In silico re-identification of properties of drug target proteins

Background

Computational approaches in the identification of drug targets are expected to reduce time and effort in drug development. Advances in genomics and proteomics provide the opportunity to uncover properties of druggable genomes. Although several studies have been conducted for distinguishing drug targets from non-drug targets, they mainly focus on the sequences and functional roles of proteins. Many other properties of proteins have not been fully investigated.

Methods

Using the DrugBank (version 3.0) database containing nearly 6,816 drug entries including 760 FDA-approved drugs and 1822 of their targets and human UniProt/Swiss-Prot databases, we defined 1578 non-redundant drug target and 17,575 non-drug target proteins. To select these non-redundant protein datasets, we built four datasets (A, B, C, and D) by considering clustering of paralogous proteins.

Results

We first reassessed the widely used properties of drug target proteins. We confirmed and extended that drug target proteins (1) are likely to have more hydrophobic, less polar, less PEST sequences, and more signal peptide sequences higher and (2) are more involved in enzyme catalysis, oxidation and reduction in cellular respiration, and operational genes. In this study, we proposed new properties (essentiality, expression pattern, PTMs, and solvent accessibility) for effectively identifying drug target proteins. We found that (1) drug targetability and protein essentiality are decoupled, (2) druggability of proteins has high expression level and tissue specificity, and (3) functional post-translational modification residues are enriched in drug target proteins. In addition, to predict the drug targetability of proteins, we exploited two machine learning methods (Support Vector Machine and Random Forest). When we predicted drug targets by combining previously known protein properties and proposed new properties, an F-score of 0.8307 was obtained.

Conclusions

When the newly proposed properties are integrated, the prediction performance is improved and these properties are related to drug targets. We believe that our study will provide a new aspect in inferring drug-target interactions.

     

Fpocket: An open source platform for ligand pocket detection

Background  

Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.     

Predicting drug targets based on protein domains

The identification of interactions between drugs and proteins plays key roles in understanding mechanisms underlying drug actions and can lead to new drug design strategies. Here, we present a novel statistical approach, namely PDTD (Predicting Drug Targets with Domains), to predict potential target proteins of new drugs based on derived interactions between drugs and protein domains. The known target proteins of those drugs that have similar therapeutic effects allow us to infer interactions between drugs and protein domains which in turn leads to identification of potential drug-protein interactions. Benchmarking with known drug-protein interactions shows that our proposed methodology outperforms previous methods that exploit either protein sequences or compound structures to predict drug targets, which demonstrates the predictive power of our proposed PDTD method.     

Structural assembly of two-domain proteins by rigid-body docking

Background  

Modelling proteins with multiple domains is one of the central challenges in Structural Biology. Although homology modelling has successfully been applied for prediction of protein structures, very often domain-domain interactions cannot be inferred from the structures of homologues and their prediction requiresab initiomethods. Here we present a new structural prediction approach for modelling two-domain proteins based on rigid-body domain-domain docking.     

Identification of a novel <Emphasis Type="Italic">Plasmopara halstedii</Emphasis> elicitor protein combining <Emphasis Type="Italic">de novo</Emphasis> peptide sequencing algorithms and RACE-PCR

Background  

Often high-quality MS/MS spectra of tryptic peptides do not match to any database entry because of only partially sequenced genomes and therefore, protein identification requires de novo peptide sequencing. To achieve protein identification of the economically important but still unsequenced plant pathogenic oomycete Plasmopara halstedii, we first evaluated the performance of three different de novo peptide sequencing algorithms applied to a protein digests of standard proteins using a quadrupole TOF (QStar Pulsar i).     

Analysis of protein-protein docking decoys using interaction fingerprints: application to the reconstruction of CaM-ligand complexes

Background  

Protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. Here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. This combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles.     

Correcting ligands, metabolites, and pathways

Background  

A wide range of research areas in bioinformatics, molecular biology and medicinal chemistry require precise chemical structure information about molecules and reactions, e.g. drug design, ligand docking, metabolic network reconstruction, and systems biology. Most available databases, however, treat chemical structures more as illustrations than as a datafield in its own right. Lack of chemical accuracy impedes progress in the areas mentioned above. We present a database of metabolites called BioMeta that augments the existing pathway databases by explicitly assessing the validity, correctness, and completeness of chemical structure and reaction information.     

RepSeq – A database of amino acid repeats present in lower eukaryotic pathogens

Background  

Amino acid repeat-containing proteins have a broad range of functions and their identification is of relevance to many experimental biologists. In human-infective protozoan parasites (such as the Kinetoplastid and Plasmodium species), they are implicated in immune evasion and have been shown to influence virulence and pathogenicity. RepSeq is a new database of amino acid repeat-containing proteins found in lower eukaryotic pathogens. The RepSeq database is accessed via a web-based application which also provides links to related online tools and databases for further analyses.     

Proteomic identification of Drosophila melanogaster male accessory gland proteins, including a pro-cathepsin and a soluble γ-glutamyl transpeptidase

Background  

In Drosophila melanogaster, the male seminal fluid contains proteins that are important for reproductive success. Many of these proteins are synthesised by the male accessory glands and are secreted into the accessory gland lumen, where they are stored until required. Previous studies on the identification of Drosophila accessory gland products have largely focused on characterisation of male-specific accessory gland cDNAs from D. melanogaster and, more recently, Drosophila simulans. In the present study, we have used a proteomics approach without any sex bias to identify proteins in D. melanogaster accessory gland secretions.     

Utilization of tmRNA sequences for bacterial identification

Background  

Ribosomal RNA molecules are widely used for phylogenetic and in situ identification of bacteria. Nevertheless, their use to distinguish microorganisms within a species is often restricted by the high degree of sequence conservation and limited probe accessibility to the target in fluorescence in situ hybridization (FISH). To overcome these limitations, we examined the use of tmRNA for in situ identification. In E. coli, this stable 363 nucleotides long RNA is encoded by the ssrA gene, which is involved in the degradation of truncated proteins.     

Large-scale prediction of protein-protein interactions from structures

Background  

The prediction of protein-protein interactions is an important step toward the elucidation of protein functions and the understanding of the molecular mechanisms inside the cell. While experimental methods for identifying these interactions remain costly and often noisy, the increasing quantity of solved 3D protein structures suggests that in silico methods to predict interactions between two protein structures will play an increasingly important role in screening candidate interacting pairs. Approaches using the knowledge of the structure are presumably more accurate than those based on sequence only. Approaches based on docking protein structures solve a variant of this problem, but these methods remain very computationally intensive and will not scale in the near future to the detection of interactions at the level of an interactome, involving millions of candidate pairs of proteins.     

Virtual screening of GPCRs: An <Emphasis Type="Italic">in silico</Emphasis> chemogenomics approach

Background  

The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules in the transmembrane ligand-binding site is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies.     

Properties and identification of antibiotic drug targets

Background  

We analysed 48 non-redundant antibiotic target proteins from all bacteria, 22 antibiotic target proteins from E. coli only and 4243 non-drug targets from E. coli to identify differences in their properties and to predict new potential drug targets.     

Columba: an integrated database of proteins,structures, and annotations

Background  

Structural and functional research often requires the computation of sets of protein structures based on certain properties of the proteins, such as sequence features, fold classification, or functional annotation. Compiling such sets using current web resources is tedious because the necessary data are spread over many different databases. To facilitate this task, we have created COLUMBA, an integrated database of annotations of protein structures.     

Stratification of co-evolving genomic groups using ranked phylogenetic profiles

Background  

Previous methods of detecting the taxonomic origins of arbitrary sequence collections, with a significant impact to genome analysis and in particular metagenomics, have primarily focused on compositional features of genomes. The evolutionary patterns of phylogenetic distribution of genes or proteins, represented by phylogenetic profiles, provide an alternative approach for the detection of taxonomic origins, but typically suffer from low accuracy. Herein, we present rank-BLAST, a novel approach for the assignment of protein sequences into genomic groups of the same taxonomic origin, based on the ranking order of phylogenetic profiles of target genes or proteins across the reference database.     

Identification and bioinformatic analysis of the membrane proteins of synechocystis sp. PCC 6803

Background  

The membranes of Synechocystis sp. PCC 6803 play a central role in photosynthesis, respiration and other important metabolic pathways. Comprehensive identification of the membrane proteins is of importance for a better understanding of the diverse functions of its unique membrane structures. Up to date, approximately 900 known or predicted membrane proteins, consisting 24.5% of Synechocystis sp. PCC 6803 proteome, have been indentified by large-scale proteomic studies.     

Fast dynamics perturbation analysis for prediction of protein functional sites

Background  

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy D _x . Such regions are associated with functional sites.     

Domain-based small molecule binding site annotation

Background  

Accurate small molecule binding site information for a protein can facilitate studies in drug docking, drug discovery and function prediction, but small molecule binding site protein sequence annotation is sparse. The Small Molecule Interaction Database (SMID), a database of protein domain-small molecule interactions, was created using structural data from the Protein Data Bank (PDB). More importantly it provides a means to predict small molecule binding sites on proteins with a known or unknown structure and unlike prior approaches, removes large numbers of false positive hits arising from transitive alignment errors, non-biologically significant small molecules and crystallographic conditions that overpredict ion binding sites.     

Peptide deformylase is a potential target for anti-Helicobacter pylori drugs: reverse docking, enzymatic assay, and X-ray crystallography validation

Colonization of human stomach by the bacterium Helicobacter pylori is a major causative factor for gastrointestinal illnesses and gastric cancer. However, the discovery of anti-H. pylori agents is a difficult task due to lack of mature protein targets. Therefore, identifying new molecular targets for developing new drugs against H. pylori is obviously necessary. In this study, the in-house potential drug target database (PDTD, http://www.dddc.ac.cn/tarfisdock/) was searched by the reverse docking approach using an active natural product (compound 1) discovered by anti-H. pylori screening as a probe. Homology search revealed that, among the 15 candidates discovered by reverse docking, only diaminopimelate decarboxylase (DC) and peptide deformylase (PDF) have homologous proteins in the genome of H. pylori. Enzymatic assay demonstrated compound 1 and its derivative compound 2 are the potent inhibitors against H. pylori PDF (HpPDF) with IC50 values of 10.8 and 1.25 microM, respectively. X-ray crystal structures of HpPDF and the complexes of HpPDF with 1 and 2 were determined for the first time, indicating that these two inhibitors bind well with HpPDF binding pocket. All these results indicate that HpPDF is a potential target for screening new anti-H. pylori agents. In addition, compounds 1 and 2 were predicted to bind to HpPDF with relatively high selectivity, suggesting they can be used as leads for developing new anti-H. pylori agents. The results demonstrated that our strategy, reverse docking in conjunction with bioassay and structural biology, is effective and can be used as a complementary approach of functional genomics and chemical biology in target identification.     

Signalogs: orthology-based identification of novel signaling pathway components in three metazoans

Background

Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.

Principal Findings

Here we introduce the concept of ‘signalog’ to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates.

Conclusions

Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.