Mamoon Rashid

Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs.

BackgroundIn past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.ResultsThe models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM (Support Vector Machine) model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy (mean of class-wise accuracy) of 68.47%. In order to utilize evolutionary information, a SVM model was developed using PSSM (Position-Specific Scoring Matrix) profiles obtained from PSI-BLAST (Position-Specific Iterated BLAST) and overall accuracy achieved was of 86.62% with average accuracy of 73.71%. In addition, HMM (Hidden Markov Model), MEME/MAST (Multiple Em for Motif Elicitation/Motif Alignment and Search Tool) and hybrid model that combined two or more models were also developed. We achieved maximum overall accuracy of 86.8% with average accuracy of 89.00% using combination of PSSM based SVM model and MEME/MAST. Performance of our method was compared with that of the existing methods developed for predicting subcellular locations of Gram-positive bacterial proteins.ConclusionA highly accurate method has been developed for predicting subcellular location of mycobacterial proteins. This method also predicts very important class of proteins that is membrane-attached proteins. This method will be useful in annotating newly sequenced or hypothetical mycobacterial proteins. Based on above study, a freely accessible web server TBpred http://www.imtech.res.in/raghava/tbpred/ has been developed.

READSCAN: a fast and scalable pathogen discovery program with accurate genome relative abundance estimation

Summary: READSCAN is a highly scalable parallel program to identify non-host sequences (of potential pathogen origin) and estimate their genome relative abundance in high-throughput sequence datasets. READSCAN accurately classified human and viral sequences on a 20.1 million reads simulated dataset in <27 min using a small Beowulf compute cluster with 16 nodes (Supplementary Material). Availability: http://cbrc.kaust.edu.sa/readscan Contact: arnab.pain@kaust.edu.sa or raeece.naeem@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

A Simple Approach for Predicting Protein-Protein Interactions

The availability of an increased number of fully sequenced genomes demands functional interpretation of the genomic information. Despite high throughput experimental techniques and in silico methods of predicting protein-protein interaction (PPI); the interactome of most organisms is far from completion. Thus, predicting the interactome of an organism is one of the major challenges in the post-genomic era. This manuscript describes Support Vector Machine (SVM) based models that have been developed for discriminating interacting and non-interacting pairs of proteins from their amino acid sequence. We have developed SVM models using various types of sequence compositions e.g. amino acid, dipeptide, biochemical property, split amino acid and pseudo amino acid composition. We also developed SVM models using evolutionary information in the form of Position Specific Scoring Matrix (PSSM) composition. We achieved maximum Matthewss correlation coefficient (MCC) of 1.00, 0.52 and 0.74 for Escherichia coli, Saccharomyces cerevisiae, and Helicobacter pylori, using dipeptide based SVM model at default threshold. It was observed that the performance of a prediction model depends on the dataset used for training and testing. In case of E. coli MCC decreased from 1.0 to 0.67 when evaluated on a new dataset. In order to understand PPI in different cellular environment, we developed species-specific and general models. It was observed that species-specific models are more accurate than general models. We conclude that the primary amino acid sequence based descriptors could be used to differentiate interacting from non-interacting protein pairs. Some amino acids tend to be favored in interacting pairs than non-interacting ones. Finally, a web server has been developed for predicting protein-protein interactions.

Metabolic traits of an uncultured archaeal lineage -MSBL1- from brine pools of the Red Sea

The candidate Division MSBL1 (Mediterranean Sea Brine Lakes 1) comprises a monophyletic group of uncultured archaea found in different hypersaline environments. Previous studies propose methanogenesis as the main metabolism. Here, we describe a metabolic reconstruction of MSBL1 based on 32 single-cell amplified genomes from Brine Pools of the Red Sea (Atlantis II, Discovery, Nereus, Erba and Kebrit). Phylogeny based on rRNA genes as well as conserved single copy genes delineates the group as a putative novel lineage of archaea. Our analysis shows that MSBL1 may ferment glucose via the Embden–Meyerhof–Parnas pathway. However, in the absence of organic carbon, carbon dioxide may be fixed via the ribulose bisphosphate carboxylase, Wood-Ljungdahl pathway or reductive TCA cycle. Therefore, based on the occurrence of genes for glycolysis, absence of the core genes found in genomes of all sequenced methanogens and the phylogenetic position, we hypothesize that the MSBL1 are not methanogens, but probably sugar-fermenting organisms capable of autotrophic growth. Such a mixotrophic lifestyle would confer survival advantage (or possibly provide a unique narrow niche) when glucose and other fermentable sugars are not available.

The population structure of Vibrio cholerae from the Chandigarh Region of Northern India.

Background Cholera infection continues to be a threat to global public health. The current cholera pandemic associated with Vibrio cholerae El Tor has now been ongoing for over half a century. Methodology/Principal Findings Thirty-eight V. cholerae El Tor isolates associated with a cholera outbreak in 2009 from the Chandigarh region of India were characterised by a combination of microbiology, molecular typing and whole-genome sequencing. The genomic analysis indicated that two clones of V. cholera circulated in the region and caused disease during this time. These clones fell into two distinct sub-clades that map independently onto wave 3 of the phylogenetic tree of seventh pandemic V. cholerae El Tor. Sequence analyses of the cholera toxin gene, the Vibrio seventh Pandemic Island II (VSPII) and SXT element correlated with this phylogenetic position of the two clades on the El Tor tree. The clade 2 isolates, characterized by a drug-resistant profile and the expression of a distinct cholera toxin, are closely related to the recent V. cholerae isolated elsewhere, including Haiti, but fell on a distinct branch of the tree, showing they were independent outbreaks. Multi-Locus Sequence Typing (MLST) distinguishes two sequence types among the 38 isolates, that did not correspond to the clades defined by whole-genome sequencing. Multi-Locus Variable-length tandem-nucleotide repeat Analysis (MLVA) identified 16 distinct clusters. Conclusions/Significance The use of whole-genome sequencing enabled the identification of two clones of V. cholerae that circulated during the 2009 Chandigarh outbreak. These clones harboured a similar structure of ICEVchHai1 but differed mainly in the structure of CTX phage and VSPII. The limited capacity of MLST and MLVA to discriminate between the clones that circulated in the 2009 Chandigarh outbreak highlights the value of whole-genome sequencing as a route to the identification of further genetic markers to subtype V. cholerae isolates.

Designing of peptides with desired half-life in intestine-like environment

BackgroundIn past, a number of peptides have been reported to possess highly diverse properties ranging from cell penetrating, tumor homing, anticancer, anti-hypertensive, antiviral to antimicrobials. Owing to their excellent specificity, low-toxicity, rich chemical diversity and availability from natural sources, FDA has successfully approved a number of peptide-based drugs and several are in various stages of drug development. Though peptides are proven good drug candidates, their usage is still hindered mainly because of their high susceptibility towards proteases degradation. We have developed an in silico method to predict the half-life of peptides in intestine-like environment and to design better peptides having optimized physicochemical properties and half-life.ResultsIn this study, we have used 10mer (HL10) and 16mer (HL16) peptides dataset to develop prediction models for peptide half-life in intestine-like environment. First, SVM based models were developed on HL10 dataset which achieved maximum correlation R/R2 of 0.57/0.32, 0.68/0.46, and 0.69/0.47 using amino acid, dipeptide and tripeptide composition, respectively. Secondly, models developed on HL16 dataset showed maximum R/R2 of 0.91/0.82, 0.90/0.39, and 0.90/0.31 using amino acid, dipeptide and tripeptide composition, respectively. Furthermore, models that were developed on selected features, achieved a correlation (R) of 0.70 and 0.98 on HL10 and HL16 dataset, respectively. Preliminary analysis suggests the role of charged residue and amino acid size in peptide half-life/stability. Based on above models, we have developed a web server named HLP (Half Life Prediction), for predicting and designing peptides with desired half-life. The web server provides three facilities; i) half-life prediction, ii) physicochemical properties calculation and iii) designing mutant peptides.ConclusionIn summary, this study describes a web server ‘HLP’ that has been developed for assisting scientific community for predicting intestinal half-life of peptides and to design mutant peptides with better half-life and physicochemical properties. HLP models were trained using a dataset of peptides whose half-lives have been determined experimentally in crude intestinal proteases preparation. Thus, HLP server will help in designing peptides possessing the potential to be administered via oral route (http://www.imtech.res.in/raghava/hlp/).

Hmrbase: a database of hormones and their receptors

BackgroundHormones are signaling molecules that play vital roles in various life processes, like growth and differentiation, physiology, and reproduction. These molecules are mostly secreted by endocrine glands, and transported to target organs through the bloodstream. Deficient, or excessive, levels of hormones are associated with several diseases such as cancer, osteoporosis, diabetes etc. Thus, it is important to collect and compile information about hormones and their receptors.DescriptionThis manuscript describes a database called Hmrbase which has been developed for managing information about hormones and their receptors. It is a highly curated database for which information has been collected from the literature and the public databases. The current version of Hmrbase contains comprehensive information about ~2000 hormones, e.g., about their function, source organism, receptors, mature sequences, structures etc. Hmrbase also contains information about ~3000 hormone receptors, in terms of amino acid sequences, subcellular localizations, ligands, and post-translational modifications etc. One of the major features of this database is that it provides data about ~4100 hormone-receptor pairs. A number of online tools have been integrated into the database, to provide the facilities like keyword search, structure-based search, mapping of a given peptide(s) on the hormone/receptor sequence, sequence similarity search. This database also provides a number of external links to other resources/databases in order to help in the retrieving of further related information.ConclusionOwing to the high impact of endocrine research in the biomedical sciences, the Hmrbase could become a leading data portal for researchers. The salient features of Hmrbase are hormone-receptor pair-related information, mapping of peptide stretches on the protein sequences of hormones and receptors, Pfam domain annotations, categorical browsing options, online data submission, DrugPedia linkage etc. Hmrbase is available online for public from http://crdd.osdd.net/raghava/hmrbase/.

Complete Genome Sequence of Mycobacterium phlei Type Strain RIVM601174

Mycobacterium phlei is a rapidly growing nontuberculous Mycobacterium species that is typically nonpathogenic, with few reported cases of human disease. Here we report the whole genome sequence of M. phlei type strain RIVM601174.

Genomic differentiation among two strains of the PS1 clade isolated from geographically separated marine habitats

Using dilution-to-extinction cultivation, we isolated a strain affiliated with the PS1 clade from surface waters of the Red Sea. Strain RS24 represents the second isolate of this group of marine Alphaproteobacteria after IMCC14465 that was isolated from the East (Japan) Sea. The PS1 clade is a sister group to the OCS116 clade, together forming a putatively novel order closely related to Rhizobiales. While most genomic features and most of the genetic content are conserved between RS24 and IMCC14465, their average nucleotide identity (ANI) is < 81%, suggesting two distinct species of the PS1 clade. Next to encoding two different variants of proteorhodopsin genes, they also harbor several unique genomic islands that contain genes related to degradation of aromatic compounds in IMCC14465 and in polymer degradation in RS24, possibly reflecting the physicochemical differences in the environment they were isolated from. No clear differences in abundance of the genomic content of either strain could be found in fragment recruitment analyses using different metagenomic datasets, in which both genomes were detectable albeit as minor part of the communities. The comparative genomic analysis of both isolates of the PS1 clade and the fragment recruitment analysis provide first insights into the ecology of this group.

Complete Genome Sequence of Mycobacterium xenopi Type Strain RIVM700367

Mycobacterium xenopi is a slow-growing, thermophilic, water-related Mycobacterium species. Like other nontuberculous mycobacteria, M. xenopi more commonly infects humans with altered immune function, such as chronic obstructive pulmonary disease patients. It is considered clinically relevant in a significant proportion of the patients from whom it is isolated. We report here the whole genome sequence of M. xenopi type strain RIVM700367.