Highly significant improvement of protein sequence alignments with AlphaFold2

Author:

Baltzis Athanasios1ORCID,Mansouri Leila1,Jin Suzanne1,Langer Björn E1,Erb Ionas1,Notredame Cedric12

Affiliation:

1. Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology , Barcelona 08003, Spain

2. Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF) , Barcelona 08003, Spain

Abstract

Abstract Motivation Protein sequence alignments are essential to structural, evolutionary and functional analysis, but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis. Results Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly closer to the structural reference than sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis. Availability and implementation All data, analyses and results are available on Zenodo (https://doi.org/10.5281/zenodo.7031286). The code and scripts have been deposited in GitHub (https://github.com/cbcrg/msa-af2-nf) and the various containers in (https://cloud.sylabs.io/library/athbaltzis/af2/alphafold, https://hub.docker.com/r/athbaltzis/pred). Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Centre for Genomic Regulation

Spanish Plan Nacional

Spanish Ministry of Economy and Competitiveness, ‘Centro de Excelencia Severo Ochoa’

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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