Leveraging AI Advances and Online Tools for Structure-Based Variant Analysis

Francisco J. Guzmán-Vega; Ana C. González-Álvarez; Karla A. Peña-Guerra; Kelly J. Cardona-Londoño; Stefan T. Arold

doi:10.1002/cpz1.857

Leveraging AI Advances and Online Tools for Structure-Based Variant Analysis

Francisco J. Guzmán-Vega, Ana C. González-Álvarez, Karla A. Peña-Guerra, Kelly J. Cardona-Londoño, Stefan T. Arold^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Understanding how a gene variant affects protein function is important in life science, as it helps explain traits or dysfunctions in organisms. In a clinical setting, this understanding makes it possible to improve and personalize patient care. Bioinformatic tools often only assign a pathogenicity score, rather than providing information about the molecular basis for phenotypes. Experimental testing can furnish this information, but this is slow and costly and requires expertise and equipment not available in a clinical setting. Conversely, mapping a gene variant onto the three-dimensional (3D) protein structure provides a fast molecular assessment free of charge. Before 2021, this type of analysis was severely limited by the availability of experimentally determined 3D protein structures. Advances in artificial intelligence algorithms now allow confident prediction of protein structural features from sequence alone. The aim of the protocols presented here is to enable non-experts to use databases and online tools to investigate the molecular effect of a genetic variant. The Basic Protocol relies only on the online resources AlphaFold, Protein Structure Database, and UniProt. Alternate Protocols document the usage of the Protein Data Bank, SWISS-MODEL, ColabFold, and PyMOL for structure-based variant analysis.

Original language	English (US)
Article number	e857
Journal	Current Protocols
Volume	3
Issue number	8
DOIs	https://doi.org/10.1002/cpz1.857
State	Published - Aug 2023

Bibliographical note

Funding Information:
This research was supported by the King Abdullah University of Science and Technology (KAUST) through the baseline fund and Award No. FCC/1/1976‐33, URF/1/4379‐01 and REI/1/4446‐01 from the Office of Sponsored Research (OSR). For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.

Publisher Copyright:
© 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Keywords

AlphaFold
gene variants
protein structure
structural analysis for non-experts
variant analysis

ASJC Scopus subject areas

General Neuroscience
General Biochemistry, Genetics and Molecular Biology
General Immunology and Microbiology
Pharmacology, Toxicology and Pharmaceutics(all)
Health Informatics
Medical Laboratory Technology

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10.1002/cpz1.857

Cite this

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abstract = "Understanding how a gene variant affects protein function is important in life science, as it helps explain traits or dysfunctions in organisms. In a clinical setting, this understanding makes it possible to improve and personalize patient care. Bioinformatic tools often only assign a pathogenicity score, rather than providing information about the molecular basis for phenotypes. Experimental testing can furnish this information, but this is slow and costly and requires expertise and equipment not available in a clinical setting. Conversely, mapping a gene variant onto the three-dimensional (3D) protein structure provides a fast molecular assessment free of charge. Before 2021, this type of analysis was severely limited by the availability of experimentally determined 3D protein structures. Advances in artificial intelligence algorithms now allow confident prediction of protein structural features from sequence alone. The aim of the protocols presented here is to enable non-experts to use databases and online tools to investigate the molecular effect of a genetic variant. The Basic Protocol relies only on the online resources AlphaFold, Protein Structure Database, and UniProt. Alternate Protocols document the usage of the Protein Data Bank, SWISS-MODEL, ColabFold, and PyMOL for structure-based variant analysis.",

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Leveraging AI Advances and Online Tools for Structure-Based Variant Analysis

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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