Bacterial Rieske-type aromatic ring-hydroxylating oxygenases (RHOs) constitute a large family of enzymes, primarily involved in bioremediation of diverse aromatic compounds in the environment. In the present study, we have designed a manually curated database, Ring-Hydroxylating Oxygenase database (RHObase), which provides comprehensive information on all biochemically characterized bacterial RHOs. It consists of ∼ 1000 entries including 196 oxygenase α-subunits, 153 oxygenase β-subunits, 92 ferredoxins and 110 reductases, distributed among 131 different bacterial strains implementing a total of 318 oxygenation reactions. For each protein, users can get detailed information about its structure and conserved domain(s) with motif signature. RHObase allows users to search a query, based on organism, oxygenase, substrate, or protein structure. In addition, this resource provides analysis tools to perform blast search against RHObase for prediction of putative substrate(s) for the query oxygenase and its phylogenetic affiliation. Furthermore, there is an integrated cheminformatics tool to search for structurally similar compound(s) in the database vis-a-vis RHO(s) capable of transforming those compound(s). Resources in the RHObase and multiple search/display options therein are intended to provide oxygenase-related requisite information to researchers, especially working in the field of environmental microbiology and biocatalysis to attain difficult chemistry of biotechnological importance.

Linear motifs (LMs), used by a subset of all protein–protein interactions (PPIs), bind to globular receptors or domains and play an important role in signaling networks. LMPID (Linear Motif mediated Protein Interaction Database) is a manually curated database which provides comprehensive experimentally validated information about the LMs mediating PPIs from all organisms on a single platform. About 2200 entries have been compiled by detailed manual curation of PubMed abstracts, of which about 1000 LM entries were being annotated for the first time, as compared with the Eukaryotic LM resource. The users can submit their query through a user-friendly search page and browse the data in the alphabetical order of the bait gene names and according to the domains interacting with the LM. LMPID is freely accessible at http://bicresources.jcbose. ac.in/ssaha4/lmpid and contains 1750 unique LM instances found within 1181 baits interacting with 552 prey proteins. In summary, LMPID is an attempt to enrich the existing repertoire of resources available for studying the LMs implicated in PPIs and may help in understanding the patterns of LMs binding to a specific domain and develop prediction model to identify novel LMs specific to a domain and further able to predict inhibitors/modulators of PPI of interest.Database URL: http://bicresources.jcbose.ac.in/ssaha4/lmpid

BackgroundMyc is an essential gene having multiple functions such as in cell growth, differentiation, apoptosis, genomic stability, angiogenesis, and disease biology. A large number of researchers dedicated to Myc biology are generating a substantial amount of data in normal and cancer cells/tissues including Burkitt’s lymphoma and ovarian cancer.ResultsMYCbase (http://bicresources.jcbose.ac.in/ssaha4/mycbase) is a collection of experimentally supported functional sites in Myc that can influence the biological cellular processes. The functional sites were compiled according to their role which includes mutation, methylation pattern, post-translational modifications, protein-protein interactions (PPIs), and DNA interactions. In addition, biochemical properties of Myc are also compiled, which includes metabolism/pathway, protein abundance, and modulators of protein-protein interactions. The OMICS data related to Myc- like gene expression, proteomics expression using mass-spectrometry and miRNAs targeting Myc were also compiled in MYCbase. The mutation and pathway data from the MYCbase were analyzed to look at the patterns and distributions across different diseases. There were few proteins/genes found common in Myc-protein interactions and Myc-DNA binding, and these can play a significant role in transcriptional feedback loops.ConclusionIn this report, we present a comprehensive integration of relevant information regarding Myc in the form of MYCbase. The data compiled in MYCbase provides a reliable data resource for functional sites at the residue level and biochemical properties of Myc in various cancers.

PPIMpred is a web server that allows high-throughput screening of small molecules for targeting specific protein–protein interactions, namely Mdm2/P53, Bcl2/Bak and c-Myc/Max. Three different kernels of support vector machine (SVM), namely, linear, polynomial and radial basis function (RBF), and two other machine learning techniques including Naive Bayes and Random Forest were used to train the models. A fivefold cross-validation technique was used to measure the performance of these classifiers. The RBF kernel of SVM outperformed and/or was comparable with all other methods with accuracy values of 83%, 79% and 90% for Mdm2/P53, Bcl2/Bak and c-Myc/Max, respectively. About 80% of the predicted SVM scores of training/testing datasets from Mdm2/P53 and Bcl2/Bak have significant IC50 values and docking scores. The proposed models achieved an accuracy of 66–90% with blind sets. The three mentioned (Mdm2/P53, Bcl2/Bak and c-Myc/Max) proposed models were screened in a large dataset of 265 242 small chemicals from National Cancer Institute open database. To further realize the robustness of this approach, hits with high and random SVM scores were used for molecular docking in AutoDock Vina wherein the molecules with high and random predicted SVM scores yielded moderately significant docking scores (p-values < 0.1). In addition to the above-mentioned classification scheme, this web server also allows users to get the structural and chemical similarities with known chemical modulators or drug-like molecules based on Tanimoto coefficient similarity search algorithm. PPIMpred is freely available at .

Protein-peptide interactions form an important subset of the total protein interaction network in the cell and play key roles in signaling and regulatory networks, and in major biological processes like cellular localization, protein degradation, and immune response. In this work, we have described the LMDIPred web server, an online resource for generalized prediction of linear peptide sequences that may bind to three most prevalent and well-studied peptide recognition modules (PRMs)—SH3, WW and PDZ. We have developed support vector machine (SVM)-based prediction models that achieved maximum Matthews Correlation Coefficient (MCC) of 0.85 with an accuracy of 94.55% for SH3, MCC of 0.90 with an accuracy of 95.82% for WW, and MCC of 0.83 with an accuracy of 92.29% for PDZ binding peptides. LMDIPred output combines predictions from these SVM models with predictions using Position-Specific Scoring Matrices (PSSMs) and string-matching methods using known domain-binding motif instances and regular expressions. All of these methods were evaluated using a five-fold cross-validation technique on both balanced and unbalanced datasets, and also validated on independent datasets. LMDIPred aims to provide a preliminary bioinformatics platform for sequence-based prediction of probable binding sites for SH3, WW or PDZ domains.