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Mutagenesis Objective Search and Selection Tool (MOSST): an algorithm to predict structure-function related mutations

Alvaro Olivera-Nappa1, Barbara A Andrews, Juan A Asenjo

  • 1Centre for Biochemical Engineering and Biotechnology, Institute for Cell Dynamics and Biotechnology: a Centre for Systems Biology, University of Chile, Santiago, Chile. aolivera@ing.uchile.cl

BMC Bioinformatics
|April 29, 2011
PubMed
Summary

This study introduces a novel bioinformatics method to predict protein function by analyzing amino acid conservation. It helps identify functionally significant mutations and guides protein engineering efforts.

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Molecular Biology

Background:

  • Assessing protein function is challenging without structure-function data, hindering mutation analysis and protein design.
  • Identifying functionally significant non-synonymous single nucleotide polymorphisms (nsSNPs) and planning site-directed mutagenesis require robust prediction methods.
  • Current approaches often lack explicit structure-function relationships, necessitating new predictive tools.

Purpose of the Study:

  • To develop a novel methodology for predicting protein function and guiding mutation strategies using only physicochemical properties.
  • To identify functionally significant mutable sites and specific mutagenesis targets within protein families.
  • To predict the functional impact of nsSNPs and differentiate between functionally silent and significant mutations.

Main Methods:

  • Statistical analysis of physicochemical properties (hydrophobicity, volume, charge, etc.) at each amino acid position in multiple protein alignments.
  • Combining variances of properties to create a global conservation indicator for each position.
  • Utilizing hypothesis testing on statistical variances to identify functionally significant sites and predict consensus sequences.

Main Results:

  • Defined different types of physicochemical conservation to categorize amino acid positions.
  • Identified key functional positions responsible for protein function, specificity, stability, or binding.
  • Developed position-specific statistical distributions to classify amino acids as functionally non-disruptive or deleterious.

Conclusions:

  • The approach labels conserved and non-conserved positions as functionally relevant based on physicochemical properties.
  • Provides a discriminative tool for selecting rational mutagenesis strategies and predicting the functional impact of nsSNPs.
  • Offers a valuable bioinformatics tool for developing new protein variants and understanding protein function-structure relationships.