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Sequence alignment generation using intermediate sequence search for homology modeling.

Shuichiro Makigaki1, Takashi Ishida1

  • 1Department of Computer Science, School of Computing, Tokyo Institute of Technology Ookayama, Meguro-ku, Tokyo 152-8550, Japan.

Computational and Structural Biotechnology Journal
|August 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for protein structure prediction using intermediate sequences to improve remote homology detection and alignment generation. The method enhances the accuracy of template-based modeling, especially for distantly related proteins.

Keywords:
62P1097M60BioinformaticsHomology detectionHomology modelingSequence alignment

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Protein tertiary structure is crucial for biological research, but experimental determination is costly.
  • Computational methods, particularly template-based modeling, offer economical alternatives for structure prediction.
  • Accurate template-based modeling relies heavily on sensitive homology detection and high-quality sequence alignments.

Purpose of the Study:

  • To develop an automated algorithm for accurate remote homology detection and alignment generation for template-based protein structure modeling.
  • To address limitations in current homology detection methods that produce suboptimal alignments, hindering accurate structure prediction.

Main Methods:

  • Proposed an algorithm utilizing intermediate sequence search for remote homology detection.
  • Employed intermediate sequences to generate alignments for remote homologs.
  • Evaluated the method's sensitivity and selectivity in homology detection and assessed alignment accuracy via predicted structure model quality.

Main Results:

  • The proposed method demonstrates improved sensitivity and selectivity in detecting remote homologs compared to existing approaches.
  • Generated alignments are more appropriate for template-based modeling, leading to more accurate protein structure predictions, particularly for remote homologs.
  • The accuracy of predicted structure models validates the quality of the generated alignments.

Conclusions:

  • The novel algorithm effectively enhances remote homology detection and alignment generation for template-based protein structure modeling.
  • This approach offers a more reliable computational tool for predicting protein tertiary structures, especially when sequence similarity to known structures is low.
  • The developed method provides a valuable advancement for structural bioinformatics and protein science research.