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Sequence and structure alignments in post-AlphaFold era.

Sandun Rajapaksa1, Arun S Konagurthu1, Arthur M Lesk2

  • 1Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, 3800, Victoria, Australia.

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|February 8, 2023
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Summary
This summary is machine-generated.

Aligning homologous proteins using predicted structures, like those from AlphaFold, yields better results than sequence-only alignments when experimental structures are unavailable. This approach enhances protein analysis and function prediction.

Keywords:
Amino-acid sequence alignmentProtein evolutionStructure alignmentStructure prediction

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Protein Science

Background:

  • Sequence alignment is crucial for understanding protein structure and function.
  • Structure-based alignments are generally more accurate than sequence-based alignments for non-closely related proteins.
  • A significant gap exists between available sequence data and experimentally determined protein structures, limiting structure alignment's broad application.

Purpose of the Study:

  • To investigate if structure-based alignment of predicted protein structures can overcome limitations of sequence-only alignment.
  • To evaluate the effectiveness of using predicted structures for aligning homologous proteins lacking experimental data.
  • To support the hypothesis that predicted structures improve alignment accuracy compared to sequence-based methods.

Main Methods:

  • Utilized AlphaFold and similar tools for high-quality protein structure prediction.
  • Performed pairwise alignments using both amino-acid sequences and predicted structures.
  • Quantitatively evaluated alignment accuracy by comparing predicted structure alignments against experimental structure alignments and sequence-only alignments.

Main Results:

  • Alignments based on predicted protein structures demonstrated superior accuracy compared to alignments based solely on amino-acid sequences.
  • The quantitative evaluation provided evidence supporting the hypothesis that predicted structures enhance alignment quality.
  • This method offers a viable alternative for aligning homologous proteins when experimental structures are absent.

Conclusions:

  • Leveraging predicted protein structures, particularly from advanced tools like AlphaFold, significantly improves alignment accuracy for homologous proteins.
  • This approach addresses the challenge posed by the scarcity of experimental structures, enabling more reliable protein analysis.
  • Structural alignment of predicted structures is a promising strategy for advancing protein function and structure studies.