AlphaFold2 can predict single-mutation effects

McBride, J. Michael; Polev, Konstantin; Reinharz, Vladimir; Grzybowski, Bartosz A.; Tlusty, Tsvi

doi:10.1101/2022.04.14.488301

Cited by 15 publications

(30 citation statements)

References 74 publications

(154 reference statements)

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“…93,94 For example, in a separate recent study of ours, we studied the effect of single mutations on structure (using proteins in the protein data bank, and AlphaFold), finding that structural perturbations are felt up to 2 nm away from the mutated residue. 95 This is consistent with our assertion that far-away mutations can influence molecular discrimination.…”

Section: Discussionsupporting

confidence: 92%

General theory of specific binding: insights from a genetic-mechano-chemical protein model

McBride

Eckmann

Tlusty³

2022

Preprint

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Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. Despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present a model that combines chemistry, mechanics and genetics, and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving discrimination -- by varying degrees of flexibility and shape/chemistry complementarity -- but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is aided by increasing the protein, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. Our model makes testable, specific predictions about the role of flexibility in discrimination, and how to independently tune affinity and specificity. Thus, the proposed theory of molecular discrimination addresses the natural question ``why are proteins so big?'' A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

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Section: Discussionsupporting

confidence: 92%

General theory of specific binding: insights from a genetic-mechano-chemical protein model

McBride

Eckmann

Tlusty³

2022

Preprint

Self Cite

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“…Homology models derived from high resolution structures of related proteins have been proposed to serve as sufficient templates for the interpretation of disease-causing variants (56,57), although AF2 models are expected to further improve the accuracy of the predictions (58). Due to the integration of deep multiple sequence alignments (MSAs) in the AF2 prediction pipeline, it is unclear if strict assessment of missense mutations on protein structure will uncover either strong correlations or predictive outcomes between the WT and variant sequences for a given metric (59)(60)(61). Indeed, rational manipulation of the MSA through subsampling (62,63) or introduction of strategic point mutant combinations (64) have been necessary to explore structural heterogeneity of AF2 model predictions.…”

Section: Discussionmentioning

confidence: 99%

Molecular mechanisms of catalytic inhibition for active site mutations in glucose-6-phosphatase catalytic subunit 1 linked to glycogen storage disease

Sinclair¹,

Sheehan²,

Hawes³

et al. 2023

Preprint

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Mediating the terminal reaction of gluconeogenesis and glycogenolysis, the integral membrane protein G6PC1 regulates hepatic glucose production by catalyzing hydrolysis of glucose-6-phosphate within the lumen of the endoplasmic reticulum. Because G6PC1 function is essential for blood glucose homeostasis, inactivating mutations cause glycogen storage disease (GSD) type 1a, which is characterized by severe hypoglycemia. Despite its physiological importance, the structural basis of G6P binding to G6PC1 and the molecular disruptions induced by missense mutations within the active site that give rise to GSD type 1a are unknown. Exploiting a computational model of G6PC1 derived from the groundbreaking structure prediction algorithm AlphaFold2 (AF2), we combine molecular dynamics (MD) simulations and computational predictions of thermodynamic stability with a robust in vitro screening platform to define the atomic interactions governing G6P binding within the active site as well as explore the energetic perturbations imposed by disease-linked variants. From over 15 μs of MD simulations, we identify a collection of side chains, including conserved residues from the signature phosphatidic acid phosphatase motif, that contribute to a hydrogen bonding and van der Waals network that stabilize G6P in the active site. Introduction of GSD type 1a mutations into the G6PC1 sequence causes changes in G6P binding energy, thermodynamic stability and structural properties, suggesting multiple mechanisms of catalytic impairment. Our results, which corroborate the high quality of the AF2 model as a guide for experimental design and to interpret outcomes, not only confirm active site structural organization but also suggest novel mechanistic contributions of catalytic side chains.

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“…Originally developed to solve the long‐standing protein folding problem, AlphaFold2 has already seen many spin‐off studies to assess its potential (Evans et al, 2022 ; Porta‐Pardo et al, 2022 ; Robertson et al, 2021 ; Ruff & Pappu, 2021 ; Tsaban et al, 2022 ). So far, evidence suggests that AlphaFold2 cannot effectively predict changes in folding free energy upon mutation (Buel & Walters, 2022 ; McBride et al, 2022 ; Pak et al, 2021 ). However, more studies are needed to explore this possibility fully.…”

Section: Discussionmentioning

confidence: 99%

RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

et al. 2022

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Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and proteinprotein interaction. Advances in experimental mutational scans allow highthroughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-Valentina Sora and Adrian Otamendi Laspiur equally contributed to this study.

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AlphaFold2 can predict single-mutation effects

Cited by 15 publications

References 74 publications

General theory of specific binding: insights from a genetic-mechano-chemical protein model

General theory of specific binding: insights from a genetic-mechano-chemical protein model

Molecular mechanisms of catalytic inhibition for active site mutations in glucose-6-phosphatase catalytic subunit 1 linked to glycogen storage disease

RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

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