/protein/list/api/v1/protein/PDB_ID/api/v1/protein/uuid/PROTEIN_UUID/api/v1/protein/PDB_ID/variants/api/v1/variant/VARIANT_ID/api/v1/variant/uuid/VARIANT_UUID/api/v1/ligand/CHEMBL_ID/api/v1/ligand/uuid/CHEMBL_UUID/api/v1/variant/VARIANT_ID/ligand/CHEMBL_ID/api/v1/docking/VARIANT_LIGAND_UUIDPSnpBind web interface gives users the ability to visualize every protein–ligand docking complex from the constructed dataset using Jmol , an openPSnpBind was built with FAIR principles in mind from the ground up. All the FAIR principles were addressed as far as possible. Also, structured markup using JSON-LD is provided for all the pages of proteins and ligands in the web interface. The Bioschemas.org vocabulary  was usedPSnpBind is a large database of protein–ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape. This database can be used as a source of data for different types of studies, for example, developing machine learning algorithms to predict protein–ligand affinity or SNPs effect on it which requires an extensive amount of data with a wide coverage of mutation types and small molecules. Also, studies of protein-ligand interactions and conformer orientation changes across different mutated versions of a protein can be established using data from PSnpBind. Furthermore, it provides multiple ways of accessing the data through direct download, a web application and a REST API, all provided in compliance with the FAIR principles. PSnpBind is a valuable resource for new studies in related fields like drug discovery, pharmacogenomics, and structural bioinformatics.


Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies. Proteins rarely act as isolated species to perform their biological functions . ProteinElucidation of PPIs is not trivial as many complexes are involved in diverse biological functions and the component proteins may be interconnected in multiple signaling pathways. Experimental techniques to detect PPIs integrate methods such as yeast two-hybrid (Y2H) screening, fluorescence resonance energy transfer (FRET), atomic force and electron microscopy, surface plasmon resonance, and tandem mass spectrometry (MS) . MS-baseA variety of bioinformatics methods are currently available to identify and quantify peptides and proteins obtained from MS-based proteomics experiments , providiHere, we present an interactive web server called PPI-MASS, which has been designed to identify putative proteins interacting with a target protein. Though a computational pipeline, information is extracted for a defined protein set, integrating sequence and functional data from UniProtKB , structuhttps://minicad.appsbio.utalca.cl/ppi-mass. The workflow of the web platform is shown in PPI-MASS is a computational pipeline to obtain biological information of a large protein set, obtained ideally from MS-based proteomics data, in order to identify protein-protein interactions. To make PPI-MASS open access, a web server was developed, which is currently accessible at User Input. To start its operation, PPI-MASS requires inputs as 1) a target protein specified by its identifier in UniProtKB and 2) a list of proteins associated with the target protein, ideally obtained as a result of MS-based proteomics analyses. The protein set may be entered directly into the text box using their UniProtKB identifiers separated by comma or by uploading a flat-text file with the data separated by line breaks. A 3) job name is necessary to label the processed data, whereas a 4) e-mail address is required to send a link to access the results. Since PPI-MASS does not require any user registration, providing a correct e-mail address to contact the user is highly recommended. An example option to test for proper operation of the web server is provided by pressing “Load example data”.Pipeline execution. Upon submission, the jobs are entered into a queuing system and a universally unique identifier (UUID) is assigned, which may be used later to search the job results. Once ready, the protein set associated with the target protein is analyzed. For each protein, biological information is collected from seven different public databases. The first pipeline step consists in extracting from the UniProtKB database  and the Transmembrane Serine protease 11A (TMPRSS11a).TRPM4. This protein corresponds to an Ca2+-activated non-selective cation channel expressed in several tissues and involved in a variety of physiological processes, including cell migration and contractility (γ) isoform was found, which has been previously reported as a binding partner for TRPM4 affecting its localization at the plasma membrane (γ interaction has been corroborated through co-immunoprecipitation and bimolecular fluorescence complementation (BiFC) assays, demonstrating that both proteins are located in close spatial proximity. In addition, mutations on the S88 residue of TRPM4 revealed a significant reduction in the 14-3-3γ binding, indicating a key role of this residue for the protein-protein physical association. Identifying this particular complex validates the functionality of our web platform to detect protein-protein interactions that may be used as therapeutical targets.actility . Since Tactility . Therefoactility . A set omembrane . The TRPTMPRSS11a. This protein is a Type II transmembrane serine protease (TTSP) participating in diverse biological processes. As reported recently by our group, a differentiated expression of TMPRSS11a with age was identified in gingival and skin fibroblasts, which has been associated with senescence processes and wound healing (α5β1 integrin. Proximity ligation assays (PLA) were used to validate close proximity between TMPRSS11a and beta-1 Integrin, whereas immunocytochemistry experiments revealed co-localization of both proteins at the plasma membrane. healing . Moreove healing , which wProtein-protein interactions play critical roles in different physiological processes of a wide variety of proteins. Therefore, identifying binary protein-protein complexes has become an important approach to study associated diseases. In this regard, the PPI-MASS web server represents a powerful tool for an automatized analysis of MS-based proteomics data, obtaining key information of a protein set associated with a target protein to identify protein-protein interactions with therapeutic potential. As demonstrated in the two case studies presented here, PPI-MASS has been an efficient and useful method to detect PPIs in our group, and it could be of great benefit to the scientific community working in this area.It should be noted that PPI-MASS is an ongoing project, and further developments will be focused on the incorporation of new parameters to predict protein-protein physical contacts, once a binary complex is identified. As is shown in


The study of protein–protein interactions (PPIs) is fundamental in understanding the unique role of proteins within cells and their contribution to complex biological systems. While the toolkit to study PPIs has grown immensely in mammalian and unicellular eukaryote systems over recent years, application of these techniques in plants remains under-utilized. Affinity purification coupled to mass spectrometry (AP-MS) and proximity labeling coupled to mass spectrometry (PL-MS) are two powerful techniques that have significantly enhanced our understanding of PPIs. Relying on the specific binding properties of a protein to an immobilized ligand, AP is a fast, sensitive and targeted approach used to detect interactions between bait (protein of interest) and prey (interacting partners) under near-physiological conditions. Similarly, PL, which utilizes the close proximity of proteins to identify potential interacting partners, has the ability to detect transient or hydrophobic interactions under native conditions. Combined, these techniques have the potential to reveal an unprecedented spatial and temporal protein interaction network that better understands biological processes relevant to many fields of interest. In this review, we summarize the advantages and disadvantages of two increasingly common PPI determination techniques: AP-MS and PL-MS and discuss their important application to plant systems. The study of biomolecular complexes is crucial in understanding the molecular mechanisms underpinning biological processes, protein function and subcellular protein localization ,2,3,4. BThe field of proteomics has witnessed the development of many innovative methods for the identification and characterization of PPIs ,3,4. As Recent studies highlight significant progress in the use of affinity purification and proximity labeling approaches combined with MS-based quantitative proteomics in studying PPIs ,13,14,15Both AP-MS and PL-MS are powerful techniques that have significantly enhanced our understanding of PPIs. While these methods have become increasingly popular in animal systems, application of these techniques in plants remain underutilized. Combined, AP-MS and PL-MS have the potential to reveal an unprecedented spatial and temporal protein interaction network that better understands biological processes relevant to many fields of interest. For example, AP-MS can be theoretically used to detect transient PPIs as well as interactions involving potentially insoluble proteins such as membrane-associated proteins. Furthermore, PL-MS has the potential to detect hydrophobic interactions under native conditions and has been recently used to investigate membrane contact sites between the endoplasmic reticulum and mitochondria in plants . In thisSimilar to immunopurification or immunoprecipitation (IP), AP utilizes antibodies which can be targeted to the bait, or to a standardized fusion moiety often referred to as an epitope tag . Using pGiven the increased sensitivity of MS and the application of novel bioinformatic approaches for accurate data analysis, affinity-based methods have improved considerably in recent years ,29. WhilArabidopsis plant cell culture system has been developed for TAP technology which allows for the high-throughput identification of protein complexes, even with very low sample volumes  [Nicotiana tabacum). Following an initial affinity purification step with immunoglobulin G agarose beads, protein complexes can be incubated with the tobacco etch virus protease to release the complex from the matrix. In a subsequent purification step, the bait protein complex associates with a streptavidin-conjugated bead trap. Following several washing steps, the protein complex is eluted and determined by LC-MS (yellow) has been developed that combines the fluorescent properties of citrine YFP with a streptavidin-binding peptide tag. This double affinity tag can not only be used to determine the subcellular localization of proteins in vivo but also the potential function of the protein through AP [The main disadvantage of AP-MS however, remains in the ability to fully characterize affinity matrix/epitope tag interaction properties. The identification of non-specific bound proteins is one of the main disadvantages of a single-step purification approach and contaminant proteins associated with either the solid-phase or the epitope tag are hard to distinguish from positive interactors. Thus, the use of proper negative controls such as protein extracts from wild-type plants, mutant lines, or tag-only expressing plants is critical . In prinprotein) . A GS tarough AP .Arabidopsis plant cell cultures [The strength of AP-MS is that it can be used to study PPIs in their relevant plant growth and development biological contexts. For example, studies on specific plant organs including leaves , flowerscultures ,39,40. Scultures . These mcultures . Furthercultures ,22,42.Chemical cross-linking is a classical approach which is used to freeze PPIs in their native form and has been shown to be especially useful for capturing transient and weak PPIs. For example, membrane protein interactions have been detected in vivo by cross linking with formaldehyde . In the Arabidopsis, stable isotope labeling combined AP-MS has been used to quantitatively investigate the B-box protein complex, involved in integrating light and hormone signaling pathways during photomorphogenesis from non-specific background proteins [www.araport.org, accessed on 28 April 2021). Assuming that the enrichment of most proteins (including non-specific background proteins) is kept constant by the design of the experiment, the algorithm promotes the investigation of proteins that are differentially enriched under the tested conditions [Zea mays) leaves, growth-regulating factors have been shown to interact with Angustifolia 3 in the division zone, while this interaction was significantly lower in the expansion zone of the same leaves [Two analytical strategies that can be applied to detect dynamic associations of protein complex partners include label-free quantification (LFQ) and stable isotopic labeling. Stable isotope labeling combined with AP-MS has been successfully used to follow the temporal dynamics of PPIs throughout the cell cycle  and to iproteins . Due to proteins . In contproteins . LFQ-basproteins , so alloproteins  by firstnditions . Moreovee leaves . Anothere leaves .Arabidopsis flower development [A high-performance affinity enrichment approach for mass spectrometry (AE-MS)- is a technique that combines AP-MS and LFQ and has become an effective method to determine positive PPIs from false positive interactions . Insteadelopment , vasculaelopment  and a glelopment .PL-MS is a high-throughput approach for the systematic analysis of PPIs in vivo. While PL-MS is already firmly established in mammalian and unicellular eukaryote systems, application of this technique in planta remains challenging. PL utilizes enzymes that produce reactive molecules that covalently interact with proteins in close proximity. Labeled proteins can be isolated using conventional affinity purification methods and identified via immunoblot analysis or by protein mass spectrometry, Proximity labeling overcomes some of the limitations of AP-MS and Y2H, as abundant soluble proteins as well as insoluble membrane proteins can be effectively enriched under stringent denaturing conditions, which in turn, facilitates their identification. PL can detect weak, transient or hydrophobic PPIs in their native state and provides an unedited spatial and temporal protein interaction network for better understanding of a specific biological process. In addition, fusion of PL enzymes to a minimal targeting motif that restricts proteins to a particular subcellular location or structure, can be used to map the protein population therein . While aEscherichia coli enzyme, BirA. First reported in 2014, BioID relies on the promiscuous activity of a modified BirA protein (mutation of R118G) that releases highly reactive and short-lived biotinoyl-5′-AMP and can modify proteins within a distance of 10 nm [Aquifex aeolicus, has also been produced with the advantage of maintaining the correct localization of a fused protein and requires less biotin for labeling compared to BioID [PL-MS has emerged as a powerful tool to characterize PPIs. Over recent years, this technology has grown immensely with the development of new PL enzymes and the application of PL in studying protein interaction networks (including protein-DNA and protein-RNA interactions). Currently, enzyme-mediated activation of radical sources (EMARS), engineered ascorbate peroxidase (APEX) and proximity-dependent biotin identification (BioID) are three commonly used PL technologies . As BioIto BioID .2O2 and biotin-phenol at 37 °C [2O2 and biotin-phenol on living cells [Similar to BioID, APEX is a 27 kDa monomeric protein which biotinylates prey near the target protein when supplied with ATP, Hat 37 °C . The advng cells . Compareng cells . The twong cells ,67. Furtng cells .Arabidopsis, tomato root cultures and N. benthamiana [PL technology overcomes several limitations of traditional interaction detection approaches and has been widely used in different biological contexts to highlight different molecular interactions. Furthermore, PL methods demonstrate its potential in detecting interactions with rapid kinetics ,69. Sevethamiana ,75,76,77thamiana .The use of PL methods is being increasingly applied to different fields of research. This is due to their accessibility, simplicity, and most importantly, potency in probing transient or weak PPIs as well as membrane bound proteins or proteins of low abundance. When performing PL, the first and perhaps most critical step is to choose the enzyme appropriate for ones needs. Secondly, researchers should make sure that the fusion of the PL enzyme to a bait protein does not interfere with its localization or its functions. Lastly, it is important to include appropriate controls to minimize false positives or false negatives. The emerging developments in PL technology provide an incredible opportunity to profile dynamic interaction networks under different conditions, thus offering a global vision of the entire cellular functions, which will greatly advance our understanding of various biological processes.While AP-MS results in the identification of proteins that form stable complexes, PL enables the identification of proteins that are in close proximity to the bait, which results in overlapping yet distinct protein identifications. By integrating AP- and PL-MS data, one has the ability to comprehensively characterize a protein’s molecular context and so several combined AP and PL experiments have been trialed. Enzyme combinations allow for both AP-MS and BioID analysis within a single construct and with almost identical protein purification and mass spectrometry (MS) identification procedures such as FLAG-BirA* tag ,79, MultThe study of PPIs is a rapidly evolving field. AP-MS and PL-MS possess different specificities that can be used according to the type of interactions studied. Moreover, every method has its own strengths and weaknesses. In the future, it is likely that new enzymes will be developed and current systems such as BioID and PL will be further optimized to enhance the applicability of such methods. For AP based methods, the most relevant improvements will be in the reduction of contaminants through new digestion/purification procedures. Another goal will be the extension of their use to different subcellular environments such as vacuoles or peroxisomes, as well as application of these methods in plant species other than in model species ,77,80. A


Escherichia coli (LTEE). Specifically, the density of all observed mutations per gene, as measured in metagenomic time series covering 60,000 generations of the LTEE, significantly anticorrelates with mRNA abundance, protein abundance, and degree of protein–protein interaction. The same pattern holds for nonsynonymous mutation density. However, synonymous mutation density, measured across the LTEE hypermutator populations, positively correlates with protein abundance. These results show that universal constraints on protein evolution are visible in data spanning three decades of experimental evolution. Therefore, it should be possible to design experiments to answer why abundant proteins evolve slowly.Although it is well known that abundant proteins evolve slowly across the tree of life, there is little consensus for why this is true. Here, I report that abundant proteins evolve slowly in the hypermutator populations of Lenski’s long-term evolution experiment with  A universal evolutionary pattern is that highly abundant and highly interacting proteins evolve slowly. This pattern was discovered in analyses that cover millions of years’ worth of sequence variation, so it is not clear how long it takes  for such patterns to emerge. Here, I report that this universal evolutionary pattern emerges in metagenomic data that cover just 30 years of experimental evolution.One consequence of the high complexity and intricate functional organization of organisms is that most mutations are deleterious. Natural selection resists the loss of function and fitness caused by mutation accumulation over time . This prEscherichia coli, often called the LTEE for short  mutations are considered . However, the weak positive correlations between mutation density and PPI degree in the nonmutators are not significant (I compared the number of protein–protein interactions (PPI) with the density of observed mutations across LTEE populations for every protein-coding gene in the  E. coli , which Inificant  online.I show that a number of well-known but poorly understood correlations between mRNA abundance, protein abundance, PPI degree, and evolutionary rates across the tree of life are also found in the hypermutator populations of the LTEE. In some cases, I find significant anticorrelation between mutation densities and mRNA abundance in exponential phase, but not during stationary phase. The simplest explanation for this finding is that mRNAs decay more rapidly than the proteins they encode. Protein abundance consistently shows a negative correlation with the density of all observed mutations  and withIt is widely believed that these correlations are driven by purifying selection on universal aspects of protein evolution , and indAn important limitation of these results is that the protein and mRNA abundance data for LTEE strains were collected in DM500 and DM4000 media . These mThe causes for why highly abundant proteins evolve slowly may emerge from a number of different, and nonmutually exclusive phenomena, so many explanations have been proposed . These icauses of patterns that span millions of years of protein evolution. I do not draw conclusions about the causes of these correlations. Rather, my results show that evolution experiments are reasonable model systems to study the causes of evolutionary rate variation in proteins. A concrete approach would be to recode the genomes of hypermutator strains to modulate the anticipated action of purifying selection per protein, based on the predictions of a particular explanation, and then ask whether those predictions are borne out during experimental evolution. Breakthroughs that allow for the inexpensive recoding of whole bacterial genomes may be needed, but it is plausible that such experiments will be feasible in the future.Differentiating among these possibilities is difficult, because it is challenging to study the Many other experimental directions are possible. First, a better understanding of how chaperones and other molecular mechanisms of protein quality control affect evolutionary rates and fitness  is needehttps://github.com/benjaminhgood/LTEE-metagenomic. Transcriptomic and proteomic data for REL606, grown in Davis minimal media with 500 mg/l glucose (DM500), were taken from the supplementary tables for https://github.com/shahlab/LTEE-gene-expression. I analyzed three different data sets of PPI in E. coli. First, I used the PPI network for E. coli K-12 MG1655 in the STRING database (E. coli PPI interactions reported by E. coli reported in the Ecocyc and Protein Databank (PDB) databases (E. coli PPI interactions and protein abundance were downloaded using the web interface to the ProteomeVis database (http://proteomevis.chem.harvard.edu/. Associated metadata for ProteomeVis were downloaded from: https://github.com/rrazban/proteomevis/blob/master/make_database/proteomevis_inspect.csv.Preprocessed LTEE metagenomic data were downloaded from: database  as curatatabases . PPI netatabases . Third, database , availabAll statistical analyses involve two-sided tests for Spearman correlation coefficients that are significantly different from zero, using the cor.test function in the R statistical programming language, version 4.0 (Genome Biology and Evolution online.evab070_Supplementary_DataClick here for additional data file.


Spiders use them to catch their prey, plants rely on them to fix carbon and mammals need them for eye vision—proteins.Proteins play critical roles in nature, and not surprisingly, synthetic biologists heavily rely on their functional diversity to build new therapeutics , catalysThe desire to optimize a protein’s function has always been a centerpiece of synthetic biology, and for decades, protein engineers have innovated the capacities of directed evolution  and ratiThe function of a protein is dictated by its amino acid sequence, and protein scientists picture the relationship between sequence and function of a protein as if it was a rugged landscape with shallow hills and high peaks, separated by valleys . ValleysRecently, researchers have started to perform this walk through a protein’s sequence space computationally, using deep learning . AlthougInstead of learning the fitness landscape of an individual protein from experimental data, they first built a deep learning algorithm that extracts the fundamental features of all functional proteins from the >20 million available unlabeled amino-acid sequences in the UniREF database . As suchinsilico–directed evolution and suggests protein sequences with various user-defined mutational loads that are likely better performing than the wild-type.Eventually—after having learned from all the available data—the algorithm only requires very few experimental data points to learn a good representation of the protein of interest’s sequence-function landscape: 24 or 96 functionally characterized mutants. The combined algorithm then performs The researchers showed with two evolutionary and functionally different model proteins—avGFP (eukaryotic fluorescent protein) and TEM-1 ß-lactamase (prokaryotic enzyme)—that 5–65% (avGFP) and 2.5–26% (ß-lactamase) of the suggested designs performed better, some up to 10-fold. The hit rate and fold difference in performance thereby depended on the chosen mutational load: the more mutations allowed, the lower the hit rate but the higher the potential gain in performance. Most importantly, those designs included regions in sequence space that had not been accessed by experimental exploration.In summary, the results suggest the feasibility of what the authors call a ‘24-to-24 design’: in order to get one to two protein variants (95% confidence) that perform better than the original protein, a researcher would just need to generate 24 characterized training mutants and synthesize and characterize 24 suggested designs. Given the ever-decreasing price for gene synthesis, this low number of required input data makes it likely that the paradigm can be applied to many proteins of interest, including those which are currently inaccessible for evolution-based protein engineering as no high-throughput assays are available.The resource opens up even further opportunities: the algorithm was tested with improving the original function of a protein. But can it be used to facilitate the engineering of new protein functions, for example, as recently experimentally achieved for the direct enzymatic catalysis of a C1–C1 condensation?  Also, ex


Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.Leprosy, caused by  Mycobacterium leprae causes leprosy in about 200,000 people each year globally. Leprosy is a dermato-neurological infectious disease with varied clinical manifestations, often resulting in peripheral sensorimotor/demyelinating neuropathy leading to permanent nerve damage and disability. The World Health Organization currently recommends a combinatorial therapy [multidrug therapy (MDT)] with Dapsone, Rifampicin (Rifampin) and Clofazimine to treat leprosy . However, the M. leprae has a reduced genome of 3.2 Mbp, compared to 4.4 Mbp in M. tuberculosis, and survive with only 1,614 protein coding genes which are largely annotated based the features of their homologues in M. tuberculosis and other mycobacterial species  for proteomes of Mycobacterial pathogens and other bacterial species  and of varying quality based on the templates used in each profile.Different groups have made several attempts to model the proteins of  species . Vivace  TOCCATA . Protein TOCCATA  and SCOP TOCCATA  database TOCCATA  and PDB. TOCCATA . FUGUE r TOCCATA  for modeMycobacterium tuberculosis  states are being generated and analyzed for surface topology, cavities (cavities  and fragcavities . The schProtein-protein interactions (homo/hetero) govern a majority of the cellular processes. Structure determination of these complexes is crucial for understanding their functions. Usually, the experimental techniques used to unravel interacting protein partners are time consuming, challenging and expensive. There have been significant advances in the development of computational methods and tools to identify interacting pairs and predict the structures of protein-protein complexes .de novo prediction methods. If the structures of the interacting partners are known, the interactions can be predicted using template-based, or template free and/or restraint-based docking. Template-based docking can provide the multi-component modeled complex but requires the presence of multi-component template structures  and CAPRI  competitions, aim to assess the field and the state-of-the-art methods and their ability to “correctly” model protein structures and their interactions, respectively. They define and use multiple scores for assessing the quality of protein structure and interfaces in the modeled complexes. CASP14 is the present ongoing competition, where deep learning approach-AlphaFold2 has outperformed and were able to accurately predict the protein structures ], menD [2-succinyl-5-enolpyruvyl-6-hydroxy-3-cyclohexene-1-carboxylate synthase (ML2270)] and coaA [Pantothenate kinase (ML1954)].To illustrate the modeling process adopted by menB converts o-succinylbenzoyl-CoA (OSB-CoA) to 1,4-dihydroxy-2-naphthoyl-CoA (DHNA-CoA) and its homologue in M. tuberculosis (Rv0548c) is reported as essential (M. tuberculosis (PDB Id: 4QII) as the template with sequence identity of 93% and sequence coverage of 100%  is noted to be essential for bacterial survival. We modeled menD of M. leprae using the structure of the M. tuberculosis orthlogue (PDB Id: 5ESD) as the template with the sequence identity of 86% and sequence coverage of 99% (coaA which synthesizes coA from (R)—Pantothenate. CoaA has been recognized as a drug target in tuberculosis (coaA using its orthologue in M. tuberculosis (PDB Id: 2GET) as the template with sequence identity of 93% and sequence coverage of 98%  database  is a comMycobacterium leprae genome is reduced to 3,268,203 bp preserving only 1,614 ORFs . Since physical synthesis of drug molecules is not required, millions of virtual chemical entities can be swiftly docked into the active site of the protein structure/model and appropriate chemical scaffolds that fit with high scores and form relevant stabilizing interactions can be shortlisted for experimental validations. Virtual screening can be applied to novel drug discovery and also in drug repositioning experiments (screening with existing approved drugs to identify new target-protein interactions). A repurposing screen of LipU, a lipolytic protein that is conserved across mycobacterial species and noted to be essential for survival of rtensive . Moleculrtensive , CCDC-GOrtensive , Autodocrtensive , Ledock rtensive , FlexX  oligomers are subjected to multiple state-of-the-art validation scores. These models can be very helpful and provide useful insights to understand protein structure and function, identify drug targets and unravel their functional roles in the pathogen. The structures of selected drug targets can also help in experimental design and prioritizing the protein targets for validation.M. tuberculosis (In-silico saturation mutagenesis experiments can guide the experimental design and help in saving the time and labor required to conduct laboratory experiments on animal models. Structure-based drug design (The emergence of drug resistance to the multidrug therapy is a major challenge in treating mycobacterial infections especially leprosy where structural features of drug-target interactions are poorly understood. To complement the computational findings, our group has employed cryoEM methods to understand the impact of mutations on the structure of catalase peroxidase in rculosis , 2021. Pg design  is a waySV, SM, and MADGE conducted the review and written the manuscript. MM, PT, and TB reviewed the manuscript and provided necessary additions to the text. All authors contributed to the article and approved the submitted version.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.


Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection. While aormation , many prormation . Correctormation  often reormation . As suchormation . ProteinExisting threading methods exploit a wide range of techniques ranging from dynamic programming to profile-based comparison to machine learning . The recα or Cβ carbons of the interacting residue pairs. Inter-residue distance map is finer-grained in that it captures the distribution of real-valued inter-residue spatial proximity information rather than the binary contacts at a fixed cutoff distance. Recent studies , UniRef , UniProtWhile the use of interaction maps is the main driving force behind the improved threading performance, the optimal granularity and information content of the predicted interaction maps remain elusive. Existing works consider various distance bins  and subsab initio protein structure prediction, significantly outperforming the other groups. The success of AlphaFold series is primarily attributed to the successful application of deep neural networks for accurately predicting inter-residue spatial proximity information coupled with end-to-end training, significantly improving the accuracy of protein structure prediction (ab initio protein structure prediction methods developed by DeepMind.Recent CASP experiments have witnessed dramatic recent advances by DeepMind’s AlphaFold series  in ab inediction . The int


Protein interactions govern the majority of an organism’s biological processes. Therefore, to fully understand the functionality of an organism, we must know how proteins work at a molecular level. This study assembled a protocol that enables scientists to construct a protein’s tertiary structure easily and subsequently to investigate its mechanism and function. Each step involved in prediction, validation, and functional analysis of a protein is crucial to obtain an accurate result. We have dubbed this the trifecta analysis. It was clear early in our research that no single study in the literature had previously encompassed the complete trifecta analysis. In particular, studies that recommend free, open-source tools that have been benchmarked for each step are lacking. The present study ensures that predictions are accurate and validated and will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could lead to drug development.in silico proteomics require the trifecta analysis, namely, prediction, validation, and functional assessment of a modeled protein. The main drawback of this endeavor is the lack of a single protocol that utilizes a proper set of benchmarked open-source tools to predict a protein’s structure and function accurately. The present study rectifies this drawback through the design and development of such a protocol. The protocol begins with the characterization of a novel coding sequence to identify the expressed protein. It then recognizes and isolates evolutionarily conserved sequence motifs through phylogenetics. The next step is to predict the protein’s secondary structure, followed by the prediction, refinement, and validation of its three-dimensional tertiary structure. These steps enable the functional analysis of the macromolecule through protein docking, which facilitates the identification of the protein’s active site. Each of these steps is crucial for the complete characterization of the protein under study. We have dubbed this process the trifecta analysis. In this study, we have proven the effectiveness of our protocol using the cystatin C and AChE proteins. Beginning with just their sequences, we have characterized both proteins’ structures and functions, including identifying the cystatin C protein’s seven-residue active site and the AChE protein’s active-site gorge via protein–protein and protein–ligand docking, respectively. This process will greatly benefit new and experienced scientists alike in obtaining a strong understanding of the trifecta analysis, resulting in a domino effect that could expand drug development.Current  Proteins play both structural and functional roles within the organization of a cell . A proteWith the completion of the human genome project and the genomes of entire organisms being decoded almost every day, the gap between proteins with known sequences and those with experimentally validated structures and functions has increased rapidly . TherefoIn silico tools for phylogenetic analysis and multiple sequence alignment (MSA) cater for this requirement.The process of protein identification is the first step in any protein study. Following protein identification, recognition of its relatedness to other closely related proteins via conserved sequence motifs and sequence similarity is crucial . This stWith the advancements in DNA sequencing and the increase in the number of predicted amino acid sequences, there is a serious need for the comprehensive analysis of these novel proteins in the study of proteomics. As proven through experimentation, a complete analysis requires identification of the amino acid sequence within a non-redundant database, then characterization and functional prediction through protein modeling techniques . HoweverDanio rerio, which codes for the protease-inhibiting cystatin C protein and its interactions with human cathepsin proteases. The second and third examples involve the AChE protein-coding sequences of Homo sapiens and Rattus norvegicus and their interactions with the organophosphate echothiophate.The present study attempts to fill this gap utilizing three examples. The first example involves the amino acid sequence of Danio rerio, is a model organism of vertebrate development and is of considerable scientific interest, especially in modeling human biology and disease [The zebrafish, otherwise known as  disease . Cystati disease . Due to  disease . Homo sapiens (hAChE) and Rattus norvegicus (rAChE). AChE proteins have been predicted to possess an active-site gorge with a tryptophan residue, and echothiophate is known to bind at this active site [Organophosphates such as echothiophate could inhibit AChE activity. The mechanism of AChE inhibition is well known, making this an ideal candidate for benchmarking the proposed pathway. This study focused on the AChE proteins of ive site . The inhive site  successfThe hAChE macromolecule has already been modeled and is readily available in the PDB data bank . The priin silico tools to generate valid protein models, prove their functionality through molecular docking techniques, and discover the immune potential against diseases in the human body by using gene–gene interaction mapping. The study aims to develop an economical in silico approach to analyzing the structure and functionality of novel proteins with a comprehensive revelation of their clinical significance.The primary focus in selecting the cystatin C protein encoded by zebrafish and the AChE proteins was to benchmark the timeline with a multitude of queries and provide a wealth of information to the scientific community. The selection of an immune-related protein provided an opportunity to benchmark all the features of the proposed pipeline. Here we report novel The proposed methodology  aims to In the present study, we have detailed the steps required for such an analysis. We have validated our pipeline through a series of analyses using a selection of computational tools. It is possible to replace these tools with others as required by the user. Our selection of these tools is based on their time-tested reliability, the fact that they are free to use, and the fact that they can be used on general hardware.D. rerio and acetylcholinesterase from humans (hAChE) and Rattus norvegicus (rAChE). The use of these three proteins enabled the testing of the protocol in multiple different situations that may arise in the analysis of the structure and function of a protein. The cystatin C of D. rerio and the rAChE proteins enabled the protocol to conduct the trifecta analysis of novel structures and test their functions in protein–protein and protein–ligand interactions. The hAChE example enabled the methodology to be comparatively tested against a protein of known structure. The actual structure of hAChE is readily available in the PDB data bank (PDB ID: 4PQE). The analyses of both cystatin C and AChE are novel. These findings are unique to this work, and we believe they help to explain the power of a standardized protocol. However, these predictions still need to be validated via experimentation. We proved the robustness of our proposed pipeline using a series of use cases. In this study, we detailed the use of the complete pipeline for three proteins, namely, cystatin C from To further prove the validity of our work, we conducted protein–ligand and protein–protein replication studies. To analyze protein–ligand interactions, we analyzed the survivin protein and 4-hydroxy pyridine 1-oxide pyridin-4-ol 1-oxide ligand, and the more well-known hemoglobin protein and haem ligand ,16. ThesTwo primary concerns were reliability and accessibility. We wanted to ensure the selected tools were time-tested and freely available to all users. These tools guide the use of our trifecta analysis but can be changed as the user deems fit. For functions involving protein characterization, such as identifying conserved domains and collecting sequences for analysis, we chose NCBI tools such as their CD-Search and database . The NCBMEGA software was selected to fulfill our phylogenetic needs. MEGA is equipped with a user-friendly graphical user interface (GUI) that simplifies complex phylogenetic analyses. It has sound statistical models that ensure the accuracy of its analyses. Additionally, it can be run on general-purpose computer hardware, in contrast to its counterparts such as PAML and BEAST, which may require high-performance computing (HPC). PAML and BEAST are known to require a steep learning curve. PAML, with its command-line-based usage, and BEAST require a sound knowledge of Bayesian statistics and MCMC methodologies ,24,25, bTo predict the proteins’ tertiary structures, we selected Zhang Lab’s I-TASSER and QUARK servers. Both services are available to academics and have quick turnaround times . I-TASSER was the number one server used in all CASP experiments from CASP7 to CASP14, and QUARK was ranked number one in both CASP9 and CASP10 experiments ,27,28. RThe selected docking software was Hex 8.0.0 for protein–protein docking and AutoDock Vina for protein–ligand docking ,31,32. HD. rerio (Accession No. AAZ29462.1), rat AChE (AAH94521.1), and human AChE (AAA68151.1) were identified using the NCBI database. The conserved functional domains present in the translated amino acid sequences were identified using the conserved domain search service (CD-Search) in NCBI. The predicted protein was annotated, and a graphical summary was obtained [The amino acid sequence coding for the putative cystatin C protein of obtained .D. rerio were identified using the HUGO Gene Nomenclature Committee search tool. The related proteins were searched for in the NCBI database, and the amino acid sequence was extracted in the FASTA format. It was ensured that at least one amino acid sequence was obtained for each of the five vertebrate classes: Pisces, Amphibia, Reptilia, Aves, and Mammalia. These amino acid sequences were subsequently aligned using ClustalW with the MEGA6 software, and a phylogenetic tree was obtained using the neighbor-joining statistical method.The protein families evolutionarily related to the predicted protein of The translated protein sequences were subjected to multiple sequence alignment by ClustalW, performed using the program Unipro UGENE . The anaThe secondary structure expressed by the amino acid sequence was predicted using the JPred secondary structure prediction server . Ab initio model prediction was performed using the QUARK server [ab initio modeling was not utilized to predict the AChE molecules due to the limitation of the server to 200 amino acids. The predicted 3D structures were visualized using PyMOL visualization software [The 3D structure of the protein was predicted using two main systems. Homology prediction of the protein was conducted using the I-TASSER online software . Ab initK server . Howeversoftware . Cystatisoftware . The successfully predicted structure was evaluated for its overall quality and stability, to identify any errors that may be present. The general model quality and the local model quality were assessed using the ProSA-web service . The staThe overall tertiary model quality was improved using the ReFOLD online software. The software was utilized to assess the global model quality, the overall structural improvement, and the accuracy . The refThe surface of the protein structure was analyzed for potential active binding sites by examining the distribution of electrostatic charges and hydrophobic amino acids using the protein surface analyzer tool from the Maestro BioLuminate 2.8 software . D. rerio as a cysteine protease inhibitor. Hex 8.0.0 CUDA was used, together with fast Fourier transform (FFT) correlation techniques [In the functional analysis of a protein, it is essential to determine the type of interactions occurring in the target macromolecule. The following protocol addresses the most common protein interactions, namely protein–ligand  and protchniques . The sofchniques ,45.For successful inhibition of the cysteine protease, the cystatin C protein had to block the cysteine active site of the target protein with an excellent negative binding energy. The protein was first subjected to molecular docking with papain (PDB ID: 9PAP), which is known to be inhibited by all cystatins. The docking was followed by papain-like proteases, namely, cathepsin B (PDB ID: 2IPP), cathepsin H (PDB ID: 8PCH), cathepsin L1 . The proteases have been proven to be actively inhibited by cystatin C . AutoDock Vina was chosen for the protein–ligand docking software. The protein–ligand substrate consisted of AChE and the organophosphate echothiophate (PubChem CID: 10548). A negative control, imidazole (PubChem CID: 795), was used to reduce the risk of false positives and ensure proper testing. To further prove the reliability of our selected software, AutoDock Vina, two replication analyses were conducted. The first, between the survivin protein (PDB ID: 1XOX) and 4-hydroxy pyridine 1-oxide pyridin-4-ol 1-oxide ligand (PubChem ID: 23321) was initially conducted by Heendeniya et al. , and thaIn AutoDock, the protein was prepared by removing water molecules followed by the addition of hydrogens and merging of non-polar hydrogens. Finally, computation of the charges was conducted using the Gasteiger function. Grid preparation was performed for the potential active sites. In AutoDock, the ligand was prepared by assigning a torsion tree and detecting the root. AutoDock Vina provided the nine best poses with default exhaustiveness. Subsequently, a 2D analysis of the protein–ligand complexes was performed using Discovery Studio 2017 or LigPlot+ .D. rerio. If the cystatin C of D. rerio had a more feasible binding energy , it was estimated to have clinical potential as a drug to solve the cathepsin overexpression.Clinically significant pathways involving cathepsin B, L1, and S overexpression in the human body were identified through gene–gene interaction mapping. However, cathepsin H was excluded due to the absence of its 3D tertiary structure of human origin. The GeneMANIA database coupled with the Cystoscape software was used to identify these protein interactions. The interactions were separated into physical interactions, co-expression, and co-localization . The selThe initial analysis of the cystatin C amino acid sequence obtained from the NCBI database (Accession No. AAZ29462.1) contained a cystatin protein-coding domain complete with an N-terminal glycine and a QxVxG sequence motif. The analysis of the AChE of rAChE (AAH94521.1) and hAChE (AAA68151.1) amino acid sequences showed an AB-hydrolase superfamily, complete with a substrate-binding pocket and a catalytic triad. AChE is a member of the AB-hydrolase superfamily, referred to as the alpha/beta hydrolase fold family of enzymes .The resulting phylogenetic tree In the AChE example, it could be observed that the proteins were grouped based on the phyla of Mammalia, Aves, Reptilia, and Pisces. The AChE of humans and rats was shown to share a common origin. The overall phylogenetic tree was sound, with confidence levels of over 80% .D. rerio possessed three evolutionarily conserved sequence motifs: the N-terminal glycine, the QVVAG motif, and the PW motif [D. rerio is also recognized as containing a mutation in the PW motif, which is instead read as the LW motif, as shown in The cystatin C protein produced by PW motif . D. reriRattus norvegicus and Homo sapiens . A stable enzyme–substrate complex was formed with a binding energy of −347.6 kJ/mol and the complete blockage of the in sites . The inh3 kJ/mol . Cathepsve sites . Cathepsblocking . FinallyCys site . The resThe protein–ligand interactions were predicted using AutoDock Vina. The predicted structures of rAChE and hAChE were subjected to virtual screening with echothiophate. The purpose of using two different types of software was to enable us to identify whether both programs would produce the same result.The active site of AChE is situated in a centrally placed gorge . The actNyctanthes arbor-tristis. These have proven their anticancer properties through anticancer assays involving cell cultures. Additionally, these authors investigated the interaction of the aforementioned active compound with survivin to identify its anticancer mechanism [18Arg  post phylogenetic analysis enables the accurate recognition of evolutionarily conserved sequence motifs among closely related proteins involved in the protein’s characteristic functions. It also allows identification of the sequence motifs that are most critical in the governance of the protein’s function . CystatiThe next step after protein characterization is the functional analysis, which requires predicting the protein’s native 3D tertiary structure. This involves the identification of the proteins’ secondary structure first . The secab initio modeling [The prediction of a protein’s tertiary structure through computational techniques has a two-fold advantage. The conventional methods of NMR and X-ray crystallography are expensive and not readily available to everyone. In addition, the structure of short-half-life proteins cannot be predicted using these methods . The curmodeling .The I-TASSER server was utilized for the homology prediction of the protein structure. The I-TASSER server uses a hierarchical approach to predicting protein structures . Its relAb initio or de novo protein modeling was conducted utilizing the QUARK server. In the absence of available templates, this technique can predict the protein structure from the amino acid sequence alone [ab initio predicted model failed the validity test, since both of its alpha-helices were long and there were no disulfide bonds.ce alone . The QUAce alone . HoweverThe structure of a protein depends on its sequence. A proper tertiary structure should have minimum free energy and be stereochemically stable . These tIt is known that electrostatically charged and hydrophobic residues on a protein’s surface play a key role in long-distance protein–protein interactions and are vital for drug design. Therefore these regions were mapped before conducting the virtual screening process . The cysCystatin C proteins are thought to be the most active protease inhibitors in the entire cystatin family. They can competitively block the active sites of papain and cathepsins B, H, L, and S . ProteinResidues that are frequently involved in protein–protein complexes are believed to be related to the function of the protein . Such reGenetic interaction mapping reveals functional pathways involved in molecular mechanisms . Some ofD. rerio. To further test the reliability and accuracy of the proposed pathway and the software used, the AChE proteins of both humans and rats were selected, enabling the extension of the pathway from protein–protein to protein–ligand analysis.Benchmarking the pathway was possible using the prediction of the structure and function of the cystatin C of The hAChE macromolecule was used to test the reliability of the I-TASSER software against already identified protein structures. The hAChE protein’s tertiary structure already exists in the PDB data bank. Even though the server predicted a structure closely resembling the accurate native model after refinement, minor deviations resulted in an RMSD with a value of 0.609 Å. Apart from this, both models were successfully analyzed for their functionality.The AChE protein was utilized to comparatively analyze the use of AutoDock Vina. When the grid box was implemented accurately to accommodate the respective active sites, AutoDock Vina was able to bind the ligands accurately. When faced with a false negative, the ligand bound away from the active site, showing that Vina’s predictions were not random.Th proposed pipeline, therefore, ensures the complete characterization of the protein under study. The final result confirms that the user can be provided with the function of the macromolecule and an analysis complete with the query sequence’s evolutionary significance, structural characteristics, and the prediction of tertiary structures that are appropriately validated and refined. The pipeline is robust enough to identify putative active sites also.In conclusion, the proposed computational pipeline utilizing only free, open-source software can be used to conduct a complete analysis of a novel amino acid sequence, revealing its identity and its functional and therapeutic potential. Using the cystatin C and AChE proteins and a series of replication studies, we were able to prove the proposed pipeline’s robustness. The pipeline characterized the protein based on its evolutionary relatedness, overcame protein modeling errors through structure refinement, and established the protein’s functionality complete with its active site and therapeutic potential. Such analysis has become crucial in developing targeted therapeutics intended to inhibit a protein’s active site. Following the successes of genome sequencing, novel proteins’ open reading frames (ORFs) are being discovered daily, and with the success of protein structure prediction techniques such as AlphaFold 2, the number of known structures is gradually increasing. Therefore, we believe that this single, economic pipeline will assist in bridging the gap between known protein sequences and those that are experimentally validated. Future work will focus on using our methodology to conduct trifecta analyses on a large scale and on further refinement of the protocol to enable expansion into large datasets.