Literature DB >> 21058401

Integrated prediction of the effect of mutations on multiple protein characteristics.

Michael A Johnston1, Chresten R Søndergaard, Jens Erik Nielsen.   

Abstract

Site-directed mutagenesis is routinely used in modern biology to elucidate the functional or biophysical roles of protein residues, and plays an important role in the field of rational protein design. Over the past decade, a number of computational tools have been developed that can predict the effect of point mutations on a protein's biophysical characteristics. However, these programs usually provide predictions for only a single characteristic. Furthermore, online versions of these tools are often impractical to use for examination of large and diverse sets of mutants. We have created a new web application, (http://enzyme.ucd.ie/PEAT_SA), that can simultaneously predict the effect of mutations on stability, ligand affinity and pK(a) values. PEAT-SA also provides an expanded feature-set with respect to other online tools which includes the ability to obtain predictions for multiple mutants in one submission. As a result, researchers who use site-directed mutagenesis can access state-of-the-art protein design methods with a fraction of the effort previously required. The results of benchmarking PEAT-SA on standard test-sets demonstrate that its accuracy for all three prediction types compares well to currently available tools. We illustrate PEAT-SA's potential by using it to investigate the influence of mutations on the activity of Subtilisin BPN'. This example demonstrates how the ability to obtain a wide range of information from one source, that can be combined to obtain deeper insight into the influence of mutations, makes PEAT-SA a valuable service to both experimental and computational biologists.
© 2010 Wiley-Liss, Inc.

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Year:  2011        PMID: 21058401     DOI: 10.1002/prot.22870

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  7 in total

1.  A collaborative environment for developing and validating predictive tools for protein biophysical characteristics.

Authors:  Michael A Johnston; Damien Farrell; Jens Erik Nielsen
Journal:  J Comput Aided Mol Des       Date:  2012-04-04       Impact factor: 3.686

2.  Highly perturbed pKa values in the unfolded state of hen egg white lysozyme.

Authors:  John Bradley; Fergal O'Meara; Damien Farrell; Jens Erik Nielsen
Journal:  Biophys J       Date:  2012-04-03       Impact factor: 4.033

Review 3.  Computational approaches for predicting mutant protein stability.

Authors:  Shweta Kulshreshtha; Vigi Chaudhary; Girish K Goswami; Nidhi Mathur
Journal:  J Comput Aided Mol Des       Date:  2016-05-09       Impact factor: 3.686

4.  Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.

Authors:  Arun Prasad Pandurangan; Tom L Blundell
Journal:  Protein Sci       Date:  2019-11-25       Impact factor: 6.725

5.  Assessing predictors of changes in protein stability upon mutation using self-consistency.

Authors:  Grant Thiltgen; Richard A Goldstein
Journal:  PLoS One       Date:  2012-10-29       Impact factor: 3.240

6.  PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality.

Authors:  Yves Dehouck; Jean Marc Kwasigroch; Dimitri Gilis; Marianne Rooman
Journal:  BMC Bioinformatics       Date:  2011-05-13       Impact factor: 3.307

Review 7.  Computer-Aided Protein Directed Evolution: a Review of Web Servers, Databases and other Computational Tools for Protein Engineering.

Authors:  Rajni Verma; Ulrich Schwaneberg; Danilo Roccatano
Journal:  Comput Struct Biotechnol J       Date:  2012-10-22       Impact factor: 7.271

  7 in total

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