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¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

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Related Experiment Video

Updated: Jul 2, 2026

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

Multiple sequence alignment by conformational space annealing.

Keehyoung Joo1, Jinwoo Lee, Ilsoo Kim

  • 1School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.

Biophysical Journal
|August 12, 2008
PubMed
Summary
This summary is machine-generated.

We developed MSACSA, a novel multiple sequence alignment (MSA) method using conformational space annealing (CSA). This approach yields more accurate alignments and diverse suboptimal solutions, improving protein structure prediction.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
09:51

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Multiple Sequence Alignment (MSA) is crucial for understanding protein evolution and function.
  • Existing MSA methods face challenges with accuracy and handling diverse sequence families.
  • Accurate MSA is fundamental for downstream applications like protein structure prediction.

Purpose of the Study:

  • To introduce MSACSA, a novel method for multiple sequence alignment.
  • To evaluate MSACSA's performance against established methods using benchmark datasets.
  • To highlight MSACSA's capability to provide diverse suboptimal alignments.

Main Methods:

  • MSACSA employs conformational space annealing (CSA), a global optimization technique.
  • It utilizes a consistency-based score function derived from pairwise sequence alignments.
  • The method was tested on BAliBASE and HOMSTRAD MSA databases.

Main Results:

  • MSACSA produced well-optimized and more accurate alignments across 450 test cases compared to SPEM.
  • The method consistently satisfied more pairwise constraints, indicating improved alignment quality.
  • MSACSA successfully aided in a recent blind protein structure prediction experiment.

Conclusions:

  • MSACSA offers a robust and accurate approach to multiple sequence alignment.
  • The ability to generate diverse suboptimal alignments addresses limitations of score function inaccuracies.
  • MSACSA demonstrates significant potential for advancing protein structure prediction and related bioinformatics tasks.