Distinctive Behaviors of Druggable Proteins in Cellular Networks

The interaction environment of a protein in a cellular network is important in defining the role that the protein plays in the system as a whole, and thus its potential suitability as a drug target. Despite the importance of the network environment, it is neglected during target selection for drug discovery. Here, we present the first systematic, comprehensive computational analysis of topological, community and graphical network parameters of the human interactome and identify discriminatory network patterns that strongly distinguish drug targets from the interactome as a whole. Importantly, we identify striking differences in the network behavior of targets of cancer drugs versus targets from other therapeutic areas and explore how they may relate to successful drug combinations to overcome acquired resistance to cancer drugs. We develop, computationally validate and provide the first public domain predictive algorithm for identifying druggable neighborhoods based on network parameters. We also make available full predictions for 13,345 proteins to aid target selection for drug discovery. All target predictions are available through canSAR.icr.ac.uk. Underlying data and tools are available at https://cansar.icr.ac.uk/cansar/publications/druggable_network_neighbourhoods/.     

in silico identification of protein-protein interactions in Silkworm,Bombyx mori

The Domesticated silkworm, Bombyx mori, an economically important insect has been used as a lepidopteran molecular model next only to Drosophila. Compared to the genomic information in silkworm, the protein-protein interaction data are limited. Therefore experimentally identified PPI maps from five model organisms such as E.coli, C.elegans, D.melanogaster, H. sapiens, S. cerevisiae were used to infer the PPI network of silkworm using the well-recognized Interlog based method. Among the 14623 silkworm proteins, 7736 protein-protein interaction pairs were predicted which include 2700 unique proteins of the silkworms. Using the iPfam interaction domains and the gene expression data, these predictions were validated. In that 625 PPI pairs of predicted network were associated with the iPfam domain-domain interactions and the random network has average of 9. In the gene expression method, the average PCC value of the predicted network and random network was 0.29 and 0.23100±0.00042 respectively. It reveals that the predicted PPI networks of silkworm are highly significant and reliable. This is the first PPI network for the silkworm which will provide a framework for deciphering the cellular processes governing key metabolic pathways in the silkworm, Bombyx mori and available at SilkPPI (http://210.212.197.30/SilkPPI/).     

Synapse-Assembly Proteins Maintain Synaptic Vesicle Cluster Stability and Regulate Synaptic Vesicle Transport in Caenorhabditis elegans

The functional integrity of neurons requires the bidirectional active transport of synaptic vesicles (SVs) in axons. The kinesin motor KIF1A transports SVs from somas to stable SV clusters at synapses, while dynein moves them in the opposite direction. However, it is unclear how SV transport is regulated and how SVs at clusters interact with motor proteins. We addressed these questions by isolating a rare temperature-sensitive allele of Caenorhabditis elegans unc-104 (KIF1A) that allowed us to manipulate SV levels in axons and dendrites. Growth at 20° and 14° resulted in locomotion rates that were ∼3 and 50% of wild type, respectively, with similar effects on axonal SV levels. Corresponding with the loss of SVs from axons, mutants grown at 14° and 20° showed a 10- and 24-fold dynein-dependent accumulation of SVs in their dendrites. Mutants grown at 14° and switched to 25° showed an abrupt irreversible 50% decrease in locomotion and a 50% loss of SVs from the synaptic region 12-hr post-shift, with no further decreases at later time points, suggesting that the remaining clustered SVs are stable and resistant to retrograde removal by dynein. The data further showed that the synapse-assembly proteins SYD-1, SYD-2, and SAD-1 protected SV clusters from degradation by motor proteins. In syd-1, syd-2, and sad-1 mutants, SVs accumulate in an UNC-104-dependent manner in the distal axon region that normally lacks SVs. In addition to their roles in SV cluster stability, all three proteins also regulate SV transport.     

ModuleRole: A Tool for Modulization,Role Determination and Visualization in Protein-Protein Interaction Networks

Rapidly increasing amounts of (physical and genetic) protein-protein interaction (PPI) data are produced by various high-throughput techniques, and interpretation of these data remains a major challenge. In order to gain insight into the organization and structure of the resultant large complex networks formed by interacting molecules, using simulated annealing, a method based on the node connectivity, we developed ModuleRole, a user-friendly web server tool which finds modules in PPI network and defines the roles for every node, and produces files for visualization in Cytoscape and Pajek. For given proteins, it analyzes the PPI network from BioGRID database, finds and visualizes the modules these proteins form, and then defines the role every node plays in this network, based on two topological parameters Participation Coefficient and Z-score. This is the first program which provides interactive and very friendly interface for biologists to find and visualize modules and roles of proteins in PPI network. It can be tested online at the website http://www.bioinfo.org/modulerole/index.php, which is free and open to all users and there is no login requirement, with demo data provided by “User Guide” in the menu Help. Non-server application of this program is considered for high-throughput data with more than 200 nodes or user’s own interaction datasets. Users are able to bookmark the web link to the result page and access at a later time. As an interactive and highly customizable application, ModuleRole requires no expert knowledge in graph theory on the user side and can be used in both Linux and Windows system, thus a very useful tool for biologist to analyze and visualize PPI networks from databases such as BioGRID.

Availability

ModuleRole is implemented in Java and C, and is freely available at http://www.bioinfo.org/modulerole/index.php. Supplementary information (user guide, demo data) is also available at this website. API for ModuleRole used for this program can be obtained upon request.     

PDBsum extras: SARS‐CoV‐2 and AlphaFold models

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS‐CoV‐2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum.     

New Insights into the Post-Translational Regulation of DNA Damage Response and Double-Strand Break Repair in Caenorhabditis elegans

Although a growing number of studies have reported the importance of SUMOylation in genome maintenance and DNA double-strand break repair (DSBR), relevant target proteins and how this modification regulates their functions are yet to be clarified. Here, we analyzed SUMOylation of ZTF-8, the homolog of mammalian RHINO, to test the functional significance of this protein modification in the DSBR and DNA damage response (DDR) pathways in the Caenorhabditis elegans germline. We found that ZTF-8 is a direct target for SUMOylation in vivo and that its modification is required for DNA damage checkpoint induced apoptosis and DSBR. Non-SUMOylatable mutants of ZTF-8 mimic the phenotypes observed in ztf-8 null mutants, including reduced fertility, impaired DNA damage repair, and defective DNA damage checkpoint activation. However, while mutants for components acting in the SUMOylation pathway fail to properly localize ZTF-8, its localization is not altered in the ZTF-8 non-SUMOylatable mutants. Taken together, these data show that direct SUMOylation of ZTF-8 is required for its function in DSBR as well as DDR but not its localization. ZTF-8’s human ortholog is enriched in the germline, but its meiotic role as well as its post-translational modification has never been explored. Therefore, our discovery may assist in understanding the regulatory mechanism of this protein in DSBR and DDR in the germline.     

Benchmarking Human Protein Complexes to Investigate Drug-Related Systems and Evaluate Predicted Protein Complexes

Protein complexes are key entities to perform cellular functions. Human diseases are also revealed to associate with some specific human protein complexes. In fact, human protein complexes are widely used for protein function annotation, inference of human protein interactome, disease gene prediction, and so on. Therefore, it is highly desired to build an up-to-date catalogue of human complexes to support the research in these applications. Protein complexes from different databases are as expected to be highly redundant. In this paper, we designed a set of concise operations to compile these redundant human complexes and built a comprehensive catalogue called CHPC2012 (Catalogue of Human Protein Complexes). CHPC2012 achieves a higher coverage for proteins and protein complexes than those individual databases. It is also verified to be a set of complexes with high quality as its co-complex protein associations have a high overlap with protein-protein interactions (PPI) in various existing PPI databases. We demonstrated two distinct applications of CHPC2012, that is, investigating the relationship between protein complexes and drug-related systems and evaluating the quality of predicted protein complexes. In particular, CHPC2012 provides more insights into drug development. For instance, proteins involved in multiple complexes (the overlapping proteins) are potential drug targets; the drug-complex network is utilized to investigate multi-target drugs and drug-drug interactions; and the disease-specific complex-drug networks will provide new clues for drug repositioning. With this up-to-date reference set of human protein complexes, we believe that the CHPC2012 catalogue is able to enhance the studies for protein interactions, protein functions, human diseases, drugs, and related fields of research. CHPC2012 complexes can be downloaded from http://www1.i2r.a-star.edu.sg/xlli/CHPC2012/CHPC2012.htm.     

The Anaphase Promoting Complex Regulates Yeast Lifespan and rDNA Stability by Targeting Fob1 for Degradation

Genomic stability, stress response, and nutrient signaling all play critical, evolutionarily conserved roles in lifespan determination. However, the molecular mechanisms coordinating these processes with longevity remain unresolved. Here we investigate the involvement of the yeast anaphase promoting complex (APC) in longevity. The APC governs passage through M and G1 via ubiquitin-dependent targeting of substrate proteins and is associated with cancer and premature aging when defective. Our two-hybrid screen utilizing Apc5 as bait recovered the lifespan determinant Fob1 as prey. Fob1 is unstable specifically in G1, cycles throughout the cell cycle in a manner similar to Clb2 (an APC target), and is stabilized in APC (apc5^CA) and proteasome (rpn10∆) mutants. Deletion of FOB1 increased replicative lifespan (RLS) in wild type (WT), apc5^CA, and apc10∆ cells, and suppressed apc5^CA cell cycle progression and rDNA recombination defects. Alternatively, increased FOB1 expression decreased RLS in WT cells, but did not reduce the already short apc5^CA RLS, suggesting an epistatic interaction between apc5^CA and fob1∆. Mutation to a putative L-Box (Fob1^E420V), a Destruction Box-like motif, abolished Fob1 modifications, stabilized the protein, and increased rDNA recombination. Our work provides a mechanistic role played by the APC to promote replicative longevity and genomic stability in yeast.     

Wide-Ranging Effects of the Yeast Ptc1 Protein Phosphatase Acting Through the MAPK Kinase Mkk1

The Saccharomyces cerevisiae type 2C protein phosphatase Ptc1 is required for a wide variety of cellular functions, although only a few cellular targets have been identified. A genetic screen in search of mutations in protein kinase–encoding genes able to suppress multiple phenotypic traits caused by the ptc1 deletion yielded a single gene, MKK1, coding for a MAPK kinase (MAPKK) known to activate the cell-wall integrity (CWI) Slt2 MAPK. In contrast, mutation of the MKK1 paralog, MKK2, had a less significant effect. Deletion of MKK1 abolished the increased phosphorylation of Slt2 induced by the absence of Ptc1 both under basal and CWI pathway stimulatory conditions. We demonstrate that Ptc1 acts at the level of the MAPKKs of the CWI pathway, but only the Mkk1 kinase activity is essential for ptc1 mutants to display high Slt2 activation. We also show that Ptc1 is able to dephosphorylate Mkk1 in vitro. Our results reveal the preeminent role of Mkk1 in signaling through the CWI pathway and strongly suggest that hyperactivation of Slt2 caused by upregulation of Mkk1 is at the basis of most of the phenotypic defects associated with lack of Ptc1 function.     

Seed Pro-Nutra Care: A tool for characterization of seed storage proteins and database of bioactive peptides having potential health benefits

Seed storage proteins, the major food proteins, possess unique physicochemical characteristics which determine their nutritional importance and influence their utilization by humans. Here, we describe a database driven tool named Seed Pro-Nutra Care which comprises a systematic compendium of seed storage proteins and their bioactive peptides influencing several vital organ systems for maintenance of health. Seed Pro-Nutra Careis an integrated resource on seed storage protein. This resource help in the (I) Characterization of proteins whether they belong to seed storage protein group or not. (II) Identification the bioactive peptides with their sequences using peptide name (III) Determination of physico chemical properties of seed storage proteins. (IV) Epitope identification and mapping (V) Allergenicity prediction and characterization. Seed Pro-Nutra Care is a compilation of data on bioactive peptides present in seed storage proteins from our own collections and other published and unpublished sources. The database provides an information resource of a variety of seed related biological information and its use for nutritional and biomedical application.

Availability

http://www.gbpuat-cbsh.ac.in/departments/bi/database/seed_pro_nutra_care/     

CFP: a web-server for constructing sequence-based protein conformational flexibility profiles

Many proteins contain conformationally flexible segments that undergo significant changes in the backbone conformation or completely lack a well­defined conformation. Previously, we have developed the generalized local propensity (GLP), a quantitative sequence-based measure of the protein backbone flexibility. In this paper, we present the CFP (Conformational Flexibility Profile) web­server that constructs the GLP flexibility profile for a user­submitted sequence and uses this profile to identify segments with high backbone flexibility. The statistical significance of a flexible sequence segment is assessed using the discrete scan statistics based on the density of flexible residues observed in this segment.

Availability

CFP is publicly available at http://cfp.rit.albany.edu     

Identification of drug target candidates of the swine pathogen <Emphasis Type="Italic">Actinobacillus pleuropneumoniae</Emphasis> by construction of protein–protein interaction network

Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein–protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.     

Evidence for a Nonendosomal Function of the Saccharomyces cerevisiae ESCRT-III-Like Protein Chm7

Endosomal sorting complex required for transport (ESCRT) proteins are involved in a number of cellular processes, such as endosomal protein sorting, HIV budding, cytokinesis, plasma membrane repair, and resealing of the nuclear envelope during mitosis. Here we explored the function of a noncanonical member of the ESCRT-III protein family, the Saccharomyces cerevisiae ortholog of human CHMP7. Very little is known about this protein. In silico analysis predicted that Chm7 (yeast ORF YJL049w) is a fusion of an ESCRT-II and ESCRT-III-like domain, which would suggest a role in endosomal protein sorting. However, our data argue against a role of Chm7 in endosomal protein sorting. The turnover of the endocytic cargo protein Ste6 and the vacuolar protein sorting of carboxypeptidase S (CPS) were not affected by CHM7 deletion, and Chm7 also responded very differently to a loss in Vps4 function compared to a canonical ESCRT-III protein. Our data indicate that the Chm7 function could be connected to the endoplasmic reticulum (ER). In line with a function at the ER, we observed a strong negative genetic interaction between the deletion of a gene function (APQ12) implicated in nuclear pore complex assembly and messenger RNA (mRNA) export and the CHM7 deletion. The patterns of genetic interactions between the APQ12 deletion and deletions of ESCRT-III genes, two-hybrid interactions, and the specific localization of mCherry fusion proteins are consistent with the notion that Chm7 performs a novel function at the ER as part of an alternative ESCRT-III complex.     

Analysis of PHA-1 Reveals a Limited Role in Pharyngeal Development and Novel Functions in Other Tissues

PHA-1 encodes a cytoplasmic protein that is required for embryonic morphogenesis and attachment of the foregut (pharynx) to the mouth (buccal capsule). Previous reports have in some cases suggested that PHA-1 is essential for the differentiation of most or all pharyngeal cell types. By performing mosaic analysis with a recently acquired pha-1 null mutation (tm3671), we found that PHA-1 is not required within most or all pharyngeal cells for their proper specification, differentiation, or function. Rather, our evidence suggests that PHA-1 acts in the arcade or anterior epithelial cells of the pharynx to promote attachment of the pharynx to the future buccal capsule. In addition, PHA-1 appears to be required in the epidermis for embryonic morphogenesis, in the excretory system for osmoregulation, and in the somatic gonad for normal ovulation and fertility. PHA-1 activity is also required within at least a subset of intestinal cells for viability. To better understand the role of PHA-1 in the epidermis, we analyzed several apical junction markers in pha-1(tm3671) homozygous embryos. PHA-1 regulates the expression of several components of two apical junction complexes including AJM-1–DLG-1/discs large complex and the classical cadherin–catenin complex, which may account for the role of PHA-1 in embryonic morphogenesis.     

Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein–protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.For well over two decades, plant scientists have studied protein interactions within plants using many different and evolving approaches. Their findings are represented by a large and growing corpus of peer-reviewed literature reflecting the increasing activity in this area of plant proteomic research. More recently, a number of predicted interactomes have been reported in plants and, while these predictions remain largely untested, they could act as a useful guide for future research. These studies have allowed researchers to better understand the function of protein complexes and to refine our understanding of protein function within the cell (Uhrig, 2006; Morsy et al., 2008). The extraction of protein interaction data from the literature and its standardized deposition and representation within publicly available databases remains a challenging task. Aggregating the data in databases allows researchers to leverage visualization, data mining, and integrative approaches to produce new insights that would be unachievable when the data are dispersed within largely inaccessible formats (Rodriguez et al., 2009).Currently, there are three databases that act as repositories of plant protein interaction data. These are IntAct (http://www.ebi.ac.uk/intact/; Aranda et al., 2010), The Arabidopsis Information Resource (TAIR; http://www.Arabidopsis.org/; Poole, 2007), and BioGRID (http://www.thebiogrid.org/; Breitkreutz et al., 2008). These databases curate experimentally established interactions available from the peer-reviewed literature (as opposed to predicted interactions, which will be discussed below). Each repository takes its own approach to the capture, storage, and representation of protein interaction data. TAIR focuses on Arabidopsis thaliana protein–protein interaction data exclusively; BioGRID currently focuses on the plant species Arabidopsis and rice (Oryza sativa), while IntAct attempts to capture protein interaction data from any plant species. Unlike the other repositories, IntAct follows a deep curation strategy that captures detailed experimental and biophysical details, such as binding regions and subcellular locations of interactions using controlled vocabularies (Aranda et al., 2010). While the majority of plant interaction data held by IntAct concern protein–protein interaction data in Arabidopsis, there is a small but growing content of interaction data relating to protein–DNA, protein–RNA, and protein–small molecule interactions, as well as interaction data from other plant species.Using the IntAct Arabidopsis data set as an example, we outline how the accumulating knowledge captured in these repositories can be used to further our understanding of the plant proteome. We compare the characteristics of predicted interactomes with the IntAct protein–protein interaction data set, which consists entirely of experimentally measured protein interactions, to gauge the predictive accuracy of these studies. Finally, we show how the IntAct data set can be used together with a recently developed Divide and Conquer k-Nearest Neighbors Method (DC-kNN; K. Lee et al., 2008) to predict the subcellular locations for most Arabidopsis proteins. This data set predicts high confidence subcellular locations for many unannotated Arabidopsis proteins and should act as a useful resource for future studies of protein function. Although this article focuses on the IntAct Arabidopsis protein–protein interaction data set, readers are also encouraged to explore the resources offered by our colleagues at TAIR and BioGRID.Each database employs its own system to report molecular interactions, as represented in the referenced source publications, and each avoids making judgments on interaction reliability or whether two participants in a complex have a direct interaction. Thus, the user should carefully filter these data sets for their specific purpose based on the full annotation of the data sets. In particular, the user should consider the experimental methods and independent observation of the same interaction in different publications when assessing the reliability and type of interaction of the proteins (e.g., direct or indirect). Confidence scoring schemes for interaction data are discussed widely in the literature (Yu and Finley, 2009).     

bPE toolkit: toolkit for computational protein engineering

We present a computational toolkit consisting of five utility tools, for performing basic operations on a protein structure file in PDB format. The toolkit consists of five different programs which can be integrated as part of a pipeline for computational protein structure characterization or as a standalone analysis package. The programs include tools for chirality check for amino acids (ProChiral), contact map generation (CoMa), data redundancy (DaRe), hydrogen bond potential energy (HyPE) and electrostatic interaction energy (EsInE). All programs in the toolkit can be accessed and downloaded through the following link: http://www.iitg.ac.in/bpetoolkit/.     

Network-based protein-protein interaction prediction method maps perturbations of cancer interactome

The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.     

Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks

As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.