Primary and secondary structure prediction were computed using Expasy ProtParam server and Phyre2 server, respectively. TMHMM server was used to identify the transmembrane regions in HSP70. Multiple sequence alignment and comparative analysis of the protein was carried out using MAFFT and visualization was created using ESPript 3.0. Phylogenetic analysis was accomplished by COBALT. Interactions of HSP70 with other proteins were studied using STRING database. Modeller 9.18, RaptorX, Swiss-Modeller, Phyre2, and I-TASSER were utilized to design the three-dimensional structure of these proteins followed by refinement; energy minimization was accomplished using ModRefiner and SPDBV program. Stereochemical quality along with the accuracy of the predicted models and their visualization was observed by PROCHECK program of PDBsum and UCSF Chimera, respectively. ClusPro 2.0 server was accessed for the docking of the receptor protein with the ligand.Bos indicus (BI) with Bos taurus as compared to BI and BB. STRING database clearly indicates the HSF1 as one of the interacting molecules among 10 interacting partners with HSP 70. The best hit of 3D model of HSP70 protein and HSF1 was retrieved from I-TASSER and Phyre2, respectively. Interacting residues and type of bonding between both the molecules which were docked by ClusPro 2.0 were decoded by PIC server. Hydrophobic interactions, protein-protein main-chain-side-chain hydrogen bonds, and protein-protein side-chain-side-chain hydrogen bonds were delineated in this study.The lower value of Grand Average of Hydropathy indicates the more hydrophilic nature of HSP70 protein. Value of the instability index (II) classified the protein as stable. No transmembrane region was reported for HSP70 by TMHMM server. Phylogenetic analysis based on multiple sequence alignments (MSAs) by COBALT indicated more evolutionarily closeness of in silico interaction of HSP70 and HSF1 proteins in BB. Several bioinformatics web tools were utilized to study secondary structure along with comparative modeling, physicochemical properties, and protein-protein interaction. The various interacting amino acid residues of both proteins have been indicated in this study.This is the first-ever study on  Environmental stresses instigate physiological responses in living organisms as part of their adaptation mechanism. Temperature deflections produce a heat shock response in living organisms, which comprise the expression of heat shock proteins (HSPs). Synonymously known as molecular chaperones, these proteins are in charge of regulating proteostasis, whenever any kind of physical or chemical trigger disturbs the internal milieu of the cellular organization -4. AmongBubalus bubalis [BB]) in India, is an important livestock species among all domestic animals because it alone contributes to the highest to the country’s milk as well as meat production [in silico studies.In the present time, the ever-changing global climatic conditions have made abiotic stresses such as heat, a major challenge for productivity in livestock ,14. In goduction ,19. Howeoduction . As the oduction ,22. Thisoduction . As a 70oduction ,24. HSP7oduction -29. The in silico tools.To date, there is no availability of experimental structure of HSP70 protein for BB, and therefore, evaluation of HSP70 in BB becomes essential to analyze the prospective mechanism of its functioning in buffalo and interaction studies with other molecules. The study has also focused on finding the other interacting partners of HSP70 protein and also to deduce their roles during cell cycle regulation and signal transduction in BB, utilizing various The present work did not involve any wet laboratory experiments. However, all the animal handling procedures followed under the project had the Institute Animal Ethics Committee approval vide sanction F.No. NBAGR/IAEC/2017 dated 21-01-2017.The primary sequence of the HSP70 (Accession No. ADQ27308.1) and HSF1 (Accession No. AHB38918.1) of BB was extracted from the reference protein database of NCBI. Both the protein sequences were retrieved in FASTA format and used for further analysis.https://web.expasy.org/protparam/). Secondary structure of this protein was predicted using the FASTA sequences of HSP70 and HSF1 using Phyre2 server [For HSP70 and HSF1 proteins’, primary structure prediction, molecular weight, theoretical isoelectric point (pI), total number of positive and negative residues, extinction coefficient , instabi2 server .Bos indicus (BI), Bos taurus (BT), Capra hircus (CH), Ovis aries (OA), Rattus norvegicus (RN), Sus scrofa (SS), Equus caballus (EC), Mus musculus (MM), and Homo sapiens (HS), were fetched from protein reference database of NCBI  was utilized to know the critical interactions displayed by HSP70 in BB for performing diverse cellular activities, and data on protein-protein interactions were collected [STRING database (ollected .https://zhanglab.ccmb.med.umich.edu/I-TASSER/) and the best hit of 3D model of HSF1 was generated by Phyre2. After model construction using I-TASSER and Phyre2 (http://www.sbg.bio.ic.ac.uk/phyre2/) server, the models were subjected to refinement and energy minimization using ModRefiner and Swiss-PdbViewer (SPDBV) program [The modeling of the three-dimensional structure of these proteins was constructed by various tools and servers such as Modeller 9.18, RaptorX, Swiss-Modeller, Phyre2, and I-TASSER; the best hit of HSP70 protein was obtained from I-TASSER  and negatively charged residues (Asp+Glu) in the sequence were found to be 83 and 92, respectively. Calculated GRAVY was too low (−0.403) which indicates better interaction of the protein with water, i.e., more hydrophilic nature. The value of the instability index (II) was computed to be 33.03 (<50), therefore, classifying the protein as stable . None ofThe MSA of 10 different species was investigated to scrutinize the evolutionary relatedness ; subsequab initio folding simulation called Poing2 has also been amalgamated to model those regions of the protein which show no homology to known structures [Phyre2 module was exploited to deduce the disorder and secondary structure of HSP70 using amino acid sequences. Phyre2 uses the alignment of hidden Markov models through HH search to pointedly improve the precision of alignment and detection rate . A new aructures . The resin vacuo with the GROMOS96 43B1 parameters set without reaction field. Ramachandran plot statistics of both models shown in The three-dimensional (3D) model for HSP70 protein  and HSF1As far as concern with Ramachandran plot, more than 98.0% of residues of both the proteins were in the allowed region which, in turn, signifies that the quality of both the models is reliable and good enough because both are as per the acceptance criteria, i.e., at least 90% of residues fall under allowed region. Again, Qualitative Model Energy Analysis termed as QMEAN analysis ,54 showeA solitary protein may not be able to play each of the committed tasks separately, as a result, these proteins have to be associated with some other proteins or different factor(s) and form channels/pathways or frame buildings that can manage multiple biological events. Similarly, the molecular chaperone(s) requires co-chaperones or some transcription factors, etc., to do their activity efficiently. By the use of STRING database, we are able to know the interacting partners of HSP70. These interacting proteins are mainly molecular chaperones/regulators of molecular chaperones and also found to be participated in signal transduction and cell cycle regulation . InteresThe HSF1 acts as a transcriptional regulator of HSP70; therefore, elucidation of these interactions by docking is very valuable for a general understanding of complex biological mechanisms and pathways. After the quality assessment and model validation step, the modeled ligand protein (HSF1) was docked with receptor (HSP70) homology model using ClusPro 2.0 docking environment. The goal of docking HSP70 and HSF1 was to investigate those docked confirmations, which shows good surface complementarity, which, in turn, can be attained by selecting those complexes that possess the lowest desolvation and electrostatic energies. Only top 10 results out of thousands of synthesized confirmations are revealed by ClusPro 2.0 and the very first model had lowest electrostatic and desolvation potential values .Final docked complex, HSP70, and HSF1 were analyzed using Protein Interactions Calculator (PIC) web server to confirm the interacting residues . Hydrophin silico interaction study of HSP70 and HSF1 in BB. The investigation has led to the prediction of the secondary structure of HSP70 along with its physicochemical properties study, comparative modeling, protein-protein interaction with HSF1, transcriptional regulation, model evaluation, and its accuracy assessment. The comparative analysis of the HSP70 protein revealed certain significant variations, but most of the sequences were found to be conserved across species. The study delineated a number of amino acid residues of HSP70 potentially interacting with HSF1, which was important to understand the mechanism of heat stress adaptation in BB.This is the first-ever in silico experimentations. CR and RSK contributed to drafting and revision of the manuscript. All authors read and approved the final manuscript.RS and RSK designed the experiment. RS and AG wrote the first draft of the manuscript; RS, AG, SKM, SR, AB, and VK performed the


Although improved strategies for the detection and analysis of evolutionary couplings (ECs) between protein residues already enable the prediction of protein structures and interactions, they are mostly restricted to conserved and well-folded proteins. Whereas intrinsically disordered proteins (IDPs) are central to cellular interaction networks, due to the lack of strict structural constraints, they undergo faster evolutionary changes than folded domains. This makes the reliable identification and alignment of IDP homologs difficult, which led to IDPs being omitted in most large-scale residue co-variation analyses. By preforming a dedicated analysis of phylogenetically widespread bacterial IDP–partner interactions, here we demonstrate that partner binding imposes constraints on IDP sequences that manifest in detectable interprotein ECs. These ECs were not detected for interactions mediated by short motifs, rather for those with larger IDP–partner interfaces. Most identified coupled residue pairs reside close (<10 Å) to each other on the interface, with a third of them forming multiple direct atomic contacts. EC-carrying interfaces of IDPs are enriched in negatively charged residues, and the EC residues of both IDPs and partners preferentially reside in helices. Our analysis brings hope that IDP–partner interactions difficult to study could soon be successfully dissected through residue co-variation analysis. Protein sequences provide rich information on structural and functional constraints in the form of residue co-variation in evolution. With the rapid expansion of available sequence data and computational power, and improvements in global statistical approaches , the proIntrinsically disordered proteins/regions (IDPs/IDRs) lack well-defined 3D structures, rather, they exist and function as ensembles of rapidly interconverting conformers ,18,19,20IDPs mostly interact with their partners through eukaryotic/short linear motifs (ELMs/SLiMs) comprised of a few specificity-determining residues embedded into a disordered sequence environment that ensures the right positioning of their mostly hydrophobic, crucial interaction residues ,31,32,33Therefore, to see if functional constraints originating from partner binding resulted in detectable interprotein ECs of  IDPs and to characterize the respective protein pairs, interfaces, and their co-evolved residue pairs, we performed a targeted screen to identify such cases.p > 0.88), hereafter referred to as ECs. For EVcomplex, interprotein ECs with an EVcomplex score >0.9 have been accepted as ECs. EVcomplex also identified ECs for 7/19 complexes   portal fp < 0.01). The descriptive distribution features  of the 100 samples have shown an average standard deviation of 2.31 which we interpret as low variation among the random samples. 18/22 visible ECs were closer than 8 Å, two were between 8 and 10 Å far, and only two had >10 Å distances  we calculated the minimal residue–residue distances using PyMOL . We compistances C. The laistances B. One ofp = 0.003) and partners (p = 0.039) compared to other residues  database . Althoug2, except for the sole complex with the permanent interaction of two subunits of the ATP synthase that only had 610.5 Å2. This might imply that, in permanent protein complexes, which are phylogenetically widely distributed, co-evolution of interacting subunits is so prominent that it can be detected even if the corresponding interaction surfaces are relatively small.Although multisubunit enzymes often contain predicted disordered chains that occupy a completely extended conformation in the complex, and such cases would be perfect candidates for IDP-partner co-evolution analysis, the structural features of these subunits are rarely analyzed on their own. Due to this reason, our DIBS-derived dataset only has 4/37 permanent protein complexes, while most of them depict transient interactions . The sev2 (for transient complexes this threshold rather seems as 1000 Å2) in size, not even for exceptionally widely conserved interactions. ECs could also not be identified for interfaces without any assigned H-bonds or salt bridges  for their interface areas, H-bond and salt bridge densities, we did not find any significant difference . However bridges C.p = 0.0519). We have to note though that we had only 28 IDP- and 30 partner EC residues, which is suboptimal for rigorous statistical analysis. Due to the small sample size, even seemingly very large differences were not statistically significant, for instance, IDP EC residues were not found to be enriched in positive amino acids compared to all IDP interface residues, despite having almost two times as high fraction of positives , in particular glutamates (p = 0.0240), than the IDP interfaces with no ECs , all interfaces , interfaces without any detectable EC pairs (No-EC IFs), EC-carrying interfaces (EC IFs) and EC residues for the IDPs and partners separately. Comparing EC IFs and ECs to all IFs, we found no significant differences either for IDPs  or theirh no ECs . For theh no ECs .Due to increased substitution rates , IDP hom2) or have no hydrogen bonds or salt bridges, no matter how widely conserved the interaction is. Since for the partners we did not find enrichment in any residue type, we do not claim that the enrichment of EC-carrying IDP interfaces in negative residues would imply the employment of increased electrostatic attraction compared to other IDP–partner interfaces. Therefore, we cannot ascertain the biological relevance of the noted preference for negatively charged residues.Supporting the validity of the predicted ECs, i.e., that they represent true physical contacts between the IDP and its partner, or at least could be spatially restrained by each other, the majority of the visible ECs fall within 8 Å. Residue contact networks from PCA also showed that several EC pairs had multiple atomic contacts; moreover, ECs had more atomic contacts than other contacting IDP–partner residue pairs. Regarding their structural preferences, EC residues tend to be located in helices in both IDPs and folded partners. Although EC-carrying interfaces did not significantly differ in size or in the surface-normalized number of H-bonds and salt bridges, it is important to note that no ECs could be identified in interfaces that are small (<600 Åmpa gene) with prokaryotic ubiquitin-like protein Pup (3M91) are located in different operons and are thus substantially less strictly associated both in gene-regulatory and evolutionary terms. Therefore, based on our findings, a phylogenetically widely preserved protein–protein interaction that buries an interface larger than SLiM-mediated interactions typically do, might be enough for the reliable identification of interprotein evolutionary couplings. No other assumption regarding permanence of the interaction or co-regulation of the corresponding genes needs to be made prior to analysis, although such factors could increase the chance of finding true ECs.Importantly, ECs could be identified for both permanent and transient IDP–partner interactions. This shows that not only obligatory, permanent associations, such as the subunits of the ribosome  or heterIn this first dedicated study of IDP–partner co-evolution, we also show that IDPs are difficult to investigate by methods addressing residue co-variation, due to their fast evolutionary changes, limited sequence representation, increased propensity for moonlighting functions, frequent use of short linear motifs for partner binding and relatively few co-evolving residue pairs both within their chains ,40 and wBacterial protein complexes were obtained from the DIBS database  Supplem. We idenInformation on the complexes representing permanent or transient interactions was taken from the literature and UniProt subunit structure annotations. To assign if the genes are encoded within a single operon/gene neighborhood, we used information in the STRING database  and EnseThe trimmed regions of the 19 remaining complexes were run with the Gremlin  and EVco−5) in searching for homologues and chose the option of selecting the closest homologs of the query sequences from the analyzed proteomes. Here, interprotein ECs with an EVcomplex score >0.9 have been accepted as ECs. EVcomplex identified ECs for 7/19 complexes.For the EVcomplex analysis, we applied the default e-value  was taken from DIBS and is indicated in In the residue and residue group composition analyses, we have calculated the compositions of the trimmed protein segments , all interfaces , interfaces with no ECs (no-EC IFs), EC-carrying interfaces (EC IFs) and EC residues for the IDPs and partners separately. Identical partner chains have been only included once into these composition analyses. The interfaces have been defined based on PDBe PISA, so regions of the trimmed protein segments falling outside the segments with PDB coordinates are not represented by the interface composition values. However, they are represented in the analyzed sequence ranges and among ECs. Residue groups were defined as: hydrophobic , aromatic , polar , negative , and positive . Residue group and residue proportion significances were obtained by the built-in test of equal proportions of R .Interchain residue–residue atomic contacts have been downloaded from the Protein Contacts Atlas  for all DSSP secondary structure assignments  have beehttps://pymol.org/2/; The PyMOL Molecular Graphics System, Version 2.0 by Schrödinger, LLC, New York, USA). Then, for each EC we have randomly selected an interface IDP residue and an interface partner residue from the same interface, and measured their distance similarly. The distances of ECs and the thereby assembled equivalent random reference interface pairs have been compared by Mann–Whitney U test. To check if the observed difference remains consistent, we have obtained 100 additional samples of random residue pairs and compared their distances to those of ECs.For the ECs being in direct atomic contact according to the Protein Contacts Atlas, the shortest atomic contact distance (between heavy atoms) has been accepted as residue–residue distance. For the rest of the ECs, the shortest distance between the heavy atoms of the two residues has been measured using PyMOL (


We have performed an update of APID database including a redefinition of several key properties of the PPIs to provide a more precise data integration and to avoid false duplicated records. This includes the unification of all the PPIs from five primary databases of molecular interactions , plus the information from two original systematic sources of human data and from experimentally resolved 3D structures . Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions . Within this data mining framework, all interaction detection methods have been grouped into two main types: (i) `binary’ physical direct detection methods and (ii) `indirect’ methods. As a result of these redefinitions, APID provides unified protein interactomes including the specific `experimental evidences’ that support each PPI, indicating whether the interactions can be considered `binary’ (i.e. supported by at least one binary detection method) or not.The collection and integration of all the known protein–protein physical interactions within a proteome framework are critical to allow proper exploration of the protein interaction networks that drive biological processes in cells at molecular level. APID Interactomes is a public resource of biological data ( The experimental mapping of all molecular interactions between pairs of proteins that occur by specific biophysical contacts [i.e. protein-to-protein physical interactions (PPIs)] is an essential step to achieve a better understanding of the molecular architecture and the wiring of proteins in living cells. This biological cartography will produce a wider and more significant view when it comes close to full coverage by including data on all known proteins within a proteome . The paswww.ebi.ac.uk/ols/ontologies/mi) for the annotation of experiments concerned with PPIs. A relevant part of this vocabulary includes the `interaction detection methods’ (accessible at www.ebi.ac.uk/ols/ontologies/MI/terms?obo_id=MI:0001), which allow the identification of the methods used to detect each PPI reported in a published article, assigning a unique identifier notation to each method (like MI:000X). Most public databases and resources of PPIs follow the PSI-MI standards and apply them when creating new records, including the interactions derived from the specific research articles, which have been analyzed and curated (The working group on Molecular Interactions of the HUPO Proteomics Standards Initiative (PSI-MI) has recently published an update of the standard format to enable the download and interchange of molecular interaction data . This st curated . In the  curated , 9, as ain vitro). It is also very important to emphasize that the classification of methods in those focused on identifying interactions in `protein complexes’ or those focused on identifying interactions between `protein pairs’ does not mean that a type is better, it is more accurate or it has more quality than the other. We simply report that they are different.The implementation of this new category for the methods (`Method_Type’) and the re-evaluation of all the `interaction detection methods’  led us tConsidering the existence of these two different methodological approaches for experimental detection of interactions, we re-evaluated all the PSI-MI terms to assign `binary’ or `indirect’ as two major types within the new category that we defined `Method_Type’ . Thus, oEscherichia coli ; and (iii) the specific interaction experimental detection method (which we labeled `detectMethod’ and corresponds to a specific ID from the `HUPO PSI-MI ontology and controlled vocabulary’: www.ebi.ac.uk/ols/ontologies/mi). Therefore, any record (or tuple) in an ideal unified PPI database will correspond to a combination of these three minimal elements: `pApB’ + `PMID’ + `detectMethod’. Its implementation is critical to define unique PPI records and avoid artificial duplicates. In For the development and implementation of a unified database of PPIs derived from experimental published data, we need to have a clear definition of what is a distinct or unique `record’ in such database. To achieve this, we have to define what are the essential information elements, attributes or fields that each record must have. A distinct PPI record has to include three essential and minimal elements: (i) the specific protein pair that defines the interaction (which we labeled `pApB’); (ii) the specific publication where the interaction of these two proteins have been experimentally demonstrated , it is very important to consider other attributes that should be added to this tuple (record) when there are several source databases or biological data resources that must be merged. The first attribute that has to be added when there is an integration of different databases is the one that defines each source (that we labeled `sourceDB’). In the case of APID, we have indicated that it collects and integrates all PPIs from five primary databases of molecular interactions: BioGRID, DIP, HPRD, IntAct and MINT.In many cases, adding just the `sourceDB’ field to the records is not enough. For example, when the same interaction (defined by a record with four attributes: `pApB’ - `PMID’ - `detectMethod’ - `sourceDB’) is curated more than once by a primary database, and thus it has several IDs in the same source database. Therefore, the record also needs to have another element: the `sourceDB’ identifier that corresponds to each time a PPI record had been analyzed and re-annotated in that source database (i.e. the `sourceDBid’).These five field records (`pApB’ + `PMID’ +`detectMethod’ + `sourceDB’ + `sourceDBid’) are what we define as `curation_event’. The exhaustive and reliable recognition of all the curation events is essential to ensure the quality of the unified database building process and to keep the traceability of all the unified PPI records.Finally, after these definitions, it is important to underline the difference between `curation_event’ and `experimental_evidence’. The `curation_event’ is only determined by the action of a primary database to read and curate a PPI from a PubMed article and report it, and, therefore, it is independent of the experimental evidence, since a single experiment shown in an article as a demonstration of the interaction between proteins X and Y can be curated by several databases producing several `curation_events’ but only one single `experimental_evidence’. In fact, we do not count as an `evidence’ each time that one, two or three primary databases curate the same PPI from a PubMed article tested with the same method, and this is a clear difference with other PPI meta-databases.As mentioned above, the two biggest primary databases for PPIs (IntAct and BioGRID) make a different use of the available ontology terms, which leads to differences in the curation events reported by each one for common published papers. To validate our proposal, we measured the ability of this reunification step to solve curation mismatches among IntAct and BioGrid. Based on the same 6689 common papers, there were 86 382 curation events  including at least 1 binary `detectMethod’. Out of these records, 35 673 curation events (41.29%) show differences between IntAct and BioGrid. After the reunification step into meta-terms, we matched registers from both primary databases obtaining an almost total agreement (99.91%).The analysis of the essential elements necessary to produce unique records for a PPI database led us to redefine what is a single `experimental evidence’ and what is a `curation event’. These redefinitions allow us to reconstruct the protein interactomes included in APID database, based on the integration of currently known data derived from multiple primary sources. Thus, the new version of the APID database presented in this article only counts unique `experimental evidences’ for the protein interactions included in the interactomes provided. In addition, the identification of binary interaction detection methods within the list of registered PSI-MI ontology terms allows us to generate a new compendium of `binary interactomes’ for multiple species, with reliable metrics to weight every interaction included in each of those interactomes.Instituto de Salud Carlos III ; FEDER program of the European Union; and European Project H2020 ArrestAD .Conflict of interest. None declared.Supplementary Table 1Click here for additional data file.Supplementary Table 2Click here for additional data file.


The accumulation of intra- and extracellular inclusions and protein aggregates are well-known hallmarks of aging that are associated with the incidence and progression of diseases of multiple tissues. While the pathology and structure of some varieties of inclusions are well-characterized and described, the protein components of most are not known.A common feature of protein aggregates is extensive post-translational modification of proteins with ubiquitin. Ubiquitination is commonly associated with the turnover of proteins. The ubiquitin-proteasome system, one of the major protein quality control pathways of the cell, uses poly-ubiquitination of proteins as a signal for protein degradation by the proteasome. Ubiquitination also promotes autophagic turnover pathways such as mitophagy and aggrephagy, a selective disposal of protein aggregates . The ubiin vivo have now been described in numerous organisms [ad libitum, calorie restricted, or treated with rapamycin [Proteomic methods for measuring rates of protein turnover rganisms . In micerganisms . A critirganisms , enable apamycin . The autapamycin ,4. AlongTo examine the link between increased ubiquitination and altered protein turnover, Basisty et al. developed a method to measure the turnover of ubiquitinated proteins . FollowiA key consideration in the determination of ubiquitinated protein turnover is combining a stable isotope- heavy leucine -feeding study with PTM enrichment and mass spectrometric analysis and specialized software tools and data processing to perform the calculation of ubiquitinated protein turnover .In aged mice there is a striking reduction in protein turnover and an increase in abundance of insoluble ubiquitin-modified proteins compared to young mice. This accumulation of ubiquitinated proteins suggests that old mice lack the proteostatic capacity to eliminate ubiquitinated proteins. Moreover, calorie restriction and rapamycin treatment, interventions known to enhance proteostasis, restored the turnover of ubiquitinated proteins in old mice.Given that protein aggregates are insoluble, often ubiquitinated, and difficult to degrade, it was reasoned that the slowly turned-over ubiquitinated proteins in less soluble protein fractions that accumulated with age may be overrepresented in protein aggregates. To test this, two of the top “aggregator” protein candidates, keratin 8 and catalase, were measured by immunofluorescence. Both keratin 8, known to be the primary component of liver aggregates known as ‘Mallory Bodies’, and catalase, a previously unknown component of protein aggregates, were confirmed. These findings suggest that the inability of old mice to turn over ubiquitinated proteins is in part due to their sequestration into insoluble protein aggregates.This work demonstrates that using a multi-omic approach which integrates changes in relative protein abundances, rates of turnover, and modification of proteins may provide novel mechanistic insights into protein homeostasis, particularly in identifying novel aggregating proteins . This apIn future studies, it will be of considerable interest to perform similar analysis in other aged tissues, particularly in tissues with a susceptibility to the formation of protein aggregates and inclusions, such as the brain, heart, and eye. Identifying the protein components of insoluble age-related aggregates will provide mechanistic insights into the formation of aggregates and new possible targets for therapeutic interventions in age-related diseases.


The accessibility of a huge amount of protein-protein interaction (PPI) data has allowed to do research on biological networks that reveal the structure of a protein complex, pathways and its cellular organization. A key demand in computational biology is to recognize the modular structure of such biological networks. The detection of protein complexes from the PPI network, is one of the most challenging and significant problems in the post-genomic era. In Bioinformatics, the frequently employed approach for clustering the networks is Markov Clustering (MCL). Many of the researches for protein complex detection were done on the static PPI network, which suffers from a few drawbacks. To resolve this problem, this paper proposes an approach to detect the dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach (DMCL-EHO). Initially, the proposed method divides the PPI network into a set of dynamic subnetworks under various time points by combining the gene expression data and secondly, it employs the clustering analysis on every subnetwork using the MCL along with Elephant Herd Optimization approach. The experimental analysis was employed on different PPI network datasets and the proposed method surpasses various existing approaches in terms of accuracy measures. This paper identifies the common protein complexes that are expressively enriched in gold-standard datasets and also the pathway annotations of the detected protein complexes using the KEGG database. The protein complexes are molecular combinations of proteins accumulated by multiple PPI networks, which plays a significant part in numerous biological processes. Several proteins are biologically functional only when they interact with their neighbour proteins and create their protein complex. It is crucial to recognize the sets of proteins that form complexes. Thus, numerous computational approaches have been developed to detect and predict protein complexes from the PPI networks.et =  where Ver is the set of nodes and Edg is the set of edges. The set of nodes signifies the proteins and set of edges signifies the interaction between proteins.High-throughput approaches have created a huge quantity of protein interactions that helps to discover the protein complexes from a large PPI network. During the clustering process, the PPI network is considered as an undirected graph N1.To cluster the PPI, the network has been modelled into two types, static PPI network that detects the protein functional modules and the second is the dynamic PPI network that detects protein complexes. The dynamic PPI network is defined as the division of static PPI in a series of time-sequenced subnetworks using gene expression data. There exists the variance between protein functional module and protein complexes. The protein functional module is defined as the cluster of proteins which contributes to a specific cellular process and binds with each other at various time points, whereas protein complexes are defined as the cluster of proteins that interacts with each other at the same time point2, CFinder3, MCL4, COACH5, ClusterOne6, RNSC7, CMC8, and many more. Maulik et al., identified the protein complexes using non-cooperative sequential game9.Many computational approaches of protein complex detection have been focused on static PPI that extract the dense region in PPI networks, which concentrates only on the topological structure of PPI. Some of the methods that use the static PPI for protein complex detection are MCode1, ACO-MCL10, cuckoo search optimization (CSO)11, BiCAMWI using genetic algorithm12, Soft Regularized-MCL13, particle swarm optimization (PSO-MCL)14 and artificial fish school algorithm (AFA-MCL)15. The firefly optimization was employed along with Markov Clustering (F-MCL) on the dynamic PPI network for predicting complexes. The execution time for F-MCL is higher as all the fireflies (proteins) in the population (network) tries to reach the optimal solution (cluster). There are few proteins that are not eligible to come under the cluster and take more iterations to reach the cluster, which may take a long time16.As PPI network continuously transforms with respect to the environment, time and various phases of the cell cycle, the clustering analysis on static PPI does not emulate these dynamic attributes and it is far from optimal solution. Thus, in recent times, various attempts on the clustering process of dynamic PPI network has been initiated along with the gene expression data to enhance the protein complex detection. Also, many evolutionary approaches were employed for analysing the clustering process of the PPI network such as ant colony optimization ACC-DPCThe above-mentioned approaches were effective, but they do not promise a global outcome since they suffer from the effect of unwanted clusters which leads to time consuming. In order to discard the drawbacks of the above-mentioned approaches, a novel approach was proposed to detect the dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach. One of the most important advantages for EHO is that it is the most computationally efficient and has less time consuming compared to F-MCL and other approaches. This is because the unwanted noisy data (unclustered proteins) will be removed from the clan separating operation of EHO approach. The remaining sections of this paper is ordered as follows: Section 2 discusses briefly about the methodology of the proposed approach. Section 3 illustrates the experimental results with various performance measures, Section 4 deliberates about the implementation and discussion of the proposed method in detail and finally Section 5 concludes the paper and recommends for the future enrichments.17. The expression level of a protein will be increased before its expression and will be decreased once the protein has completed its function, and the time points are identified with its expression level, which are higher than a threshold.For detecting the protein complexes, initially, the proposed method divides a static PPI network into a sequence of subnetworks below diverse time points by combining gene expression data to form dynamic model. In order to build a dynamic model, the static PPI network is integrated with gene expression data, which declare the level of gene expression, as well as protein expression. As a protein does not always becomes active at a cell cycle, it is assumed that a protein was active at the time points with its highest expression levelPP = , where Pver, is a set of proteins and PEdg, is a set of interactions between these proteins. In gene expression data, there is a series of T time stamps coming with |Pver| × (T * TR) matrix M, where TR is the number of repetitions of the time series. Each element M of this matrix represents the level of gene expression.Given is a static PPI network Pver, the gene expression is defined as given in the following Eqs (Evi(Pver) is the mean of the expression value of gene Pver at timestamp i, UE(Pver) is the mean of its expression values over times ranging from 1 to T, σ(Pver) is the standard deviation of its expression values, Fl(Pver) is used to show fluctuation of the expression curve of gene Pver. Suppose that the gene expression data is governed by a normal distribution, then S1(Pver) and S2(Pver) are the associated mean and three-sigma value, that is S1(Pver) = UE(Pver) and S2(Pver) = UE(Pver) + 3σ(Pver). In virtue of three-sigma principle, the probability that a value greater than S2(Pver) is not an active point is less than 0.1%. AT(Pver) is the active threshold of gene Pver. Consider the gene (Pver) at timestamp i. If Evi(Pver) > AT(Pver), then the gene Pver is expressed and the gene product exists16.The three-sigma principle is employed to determine if a gene is expressed in a single stamp. For each gene wing Eqs –51\docum1. To clearly demonstrate the three steps, a subnetwork of time point t with t.Selecting seed nodes: This step first computes the clustering coefficient of every node. Then it selects the nodes whose clustering coefficients are greater than a given threshold λc as seed nodes, and puts them into the set of seed nodes at the current time point t, denoted by St. The seed nodes are considered as the candidate clustering centers and represent different clusters of protein complexes. The clustering coefficient of any node i is defined in Eq. (Neigh(i) = {j єi · j) є i, and |Neigh(i)| is the number of neighbor nodes of node i, i at the time point t.d in Eq. :6\documeAttachment nodes addition: For any seed node i , then the nodes which are in the cluster i at the previous time point (t − 1) and also exists in the subnetwork t are put into the cluster i of current time point t. In this way, initial clusters are built. However, some clusters may be too sparse since that not all proteins of previous time point (t − 1) exist at the current time point t. Thus, a refining step is needed to be carried out on the initial clusters.Refining: For any initial cluster of protein complex t, if its density is smaller than a given threshold λd all the nodes in λd. The density of a protein complex d by Eq. :ni and li are number of nodes and edges in cluster 1.In the clustering procedure of every subnetwork, the proposed method starts with constructing the initial protein clusters depending on the protein complexes attained at the prior time point. The initial clusters constructed in the first generation have three steps The procedure for constructing initial clusters has three steps: seed node selection, attachment nodes addition and finally refiningMat for a graph description and the process of Markov clustering method is briefly described18.Now, the clustering analysis of the remaining generations is employed by utilizing the Markov Clustering technique along with the EHO algorithm on every subnetwork. The matrix is constructed that depicts the probabilities of transition of a Markov Chain  based on the graph. The MCL procedure comprises of two activities such as expansion and inflation, which was applied to the matrix that was constructed. The construction of matrix Pp = , where Pver, is a set of proteins and PEdg, is a set of interactions between these proteins. Denote a node in Pver by pvi and an edge between pvi and pvj in PEdg by , in which i and j are the indexes of the corresponding nodes16. W is the weight of edge , which represents the confidence level of the interaction in a weighted PPI networks. Adj is the adjacency matrix of a weighted graph given as Eq.  matrix that shows the probabilities of transition of a random walk defined on the graph. Mat represents the probability of a transition from node pvito pvj. The transition probability from pvito pvj is referred to as the stochastic flow from pvito pvj. All the elements in each column of Mat will sum up to 1 and the matrix is expressed as given in Eq. , balance (b) and penalty proportion (Pp), where ic defines the size of each cluster, b defines the user-specific balance constant that is employed for penalizing higher-propensity neighbours and Pp defines the penalty ratio of the protein nodes, which is also user-specified16. The clustering process using EHO algorithm is briefly explained here for clustering protein complexes. The overall flowchart of the proposed method is shown in Fig. The three crucial parameters of MCL are inflation constant .The static PPI is initially separated into wing Eq. .10\docume for best fitness can be updated using Eq.  which is a scale to regulate the effect of cli and for the dith dimension it can be computed using the Eq. . The pseudocode for the clan separating operator is given in Algorithm 2.To enhance the search capacity of the proposed method, the unclustered proteins and clusters with the lowest fitness will exit in every generation as given in Eq 19.13\docDepending on the clan updating and separating operator, the module of the proposed algorithm is framed as given in Algorithm 3.The relationship between the DMCL-EHO and the protein complex is given in the Table Saccharomyces cerevisiae and Homo Sapiens are DIP21, BioGRID22 and STRING23. The benchmark PPI datasets employed only for Saccharomyces cerevisiae are Gavin2 and Gavin624, Krogan-core and Krogan-extended25, Collins26, and WI-PHI27. The Gavin + Krogan dataset was generated by merging Gavin and Krogan Core datasets. The PPI datasets employed only for Homo Sapiens are HPRD28, HPID29 and PIPs30. Table In this experiment, the datasets which consists of interactions for both Saccharomyces cerevisiae (GSE3431)31 and Homo Sapiens (GSE3933)32 are taken from the GEO database.The gene expression data used in this study for 33, MIPS34, SGD35 for Saccharomyces cerevisiae organism and PCDq36 benchmark dataset for Homo sapiens organism. The percentage of overlapping interactions among the datasets in Gavin2 is 32%, Gavin6 is 53%, Krogan-core is 46%, Collins is 56%, HPRD is 23%, PIPs is 57%, DIP is 2%, BioGRID is 55% and STRING is 47%38.The predicted complexes are compared to gold standard benchmark databases such as CYC2008r and Be be the set of vertices (proteins) and edges (interaction) of a predicted protein complex and benchmark protein complexes.To evaluate and compare the clustering results of predicted protein complexes, the generated complexes were compared and matched with the gold standard benchmark protein complexes. Assume Pr) and benchmark (Be) protein complexes and they are computed based on Eq. Vpr and VBe denotes the set of proteins in predicted and benchmark protein complexes. If CSS is equal to 0, it denotes that the predicted and benchmark protein complexes do not have any common protein complexes. On the contradictory, if CSS is equal to 1, then the predicted complex Pr has the same equal nodes as the benchmark complex Be. Here, if CSS > 0.2, it is considered as the predicted and benchmark protein complexes are identical39.CSS is defined as the closeness of two protein complexes namely predicted  or added noise  in the PPI network. The DIP dataset is used for evaluation of adding and removing random interactions. The missing information of PPI network is processed by removing the proportion of edges randomly  and the false positive information of PPI network is processed by adding the proportion of edges randomly . The performance of the proposed method by adding and removing the random interactions are depicted in Figs From the Figs 27 dataset of Saccharomyces cerevisiae was employed which comprises of 5955 proteins and 50,000 protein interactions. The proposed method and also the existing methods were executed on this large dataset and compared the predicted clusters with the various gold standard benchmark databases. The comparison of the existing and proposed method on WI-PHI dataset is depicted in Figs In addition to analyse the performance of the proposed method on the large PPI dataset, WI-PHIth generation. For all the performance measures, there were fluctuations during the first 10 runs of the experiment and in the future runs reliability was observed. Figures Generally, every metaheuristic approach is based on certain stochastic dissemination. Hence, diverse runs will produce various diverse results. This work implements 500 independent runs in order to score optimal solution. In general, 20 numbers of clans were employed as per literature. The execution process will be terminated, if the best result generated in each iteration remains interchangeable for 100 successive iterations or the maximum number of generations is attained. The assignment of parameter values was adjusted based on the experimental results. It was identified that the parameters of the proposed method that has values of α = 0.5 and β = 0.1 produced better solution among different values and hence were allocated. It was observed that the optimal solution was identified after 315The proposed method was also assessed by utilizing non-parametric test such as, Wilcoxon Matched-Pair Signed-Rank Test among each pair of approaches that produces the statistical consequence. The discrepancy between the F-Measure and Accuracy for every entry in Figs Saccharomyces cerevisiae and the PCDq database for Homo sapiens are employed for assessing the proposed method. Table Many of the existing methods solve the protein complex detection problem based on the topological similarity. But to obtain some useful biological information, the computational methods should be biologically significant in nature. This proposed method is evaluated in the biological significance test. The gold standard benchmark databases are manually annotated based on the information from biologically experimental analysis. Thus, the detected protein complexes obtained from the proposed method is compared and matched with the benchmark databases. Few benchmark databases such as CYC2008, MIPS, SGD databases for 42. The pictorial representation of the Top 5 common protein complexes, gene ontology functions and KEGG pathways of the predicted complexes of proposed method is given as Venn diagram in Figs The predicted complex gene ontology and KEGG pathway enrichment analysis were predicted by using the DAVID gene function classification online tool. The overall predicted complex enrichment score and the respective gene ontology elements and KEGG pathway enrichment scores are displayed in Table Besides the accuracy, the time required to detect the dynamic protein complexes is also an important factor. Processing the various benchmark datasets with various numbers of proteins and different interactions requires more time complexity due to stochastic optimization methods. Subsequently, not all methods were available under the same platform, the execution of many of the approaches were done on virtual machines, which prohibited us from accomplishing an exact comparison of their relative execution times. Thus, here the average execution time of SR-MCL, ACO-MCL, PSO-MCL, AFA-MCL, F-MCL, FOCA AND EHO-MCL is displayed in Fig. From Fig. The Protein Complex detection is an exposed problem for scientists. The solution for the complex problem should be recurrently improved as they are important in the analysis of the biological process. The volume of PPI networks has also been increased due to high-throughput experiments, the lack of accurate computational model for protein complex detection exists. Many of the existing researches were employed on the static PPI data that do not provide accurate biological results. Thus, in this proposed method initially, the static PPI data is converted into dynamic PPI data by integrating the gene expression data. Later, every dynamic subnetwork was clustered based on the popular clustering technique MCL along with the elephant herd optimization method for exploring and exploiting the better solution. The proposed method was employed on various 11 widespread datasets and the predicted complexes were compared with 4 different benchmark databases. Also, the proposed method was evaluated based on noise removal, insertion and deletion of random protein interactions, using the large PPI dataset, various parameter analyses, statistical significance and biological significance. On every evaluation phase, the proposed method was outperforming all other existing approaches and identified the common protein complexes, Gene Ontology functions and KEGG pathways of predicted protein complexes. As a future work, additional information on the unknown protein complexes predicted by the proposed method is to be addressed with the help of biological experts. The proposed method can also be applied and analyzed on weighted PPI networks. Also, various other diseased databases can be used to experiment.Supplementary Information


The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein–peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein–peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution. There is a growing body of evidence that some proteins act in multiple structural states . It has Internal protein motions may be studied both experimentally and with computational methods ,7. For eA variety of computational techniques have been developed to assist these challenging experimental studies ,9. In thRecent works show that methods combining experimental data and computational approaches may produce the most promising pictures of protein equilibrium dynamics ,16. HoweMolecular dynamics (MD) has been so far the most widespread computational method for the investigation of protein motions . HoweverA number of non-MD sampling methods have also been developed to provide a comprehensive image of protein dynamics using limited computational resources. Of these, Monte Carlo (MC) is perhaps the most commonly used and generally applicable sampling method . Monte CAside from the sampling method, a further extension of effective timescales is possible by using a simplified representation of protein structures to reduce the number of a system’s degrees of freedom. The accuracy of the available coarse-grained (CG) models may vary from detailed, almost atomistic representations  interaction terms , and thoThis review briefly describes one of these approaches: an MC-based and knowledge-based interaction scheme for modeling protein–peptide interactions and unfolded states of globular proteins using the CABS coarse-grained protein model. Firstly, the main features of the CABS method will be described, with a focus on their applicability for modeling disordered or unfolded proteins or their fragments. Subsequently, representative case studies will be discussed to provide detailed insights into the modeling results obtained for systems characterized by a varying level of disorder.Since its development, the CABS model  has been applied to a variety of modeling problems, such as protein folding mechanisms ,55,56,57In this review, we discuss the applicability of the CABS CG model and its knowledge-based statistical force field  to the mThe CABS model uses a knowledge-based statistical force field that consists of generic, sequence-independent interaction terms that favor protein-like conformations, and sequence-dependent interaction terms that determine some structural details ,28,78. TThe resulting force field takes a form of a precomputed matrix of contact pseudo-energies, presented schematically in The main difference between CABS and other statistical force fields used in CG models of similar resolution  is the cUsing the mean-force force field derived from folded proteins to simulations of less-structured systems raises justified questions about the validity of this approach in studies of the disordered protein regions. The folding events observed in simulations performed using the CABS force field are consistent with both the experimental data and all-atom MD simulations ,52,80,81s number of MC steps are organized into the y number of MC cycles, and these in the a number of annealing cycles. Each of the MC steps consists of a per-set number of attempts to perform each of the five standard precomputed moves. The available motions and the details of implementation of the sampling scheme are presented in The CABS method uses the MC asymmetric Metropolis sampling scheme that governs a set of local motions as well as multi-residue, small distance moves of the C-alpha atoms see . The met3 cheaper in terms of computational cost than the classical MD). The chosen micro-motions allow (via accumulation over simulation steps) cooperative, large-scale motions. The ensemble of structures produced by the CABS method resembles a dynamic ensemble averaged over the effective timescale. Due to the nature of the method, the picture of local dynamics is distorted ; however, it may be argued  that the long-time pseudo-dynamics recovers the realistic picture of protein motions averaged over time.The combination of the key features of CABS—its representation, force field and the scale of the movements used in the MC scheme—makes it suitable for the investigation of protein pseudo-dynamics. As mentioned above, the fine-grained lattice improves sampling efficiency, achieving effective timescales of milliseconds. As compared with MD, this is a considerably broader time range  (accessed on 30 January 2019). In comparison to other protein–peptide docking tools, reviewed recently [http://biocomp.chem.uw.edu.pl/CABSflex and a standalone application [https://bitbucket.org/lcbio/cabsflex/, accessed on 30 January 2019). This approach has been also incorporated as the module in the Aggrescan3D method for prediction of protein aggregation properties . By using CABS-flex predictions, Aggrescan3D enables predicting the impact of protein conformational fluctuations on aggregation properties. Finally, the CABS model is used in the CABS-fold method for protein structure prediction: in the de novo fashion (from an amino acid sequence only), guided by user-provided templates or user-provided distance restraints . The access to CABS-based tools, together with the tools description, is also available from websites of the laboratories: http://biocomp.chem.uw.edu.pl/ and http://lcbio.pl/ (accessed on 30 January 2019).The CABS model is presently used as a simulation engine of a few multiscale modeling tools that merge CABS with models reconstruction to all-atom resolution. Those include the CABS-dock method for flexible protein-peptide docking  for several model systems that have been extensively studied by experiment and simulation tools. Those studies include barnase ,52, chymFor example, simulation of barnase folding resulted in the adequate reproduction of the folding pathway in strong agreement with NMR data for denatured states and phi-value analysis . The perThe presented case studies review the applications of the CABS model in simulations of disordered or unfolded protein states. As discussed, the method succeeded in capturing the experimentally determined features of the investigated systems, such as binding site localization, key contacts, peptide hot-spot areas, distinctive conformational states of the system, transient encounter complexes and intermediate states in protein folding ,52,63,64There is a number of tools commonly used for sampling of disordered protein states, which predictions agree with the experimental studies ,93,94,95On the other hand, CABS force field side-chain interactions escape a clear interpretation, which may be a disadvantage compared to physics-based approaches that allow for straightforward and detailed description of each of the terms ,94.It is, however, noteworthy that statistical force fields suffer from inherent limitations, depending on the chosen method of derivation. The most commonly discussed challenges include the transferability, solvent interactions and integration of experimental data. Here, we briefly summarize these topics, a detailed discussion of the limitations of this approach, and possible workarounds may be found in review works ,17. The One of the most challenging tasks in modeling protein systems is the effective incorporation of sparse experimental data to drive the modeling procedure. In the CABS model, the experimental data may be readily introduced into the simulation as geometry distance restraints and weighted according to their certainty. A thorough discussion of this possibility is presented in the documentation of CABS-based tools for the fast modeling of protein flexibility and protein–peptide docking ,72,73. O


Our score takes into account the optimal number of atoms that mediate the interaction between pairs of residues and whether these contacts emanate from the main chain or side chain. We tested the score on 174 native interactions for which 100 decoys each were constructed using ZDOCK. The native structure scored better than any of the decoys in 146 cases and was able to rank within the 95th percentile in 162 cases. This easily outperforms a competing method, CIPS. We also benchmarked our scoring scheme on 15 targets from the CAPRI dataset and found that our method had results comparable to that of CIPS. Further, our method is able to analyse higher order protein complexes without the need to explicitly identify chains as receptors or ligands. The PIZSA server is easy to use and could be used to score any input three-dimensional structure and provide a residue pair-wise break up of the results. Attractively, our server offers a platform for users to upload their own potentials and could serve as an ideal testing ground for this class of scoring schemes.Our web server, PIZSA ( Protein-protein interactions (PPIs) are key to almost all cellular functions. In order to better appreciate and/or get insights into these cellular processes, it is crucial that we discover the entire network of underlying interactions. Determining protein interacting partners experimentally is both, time-consuming and expensive. Also, given the magnitude of the problem, where estimates say there could be upto 130,000 PPIs among the Human proteome alone , it is iComputational predictions usually consider geometric and chemical complementarity of the interactors to suggest viable PPIs. Typically, the problem involves sampling various conformations of the interactors (with respect to one another) and then scoring each of these proposed associations. In this manuscript, we present a platform for scoring putative PPIs demonstrated with our newly developed statistical potential that builds on an older formulation . Our scoOur scoring scheme involves the computation of three matrices of amino acid pair preferences for interactions mediated by main chain–main chain, main chain–side chain and side chain–side chain atomic contacts. We explored and optimized values for the most effective contact distance based on a benchmark . We haveIn the analysis of our scoring scheme presented here, we have compared its performance on two datasets of native PPIs and decoys . We haveZ score that predicts the likelihood of a protein complex to form a stable association by comparing its score to a background distribution of scores obtained from non-native interactions. Protein complexes are predicted to form stable associations if they acquire Z scores above certain thresholds. Z score thresholds have been optimized at different distance cut-offs, the details of which along with the details of our scoring functions are reported elsewhere . Every residue pair score is further normalized by their atomic propensities and a clash penalty. Atomic propensities are a measure of how frequently a specific number of atoms are involved in a residue pair interaction and clash penalties are used to penalize interactions with steric clashes. The final score assigned to a protein–protein complex is the sum of all the scores assigned to its interacting residue pairs. Every protein complex is also assigned a The ability of our method to identify the native complex amongst a set of decoys was tested on the ZDOCK Docking Benchmark 4.0 ,12 and CWe tested our method’s ability to score native protein complexes optimally on the ZDOCK Docking Benchmark 4.0 and compared our performance with that of CIPS. Of the 174 native protein complexes, we were able to correctly identify the native structure as the best scoring in 146 cases (84%) whereas CIPS identified the native to be the best in only 26 cases (15%). Furthermore, our method ranked native interactions among the top three interactions in 160 cases (92%), among the top 5 interactions in 162 cases (93%) and among the top 10 interactions in 166 cases (95%) whereas CIPS ranked the native complexes among the top 3, 5 and 10 interactions in 50 (29%), 59 (34%) and 79 (45%) cases respectively (Table Z Score assessment. Since the dataset has a larger representation of the negatives as compared to the positives (100 decoys for a single native complex), we report our performance in terms of rates instead of absolute numbers. We were able to correctly predict 158 of 174 native structures as stable associations at a distance threshold of 4 Å and a Z Score threshold of 1.5 with a true positive rate of 0.91, true negative rate of 0.89, false positive rate of 0.11 and false negative rate of 0.09. PIZSA achieved a classification accuracy of 0.89, balanced accuracy of 0.90 and an MCC of 0.80 .We also tested our method’s ability to classify native protein–protein interactions as stable associations using a P-value of 0.5 on a Wilcoxon signed-rank test).We tested our method’s ability to identify native structures among a set of decoys in different CAPRI rounds. 15 CAPRI targets were evaluated of which 12 were dimeric complexes and 3 were higher order associations (>2 chains). PIZSA was able to identify the native complex in the top 10% of the decoys in six cases and in the top 20% of the decoys in 10 cases. We compared the ranks of native complexes assigned by PIZSA with those assigned by CIPS  to define amino acid interactions and also of using their own custom scoring matrices for different modes of interactions. The default settings for PIZSA utilize the pairwise amino acid preferences defined for all atom types at a distance threshold of 4 Å. The server also has a provision for carrying out mutational analysis. In the current version of the server, this utility has not yet been optimized and is currently being tested and refined and as such the results produced by this module is outside the consideration of this study. Copious help pages guide users through the website. Finally, users of the web server also have the option of providing an email address to receive notifications especially for computationally intensive analyses.The PIZSA web server provides an easy-to-use graphical interface for the assessment of the stability of quaternary protein assemblies. The web server is freely accessible without login requirements at Z Score obtained by the protein complex and also whether or not the complex was predicted as a stable association  for the input protein complex. Individual scores for every interacting residue pair are reported in the ‘Interacting Interface Residues’ tab. All interface residues are listed by the residue number, residue name, the polypeptide chain that they belong to, their interacting partner and their interaction score. Users have the option to filter this list by searching for specific residue names or residue numbers. Hovering the cursor over this list highlights the interacting residue pairs on the structure that is displayed by default as a ribbon diagram on the right side of the window and is color-coded to distinguish different chains. Since the protein complex is rendered using JSmol  that are rotated by 180○ with respect to each other and acquire two-fold symmetry around a central water-filled cavity. The haemoglobin molecule can exist in two states: oxygenated (or R-state) and deoxygenated (or T-state). Upon the binding of oxygen, the haemoglobin molecule transitions from the T-state to the R-state undergoing a conformational change in which the α2β2 dimer rotates ∼15○ relative to the α1β1  arising from more extensive interactions in the deoxy-haemoglobin . Such a distinction is necessary as the same interacting residue pairs have varying preferences for different modes of interactions . InteracAmino acid pairs on the interface are scored based on their pairing preferences and their tendencies to interact with a certain number of atoms . These pWe demonstrated the ability of PIZSA to identify native complexes and compared it with that of CIPS on two different datasets, the ZDOCK Docking Benchmark 4.0 and the CAPRI decoy set. Our method identified 146 out of 174 native complexes as the best interaction in the ZDOCK data set and outperformed CIPS that was able to identify only 26 native complexes as the best interactions. On the CAPRI dataset, both methods had comparable results, however CIPS can only score multimeric assemblies if they are bifurcated into chains for receptors and chains for ligands. The CAPRI dataset consists of many more decoys that are close to the native structure, or in the immediate proximity of the true binding site, than the ZDOCK decoys. This follows from the method ZDOCK uses to create the decoys - by design 5/6 of the complexes have non-native contact while the other 1/6 explore different conformations in the true binding site. We believe that in comparison to CIPS, PIZSA can better discern between completely non-native structures and native like structures.PIZSA successfully identified the native complexes of a dodecamer (T95: 4R8P), and two trimers (T99: 4UEL and T100: 4UF6) with percentile ranks of 80, 97 and 71 respectively. Our method can potentially be used to assess models of higher order multimeric protein complexes such as those constructed by fitting monomeric subunits onto electron density maps .We have demonstrated the utility of PIZSA using two case studies. In the first of the two, we show that PIZSA is able to identify alternative binding modes of Ras with SOS along with the Ras:SOS:Ras ternary complex. In the second case study we demonstrate that our scores concur with experimental observations in suggesting that the deoxygenated tetrameric complex of Haemoglobin is more stable than its oxygenated counterpart.gkz368_Supplemental_FilesClick here for additional data file.


In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions. Protein-protein interactions are receiving considerable attention as targets for rational drug design. Interest in both inhibitors of protein-protein interactions ,3,4,5,6 i.e., below a cut-off distance). Introduced to provide a more reduced representation of a protein structure than its full 3D atomic coordinates, contact maps have been successfully exploited for describing similarity between protein structures [i.e., lists of interacting residues (defined on the basis of a cut-off distance), residues at the interface (defined on the basis of the buried surface upon complex formation), intermolecular H-bonds, etc., plus a 3D visualization with interface residues highlighted.Details regarding the predicted or experimental structure of a protein complex can be automatically or manually extracted from the 3D model and summarized in tables. However, a simplified representation of the complex interface delivering the relevant information at a single glance is desirable, especially when the comparison between different binding modes is required. With these considerations in mind, we developed a web tool for the analysis and visualization of protein complexes based on the use of intermolecular contact maps, which we named COCOMAPS (COmplexes COntact MAPS) . Intermoructures . Analogoructures . In addii.e., a map where the conservation rates of the different contacts are represented on a gray scale . When measuring the conservation of the various contacts in several ensembles of docking models submitted to CAPRI , a community-wide blind docking experiment, we interestingly found that most conserved contacts in each ensemble usually correspond to the “native” ones, i.e., those observed in the experimental structure [Since contact maps are demonstrated to be a particularly efficient way to represent the interface in protein complexes, we decided to extend this kind of visualization to analyze ensembles of docking models. As an ensemble of models will include both incorrect and hopefully correct solutions, several different contacts will be featured in the ensemble, with different conservation rates (or frequencies). To represent them, we introduced a “consensus contact map”, tructure .per se, the frequency of inter-residue contacts used by CONSRANK is a particularly effective measure to highlight the consensus itself. The CONSRANK algorithm was also shown to perform very well on a limited number of models, provided that more than one correct solution is included in the ensemble, thus extending its applicability to cases where a few dozens of models are available [Based on this promising observation, we then developed CONSRANK (CONSensus-RANKing), a consensus approach to the scoring and ranking of docking models, which ranks models based on their ability to match the most conserved contacts in the ensemble to which they belong . Traditivailable .https://www.molnac.unisa.it/BioTools/consrank/) [More recently, we implemented a web server for CONSRANK, which integrates the above analyses, extends them to protein-DNA and protein-RNA complexes and makes them easily available to the scientific community through an advanced interactive web interface (at the URL: nsrank/) . One thoIn the following, the potential of the above tools is illustrated by application to exemplary cases, including a possible application in rational drug design targeting protein-protein interactions.S. sagittifolia (PDB ID: 3E8L) [2 (vs. 776 Å2 of the AC interface) and that buried residues (more than 90%) are all hydrophobic: besides Leu87 at P1, Ile88 and Phe115.As an exemplary case, we will use in the following a complex between bovine trypsin and the inhibitor API-A from D: 3E8L) . In thisD: 3E8L) ,39. The i.e., T40CA and T40CB. This means that the same models were assessed twice, using as a reference the two crystallographic interfaces, one at a time. Assessment showed that models included 97 and 66 correct solutions, according to the first (Arg145) and the second (Leu87) binding modes, respectively.As mentioned above, this double interface trypsin-inhibitor complex was a target in the CAPRI experiment (Round 18). Therefore, before the experimental structure shown in a was madIn the following, we apply our analysis and ranking approach to this ensemble of 368 models (T40 prediction set). The 2D consensus map from the 368 available models for target T40 is shown in We recently applied a CONS-COCOMAPS/CONSRANK combined approach to analyze and score the models that we obtained for an antibody-to-antibody complex, related to celiac disease. The two interactors were an anti-type 2 transglutaminase (TG2) antibody, Ab1-MB2.8, derived from celiac patients, and a specific anti-idiotype antibody, Ab2-AIM2, elicited in mouse and competing with TG2 for anti-TG2 binding .a posteriori comparison between the selected model and the available experimental structures of idiotype-anti-idiotype antibody complexes showed that it is strikingly similar to most of them, and particularly similar to the complex between Ab1-D1.3 and Ab2-E5.2 , despitThe approach illustrated above, based on the conservation of inter-residue contacts and their visualization in contact maps, can also be usefully applied for analyzing ensembles of structures other than docking decoys. For instance, it can be extended to usefully characterize the molecular dynamics (MD) evolution of the interface in a protein-protein complex, as well as the interface between domains and sub-domains of a single-chain protein.We recently introduced a tool specifically devoted to the analysis of the MD trajectories of protein complexes, MDcons . To demoLater, we used the above approach to analyze the MD trajectories for the wild-type (WT) serine protease FXa, playing a major role in the coagulation cascade, and a panel of five pathogenic point mutations . We partAnother recent application of the method was the study of the dynamic behavior of the ADAMTS13 Asp173Gly mutant, causing a severe deficiency of the enzyme activity and antigen level, associated with periodic thrombocytopenia and hemolytic anemia in patients. Our approach to the analysis of molecular dynamics simulations allows one to outline that in the mutant, the interface area between two adjacent domains  significantly decreases during the simulations, while the proline-rich linker region between them undergoes extensive conformational changes . FurtherThe availability of the three-dimensional structure of a protein-protein complex is key to the possible rational design of inhibitors targeting protein-protein interactions. Deep analysis of the complex interface is in fact the first step toward a successful design. We suggest that COCOMAPS may assist in such analysis, by providing detailed information on the interface, complemented by intuitive and immediate visualization. As a possible example, we show in in vitro and in cellular assays, were identified by high throughput screening, as potential antivirals for the treatment of Human papillomavirus (HPV) infections [The complex is shown in the default 3D representation provided by COCOMAPS a, with rfections ,49. The fections ). In thiAlternatively, when an experimental structure of the protein-protein complex of interest is missing, while structures for the two separate proteins are available, CONSRANK may be used in the analysis of 3D models obtained by docking simulations. In particular, analysis of the conservation of inter-residue contacts provided by the CONSRANK server may be used to help identify possible hotspots for the interaction, to be targeted by designed inhibitors.Through the examples illustrated above, we have shown that visualization by inter-molecular contact maps is a powerful way to analyze the interface in protein-protein complex structures, providing the needed information in a compact ready-to-read form. Integrated with the measure of the conservation of inter-residue contacts, it also represents a valuable tool to both analyze and rank protein-protein docking models. CONSRANK, the consensus approach we developed based on the above idea, has indeed widely tested and shown to perform competitively with state-of-the-art scoring methods ,36. The We believe that the approach we reviewed here has great potential, not yet fully exploited, in all applications implying the use of structural information for protein-protein complexes, including drug discovery. As a final remark, we are aware that our approach could usefully be extended to study protein-small ligand complexes, and we are currently working in this direction.http://www.pymol.com).Within the COCOMAPS, CONSRANK and MDcons analyses, two residues are considered in contact, accordingly to the definition used in the CAPRI experiment , if they


Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a proper analysis of the structure-function relationship of proteins: (i) the lack of a formal definition of what critical residues are and (ii) the lack of a systematic evaluation of methods and protein structure features. To address this problem, here we introduce an index to quantify the protein-function criticality of a residue based on experimental data and a strategy aimed to optimize both, descriptors of protein structure  and machine learning algorithms, to minimize the error in the classification of critical residues. We observed that both physicochemical and centrality descriptors of residues effectively relate protein structure and protein function, and that physicochemical descriptors better describe critical residues. We also show that critical residues are better classified when residue criticality is considered as a binary attribute . Using this binary annotation for critical residues 8 models rendered accurate and non-overlapping classification of critical residues, confirming the multi-factorial character of the structure-function relationship of proteins. The study of the relationship between protein structure and protein function constitutes an open problem in biochemistry and bioinformatics. The main motivation for addressing this problem is dual: on the one hand it may facilitate the annotation of protein function and on the other hand it may help understand the mechanism(s) by which a given protein structure renders a particular function. Given the accumulation of protein sequences most efforts to predict protein function are based on this information. Alternatively, the mechanisms of protein function are studied based on the atomic three-dimensional structure of proteins (hereafter referred to as protein structure). Despite this difference in the data source, these two approaches converge in the prediction of critical residues for protein function revealing the relevance of this subject . Criticapi as part of the set of protein structures P and a protein function ai as part of the set of protein functions A. Protein structure is defined in turn by two sets: the group of amino acid residues an i-protein is made of (AAi) and the group of relationships between these residues within the i-protein (Ei). Alternatively, protein function may be represented by the set of critical residues (CR). Hence, P may be related to A through these three sets . We have previously noted that the relationship between the protein function and the protein structure may be modeled by a mathematical function of the bijection type :[path-to-weka.jar] -Djava.io.tmpdir = [path_to-save-autoweka-tmp-files] weka.classifiers.meta.AutoWEKAClassifier –t [path-to-arff-training-dataset] –d [path-to-model-output-filename] –timeLimit [1500 or 3000] -memLimit 32000 > [path-to-output-file-with-statistics]The arff file used for training is available as For every model trained to classify critical residues we compared their classifications to determine how similar these were. To do so, we identified every residue classified as critical for each model and that number was used to normalize the number of identical classifications between each pair of models. The predictions as critical residues for each model and test set are available as We implemented a two-stage feature selection procedure that uses a filter in the first stage and a wrapper at the second stage. The filter method was employed to remove redundant and irrelevant descriptors for each descriptor set. For the remainder descriptors, a wrapper method was used to determine the subset of descriptors that improve the performance for critical residue classification. The main details of the feature selection method are described below.https://github.com/3eltran/RemoveCorrelatedAttributes). The command line used was: The Spearman’s correlation coefficient and the information gain were used to evaluate the redundancy and irrelevancy of each descriptor, respectively. To remove highly correlated descriptors from the dataset we used in-house remove Correlated Attributes program (available as Python3.6 (path-to removeCorrelatedAttributes.py) -i (path-to-arff-training-dataset) -o (path-to-output-filename).WEKA filter attribute selection was used to remove descriptors with information gain values equal to zero. The used command line was: java -cp [path-to-weka.jar] weka.filters.supervised.attribute.AttributeSelection -E “weka.attributeSelection.InfoGainAttributeEval” -S “weka.attributeSelection.Ranker -T 0 -N -1” -i [path-to-arff-training-dataset] -o [path-to-output-filename].Training sets to be used in this command were derived from the removeCorrelatedAttributes.py program  and are To determine the subset of descriptors that improves classification accuracy, we implemented a wrapper method. It consists of two modules: a search strategy, and an evaluation measure.m descriptors is 2m − 1. For this reason, we implemented a genetic algorithm to explore efficiently the search space. Genetic Algorithms (GAs) are known to work well in large-scale problems with more than 50 descriptors, furthermore GAs are capable of avoiding getting stuck at local optima [Search strategy. In practice, searching over all possible subset of descriptors is not feasible because the size of the search space for l optima . The maiFirst, a chromosome was constructed with a subset of indices, where each index corresponds to a selected descriptor. The initial population consists of 50 randomly generated chromosomes with a size between 1 and (m/10), m denotes the number of descriptors. Afterwards we employed a binary tournament with replacement as parent selection scheme. For each pair of parents, we applied the Subset Size-Oriented Common Feature (SSOCF)  crossoveIn the evaluation measure module, the quality of each chromosome was evaluated. First, the chromosome was decoded into a dataset with only the selected features. Next, the dataset was employed as an input to the support vector machine (SVM) in order to generate the classification model. The SVM was used from WEKA where it is referred to as LIbLINEAR. Considering the class imbalance in JAMMING1SET and JAMMING3SET, we applied regularization to rescale the penalty cost C for the critical and non-critical classes. The weights employed for the JAMMING1SET were 9 and 3 for the critical and non-critical classes, respectively. Alternatively, the weights for the JAMMING3SET were 5 and 1.22 for the critical and non-critical classes. In order to evaluate the generated model, we performed a 10-fold cross-validation and the Matthew’s Correlation Coefficient (MCC) was used as a performance measure to select the best subset of descriptors.In this work a systematic strategy to test descriptors and machine-learning algorithms to classify critical residues for protein function is presented. Also, a mathematical criterion to represent mutagenesis experiments is introduced to quantify the criticality of a residue for protein function. Our results show that our function criticality index recapitulates different definitions of critical residues and provide evidence that physicochemical and centralities descriptors of protein structure effectively classify critical residues. Our results provide the basis to further improve on the development of models to predict critical residues.


Protein is the most exposed biomolecule in the aqueous environment of the cell. Its structure maintains a delicate balance between therigidity and the flexibility that imparts binding specificity to its substrate/ligand, etc. Intramolecular interactions of polar and non-polargroups of amino acid residues and intermolecular weak interactions between these groups and shell-waters may contribute to the overallstability of the tertiary structure. However, the question as to what are the dynamics of interactions of shell-water with respect to weakforces and atom-groups of protein (AGP), requires systematic investigations. In this end, we have developed a procedure POWAINDv1.0that analyzes interactions of crystallographic shell-waters (CSH) in residues and AGP specific manner. The shell-water and AGP specificbridge-interactions are also extracted. Further, the program analyzes favorable and unfavorable nature of each interaction based on theactual and 75% of the sum of van der Waals (vdW) radii of interacting atoms. The EXCEL-outputs are useful in understanding the profilefor AGP-CSH interactions and contribution of each component in AGP. Taken together, the program provides intricate details on CSHproteininteractions and finds application in the structural Bioinformatics Water is the solvent for structure and stability of biomolecules X-ray crystallography, NMR and molecular dynamics simulationare the available methods that potentially detect the presence ofshell-water in protein molecules To gain insight into the water-protein interactions, one needs tounderstand PAGs of each amino acid. For example, if we considerASP, it has two polar atoms in the main-chain (i.e. O and N) thatmay mediate polar interaction with water via HB. alpha-, beta- andgamma-carbons can mediate van der Waals interactions with watermolecules. There are two delta-oxygens that can mediate HB andELS interactions with waters, as -COOH dissociates as -COO(-) in the cellular aqueous medium. Overall, theneutral, positively and negatively charged PAGs of 11 amino acids(DESTRKHYNQC) forming the complete subset for polarinteractions. In these aspects, the following questions arenoteworthy. Which PAG/residue preferably binds more waters?Which PAG/residue dominates in HB/ELS interactions overothers? Which PAG/residues involve in maximum bridgeinteractions? These questions are relevant in understanding therole of a polar component of protein in protein-water interactionsin the surface and in the core of the protein. Similarly, the role ofnon-polar atom groups (NPAGs) is required to be understood. It is,however, difficult to address these interactions manually. Atpresent, there are about 1.3 lacs of protein structures in the RCSBdatabase of which 26,000 have small molecule ligands. Almost allof these structures possess CSH as an essential component of thestructure. Since the input data is highly enriched in the database,and since the questions raised above are highly relevant, systematicextraction of PGAs and NPGAs specific water-protein interactionsusing appropriately screened (e.g. resolution and see above)dataset appears to be demanding.In this work, we present details on our procedure, POWAINDv1.0that function in CYGWIN-32bit UNIX like operating system in awindow environment. It takes single crystal structure (may havemultiple chains) and multiple distance-containing file as input toredirect outputs, which reveal detailed insight into the CSH-AGPinteractions. While flow-chart shows operational details of theprogram, analysis of the results using a representative crystalstructure provide application and scope of the program. Overall,the program provides details on CSH-AGP interactions and findsapplication in structural bioinformatics.Upon start of the program, it verifies the directory structure and distance-input-file (DIF) in the working directory . In theThe program runs in CYGWIN 32bit OS in a window environmentfrom C/B-shell. Before the run of the program, the input distancefile ("inp_dist") is to be edited as per the requirement. The programcan interpret distances up to third decimal points, which may berequired to increase the precision of analysis and dynamics ofinteractions of water as a function of weak forces .Two inputs are necessary for a successful run of the program. Firstis a protein data-bank (PDB) file in X-ray format and the second isthe input distances in the "inp_dist" file. Although, the programcan analyze an X-ray structure file with any number of chains, suchPDB files not necessarily provide details on water-proteininteractions for the sites of protein-protein, protein-ligandinteractions. Such a problem could be avoided by the choice ofmonomeric protein structure, apart from other precautions (seeabove).The output of POWAINDv1.0 includes three excel files and adistance-range-specific text file. Excel files include distance-rangespecificfrequency of bound-water for i] residue-types and ii] atomtypesof protein. These two outputs are also repeated to givenormalized outputs of these items. Third excel file details ondistance-range-specific bridge-interactions. The text file is adistance-range-specific file that reports favorable/unfavorableinteractions along with other details .To understand the functioning, reliability, and interpretation ofoutputs of POWAINDv1.0, we performed detailed analysis on3E9L.pdb, a monomer with resolution 1.95Å; UniProt ID Q6P2Q9.The protein, 3E9l is the domain of human Pre-mRNA-processingsplicingfactor 8, for the range 1755-2016. It has 257 residues thatbind 315 moles of crystal waters.3E9l has been analyzed using PHYSICO2 Forth, since we were interested to explore the interactions betweenshell-water and protein, and since salt-bridge/ion-pair To address the above-mentioned issues, we have analyzed3E9L.pdb using POWAINDv1.0. The profile of normalized HOHproteininteractions by various atom-types  are presented in First, the profile for polar atom-types , red broThe charged atom-types of acidic and basic (cyan) also contribute.It is interesting to note that the non-polar atom-types  have no interactions for the region.Second, the profile for the C-type , pink, nThird, the region R3 is centered on 5Å, which is beyond hydrogenbonding limit. The region is suitable for ion-pair, van der Waals,cation-p and anion-p types of interactions Binding of waters by different amino acid residues depends onmany factors such as their side-chains, location in proteinsstructure First, of all components of polar type , OD1 of In water-protein interactions, the most interesting observations arethe bridge interactions, which are known to contribute to theoverall stability of local structure and topology We have developed a program POWAINDv1.0 that takes a PDBfile and a distance file as input and redirects four types of outputs.Residue-specific and atom-type-specific absolute and normalizedfrequency of water-protein interactions is written in a range-ofdistancespecific manner. Different types of bridge interactions areseparately redirected in EXCEL file. A text output is also producedwith details on residues of a protein in the range of distancespecific manner. The case study with 3E9L.pdb using the programshows a number of interesting observations. While polar and nonpolarinteractions are dominated in short (≤3.5Å) and intermediateranges (≤4.3Å) respectively, both these interactions making almostequal contributions at long range (≤5.5Å). Further insight isrevealed in the analyses of the contribution of components in eachof this type . The rangeof distance specific analysis of bridge interactions would highlightthe importance of crystal water. Taken together, the programprovides details on protein-water interactions, the knowledge ofwhich seems to have potential applications in protein engineeringand structural bioinformatics.


Glycine max leaves” [1]. Phosphorylation plays a critical role in the regulation of the biological activities of proteins. However, phosphorylation-mediated regulation of proteins and pathways involved in ethylene (ET) and abscisic acid (ABA) signaling is currently poorly understood. Therefore, we used a shotgun proteomics approach to identify the phosphopeptides and phosphoproteins in response to ET, ABA and combined ET+ABA treatments. Here, we present the Mass spectrometry, protein–protein interaction, Gene ontology and KEGG data associated with the ET and ABA signaling in soybean leaves [1].The data reported here are associated with the article “Comparative phosphoproteome analysis upon ethylene and abscisic acid treatment in  Daewon seeds were germinated in the soil and allowed to grow in a growth chamber at 25 °C  for one month 2.22 based phosphopeptide enrichment kit (Pierce Biotechnology) following manufacturer׳s protocol.Protein extraction and phosphopeptides enrichment were carried out as described previously 2.3QExactive™ Orbitrap High-Resolution Mass Spectrometer  was used for the identification of enriched phosphopeptides exactly using the same protocol as described earlier 2.4https://david.ncifcrf.gov/tools.jsp) with integrated PANTHER Gene Ontology (GO) and KEGG pathways was used for the functional annotation of the identified phosphoproteins. For the construction of protein–protein interaction (PPI) network, homologs of the identified phosphoproteins were searched in the Arabidopsis database obtained from Phytozome . The obtained Arabidopsis homologs were used for the PPI analysis by the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) database (http://string-db.org/) and arranged using Cytoscape tool.DAVID functional annotation tool (