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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence

David T Jones1, Daniel W A Buchan, Domenico Cozzetto

  • 1Department of Computer Science, Bioinformatics Group, Centre for Computational Statistics and Machine Learning, University College London, Malet Place, London WC1E 6BT, UK. d.jones@cs.ucl.ac.uk

Bioinformatics (Oxford, England)
|November 22, 2011
PubMed
Summary
This summary is machine-generated.

Predicting protein residue-residue contacts is crucial for understanding protein structure. A new method, PSICOV, uses sparse inverse covariance estimation on multiple sequence alignments to accurately identify direct correlations, improving protein structure prediction.

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Area of Science:

  • Structural bioinformatics
  • Computational biology
  • Protein structure prediction

Background:

  • Accurate prediction of residue-residue contacts is vital for determining a protein's native fold.
  • Traditional methods using mutual information on multiple sequence alignments (MSAs) are often confounded by indirect evolutionary couplings and phylogenetic effects.
  • Recent advancements in algorithms and the availability of large sequence families have renewed interest in improving contact prediction.

Purpose of the Study:

  • To introduce a novel method, PSICOV, for enhanced protein contact prediction.
  • To address the limitations of existing methods in distinguishing direct evolutionary couplings from noise in MSAs.
  • To improve the accuracy of predicting residue-residue contacts for applications in protein structure and function prediction.

Main Methods:

  • Utilizes sparse inverse covariance estimation applied to protein multiple sequence alignments (MSAs).
  • Builds upon previous work to correct for phylogenetic and entropic correlation noise.
  • Enables accurate discrimination between direct and indirect mutation correlations within MSAs.

Main Results:

  • PSICOV achieves substantially higher mean precision compared to normalized mutual information and Bayesian network approaches.
  • For 118 out of 150 proteins, PSICOV reached a precision of ≥ 0.5 for top predictions of long-range contacts (sequence separation >23).
  • This level of precision offers significant benefits for protein structure prediction and model quality assessment.

Conclusions:

  • PSICOV offers a significant improvement in protein contact prediction accuracy.
  • The method effectively filters out noise, revealing true direct evolutionary couplings.
  • The enhanced prediction accuracy has direct implications for advancing protein structure prediction and related fields.