A database of calculated solution parameters for the AlphaFold predicted protein structures

Emre Brookes; Mattia Rocco

doi:10.1038/s41598-022-10607-z

A database of calculated solution parameters for the AlphaFold predicted protein structures

Emre Brookes, Mattia Rocco

Chemistry and Biochemistry

IRCCS Ospedale Policlinico San Martino

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

14 Scopus citations

Abstract

Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting “folding frenzy” has already produced predicted protein structure databases for the entire human and other organisms’ proteomes. However, rapidly ascertaining a predicted structure’s reliability based on measured properties in solution should be considered. Shape-sensitive hydrodynamic parameters such as the diffusion and sedimentation coefficients (Dt(20,w)0, s(20,w)0) and the intrinsic viscosity ([η]) can provide a rapid assessment of the overall structure likeliness, and SAXS would yield the structure-related pair-wise distance distribution function p(r) vs. r. Using the extensively validated UltraScan SOlution MOdeler (US-SOMO) suite, a database was implemented calculating from AlphaFold structures the corresponding Dt(20,w)0, s(20,w)0, [η], p(r) vs. r, and other parameters. Circular dichroism spectra were computed using the SESCA program. Some of AlphaFold’s drawbacks were mitigated, such as generating whenever possible a protein’s mature form. Others, like the AlphaFold direct applicability to single-chain structures only, the absence of prosthetic groups, or flexibility issues, are discussed. Overall, this implementation of the US-SOMO-AF database should already aid in rapidly evaluating the consistency in solution of a relevant portion of AlphaFold predicted protein structures.

Original language	English
Article number	7349
Journal	Scientific Reports
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41598-022-10607-z
State	Published - Dec 2022

Funding

Research reported in this publication was supported by the NIGMS of the National Institutes of Health under Award Number R01GM120600 and by the National Science Foundation under awards CHE-1265817, OAC-1740097, OAC-1912444, all to E.B. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health nor the National Science Foundation. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant Number ACI-1548562 and utilized Jetstream at Indiana University through allocation TG-MCB17057 to EB. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper ( http://www.tacc.utexas.edu ). The computing infrastructure at the University of Lethbridge was funded by the Canada Foundation for Innovation (CFI-37589 to Borries Demeler). We are grateful to P. Vachette (I2BC, Université Paris-Saclay, CEA, CNRS, Gif-sur-Yvette, France) for comments and suggestions. Research reported in this publication was supported by the NIGMS of the National Institutes of Health under Award Number R01GM120600 and by the National Science Foundation under awards CHE-1265817, OAC-1740097, OAC-1912444, all to E.B. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health nor the National Science Foundation. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant Number ACI-1548562 and utilized Jetstream at Indiana University through allocation TG-MCB17057 to EB. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper (http://www.tacc.utexas.edu). The computing infrastructure at the University of Lethbridge was funded by the Canada Foundation for Innovation (CFI-37589 to Borries Demeler). We are grateful to P. Vachette (I2BC, Université Paris-Saclay, CEA, CNRS, Gif-sur-Yvette, France) for comments and suggestions.

Funders	Funder number
Texas Advanced Computing Center
OAC-1740097, ACI-1548562, OAC-1912444, CHE-1265817
R01GM120600
TG-MCB17057
University of Texas at Austin
Canada Foundation for Innovation	CFI-37589
Université Paris-Saclay

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title = "A database of calculated solution parameters for the AlphaFold predicted protein structures",

abstract = "Recent spectacular advances by AI programs in 3D structure predictions from protein sequences have revolutionized the field in terms of accuracy and speed. The resulting “folding frenzy” has already produced predicted protein structure databases for the entire human and other organisms{\textquoteright} proteomes. However, rapidly ascertaining a predicted structure{\textquoteright}s reliability based on measured properties in solution should be considered. Shape-sensitive hydrodynamic parameters such as the diffusion and sedimentation coefficients (Dt(20,w)0, s(20,w)0) and the intrinsic viscosity ([η]) can provide a rapid assessment of the overall structure likeliness, and SAXS would yield the structure-related pair-wise distance distribution function p(r) vs. r. Using the extensively validated UltraScan SOlution MOdeler (US-SOMO) suite, a database was implemented calculating from AlphaFold structures the corresponding Dt(20,w)0, s(20,w)0, [η], p(r) vs. r, and other parameters. Circular dichroism spectra were computed using the SESCA program. Some of AlphaFold{\textquoteright}s drawbacks were mitigated, such as generating whenever possible a protein{\textquoteright}s mature form. Others, like the AlphaFold direct applicability to single-chain structures only, the absence of prosthetic groups, or flexibility issues, are discussed. Overall, this implementation of the US-SOMO-AF database should already aid in rapidly evaluating the consistency in solution of a relevant portion of AlphaFold predicted protein structures.",

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A database of calculated solution parameters for the AlphaFold predicted protein structures

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