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.

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. Results: SVM models have been developed on DNAaset, which consists of 1153 DNA-binding and equal number of non DNA-binding proteins, and achieved the maximum accuracy of 72.42% and 71.59% using amino acid and dipeptide compositions, respectively. The performance of SVM model improved from 72.42% to 74.22%, when evolutionary information in form of PSSM profiles was used as input instead of amino acid composition. In addition, SVM models have been developed on DNAset, which consists of 146 DNA-binding and 250 non-binding chains/domains, and achieved the maximum accuracy of 79.80% and 86.62% using amino acid composition and PSSM profiles. The SVM models developed in this study perform better than existing methods on a blind dataset. Conclusion: A highly accurate method has been developed for predicting DNA-binding proteins using SVM and PSSM profiles. This is the first study in which evolutionary information in form of PSSM profiles has been used successfully for predicting DNA-binding proteins. A web-server DNAbinder has been developed for identifying DNA-binding proteins and domains from query amino acid sequences http://www.imtech.res.in/raghava/dnabinder/.

Oxygenases belong to the oxidoreductive group of enzymes (E.C. Class 1), which oxidize the substrates by transferring oxygen from molecular oxygen (O₂) and utilize FAD/NADH/NADPH as the co-substrate. Oxygenases can further be grouped into two categories i.e. monooxygenases and dioxygenases on the basis of number of oxygen atoms used for oxidation. They play a key role in the metabolism of organic compounds by increasing their reactivity or water solubility or bringing about cleavage of the aromatic ring. We compiled a database of biodegradative oxygenases (OxDBase) which provides a compilation of the oxygenase data as sourced from primary literature in the form of web accessible database. There are two separate search engines for searching into the database i.e. mono and dioxygenases database respectively. Each enzyme entry contains its common name and synonym, reaction in which enzyme is involved, family and subfamily, structure and gene link and literature citation. The entries are also linked to several external database including BRENDA, KEGG, ENZYME and UM-BBD providing wide background information. At present the database contains information of over 235 oxygenases including both dioxygenases and monooxygenases. This database is freely available online at http://www.imtech.res.in/raghava/oxdbase/ . OxDBase is the first database that is dedicated only to oxygenases and provides comprehensive information about them. Due to the importance of the oxygenases in chemical synthesis of drug intermediates and oxidation of xenobiotic compounds, OxDBase database would be very useful tool in the field of synthetic chemistry as well as bioremediation.