Currently microorganisms are best identified using 16S rRNA and 18S rRNA gene sequencing. However, in recent years matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a potential tool for microbial identification and diagnosis. During the MALDI-TOF MS process, microbes are identified using either intact cells or cell extracts. The process is rapid, sensitive, and economical in terms of both labor and costs involved. The technology has been readily imbibed by microbiologists who have reported usage of MALDI-TOF MS for a number of purposes like, microbial identification and strain typing, epidemiological studies, detection of biological warfare agents, detection of water- and food-borne pathogens, detection of antibiotic resistance and detection of blood and urinary tract pathogens etc. The limitation of the technology is that identification of new isolates is possible only if the spectral database contains peptide mass fingerprints of the type strains of specific genera/species/subspecies/strains. This review provides an overview of the status and recent applications of mass spectrometry for microbial identification. It also explores the usefulness of this exciting new technology for diagnosis of diseases caused by bacteria, viruses, and fungi.

RNA-binding proteins (RBPs) play key roles in post-transcriptional control of gene expression, which, along with transcriptional regulation, is a major way to regulate patterns of gene expression during development. Thus, the identification and prediction of RNA binding sites is an important step in comprehensive understanding of how RBPs control organism development. Combining evolutionary information and support vector machine (SVM), we have developed an improved method for predicting RNA binding sites or RNA interacting residues in a protein sequence. The prediction models developed in this study have been trained and tested on 86 RNA binding protein chains and evaluated using fivefold cross validation technique. First, a SVM model was developed that achieved a maximum Matthew's correlation coefficient (MCC) of 0.31. The performance of this SVM model further improved the MCC from 0.31 to 0.45, when multiple sequence alignment in the form of PSSM profiles was used as input to the SVM, which is far better than the maximum MCC achieved by previous methods (0.41) on the same dataset. In addition, SVM models were also developed on an alternative dataset that contained 107 RBP chains. Utilizing PSSM as input information to the SVM, the training/testing on this alternate dataset achieved a maximum MCC of 0.32. Conclusively, the prediction performance of SVM models developed in this study is better than the existing methods on the same datasets. A web server 'Pprint' was also developed for predicting RNA binding residues in a protein sequence which is freely available at http://www.imtech.res.in/raghava/pprint/.

Background: Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation, as these proteins play a crucial role in gene-regulation. In this paper, we developed various SVM modules for predicting DNA-binding domains and proteins. All models were trained and tested on multiple datasets of non-redundant proteins.

RNA-binding proteins (RBPs) play crucial role in transcription and gene-regulation. This paper describes a support vector machine (SVM) based method for discriminating and classifying RNA-binding and non-binding proteins using sequence features. With the threshold of 30% interacting residues, RNA-binding amino acid prediction method PPRINT achieved the Matthews correlation coefficient (MCC) of 0.32. BLAST and PSI-BLAST identified RBPs with the coverage of 32.63 and 33.16%, respectively, at the e-value of 1e-4. The SVM models developed with amino acid, dipeptide and four-part amino acid compositions showed the MCC of 0.60, 0.46, and 0.53, respectively. This is the first study in which evolutionary information in form of position specific scoring matrix (PSSM) profile has been successfully used for predicting RBPs. We achieved the maximum MCC of 0.62 using SVM model based on PSSM called PSSM-400. Finally, we developed different hybrid approaches and achieved maximum MCC of 0.66. We also developed a method for predicting three subclasses of RNA binding proteins (e.g., rRNA, tRNA, mRNA binding proteins). The performance of the method was also evaluated on an independent dataset of 69 RBPs and 100 non-RBPs (NBPs). An additional benchmarking was also performed using gene ontology (GO) based annotation. Based on the hybrid approach a web-server RNApred has been developed for predicting RNA binding proteins from amino acid sequences (http://www.imtech.res.in/raghava/rnapred/).

The first release of Protein–protein Interactions Thermodynamic Database (PINT) contains >1500 data of several thermodynamic parameters along with sequence and structural information, experimental conditions and literature information. Each entry contains numerical data for the free energy change, dissociation constant, association constant, enthalpy change, heat capacity change and so on of the interacting proteins upon binding, which are important for understanding the mechanism of protein–protein interactions. PINT also includes the name and source of the proteins involved in binding, their Protein Information Resource, SWISS-PROT and Protein Data Bank (PDB) codes, secondary structure and solvent accessibility of residues at mutant positions, measuring methods, experimental conditions, such as buffers, ions and additives, and literature information. A WWW interface facilitates users to search data based on various conditions, feasibility to select the terms for output and different sorting options. Further, PINT is cross-linked with other related databases, PIR, SWISS-PROT, PDB and NCBI PUBMED literature database. The database is freely available at

Mitochondria are considered as one of the core organelles of eukaryotic cells hence prediction of mitochondrial proteins is one of the major challenges in the field of genome annotation. This study describes a method, MitPred, developed for predicting mitochondrial proteins with high accuracy. The data set used in this study was obtained from Guda, C., Fahy, E. & Subramaniam, S. (2004) Bioinformatics 20, 1785-1794. First support vector machine-based modules/methods were developed using amino acid and dipeptide composition of proteins and achieved accuracy of 78.37 and 79.38%, respectively. The accuracy of prediction further improved to 83.74% when split amino acid composition (25 N-terminal, 25 C-terminal, and remaining residues) of proteins was used. Then BLAST search and support vector machine-based method were combined to get 88.22% accuracy. Finally we developed a hybrid approach that combined hidden Markov model profiles of domains (exclusively found in mitochondrial proteins) and the support vector machine-based method. We were able to predict mitochondrial protein with 100% specificity at a 56.36% sensitivity rate and with 80.50% specificity at 98.95% sensitivity. The method estimated 9.01, 6.35, 4.84, 3.95, and 4.25% of proteins as mitochondrial in Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, mouse, and human proteomes, respectively. MitPred was developed on the above hybrid approach.The mitochondrion, popularly known as the power house of the cell, is the central unit of eukaryotic cells.2 It is a double membrane-bounded organelle with two spaces, the outer intermembrane space and inner matrix. Due to the presence of an explicit mitochondrial genome, unlike other organelles, its function is regulated by two genomes. It performs a plethora of biochemical reactions like oxidative phosphorylation, Krebs cycle, ␤-oxidation of fatty acids, DNA replication, transcription, translation, etc., some of which occur in mitochondria only. In addition mitochondria are also involved in apoptosis (1) and ionic homeostasis (2). Because of their multidimensional utility, mitochondrial proteins are associated with several human diseases, including Alzheimer disease (3), Type II diabetes (4) and Parkinson disease (5).A majority of mitochondrial proteins are synthesized in cytoplasm from where they are transported inside mitochondria. But a small number of mitochondria-resident proteins are also synthesized inside mitochondria by the mitochondrial genome. Proteins that are imported to mitochondria contain a leader sequence at the N terminus that contains all the information needed to localize to mitochondria (6). But this is not true for all mitochondrial proteins. In many cases the leader sequence is altogether absent. This poses a major challenge in predicting mitochondrial proteins in silico.In the past, a number of methods have been developed to predict the mitochondrial proteins, although most were not intended exclusively for mitochondrial proteins. Existing prediction methods can be divi...

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Recent evidences suggest that the localization of mRNAs near the subcellular compartment of the translated proteins is a more robust cellular tool, which optimizes protein expression, post-transcriptionally. Retention of mRNA in the nucleus can regulate the amount of protein translated from each mRNA, thus allowing a tight temporal regulation of translation or buffering of protein levels from bursty transcription. Besides, mRNA localization performs a variety of additional roles like long-distance signaling, facilitating assembly of protein complexes and coordination of developmental processes. Here, we describe a novel machine-learning based tool, mRNALoc, to predict five sub-cellular locations of eukaryotic mRNAs using cDNA/mRNA sequences. During five fold cross-validations, the maximum overall accuracy was 65.19, 75.36, 67.10, 99.70 and 73.59% for the extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. Assessment on independent datasets revealed the prediction accuracies of 58.10, 69.23, 64.55, 96.88 and 69.35% for extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. The corresponding values of AUC were 0.76, 0.75, 0.70, 0.98 and 0.74 for the extracellular region, endoplasmic reticulum, cytoplasm, mitochondria, and nucleus, respectively. The mRNALoc standalone software and web-server are freely available for academic use under GNU GPL at http://proteininformatics.org/mkumar/mrnaloc.