Mycobacterium tuberculosis has been extensively demonstrated. In vitro and in vivo assays have identified that Erp interacts with Rv1417 and Rv2617c proteins, forming putative transient molecular complexes prior to localization to the cell envelope. Although new insights into the interactions and functions of Erp have emerged over the years, knowledge about its structure and protein–protein interactions at the atomistic level has not been sufficiently explored. In this work, we have combined several in silico methodologies to gain new insights into the structural relationship between these proteins. Two system conditions were evaluated by MD simulations: Rv1417 and Rv2617c embedded in a lipid membrane and another with a semi-polar solvent to mimic the electrostatic conditions on the membrane surface. The Erp protein was simulated as an unanchored structure. Stabilized structures were docked, and complexes were evaluated to recognize the main residues involved in protein–protein interactions. Our results show the influence of the medium on the structural conformation of proteins. Globular conformations were favored under high polarity conditions and showed a higher energetic affinity in complex formation. Meanwhile, disordered conformations were favored under semi-polar conditions and an increase in the number of contacts between residues was observed. In addition, the electrostatic potential analysis showed remarkable changes in protein interactions due to the polarity of the medium, demonstrating the relevance of Erp protein in heterodimer formation. On the other hand, contact analysis showed that several C-terminal residues of Erp were involved in the protein interactions, which seems to contradict experimental observations; however, these complexes could be transient forms. The findings presented in this work are intended to open new perspectives in the studies of Erp protein molecular interactions and to improve the knowledge about its function and role in the virulence of Mycobacterium tuberculosis.Nowadays, tuberculosis is the second leading cause of death from a monopathogenic transmitted disease, only ahead of COVID-19. The role of exported repetitive protein (Erp) in the virulence of  Before the COVID-19 pandemic, tuberculosis (TB) was considered the foremost cause of death from an infectious disease worldwide . In addiMycobacterium tuberculosis (Mtb) [Mtb [TB is a chronic disease caused by the intracellular pathogen is (Mtb) ,8,9. Desis (Mtb) . The ignis (Mtb) ,11. Furttb) [Mtb . Four drtb) [Mtb . Unfortutb) [Mtb . Approvetb) [Mtb . The mostb) [Mtb ,14.Mtb [Mtb does not produce a single dominant virulence factor responsible for causing the disease but induces it through several groups of factors that intervene in the different stages of pathogenesis. In this sense, in vitro cell culture assays, and in vivo, have demonstrated the determinant role of the exported repetitive protein (Erp) in the virulence factor of Mtb [Mtb mutant strains that were rapidly cleared from the lungs of infected animals [Mtb-induced phagosome maturation and is involved in the oxidative stress resistance mechanism [Considerable efforts have been made in recent years to understand the pathogenesis, virulence, and genomics of Mtb ,16,17,18Mtb  mentionsr of Mtb ,20,21. B animals . However animals . To clar animals . Despite animals ,25. Klep animals . Recent echanism .Mtb-secreting proteins and their relationship with infection and virulence factors may undermine the development of new anti-TB drugs with great potential. Erp is one of the main proteins involved in Mtb virulence [The lack of knowledge of the molecular interactions between irulence ,20,21,22irulence . The posThe aim of this work is to explore the conformational dynamics of Rv1417 and Rv2617c proteins under membrane and solution conditions on a nanosecond timescale. Furthermore, their interactions with the Erp protein have been investigated. MD simulations and various in silico techniques are carried out to identify important residues involved in protein–protein interactions. The structural insights gained from this study shed light on the binding modes and active sites of the analyzed proteins.Mtb protein structures. Sequences and structures of the exported repetitive protein, Erp (Rv3810) [Mtb bacterium were retrieved from the Uniprot and AlphaFold databases [To explore the protein–protein interactions, we selected three (Rv3810) , unchara(Rv3810) , and pro(Rv3810)  of Mtb batabases ,30,31.Prior to preparing the protein systems for the MD simulations, the initial 3D structures were obtained in two ways. First, the structures predicted by the AlphaFold methodology were retrieved from the server database and used as initial models. Second, based on the amino acid sequences, the structures were modeled using the I-TASSER ,33,34 seFor the protein membrane, explicit water molecules were considered as solvent. The Rv1417 and Rv2617c proteins (RvPs) were embedded in a dipalmitoylphosphatidylcholine (DPPC) bilayer with 512 and 509 lipids, respectively. A 128-DPPC bilayer with 64 lipid molecules for each layer was replicated four times (twice in the x and y directions) to obtain the membrane model. The InflateGRO methodology was used for the incorporation of proteins into the lipid membrane . Only prz axis. TIP4P water model molecules were used to solvate the systems [Molecular dynamics (MD) simulations were performed in Gromacs 2020.3 ,39 with  systems . Next, w systems . To neutOnce the minimum energy structures were obtained, several docking calculations were performed to analyze the multiple interactions. A first docking was performed between RvPs and Erp protein to obtain the main interacting residues in the protein–protein complexes. For this, and based on complementary forms, the PatchDock server was used to obtain the complexes ,43. The gmx sham module, while the RMSD and RG were considered as the atomic position variables with respect to their mean structure. Figures related to the latter were constructed using Wolfram Mathematica 12.1 [The statistical results, root mean square deviation (RMSD), root mean square fluctuation (RMSF), radii of gyration (RG), solvent-accessible surface area (SASA), hydrogen bonds (HB), binding free energies (BFE), structures, trajectories and B-factor maps were obtained using the Gromacs modules. Analysis of the structure properties was performed using the MD trajectories of the last 200 ns of each simulation and then visualized using the Visual Molecular Dynamics (VMD)  and the ica 12.1 .Mtb [Experimental evidence has shown that RvPs interact with the Erp protein, which is one of the important virulence factors of Mtb . These iMtb . DespiteTo address these issues, MD simulations and docking analyses were performed to evaluate protein–protein interactions. For this purpose, RvPs structures were embedded in a DPPC bilayer membrane in an aqueous solution, and the final MD complexes were docked with the isolated Erp protein. Finally, electrostatic potential and dynamic pocket analyses were performed to identify the interaction sites of potential drug targets.The characterization of protein structure is an important key to understanding protein functions. Prior to MD simulations, analyses of the topology of transmembrane segments and intrinsically disordered regions (IDRs) were performed to gain insight into the flexibility domains as well as the position and orientation of proteins in the lipid bilayer. The membrane topologies of proteins play an important role in their physiological function and are crucial in the recognition of novel therapeutic targets ,55,56. TThe results show important differences with respect to previous analyses on the orientation of RvPs. Klepp et al. mention that Rv2617c has three transmembrane helices, and the probability that the C-terminal domain is oriented outward is about 82%. Using the improved TMHMM server  and Rv2617c . Finally, the Erp protein was modeled as an unbound structure due to a large number of disordered residues. While AFMs consider it as an unfolded protein, ITMs constructed it as a compact protein.Based on the above results, protein–membrane systems were constructed using AlphaFold models (AFMs) due to their well-defined membrane motifs: two alpha-helices in the structure of Rv1417  and four in Rv2617c . Second modeling of the structure was performed to compare these models with those obtained with the I-TASSER algorithms. Initially, the three protein systems were prepared to evaluate the stability of the proteins (Rv1417 and Rv2617c) within their membranes, or under unrestricted conditions (Erp). For this purpose, 500 ns of MD simulations were performed using water molecules as solvent. Because the precise sites where protein–protein interactions occur have not been established, second MD simulations were performed to determine conformational stability under unrestricted membrane conditions. Considering that interactions could occur close to the membrane surface, a semi-polar environment was considered to mimic the interfacial region of the membrane ,64. For  309.65K ) were usassembly ,66, whicassembly .The predicted stability of the AFMs structures can be observed in the RMSD plots A. Both senvelope .Hydrogen bonding analyses show a similar number of intramolecular polar interactions in both AFMs (100/109) and ITMs structures (106/101) for Rv1417 and Rv2617c, respectively, at the beginning of the MD simulations. Despite the loss of compactness and secondary structure, the average hydrogen bond (HB) values of ITMs were comparable to those of the AFMs structures . This faBy analyzing the MD trajectories, it could be observed that the proteins had some diffusion across the membrane A. ContinThe density profiles show good membrane behavior throughout the MD simulations B. Takingectively C,D.msd was used to calculate the lateral diffusion of the proteins. The previous visual analysis of the protein trajectories showed the higher mobility of the Rv2617c protein across the lipid bilayer. However, mean square displacement (MSD) graph  graph E and difHowever, the displacement of membrane molecules was larger in Rv2617c than in Rv1417 . The loss of helical structure is higher in AFMs (60.7%) compared to ITMs (32.9%), with the formation of a small anti-parallel As mentioned above, disordered structures have a greater ability to interact with other structures, and this fact can be partially explained in To analyze protein–protein interactions, minimum energy structures were achieved through FEL analysis, and molecular docking calculations were performed to obtain the protein complexes. In total, 1000 protein complexes were analyzed for each heterodimer formed with the Erp protein, two for interactions with membrane-embedded RvPs, and two for unanchored systems in a semi-polar environment. All complexes were evaluated based on their binding energy obtained by a flexible refinement algorithm using the FireDock server. For complexes on a membrane, the 100 best structures were chosen to evaluate the interactions of residues in the contact zone. For the case of complexes in a semi-polar medium, the 20 best structures were analyzed.Evaluation of the contacts at the protein interface shows a high interaction site in the N-terminal domain of the Rv1417 protein. This interaction hot spot is formed by residues M1-W8 and S79 zoom in E. Other Following the same methodology, the complexes obtained in the Rv2617c–Erp docking were analyzed. Despite having a smaller contact area with respect to the Rv1417 protein, Rv2617c interacted with the Erp protein in a greater number of complexes. Contact analysis shows 19 structures with protein–protein interactions, seven of them in the cytoplasmic region and 12 in the extracellular region of the membrane A. The anThe heat map of Erp interactions shows a higher affinity for the Rv2617c protein. Interactions between Erp protein and RvPs can take place at the cell envelope and under unanchored or free protein conditions ,24. In tThe results show that all complexes exhibit augmented interaction at the contact interface due to the increased contact area. An example of this is shown in the zooms of the protein interfaces in both Erp–RvPs complexes, where large contact areas with more than 18 interacting residues are observed. It is interesting to note the fact that in the membrane complexes, all interactions found were of hydrophobic character, whereas in these complexes, electrostatic interactions appear . The heaRemarkable results were observed in the comparison of the binding energies (BEs) of the Erp–RvPs complexes. The Circos graphs show the binding energies of the first 1000 solutions obtained with the FireDock server G. The ovFinally, a comparison of the binding energy of the best complexes shows a higher energetic affinity of the unanchored structures, mainly in the Erp–Rv1417 complexes, which were 27.8% more energetically favorable as opposed to 11.9% for the Erp–Rv2617c complexes H. Even sTo clarify the nature of protein interactions, an ESP analysis was carried out to correlate the energy affinity with the electrostatic properties of the proteins. For this purpose, all structures were subjected to hydrogen optimization to avoid overlaps and to improve the hydrogen-bonding networks in the protein structures ,70. Two The results show remarkable differences in protein interactions due to the electrostatic effect in both environments . The mosOn the other hand, in a semi-polar medium, drastic changes in the ESP surfaces were observed. As can be seen in Mycobacterium tuberculosis, which causes tuberculosis disease, has been increasing. Although the virulence factor Erp protein has been demonstrated in several experimental studies, its function and molecular interactions with other proteins have been poorly addressed. In this work, using MD simulations, docking calculations, and various in silico techniques, we have explored the molecular interactions of the Erp protein with two putative proteins identified by Klepp et al., Rv1417 and Rv2617c. To understand the importance of the medium in Erp–RvPs interactions, two dielectric conditions, a polar solvent with water molecules and a semi-polar solvent with ethanol molecules, were studied.The search for new therapeutic targets to counteract the propagation of the bacterium Our results show several highlights in the molecular characterization of Erp–RvPs interactions. The structural characterization of the proteins showed that both RvPs possess domains characteristic of transmembrane proteins. The low probability of having intrinsically disordered regions corroborated this feature. Contrary to previous studies, the N- and C-terminal regions were found to be located in the cytoplasmic area of the cell. These results coincide with the structures obtained by the AlphaFold methodology. MD simulations showed that under polar conditions, the proteins adopt a closed globular conformation, whereas under semi-polar conditions, open structures were observed. These open forms allowed the proteins to increase their interaction capacity by increasing their contact area. In addition, protein promiscuity could be favored by the high vibrational motion of the residues in the unfolding process. Mean square displacement analysis showed a higher affinity of the Rv1417 protein with membrane molecules. This affinity, whose effect is a smaller translation of the protein, could indicate that in the formation of the RvPs heterodimers, this protein would act as a receptor of the complexes.Important results were obtained when performing the molecular dockings between the RvPs and the Erp protein. When the binding energy of the docking calculations was analyzed, we could observe that the energetic affinity was higher in membrane-bound systems than in unanchored ones. Furthermore, about 90% of the solutions obtained in unanchored systems had positive interaction energies. It is noteworthy that the contact analyses showed that all residue contacts in the membrane systems were hydrophobic in nature. Moreover, we observed that the C-terminal residues of the Erp protein were involved in most of the complexes analyzed, forming hot spots in the contact frequency analysis. This latter result suggests the possibility of the formation of transient complexes involving the C-terminal domain. Interactions were also found in the Erp repeat domains, which could confirm that these regions are involved in interactions with RvPs. An important result was to corroborate the higher energetic affinity between the Erp and Rv2617c protein under membrane conditions, which is in agreement with experimental studies.Finally, ESP analysis showed the importance of the dielectric conditions of the medium in protein interactions and confirmed the directionality of the Erp protein in complex formation. In the case of polar conditions, the protein interactions had a hydrophobic character denoted by the white hues in both structures. However, under semi-polar conditions, the electrostatic nature of proteins changes dramatically, causing the interactions to be hydrophilic. The latter is of great relevance, since it has been observed that the electrostatic environment has a low dielectric constant in the areas close to the cell membrane.Several limitations are observed in our study. However, it opens different perspectives regarding the analysis of one of the main virulence factors of MTB, such as the Erp protein, from a molecular point of view. It is suggested for future work the analysis of the free binding energies of protein complexes as well as the study of drugs that can inhibit these interactions. Overall, this work aims to provide novel insights into the study and elucidation of the molecular interactions between Erp and Rv proteins.


Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at  In recent years, protein function prediction has started using integrated function predictive information from several sources  instead The functionality of a protein can be attributed to the physical interactions represented in Protein-Protein Interactions (PPIs). Using proximity relationships between connected proteins, computational methods attempt to propagate the labels of known proteins to unknown proteins across the network. PPIs are mediated by their constituent domain-domain interactions . Genes eMoreover, a recent trend shows that hierarchical relationships between functional classes motivate the development of hierarchy-aware prediction methods, which are significantly better than hierarchy-unaware “flat” prediction methods . FunctioMotivated by the facts mentioned above, here we propose an integrated approach where their orthogonal methods, namely, constituent domains of the protein and their interactions, sequence homology, and protein interaction data, are used to assign functional GO terms for unknown proteins and then these prediction decisions are combined into a consensus decision using n-star approach and functional enrichment. The following section discusses the current state of the arts of computational techniques in the protein function prediction domain based on raw amino sequence, domain, and protein interaction data.Genes that evolved through duplication and rearrangement from single ancestral genes are known to be homologous. The homologous genes are found at various places in the same genome. However, duplication is considered paralogous, whereas some orthologous genes diverge through speciation events found in different organisms. Inference of functional terms from sequence similarity is well supported by Anfinsen’s dogma claiming protein’s sequence determines protein’s tertiary structure . The basPre-computed profiles of protein domains or portions of the conserved region can be used for assigned tasks. BLOCKS , ProDom Sequence-based prediction is easy to use because most proteins are available with their sequence and functional annotation. However, limitations to sequence-based methods arise when 60% of the sequences are similar . A correstate-of-the-art techniques use domain information for function prediction. Protein domains are independent units of protein function which have unique three-dimensional structures. Protein functions are collective results of functions of its constituent domains. So, to predict function, exploiting the domain architecture of proteins is the need of the hour and their interaction and cooperation pattern. Some current INGA  is anothProtein interactions have great importance in protein function, so the function of an unknown protein can be extrapolated from the functional annotation of its interaction profiles exploiting proximity relationships. There are mainly four categories of network-based approaches in functional inferences of protein: Markov random field based, optimization-based, Clustering based, and neighbor based . The treInference of functional terms from sequence similarity is well supported by Anfinsen’s dogma claiming protein’s sequence determines protein’s tertiary structure . The basPrediction of protein function can also be performed by domain information of protein. Protein domains are independent folding units that represent basic functional units of protein. In another work , the funProteins perform their functions through interaction with each other. Moreover, a protein is associated with multiple functions. So, inference of function for any unknown protein can be made from its interaction information with its interacting partner. Recently, computational function prediction techniques are gaining importance from PPIN also. Network-based approaches are classified into the following groups: neighbor-based , optimizhttps://sites.google.com/view/pfp-go/.Some of the other relevant works of protein function prediction have been summarized in PFP-GO assigns functional groups to target proteins based on the information integration of sequence similarity, PPI networks, and domain assignments. This method combines these orthogonal predictions and derives consensus predictions for GO terms using functional enrichment. As different heterogeneous information sources are used, mapping data from one source to another is essential in this regard. Protein interaction networks may contain specific false positive and false negative data. So, finding biologically essential proteins is challenging as they are promising candidates for finding drug targets. This work categorizes PFP-GO into four sections: 1) It identifies the functionally active target proteins  whose functional groups are predicted. 2) Each target protein’s level-2 neighborhood graph is considered, and non-essential proteins, i.e., shore, bridge, and fjord proteins , are eraPFP-GO can be centrally categorized into three sections: 1) Sequence-based prediction, 2) Domain-Domain interaction-based prediction, and 3) Topology or neighborhood-based prediction from the PPI network. In topology or neighborhood-based prediction from PPI networks, String  and UnipIn this section, the STRING database id used to fetch interactions , and theFor each target protein, interaction information is retrieved from the STRING database , and theBesides this bridge, fjord and shore proteins, Network centrality-based Edge Clustering Coefficient (ECC) , and edgExtinction Coefficient (Eprotein)• Absorbance • Number of Negatively Charged Residues (Nneg)• Number of Positively Charged Residues (Npos)• Aliphatic Index (AI)• IP/mol weight• Hydrophobicity (Hphb)• In this section, the functions of target proteins are predicted using protein sequences. Since proteins are formed of amino acids, protein sequence always plays a significant role in target protein function prediction. Sequence-based information is extracted by computing and assigning a Physico-chemical property score to all proteins in the target neighborhood graph, including the target itself. Physico-chemical property score  is the aInitially, node degree is computed for each member belonging to the refined neighborhood network for each target protein. The node degrees are then sorted in descending order. Now protein clusters are formed for each target protein. The protein with the highest node degree is selected as the first seed of the cluster. Then the distance between the seed and other proteins in the neighborhood of each target protein is computed based on the Euclidean distance. The Physico-chemical property score serves as an input to the Euclidean distance. If the distance is less than a specific threshold, then the inter-connected protein of the seed is incorporated in the cluster and is removed from the node degree list. Then the next node with the highest degree is considered a seed, and its corresponding clusters are formed similarly to the previous one. Thus, the clusters created are validated using inter-cluster and intra-cluster distances so that no miss classification or overlap is present.Once the cluster formation in each target protein’s neighborhood is finished, each cluster’s Physico-chemical property score is evaluated. Physico-chemical property score of a cluster is nothing but the average of the earlier computed Physico-chemical score of each constituent protein in the corresponding cluster. Then the Euclidean distance of the Physico-chemical property score between each target protein and its corresponding neighborhood clusters is calculated, and the target protein is assigned to the nearest cluster. Now the cluster contains more than one protein. So, it is not desirable that the functions of all the existing proteins in the chosen cluster are assigned to the target since it will enhance the false positives leading to an abrupt fall in the prediction accuracy level. Considering this fact, intra-cluster distance based on the Euclidean distance of the Physico-chemical property score is computed between the target and the other remaining proteins in the corresponding cluster. Functions of the selected protein having the least distance are assigned to the target protein. The schematic diagram of the entire sequence-based prediction is shown in Protein domains are the independent units that are responsible for protein function. The study of domain-domain interaction may lead to better protein function prediction. So, this proposed methodology uses PFP-GO protein domains for target protein function prediction. For each node in the refined neighborhood graph of the target protein, its STRING-id is fetched from the STRING database. Each of these STRING-ids is mapped to the Uniport database to obtain its corresponding Uniprot-id. These Uniprot-ids are mapped to their respective PFAM entries (ids), which are used to fetch the PFAM domains using the PFAM database. These derived PFAM domains of the neighborhood graph of each target protein are also checked and validated using the DOMINE database. All the mappings are considered if the one-to-many mapping occurs during the linking as mentioned earlier between databases. Once all the PFAM domains are obtained, the GO terms (protein functions) corresponding to these interacting domains are deduced from the GO Consortium database. Each of these GO terms is assigned a particular ranking based on the frequency of their occurrence. GO terms with the highest ranking are back propagated from the bottom to the top and allocated to the target protein. The entire schematic diagram for this phase has been highlighted in p value with Fisher’s exact test  with these Uniprot-ids are derived from the Uniprot database. The same is also implemented for level-2 of the target protein . Once alact test . Top-ranThe consensus technique is more effective if the results from orthogonal sources are merged to generate consistent results. In PFP-GO, PPI network, domain, and sequence data are used. For the final prediction, the n-star consensus method is used. As discussed earlier, the proposed method uses a 3-star consensus between three different predictors. 1-star consensus assigns those GO terms predicted by at least one of the predictors to the target protein. The 1-star is the least reliable as it gives the maximum number of GO terms. The 2-star consensus assigns the GO terms commonly predicted by at least two different predictors to the target protein. In contrast, the 3-star consensus  is the mPFP-GO first selects a functionally active protein set from the database, considered target proteins. It then predicts the functions of the target proteins using sequence-based, domain-based, and neighborhood Protein Interaction based approaches discussed earlier in the methodology section. The proposed method fetches the proteins in the String database for the target set selection. These proteins are mapped to the UniProt database. The STRING database contains 19,247 human proteins mapped to the UniProt database to retrieve the associated GO terms. PFP-GO utilizes the mapping of UniProt to retrieve GO terms because String database entries are not directly associated with GO annotation. In this proposed work, the human PPI network of String databases is used because it has 85, 58,002 interactions which is significantly higher than UniProt, which has only 46,410 interactions. A network diagram of the Human String database consisting of 2000 nodes is highlighted in The performance measure of PFP-GO is evaluated using Precision (P), Recall (R), and F-Score (F). PFP-GO is a combined methodology based on three heterogeneous resources, i.e., protein sequence, protein domain, and PPI network. It achieves an overall precision, recall, and F-score of 0.67, 0.58, and 0.62, respectively. It is initially compared with INGA , as showThe same has also been compared with four of the existing methods: FunApriori , the neiThe major limitation of the chi-square method is that it is suitable only for the denser part of the network. Thus, network sparseness may lead to the degradation of performance evaluation compared to the others. The inclusion of level-1 and level-2 neighbors increases the accuracy rates in all except chi-square #1 and FS-weight #1 (using only the first level).The neighborhood counting method is simple but still lags more like NRC and FS-weight #1 and #2 (using both levels) as it fails to differentiate between them. Although NRC and FunApriori perform better than the other, they fall behind PFP-GO since it does not focus on eliminating non-essential proteins from the target neighborhood.MAX score for BP, MF and CC has also been taken into account, and the same is compared with other methods like NetGO 3.0 (PFP-GO based on only PPI network and sequence is also tested against similar kinds of existing methodologies like NAIVE  and BLASetGO 3.0 , DeepGOPetGO 3.0 , BLAST (etGO 3.0  and NAÏVetGO 3.0 . The resp value with Fisher’s exact test (To further validate the predicted and ranked Gene Ontology terms, we used the meta-network created in  and Chi to studyact test . Top-ranFrom https://sites.google.com/view/pfp-go/.Besides, it should be noted that PFP-GO can perform better whether it considers the PPI network alone, PPI network and sequence, or the combination of the trio: PPI network, sequence, and domain. It can also identify functionally active proteins which may be transmitted in identifying possible drug targets in the future . Recentl


SubcellulaRVis will be useful for experimental biologists with limited bioinformatics expertise who want to analyse data related to protein (re)localisation and location-specific modules within the intracellular protein network.Cells contain intracellular compartments, including membrane-bound organelles and the nucleus, and are surrounded by a plasma membrane. Proteins are localised to one or more of these cellular compartments; the correct localisation of proteins is crucial for their correct processing and function. Moreover, proteins and the cellular processes they partake in are regulated by relocalisation in response to various cellular stimuli. High-throughput ‘omics experiments result in a list of proteins or genes of interest; one way in which their functional role can be understood is through the knowledge of their subcellular localisation, as deduced through statistical enrichment for Gene Ontology Cellular Component (GOCC) annotations or similar. We have designed a bioinformatics tool, named SubcellulaRVis, that compellingly visualises the results of GOCC enrichment for quick interpretation of the localisation of a group of proteins (rather than single proteins). We demonstrate that SubcellulaRVis precisely describes the subcellular localisation of gene lists whose locations have been previously ascertained. SubcellulaRVis can be accessed via the web (  The localisation of proteins within cells is critical for their processing, function and regulation. Cellular homeostasis and response to environmental signals are also dependent on sequestering or dynamic re-localisation of proteins in specific intracellular compartments . RegardlCo-regulated proteins or genes are commonly identified using high-throughput ‘omics experiments, such as those based on transcriptomics, proteomics and phosphoproteomics. The regulated proteins or genes identified in these experiments are often assessed based on enrichment for annotations to particular biological characteristics or participation in biological pathways. Annotations based on subcellular localisation are stored in the Gene Ontology aspect Cellular Component (GOCC) or the Jensen COMPARTMENTS database ,11. EnriSeveral web-based tools, such as EnrichR , can be http://phenome.manchester.ac.uk/subcellular/.We have implemented a Shiny web app, named SubcellulaRVis, that provides graphical visualisation of subcellular compartments enrichment from a list of multiple genes or proteins. By performing the enrichment test using condensed annotations, so that there is a single term for each major intracellular compartment in the results, the analyses can be concisely visualised and interpreted. SubcellulaRVis represents an alternative to the visualisation provided on the web interfaces of UniProt and COMPARTMENTS which only visualise the localisation of a single protein ,17. A maSubcellulaRVis aims to simplify the interpretation of GOCC enrichment analyses through visualisation of a standardised schematic of the cell. We selected subcellular compartments that are non-transient and common to eukaryotes, though it is a non-exhaustive list.H. sapiens, M. musculus, D. melanogaster, S. cerevisiae, R. rattus and X. laevis.We first defined each cellular compartment visualised on the schematic , to calculate the enrichment for each SubcellulaRVis compartment in the user-supplied gene list, and corrected using the false discovery rate (FDR) using the p.adjust function in R with the ‘method’ argument set to ‘fdr’. Though it has limitations  symbols or UniProt IDs Figure . Firstlyitations ,21, the itations ). In thehttp://phenome.manchester.ac.uk/subcellular/) and as a standalone app with R package (https://github.com/JoWatson2011/subcellularvis). The R package will be submitted to Bioconductor upon acceptance of the article. The visualisation within the app has been implemented using ggplot2 (version 1.7.1) and Plotly (version 4.10.0), allowing interactive exploration of the results when viewed on a web browser. All dependencies are described in the DESCRIPTION file of the R package and will be installed along with the package, as described in the README file.The SubcellulaRVis tool has been implemented as a Shiny (version 1.7.1) web app  can be calculated and can be viewed in a separate tab on the app; the table also specifies the SubcellulaRVis compartment each of these terms is assigned to, allowing the user full understanding of the way their data has been analysed and visualised. To evaluate genes or proteins that have been annotated to multiple categories, the user can use the results found in the ‘overlap’ tab, which contains an UpSet plot and the results in tabular form. Finally, we have also implemented a feature to allow users to compare multiple gene lists. We anticipate this being useful to users who have multiple experimental conditions or want to quickly perform and visualise multiple comparisons.To assess the precision of the SubcellulaRVis compartment assignments, we validated the tool using various data sets describing subcellular localisation of proteins.https://www.proteinatlas.org/about/download), filtered for proteins that were associated to only one subcellular compartment and protein lists were created associated to each subcellular compartment. Analysis of proteins with limited spatial distribution allowed for simpler validation, as we could perform a one-to-one comparison between the true localisation and the highest enriched SubcellulaRVis compartment. We found that this was indeed the case for many of the protein lists (Table The first validation data set came from The Human Protein Atlas . The subts Table . Encourats Table ; this iset al. (We then validated SubcellulaRVis with two experimental datasets. The first, from Go et al.  (Supplemet al. . For exaSaccharomyces cerevisiae. SubcellulaRVis found the highest enrichment for the compartment most closely describing the experimentally associated cellular compartment for the cytosol, nucleus, Golgi apparatus and plasma membrane (Table S. cerevisiae. Moreover, the SubcellulaRVis visualisation (as demonstrated in Figure To assess the precision of the SubcellulaRVis tool for the analysis of a non-human experimental data set we extracted the data from Nightingale et al.  from Su to reconne Table  and the ne Table . The latWe have introduced an app named SubcellulaRVis, which provides easy visualisation of the enriched subcellular locations from a user-supplied list of HGNC symbols or UniProt IDs. The visualisation is more biologically insightful than a bar chart and will aid in efficient interpretation of the characteristics of gene lists and standardisation of subcellular localisation analyses. Moreover, SubcellulaRVis complements existing visual tools such as COMPARTMENTS and UniProt which use similar schematics to visualise the known locations of individual protein species. We anticipate our software will be particularly useful for cellular and molecular biologists with limited bioinformatics expertise wanting to perform precise and quick enrichment analysis and immediate visualisation of gene lists. SubcellulaRVis will be beneficial in analysing data from spatial proteomics, as we have demonstrated here, which is increasingly used to resolve organelle proteomes and location-specific protein interaction networks. In this context, our tool will provide impactful visualisation of how protein localisation varies upon different extracellular perturbations or for providing confirmation of correct tagging of proteins for spatial proteomics experiments. Moreover, the method is useful for analysing high-throughput data from all ‘omics contexts. For example, RNA transcripts can be localised in order to be translated at the location in which the protein will be active . This inThere are some features of SubcellulaRVis that will be developed further based on increasing and more resolved information on the subcellular localisation of proteins. We have included the option to calculate enrichment within human protein or gene lists based on predicted subcellular localisations from the Human Protein Atlas rather than GOCC annotations. At the time of writing, the subcellular map of the Human Protein Atlas has coverage of 65% of human protein-coding genes. This makes it an invaluable resource for researchers and a good fit for SubcellulaRVis. SubcellulaRVis also provides an alternative view for the visualisation of the endosomal system Figure . The endIn summary, we have described an easy-to-use and powerful tool for the visualisation and simplification of GOCC enrichment analyses on gene or protein lists.http://phenome.manchester.ac.uk/subcellular/ and the software can be installed at https://github.com/JoWatson2011/subcellularvis.The SubcellulaRVis web app is hosted at gkac336_Supplemental_FilesClick here for additional data file.


The in vitro pulldown/coprecipitation assay directly observes the protein that binds to the target. This method involves electrophoresis, which is a technique of a low resolution as well as a low throughput. As a better alternative, we wish to propose a new method that is based on the NMR spectroscopy. This method utilizes the aggregation of the target protein and the concomitant signal disappearance of the interacting partner. The aggregation is accomplished by the elastin-like polypeptide, which is fused to the target. If a protein binds to this supramolecular complex, its NMR signal then becomes too broadened in order to be observed, which is the basic phenomenon of the NMR spectroscopy. Thus, the protein that loses its signal is the one that binds to the target. A compound that interferes with these types of bindings among the proteins can be identified by observing the reappearance of the protein signals with the simultaneous disappearance of the signals of the compound. This technique will be applied in order to find an interaction pair in the information transfer pathway as well as a compound that disrupts it. This proposed method should be able to work with a mixture of proteins and provide a higher resolution in order to find the binding partner in a higher throughput fashion.The interaction among proteins is one of the most fundamental methods of information transfer in the living system. Many methods have been developed in order to identify the interaction pairs or groups either  The metabolite mixture will play a role as a small molecule library. As for the observation of the disappearance/restoration of the ligand signals, it is possible to identify which signals have disappeared upon adding the partner-target aggregate. The 2D HSQC will become necessary in this process as the spectrum of the ligand mixture will undoubtedly become very complex.The research will proceed from proving our concept with a couple of known interaction pairs, then to identifying/extracting a partner protein from a protein mixture, and finally to identifying a binding inhibitor from a mixture of small molecules. The first step is the main focus of this protocol. After this step is successfully completed, this method can be expanded to probing the protein library, which we aim as the second step. The protein library can be constructed from the total proteins of oteomics . Each bioteomics . With thoteomics . The finectively . Howeversolution . The ligThe discovery and elucidation of the protein-protein interactions is a very important example of the recently-discovered novel coronavirus (nCoV) . The mec


Accurate identification of protein complexes in protein-protein interaction (PPI) networks is crucial for understanding the principles of cellular organization. Most computational methods ignore the fact that proteins in a protein complex have a functional similarity and are co-localized and co-expressed at the same place and time, respectively. Meanwhile, the parameters of the current methods are specified by users, so these methods cannot effectively deal with different input PPI networks.To address these issues, this study proposes a new method called MP-AHSA to detect protein complexes with Multiple Properties (MP), and an Adaptation Harmony Search Algorithm is developed to optimize the parameters of the MP algorithm. First, a weighted PPI network is constructed using functional annotations, and multiple biological properties and the Markov cluster algorithm (MCL) are used to mine protein complex cores. Then, a fitness function is defined, and a protein complex forming strategy is designed to detect attachment proteins and form protein complexes. Next, a protein complex filtering strategy is formulated to filter out the protein complexes. Finally, an adaptation harmony search algorithm is developed to determine the MP algorithm’s parameters automatically.Experimental results show that the proposed MP-AHSA method outperforms 14 state-of-the-art methods for identifying protein complexes. Also, the functional enrichment analyses reveal that the protein complexes identified by the MP-AHSA algorithm have significant biological relevance.The online version contains supplementary material available at 10.1186/s12859-022-04923-4. Upon completing the human genome project, proteomics has become the focus in the post-genomic era. Proteins do not function only as single units. Instead, they form protein-protein interaction (PPI) networks and/or functional protein complexes . Since mGenome-scale PPI data can be obtained through high-throughput approaches, such as yeast-two-hybrid . These POver the past decade, various computational methods have been proposed to identify protein complexes in PPI networks automatically . Among tHigh-throughput experiment-derived PPIs may have false-positive or false-negative results, significantly affecting protein complex identification. Therefore, some computational methods, such as PEWCC  and EWCAFurthermore, some other methods attempt to integrate biological properties. For instance, WEC  uses genIn recent years, some supervised learning methods have been developed, including ClusterEPs  and ClusSome researchers have illustrated that a protein complex with a core-attachment structure consists of two parts: a protein complex core and attachment proteins . VariousHowever, protein complexes include both global and local protein complexes. Moreover, although various definitions of protein complexes have been proposed, most only consider single properties. Thus, a novel structural description of protein complexes considering multiple topological properties is urgently needed. Additionally, most protein complex detection methods have a common disadvantage: their parameters are specified by users, making it difficult to deal with various PPI networks effectively. In recent years, the harmony search algorithm has paid much attention in the fields of bioinformatics, such as the detection of high-order SNP epistasis and protein interactions , combinaA fitness function is defined, and it can detect protein complexes with multiple properties;The MP algorithm based on the core-attachment structure is proposed, and it can detect co-localized, co-expressed protein complexes with similar functions;The AHSA algorithm is developed to automatically determine the parameters of the MP algorithm for the input of different PPI networks;The experiments on various widely used PPI networks show that the proposed MP-AHSA algorithm outperforms 14 state-of-the-art methods.To overcome the disadvantages of existing methods, this paper proposes a novel approach called MP-AHSA, which combines the MP algorithm and an adaptation harmony search algorithm (AHSA) to automatically determine the parameters of the MP algorithm for the input of different PPI networks. The MP algorithm is based on the core-attachment structure and multiple properties, and it is developed to identify protein complexes in PPI networks. First, the Topological Clustering Semantic Similarity (TCSS) method  based onV represents a set of proteins, E is a set of interactions, and W is a v is defined as Herein, a PPI network is generally described as a weighted graph mentclass2pt{minimThis paper proposes the MP-AHSA algorithm to identify protein complexes in PPI networks. The pipeline of the algorithm is shown in Algorithm 1, and it consists of the MP and AHSA methods. Figure Recent studies , 24, 36 In the following subsections, the steps of the MP algorithm are explained in detail.The identification of protein complex cores consists of two steps in Algorithm 2. In step 1, the initial seeds are identified, and local protein complex cores are mined based on the initial seeds. In step 2, global protein complex cores are detected by employing the MCL method . Additiow represents the weight of the edge , V(G) is the set of proteins in G.To detect local protein complex cores, we first introduce a weighted local clustering coefficient to detect initial seeds. Research has shown that PPI networks have a small world , scale-fWCC(pi))  is shownentclass1pt{minimaSLD, the proteins in the same protein complex tend to have the same subcellular localization term. Second, gene expression data, GED, are used to estimate proteins in the same protein complex core co-expression based on the person correlation coefficient.Next, based on initial seeds, we use subcellular localization data and gene expression data to form local protein complex cores. Because some studies , 58 havev at the time point i, and v during the experimental procedure.Generally, the gene expression data can reflect the features of proteins in a biological process under various conditions. However, for a protein, the fluctuation range of its expression is not the same. We normalize its expression value. As a result, its value is normalized using Eq. :3\documev and u in the PPI network, their gene expression profiles are denoted as CEV , this paper replaces CEV with CEV, the more likely the proteins v and u to be co-expressed, and form the same protein complex.Furthermore, for a pair of proteins EV , and itsratioV(G) are selected as the initial seeds (line 7). For each protein s in InitialSeeds, if it is not visited, it is first initialized as a protein complex core, PCC. Meanwhile, it is marked and no longer used as a seed protein to form the protein complex core (lines 8-12). Second, the subcellular location data of the seed protein s (SLD(s)) are obtained. The direct neighbors of the initial protein complex core PCC (Neighbor(PCC)) are determined (lines 13-14). Third, for each protein CEV of the edge between the seed protein s and its neighbor u is larger than GCE, and the seed protein s and its neighbor u have at least one common subcellular location term, the neighbor u is considered a part of the protein complex core PCC. It is added to the protein complex core PCC and marked (lines 15-23). Finally, if the protein complex core PCC is larger than two and does not exist in PCCs, it is saved (lines 24-26). The entire procedure terminates when no seed proteins need to be considered in InitialSeeds (lines 9-27). Then, the MCL method is employed, and its parameter inflate is set to detect global protein complex cores, MCLcluster (lines 28-29). MCL , and it is compared with HM is selected from the harmony memory (HMs) using the roulette wheel selection strategy (line 10). Otherwise, a new harmony is randomly generated according to the parameters and their value ranges in Table HMs are determined and recorded as fitnessmax and fitnessmin, respectively. Next, the value of i, fitnessmax, and fitnessmin. If the harmony is obtained from the harmony memory (lines 10 and 20), a random number R2 between  is generated. The value of HM are adjusted to obtain a new harmony (lines 23-28).In the stage of harmony selection, a variable entclass1pt{minimaHMs. In that case, it is replaced to update the harmony memory HMs (line 36). Step 3 is repeated many times until a certain termination time Maxiter is satisfied (lines 6-39).If g to Eq.  (line 35HMs is obtained. It is considered the best clustering output (IPCs)(lines 40-42).Finally, in step 4, according to the s in Eq. , the higIn this study, three PPI networks are used to conduct the verification experiments: the Collins , the Gav5/07/01) , and Bio5/07/01)  datasetsentclass1pt{minimaWe used two standard protein complexes of the yeast Saccharomyces cerevisiae (SGD) taken from the literature . The proIn this study, GO-slim data (available at https://downlo ads.yeastgenome.org) are used to describe the functional similarity of the interactions. Gene expression data is obtained from https://www.ncbi.nlm.nih.gov/sites/GDSbrowser. In addition, subcellular localization data is obtained from https://compartments.jensenlab.org/Dow nloads. If you want to obtain the these biological data, please see the Additional files S and D denoting the known and identified protein complexes by a detection method, respectively.In the present study, F-measure, accuracy (ACC), maximum matching ratio (MMR), Jaccard, and total score are used as the computational evaluation metrics to evaluate the performance of protein complex detection algorithms, with S, and D. Thus, their neighborhood affinity score , given by Eq. , to measTo demonstrate the performance of MP-AHSA, we compared it with 14 state-of-the-art protein complex identification methods using the Collins , Gavin , Krogan Figures To further verify the performance of our algorithm, we also use three new PPI networks to evaluate these identification algorithms. The evaluation results are shown in Additional files Altogether, these comparative experimental results show that the MP-AHSA can achieve a higher total score than all the compared methods in most datasets. According to the above-described analysis, multiple PPI datasets and standard protein complexes are used. The MP-AHSA algorithm consistently achieves superior results in most evaluation metrics.In this study, we provide an example of the 148th protein complex comprising 6 proteins in standard protein complexes 1 to show the performance of the described approach. Figure As shown in Figure C with respect to a functional group F is denoted by Eq. (k represents the number of proteins covered in C and F, and V represents the set of proteins in a PPI network. If the smallest p-value of C concerning all functional groups is smaller than 0.01, the detected protein complex C was regarded as biologically significant. Herein, we use the fast tool LAGO [Additionally, we also use the proportion of biologically significant protein complexes to evaluate the detected protein complexes. The p-value of a protein complex d by Eq. :18\documool LAGO  to compuTo further estimate the effectiveness of the MP-AHSA algorithm, we investigate the biological significance of the identified protein complexes. Here, each protein complex is identified by the various methods associated with a p-value for GO annotation. The percentage of biological significant protein complexes detected by different methods is shown in Table As Table mentclass2pt{minimMeanwhile, we also calculate the p-value of three new PPI networks to obtain functional enrichment analysis to measure the biologically significant of identified protein complexes by different algorithms. The evaluation results are shown in Additional file In conclusion, MP-AHSA can identify more protein complexes with significant GO terms. Although some of those identified protein complexes are not known, they are more likely to be experimentally verified as factual protein complexes by biologists. Therefore, based on the p-value results, the MP-AHSA algorithm can effectively detect biologically meaningful protein complexes.Detection of protein complexes is essential to understanding cellular mechanisms. In this study, the MP-AHSA algorithm is proposed to identify protein complexes. First, a weighted PPI network is designed using the TCSS method based on functional annotations. Then, local protein complex cores are identified based on co-subcellular localization and gene co-expression datas. Global protein complex cores are detected using the MCL method. Second, a new fitness function is defined to guide mining attachment proteins. Third, all candidate protein complexes are filtered to obtain the filtered protein complexes. Finally, the AHSA algorithm is used to determine the parameter settings of the MP algorithm based on the input PPI network. The experimental results on widely used PPI networks indicate that the MP-AHSA algorithm outperforms 14 competing methods and can effectively detect biologically meaningful protein complexes. In the future, advanced machine learning techniques, such as ensemble learning and graph attention networks will be applied to this field.Additional file 1. Collins PPI network.Additional file 2. Gavin PPI network.Additional file 3. Krogan PPI network.Additional file 4. String PPI network.Additional file 5. DIP PPI network.Additional file 6. Biogrid PPI network.Additional file 7. Gene expression data.Additional file 8. Go slim mapping.Additional file 9. Subcellular localization data.Additional file 10. Standardcomplexes1.Additional file 11. Standardcomplexes2Additional file 12. An example diagram to describe the Algorithm 2.Additional file 13. An example diagram to describe the Algorithm 3.Additional file 14. An example diagram to describe the Algorithm 5.Additional file 15. Experimental results on String dataset.Additional file 16. Experimental results on DIP dataset.Additional file 17. Experimental results on Biogrid dataset.Additional file 18. Functional enrichment analysis of protein complexes detected in String, DIP and Biogrid datasets.


The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future. Twenty years ago Hopkins and Groom  publisheHopkins and Groom stated “druggable does not equal drug target”. Their original definition of “druggable” focused on proteins that can bind orally bioavailable drug-like molecules; however, this would now be thought of as drug-like ligandability. Contemporary definitions of druggability  expand uManually assessing all the factors that contribute to a suitable drug target is a time-consuming exercise. Fortunately, public resources such as Open Targets  and canSThese platforms provide function-rich user interfaces (UIs), which are powerful tools for scientists looking to discover future drug targets, but they do not allow exploration with AI approaches. Incorporating this data into a knowledge graph would allow additional data sources to be layered on top, allowing more complex queries and graph-based algorithms for data interrogation.A good example of using knowledge graphs to annotate proteins with data is the PDBe Knowledge Base (PDBe-KB) , which pStructure-based druggability assessments typically focus on a single static protein structure , providiIn addition to structure-based assessment, drug discovery precedence for a target can be searched. This could mean identifying crystal structures in the PDB corresponding to drug-like compounds, or active drug-like compounds in ChEMBL . The preAutomation and scalability are essential in confidently expanding the druggable genome into novel and overlooked areas. Capturing all relevant data for target selection, from target-level evidence to per-residue data, in a single knowledge graph is a daunting but important task. Doing so will enable AI methods to undertake the work normally performed by large multidisciplinary teams and identify the very best novel targets.The human proteome comprises the protein sequences of all coding genes, including splice variants, from the human reference genome . There aWhere protein structures are available, they are often missing atoms, contain alternate atom placements, and are missing hydrogens. To obtain consistent high-quality structures, a method of automating the preparation of structures for computational experiments is required. Once a prepared set of structures is available, large-scale analysis requires a robust automation platform to locate target-binding sites across multiple structures per target.It has long been known that considering proteins as rigid structures fails to consider energetic fluctuations that lead to proteins exploring a multitude of complex conformational states . MoreoveExscientia’s pipeline for automated target druggability assessments, summarised in The first step leverages PDBe information relative to the structural coverage of each full-length protein characterised by a UniProt ID. The PDBe provides crucial information on the protein segments that are structurally enabled. Structural studies of larger proteins tend to yield multiple structures of shorter, non-overlapping segments. Our pipeline treats each of these segments separately, identifying the best representative structures available for each based on sequence coverage, resolution, and data quality.For each individual target, publicly available structures are combined with in-house simulated and generated structures, and then aligned to the previously determined reference structure. All structural files undergo the same processing steps  to ensure data compatibility for downstream modelling tasks such as docking and molecular dynamics. The result of this step is a standard dataset of all structural data available for each target. Tractable binding pockets on each prepared structure are then assessed using Fragment Hotspot Maps with 3D grids, which highlight the areas most attractive to small molecules . The cenThe centres of mass from all structures are clustered, shown in With the ongoing COVID-19 pandemic, target selection for pandemic preparedness is of particular importance. The conservation of a drug target is assessed to capture the robustness of the protein and its binding pockets and used to provide valuable insights into the longevity of the drug in the face of resistance and pathogen emergence. These insights are obtained by computing a range of conservation metrics that rely on sequence alignments generated by MUSCLE  for eachAt each residue in these alignments, we identify variants, the amino acid feature frequency across variants, and the most common amino acid frequency, which are mapped onto the reference PDB numbering. Additionally, we use Jensen-Shannon divergence  (JSD) toFunctional insights from UniProt and FunPDBe are collated to identify amino acids that have interactions with small molecules or play a role in the target’s function. By extracting residue-level hotspot scores and mapping the conservation scores to them, we can quickly identify the most tractable and robust positions that can be used to inform design.Our recent assessment of the papain-like protease (PLpro), an attractive target for the treatment of COVID-19 , highligA fundamental requirement of viral target selection in the context of pandemic preparedness is that future variants and species will not develop drug resistance. Here we used conservation across multiple coronavirus species, combined with per-residue tractability scores, to assess the risk of drug resistance. In the case of PLpro, most of the residues with high tractability scores, and therefore essential for binding, were highly variable across a set of coronaviruses . As a result, there is a high risk of future variants and novel coronaviruses being resistant to drugs developed against SARS-CoV-2 PLpro, making it unsuitable for pandemic preparedness.Our pipeline focuses on automating the process for single targets and runs using only a UniProt ID as input, which facilitates running the pipeline on a proteome-wide scale. Once this large-scale calculation is complete, and the results are captured in a knowledge graph, it can be layered together with public data from the PDBe and Open Targets, and our own internal knowledge graphs. Such a complete and data-rich description of the drug target space will enable far more precise search queries.The more data included in the knowledge graph, the more complex these queries can become. We can consider further target annotations such as the subcellular location or tissue expression, or create edges between targets based on homology thresholds, pathway information or protein-protein interactions, mirroring the biological systems they represent. The benefits of introducing this structure and complexity can be exemplified by synthetic lethality, a promising area for novel cancer therapeutics. The first approved synthetic lethal therapy, for indications including breast and ovarian cancer, targets poly(ADP-ribose) polymerase (PARP). PARP is essential in cancer cells with BRCA1/2 mutations due to their defective homologous recombination (HR) pathway for DNA damage repair . HealthyMany of the annotations do not rely on protein structure, but ultimately protein structures are vital for a pocket-centric target view. As well as helping the assessment of target tractability, a protein structure makes a project more doable by enabling structure-based design. Increased structural coverage of the proteome will have huge consequences on how much of the target space can be assessed and pursued.de novo protein structure prediction methods  and global (PAE) structural accuracy . These mSince CASP14, DeepMind has released the AF2 code, model parameters and a database with AF2 model predictions . The AF2Having structural annotation and prediction for unannotated proteins and regions will benefit ligand binding-pocket predictions on those regions. Work by Beltrao  indicateWhile AF2 models can be good starting points for ligand-binding or pocket prediction, especially where there are few or no homologous structures available, it is essential to account for both confidence metrics and homology of proteins or regions of interest to the PDB . UnderstIn the 20 years since the publication of the druggable genome, tremendous advances have been made in multiple areas. These include improved structure prediction, structure-based assessment, public resources of collated data, and new architecture for data storage and methods for data interrogation. This provides the opportunity to build a detailed description of drug target space, an opportunity we must seize to select the very best future drug targets. The original druggable genome was represented as a simple Venn diagram, with “drug targets” at the intersection of “druggable genes” and “disease-modifying genes.” It is increasingly becoming a multi-dimensional problem, difficult to represent for human minds, particularly at large scales.Graph-based AI algorithms can effectively work with data of this scale and complexity. Identifying the best method for selecting targets from a knowledge graph of this scale will be the subject of future research. Databases such as the Cambridge Structural Database  (CSD), C


Huntington’s disease (HD) is caused by the production of a mutant huntingtin (HTT) with an abnormally long poly-glutamine (polyQ) tract, forming aggregates and inclusions in neurons. Previous work by us and others has shown that an increase or decrease in polyQ-triggered aggregates can be passive simply due to the interaction of proteins with the aggregates. To search for proteins with active  effects, which might be more effective in finding therapies and mechanisms of HD, we selected among the proteins that interact with HTT a total of 49 pairs of proteins that, while being paralogous to each other (and thus expected to have similar passive interaction with HTT), are located in different regions of the protein interaction network (suggesting participation in different pathways or complexes). Three of these 49 pairs contained members with opposite effects on HD, according to the literature. The negative members of the three pairs, MID1, IKBKG, and IKBKB, interact with PPP2CA and TUBB, which are known negative factors in HD, as well as with HSP90AA1 and RPS3. The positive members of the three pairs interact with HSPA9. Our results provide potential HD modifiers of functional relevance and reveal the dynamic aspect of paralog evolution within the interaction network. Huntington’s disease (HD) is one of nine autosomal dominant neurodegenerative disorders caused by the expansion of a CAG trinucleotide repeat. For HD, this expansion is located in the first exon of the huntingtin gene (htt) and results in an abnormally long poly-glutamine (polyQ) tract within the N-terminus of the huntingtin protein . ExpansiThe mechanism of polyQ-mediated toxicity is still under study; however, there is evidence supporting aberrant protein–protein interactions in the pathogenesis of HD ,5,6. SevReports are accumulating on a variety of positive or negative effects that the expression or inhibition of multiple proteins have on HD’s progression or its effects ,16,17,18We hypothesized that, given the large size of HTT and its large number of interactors , it shouWe assumed that the identification of pairs of paralogs interacting with HTT with opposite effects on HD might reveal active functions relevant to HD, under the assumption that these paralogs might interact identically with HTT, but their different effect on HD would arise from different interactions with other functional components of the protein interaction network. To maximize the divergence in protein interactions, we would need to account for the entire human protein interaction network (hPIN) in an unbiased approach. This is facilitated by techniques that project networks in a geometric space where closeness means a higher connection probability.Since proteins are very complex machines, and their interactions with other proteins form a very complicated molecular system, their study as a protein–protein interaction network has gained traction in recent years . SeveralMotivated by these results, we followed a step-by-step computational filtering strategy, starting from a large protein–protein interaction (PPI) dataset embedded in the hyperbolic disc to obtain a network that consists of HTT interactors. This mapping enabled us to select pairs of paralogs of HTT interactors located in different regions of the hPIN. Proteins in each of these pairs are expected to interact similarly with HTT, but their different positions in the hPIN suggest their different involvement in pathways or complexes. The evaluation of protein pairs with opposed effects on HD was interpreted to find common partners for positive or negative effectors, which we propose as potential candidates for powerful effects in HD models.2 using LaBNE+HM [In the first step of our analysis, we created a protein–protein interaction network from the HIPPIE database with high-quality interactions formed with a confidence score of ≥0.71 ,33. The LaBNE+HM ,35,36, a2) abstracts the characteristics that make a node similar to others [protein lipidation. Proteins agglomerate in similarity-based clusters since each of them is enriched in different aspects of the GO BP terms.The similarity component of the PSM . We observed, in this network, 382 proteins that are directly linked to HTT .To reveal the functional modularity of the HTT interactors, following the procedure used above to cluster the hPIN, we partitioned the angular dimension of the nodes of the HttPIN into several sectors according to large gaps between the consecutive inferred angles of the HTT interactors , and IKBKG/OPTN are paralogous pairs with experimental evidence suggesting their different effect on HD. MID1 leads to an aberrant overproduction of the mutant polyglutamine protein, inhibition of IKBKB has a protective effect on neurodegeneration, and IKBKG binds mutant HTT contributing to HD neurotoxicity ,49,51. TFinally, we hypothesized that the existence of common interaction partners of the three negative paralogs could reveal functions that negatively influence HD and whose inhibition could have therapeutic effects. The three negative paralogs are IKBKB, IKBKG, and MID1. Using the HIPPIE database, we obtained their common interactors, besides HTT. Filtering interactions with a confidence score of ≥0.71 resulted in only PPP2CA. At a confidence score of ≥0.63, TUBB, HSP90AA1, and RPS3 were also found see .We used the same approach with all three positive partners to find proteins that might be effective as a therapy for HD. The three positive paralogs are OPTN, PML, and CHUK. Using the HIPPIE database and a confidence score of ≥0.49, besides HTT, only one partner was found, chaperon HSPA9 , which is not a direct interactor of HTT .Finally, we checked the connectivity of the common interacting partners of the paralogs with HTT. All five  rank very high on the list of human proteins ordered by the number of interactions with HTT-interactors  that the radial coordinates of nodes (proteins) correlate with protein age, with older proteins occupying more central positions, and (ii) that proteins with related biological functions and cellular localizations cluster together along the angular coordinates . The reaIn this paper, the latent geometry of the hPIN proves useful, namely in the network-based analysis of huntingtin’s interactors. Considering pairs of paralogs that (a) both interact with huntingtin, (b) are located in different regions of the hPIN, and (c) have opposite effects on HD, we found three pairs that correspond to these criteria. Particularly, MID1 is an aberrant interaction partner of HTT. Its binding leads to the induction of an aberrant translation of the mutant HTT mRNA. MID1 assembles a protein complex with its interaction partners, PP2A and 40S ribosomal S6 kinase (S6K), and recruits this complex to the mutant HTT mRNA. This recruitment induces translation in a CAG repeat length-dependent manner, resulting in a toxic gain of function . The traAnother paralog pair with opposite effects on HD is IKBKB/CHUK(IKKA). Most of the IKKA and IKBKB molecules in the cell are part of IKK complexes. The IKK complex also contains a regulatory subunit called IKKγ or NEMO . ConcurrIKBKG, which is the regulatory module of the IKK complex, binds to mutant HTT through polyQ and polyP regions. This binding activates the IKK complex and promotes the activation and nuclear localization of the nuclear factor kappa (NF-κB) . ActivatWe next focus on common partners of the three negative proteins: TUBB, PPP2CA, HSP90AA1, and RPS3. It is interesting to note that TUBB is part of our paralog interacting set with a negative effect . FurtherPPP2CA is an interesting candidate because it dephosphorylates S421 in HTT and, blocking its activity, was found to protect striatal neurons from NMDA-induced cell death . This prThe hPIN is a subset of the Human Integrated Protein–Protein Interaction rEference (HIPPIE) ,33. HIPP2), where nodes that join the system connect with the existing ones that are hyperbolically closest to them [i, represents the popularity dimension with nodes that joined the system first being close to the disc’s center. The angular coordinate, θi, represents the similarity dimension. The network was embedded in the two-dimensional hyperbolic plane using the LaBNE + HM algorithm to infer the hyperbolic coordinates of each protein, with parameters γ = 2.74, T = 0.8, and w = 2π.In order to embed the hPIN into the two-dimensional hyperbolic plane, we used the R package “NetHypGeom,” which implements the LaBNE + HM algorithm . This al to them ,36,76. Ti and θi+1 to identify large gaps between groups of proteins. The gap size, g, that was chosen to separate protein clusters produces sectors with a minimum of ten components  were kept.To cluster proteins in the similarity dimension, we sorted the nodes increasingly by their angular coordinates and computed the difference between θi and θi+1 was computed. The gap size = 0.059198 was chosen  were extracted.We then obtained the list of human HTT interactors from the HIPPIEv2.2 database ,33 and is chosen . The enr2 to explore functional interpretations based on the angular similarity dimension. We then conducted a literature review to identify pairs with opposite effects on HD. For the latter analysis, we used the HIPPIEv2.2 database [From the HTT interacting proteins dataset, we detected pairs of paralogs. This information was derived from the Ensembl BioMart database , using tdatabase ,33 to ob2 and found pairs with opposite effects on HD. We then explored the components closely connected to the positive or negative effectors. Our findings confirm proteins with relevant effects in HD and suggest RPS3 and HSPA9 as non-direct interactors of HTT that could have a negative and positive effect in HD, respectively. With our approach, we have shown how the interaction network connects the effects of HD modifiers to the literature, and the finer details of each experiment can ultimately be examined to make sense of these results and select or discard ideas for experimental work.We approached the heterogeneity of the measurements of the effects of various proteins in HD models by providing a common framework for evaluation. Our hypothesis is that strong HD modifiers should produce collective effects through multiple pathways and complexes. These strong functional effectors may be obscured by the supposedly abundant but weaker effects of proteins that influence HD aggregates by their passive interaction with HTT. To avoid this problem, we focused on pairs of HTT interactor paralogs occupying divergent positions in the protein interaction network mapped to H


Despite the recent advances in the prediction of protein structures by deep neutral networks, the elucidation of protein-folding mechanisms remains challenging. A promising theory for describing protein folding is a coarse-grained statistical mechanical model called the Wako-Saitô-Muñoz-Eaton (WSME) model. The model can calculate the free-energy landscapes of proteins based on a three-dimensional structure with low computational complexity, thereby providing a comprehensive understanding of the folding pathways and the structure and stability of the intermediates and transition states involved in the folding reaction. In this review, we summarize previous and recent studies on protein folding and dynamics performed using the WSME model and discuss future challenges and prospects. The WSME model successfully predicted the folding mechanisms of small single-domain proteins and the effects of amino-acid substitutions on protein stability and folding in a manner that was consistent with experimental results. Furthermore, extended versions of the WSME model were applied to predict the folding mechanisms of multi-domain proteins and the conformational changes associated with protein function. Thus, the WSME model may contribute significantly to solving the protein-folding problem and is expected to be useful for predicting protein folding, stability, and dynamics in basic research and in industrial and medical applications. Many proteins fold into specific three-dimensional 3D) structures to perform their functions and drive various biological processes. Therefore, elucidating how proteins fold is essential for understanding the fundamental processes of life. Since the existence of protein-folding pathways was first proposed , the detD structuThe Wako–Saitô–Muñoz–Eaton (WSME) model is promising for describing protein-folding reactions . The WSMIn this review, we first describe the details of the basic WSME model and outline how to calculate the free-energy landscapes of proteins in The WSME model is a coarse-grained statistical-mechanical model based on the 3D structures of proteins. In 1978, Wako and Saitô originally proposed the basic ideas and calculation methods of this model  ,19. Apprkm is assigned to each residue of a protein. The index k represents the residue number. km is 1 when the residue is in the native-like conformation and 0 when the residue is in other conformations. The protein state {m} is defined as a set of residue states , where N is the total number of residues. Next, the protein state has 2N possible conformations. The Hamiltonian of the WSME model is defined as:i,j represents the native contact between residues; Δi,j = 1 when i- and j-th residues are in contact with each other in the native state, otherwise Δi,j = 0  is the entropic reduction attributed to the formation of the native conformation. Then, the partition function is described as:S0 is neglected in Equation (4) because it is a constant and does not affect the results of the free-energy calculation. Thus, the effective free energy of a native stretch from i- to j-th residue can be described as:The basic WSME model is as follows . First, Δi,j = 0 . εi,j isve state . mi,j isrmations . Thereformations . The numordinate .The WSME model was originally developed by Wako and Saitô in 1978 to study the statistical mechanical properties of ideal biopolymers, including proteins ,19. SubsN-residue protein is 2N in the WSME model, and the computational complexity increases exponentially with increasing protein size, it is impossible to numerically calculate the partition function using Equation (4), even for a protein with ~50 residues. Thus, approximations that consider only specific protein states along the folding reaction coordinates are sometimes used. For example, single, double, and triple sequence approximations  assume that up to one, two, and three independently folded segments, respectively, are allowed during the folding process . The finding that the calculations with DSA and TSA yielded similar results to the exact calculation suggests that the number of native segments formed during the folding process is 2 or 3 for small proteins. This was also shown in long-duration all-atom MD simulations, confirming that simple assumptions, such as DSA/L, are sufficient to explain the folding mechanisms of small proteins . I. I45]. IIn addition to protein folding, the WSME model is also applicable to the conformational changes associated with protein function. Intrinsically disordered proteins (IDPs) have disordered structures in isolation but fold into specific structures upon binding to their partners ,134,135.The free-energy landscape of an intrinsically disordered DNA-binding domain of the transcriptional regulator CytR, calculated using the WSME model, suggests that the conformational ensemble of the disordered state involves competition for several specific conformations . By intrThus, the WSME model comprehensively explains both the folding of globular proteins and the structures of IDPs in free and bound forms based on free-energy landscapes. In addition, the model can predict the effects of temperature, osmolytes, and amino-acid substitutions on IDP structures and may be useful for controlling the conformations of IDPs ,142. It Many proteins dynamically change their conformations and exert their functions by binding to specific targets or through post-translational modifications. Free-energy calculations using the WSME model have also been applied to the theoretical analysis of the conformational changes associated with protein functions, such as photocycles and allosteric transitions ,145,146.Photoactive yellow protein (PYP) is a model protein for photoreceptors that has a photocycle consisting of three states . The cycSasai et al. further modified the WSME model to allow multiple native states and developed an allosteric WSME (aWSME) model that can calculate a free-energy landscape reflecting protein allostery ,145,146.Cnu is a transcriptional co-repressor that regulates gene expression upon temperature changes and has also been proposed to be involved in pH-dependent gene expression ,149,150.Amyloids are insoluble fibrous aggregates of proteins stabilized primarily by hydrogen bonds and hydrophobic interactions and have a cross β-sheet structure with parallel β-strands aligned perpendicular to the fibril axis ,152. BecSince the WSME model can be regarded as a simple 1D lattice model, the exact solution of the model can be calculated using the transfer matrix method, even for systems with non-uniform interactions. Taking advantage of this feature, the WSME model has also been used to describe the growth of strained epitaxy ,156. FurIn this review, we summarized how the WSME model and its extended versions describe protein folding and dynamics. The WSME model can calculate the free-energy landscapes of proteins, which predict the thermodynamic quantities involved in equilibrium-unfolding transitions and the pathways and structures involved in kinetic folding processes. These calculations are consistent with the experimental results of protein folding, especially for small single-domain proteins, suggesting that the WSME model enables the prediction of detailed protein-folding processes that are difficult to measure experimentally, and contributes to our understanding of protein-folding mechanisms. Surprisingly, although the WSME model is a simple coarse-grained model, it can reproduce various aspects of protein folding obtained by all-atom MD simulations. This agreement strongly supports the hypothesis that folding reactions are primarily driven by native interactions and that the free-energy landscape is globally biased toward the native state. This also indicates that the WSME model adequately captures and deciphers the bias encoded in protein conformation. Therefore, the WSME model, when combined with rigorous contact-energy calculations, provides theoretical predictions that are in good agreement with the experimental results for small proteins.The WSME model has also been applied to predict the folding mechanisms of multi-domain proteins, especially those consisting of tandemly connected small globular domains. Although it is difficult to compute entire folding reactions of large multi-domain proteins using all-atom MD simulations, the WSME model can calculate the free-energy landscapes of such proteins with low computational complexity. Therefore, the WSME model and MD simulations are expected to be important tools for predicting protein-folding mechanisms.Nevertheless, it is still challenging to predict the folding mechanisms of proteins with complex structures, such as multi-domain proteins with domain insertions and those with strong interactions between domains. Although non-local interactions between distant segments in the amino-acid sequence may be formed early in folding reactions by the hydrophobic collapse mechanism , they caAnother type of interaction that complicates the protein structure is a disulfide bond. The WSME model has never explicitly considered the folding reactions of disulfide-intact proteins or those involving oxidative formation of disulfide bonds. The prediction of such folding reactions is also challenging, but it may be achieved by replacing the virtual linkers introduced above as non-local interactions with covalent linkers.Because of its simplicity and versatility, the WSME model can be used to analyze various biological events other than protein folding under equilibrium and non-equilibrium conditions by calculating free-energy landscapes using exact or approximate solutions and, subsequently, performing Monte Carlo simulations. Due to this utility, the extended version of the WSME model provides reasonable predictions for protein-conformation changes in IDPs and allosteric conformational changes accompanied by protein functions, such as protein–protein interactions and ligand binding. Furthermore, the model may be applicable to multimer formation, domain swapping, and the coupled folding and binding reactions of IDPs.Recently, protein-structure prediction has made great advances, through deep-learning approaches, towards solving the structure-prediction component of the “protein-folding problem” ,164. How