Piero Fariselli1, Pier Luigi Martelli2, Castrense Savojardo2, Rita Casadio2. 1. Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna and Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy. 2. Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna and.
Abstract
MOTIVATION: A tool for reliably predicting the impact of variations on protein stability is extremely important for both protein engineering and for understanding the effects of Mendelian and somatic mutations in the genome. Next Generation Sequencing studies are constantly increasing the number of protein sequences. Given the huge disproportion between protein sequences and structures, there is a need for tools suited to annotate the effect of mutations starting from protein sequence without relying on the structure. Here, we describe INPS, a novel approach for annotating the effect of non-synonymous mutations on the protein stability from its sequence. INPS is based on SVM regression and it is trained to predict the thermodynamic free energy change upon single-point variations in protein sequences. RESULTS: We show that INPS performs similarly to the state-of-the-art methods based on protein structure when tested in cross-validation on a non-redundant dataset. INPS performs very well also on a newly generated dataset consisting of a number of variations occurring in the tumor suppressor protein p53. Our results suggest that INPS is a tool suited for computing the effect of non-synonymous polymorphisms on protein stability when the protein structure is not available. We also show that INPS predictions are complementary to those of the state-of-the-art, structure-based method mCSM. When the two methods are combined, the overall prediction on the p53 set scores significantly higher than those of the single methods. AVAILABILITY AND IMPLEMENTATION: The presented method is available as web server at http://inps.biocomp.unibo.it. CONTACT: piero.fariselli@unibo.it SUPPLEMENTARY INFORMATION: Supplementary Materials are available at Bioinformatics online.
MOTIVATION: A tool for reliably predicting the impact of variations on protein stability is extremely important for both protein engineering and for understanding the effects of Mendelian and somatic mutations in the genome. Next Generation Sequencing studies are constantly increasing the number of protein sequences. Given the huge disproportion between protein sequences and structures, there is a need for tools suited to annotate the effect of mutations starting from protein sequence without relying on the structure. Here, we describe INPS, a novel approach for annotating the effect of non-synonymous mutations on the protein stability from its sequence. INPS is based on SVM regression and it is trained to predict the thermodynamic free energy change upon single-point variations in protein sequences. RESULTS: We show that INPS performs similarly to the state-of-the-art methods based on protein structure when tested in cross-validation on a non-redundant dataset. INPS performs very well also on a newly generated dataset consisting of a number of variations occurring in the tumor suppressor protein p53. Our results suggest that INPS is a tool suited for computing the effect of non-synonymous polymorphisms on protein stability when the protein structure is not available. We also show that INPS predictions are complementary to those of the state-of-the-art, structure-based method mCSM. When the two methods are combined, the overall prediction on the p53 set scores significantly higher than those of the single methods. AVAILABILITY AND IMPLEMENTATION: The presented method is available as web server at http://inps.biocomp.unibo.it. CONTACT: piero.fariselli@unibo.it SUPPLEMENTARY INFORMATION: Supplementary Materials are available at Bioinformatics online.
Authors: Tamar E Carter; Seleshi Kebede Mekonnen; Karen Lopez; Victoria Bonnell; Lambodhar Damodaran; Abraham Aseffa; Daniel A Janies Journal: Am J Trop Med Hyg Date: 2018-01-01 Impact factor: 2.345
Authors: Arun Prasad Pandurangan; Bernardo Ochoa-Montaño; David B Ascher; Tom L Blundell Journal: Nucleic Acids Res Date: 2017-07-03 Impact factor: 16.971