Accurate protein stability predictions from homology models

Valančiūtė, Audronė; Nygaard, Lasse; Zschach, Henrike; Jepsen, Michael Maglegaard; Lindorff‐Larsen, Kresten; Stein, Amelie

doi:10.1016/j.csbj.2022.11.048

Cited by 13 publications

(11 citation statements)

References 42 publications

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“…Given a high homology template for the regions of interest in a protein, the absence of a crystal structure is no longer a major roadblock when predicting thermostabilities. The results presented here agree with the conclusions from recent studies where thermostabilities were calculated for models generated by AlphaFold and homology modeling (Akdel et al, 2021; Jumper et al, 2021; Valanciute et al, 2022). To get results consistent with experiment, the authors suggest using templates with at least 40% sequence identity.…”

Section: Discussionsupporting

confidence: 92%

Target‐template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations

2023

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Improving protein thermostability has been a labor-and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of stabilizing mutants. Among these is FEP+, a free energy perturbation implementation that uses a thoroughly tested physicsbased method to achieve unparalleled accuracy in predicting changes in protein thermostability. To gauge the applicability of FEP+ to situations where crystal structures are unavailable, here we have applied the FEP+ approach to homology models of 12 different proteins covering 316 mutations. By comparing predictions obtained with homology models to those obtained using crystal structures, we have identified that local rather than global sequence conservation between target and template sequence is a determining factor in the accuracy of predictions. By excluding mutation sites with low local sequence identity (<40%) to a template structure, we have obtained predictions with comparable performance to crystal structures (R 2 of 0.67 and 0.63 and an RMSE of 1.20 and 1.16 kcal/mol for crystal structure and homology model predictions, respectively) for identifying stabilizing mutations when incorporating residue scanning into a cascade screening strategy. Additionally, we identify and discuss inherent limitations in sequence alignments and homology modeling protocols that translate into the poor FEP+ performance of a few select examples. Overall, our retrospective study provides detailed guidelines for the application of the FEP+ approach using homology models for protein thermostability predictions, which will greatly extend this approach to studies that were previously limited by structure availability.

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Section: Discussionsupporting

confidence: 92%

Target‐template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations

2023

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“…To test this, we used template-based (homology) modelling to generate structures of the four proteins selected from ProTherm that we analysed above. To minimize issues of leakage between training and testing data, we have recently used MODELLER ( Martí-Renom et al, 2000 ; Webb and Sali, 2016 ) to construct models of the four proteins, using templates with decreasing sequence identities to the original structures ( Valanciute et al, 2023 ). We used these structures as input to RaSP in order to calculate

values to compare with experiments; we also performed similar tests using

values calculated using Rosetta ( Figure 4 ).…”

Section: Resultsmentioning

confidence: 99%

Rapid protein stability prediction using deep learning representations

Blaabjerg

Kassem

et al. 2023

eLife

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Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 300 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.

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“…It is widely recognized that systematic exploration of protein structure and function is essential to defining pathogenic mechanisms arising from missense mutations and that reliable protein models are a prerequisite for contextualizing such biochemical data (53)(54)(55). 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).…”

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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Accurate protein stability predictions from homology models

Cited by 13 publications

References 42 publications

Target‐template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations

Target‐template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations

Rapid protein stability prediction using deep learning representations

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

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