M. N. Ponnuswamy

Bio: M. N. Ponnuswamy is an academic researcher from University of Madras. The author has contributed to research in topics: Protein folding & Chaperone (protein). The author has an hindex of 7, co-authored 14 publications receiving 186 citations.

Average assignment method for predicting the stability of protein mutants.

Abstract: Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and it will be helpful for designing stable mutants. In this work, we have analyzed the stability of protein mutants using three different data sets of 1791, 1396, and 2204 mutants, respectively, for thermal stability (ΔTm), free energy change due to thermal (ΔΔG), and denaturant denaturations (ΔΔG), obtained from the ProTherm database. We have classified the mutants into 380 possible substitutions and assigned the stability of each mutant using the information obtained with similar type of mutations. We observed that this assignment could distinguish the stabilizing and destabilizing mutants to an accuracy of 70–80% at different measures of stability. Further, we have classified the mutants based on secondary structure and solvent accessibility (ASA) and observed that the classification significantly improved the accuracy of prediction. The classification of mutants based on helix, strand, and coil distinguished the stabilizing/destabilizing mutants at an average accuracy of 82% and the correlation is 0.56; information about the location of residues at the interior, partially buried, and surface regions of a protein correctly identified the stabilizing/destabilizing residues at an average accuracy of 81% and the correlation is 0.59. The nine subclassifications based on three secondary structures and solvent accessibilities improved the accuracy of assigning stabilizing/destabilizing mutants to an accuracy of 84–89% for the three data sets. Further, the present method is able to predict the free energy change (ΔΔG) upon mutations within a deviation of 0.64 kcal/mol. We suggest that this method could be used for predicting the stability of protein mutants. © 2006 Wiley Periodicals, Inc. Biopolymers 82: 80–92, 2006 This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com

CUPSAT: prediction of protein stability upon point mutations

Abstract: CUPSAT (Cologne University Protein Stability Analysis Tool) is a web tool to analyse and predict protein stability changes upon point mutations (single amino acid mutations). This program uses structural environment specific atom potentials and torsion angle potentials to predict ΔΔG, the difference in free energy of unfolding between wild-type and mutant proteins. It requires the protein structure in Protein Data Bank format and the location of the residue to be mutated. The output consists information about mutation site, its structural features (solvent accessibility, secondary structure and torsion angles), and comprehensive information about changes in protein stability for 19 possible substitutions of a specific amino acid mutation. Additionally, it also analyses the ability of the mutated amino acids to adapt the observed torsion angles. Results were tested on 1538 mutations from thermal denaturation and 1603 mutations from chemical denaturation experiments. Several validation tests (split-sample, jack-knife and k-fold) were carried out to ensure the reliability, accuracy and transferability of the prediction method that gives >80% prediction accuracy for most of these validation tests. Thus, the program serves as a valuable tool for the analysis of protein design and stability. The tool is accessible from the link http://cupsat.uni-koeln.de.

On prediction using variable order Markov models

Abstract: This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a "decomposed" CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems.

SDM—a server for predicting effects of mutations on protein stability and malfunction

Abstract: The sheer volume of non-synonymous single nucleotide polymorphisms that have been generated in recent years from projects such as the Human Genome Project, the HapMap Project and Genome-Wide Association Studies means that it is not possible to characterize all mutations experimentally on the gene products, i.e. elucidate the effects of mutations on protein structure and function. However, automatic methods that can predict the effects of mutations will allow a reduced set of mutations to be studied. Site Directed Mutator (SDM) is a statistical potential energy function that uses environment-specific amino-acid substitution frequencies within homologous protein families to calculate a stability score, which is analogous to the free energy difference between the wild-type and mutant protein. Here, we present a web server for SDM (http://wwwcryst.bioc.cam.ac.uk/� sdm/sdm.php), which has obtained more than 10 000 submissions since being online in April 2008. To run SDM, users must upload a wild-type structure and the position and amino acid type of the mutation. The results returned include information about the local structural environment of the wild-type and mutant residues, a stability score prediction and prediction of disease association. Additionally, the wild-type and mutant structures are displayed in a Jmol applet with the relevant residues highlighted.