Modelling SARS-CoV-2 spike-protein mutation effects on ACE2 binding

Thakur, Shivani; Verma, Rajaneesh Kumar; Kepp, Kasper Planeta; Mehra, Rukmankesh

doi:10.1016/j.jmgm.2022.108379

Cited by 6 publications

(9 citation statements)

References 72 publications

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“…The binding free energy changes were computed by averaging the results over 1000 equilibrium samples for each of the studied systems. The BeatMusic method demonstrated promising accuracy in various benchmark studies − and was successfully used in related studies to compute the change in the ACE2 affinity caused by mutations in the S-protein structures . The advantages of this approach are fast and accurate predictions of the effect of mutations on both the strength of the binding interactions and on the stability of the complex using statistical potentials and neural networks.…”

Section: Methodsmentioning

confidence: 99%

“…The BeatMusic method demonstrated promising accuracy in various benchmark studies 100−103 and was successfully used in related studies to compute the change in the ACE2 affinity caused by mutations in the S-protein structures. 104 The advantages of this approach are fast and accurate predictions of the effect of mutations on both the strength of the binding interactions and on the stability of the complex using statistical potentials and neural networks. The benchmark studies and our analysis of different approaches for assessment of mutational effects on the S-RBD binding 62−71 showed that the BeatMusic approach is comparable to other knowledge-based structural methods such as D-complex, 105 physics-based FoldX potentials, 106,107 and BindProfX approach.…”

Section: Ai-based Structural Modeling and Statisticalmentioning

confidence: 99%

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AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape

Raisinghani,

Alshahrani,

Gupta

et al. 2024

J. Chem. Inf. Model.

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The latest wave of SARS-CoV-2 Omicron variants displayed a growth advantage and increased viral fitness through convergent evolution of functional hotspots that work synchronously to balance fitness requirements for productive receptor binding and efficient immune evasion. In this study, we combined AlphaFold2-based structural modeling approaches with atomistic simulations and mutational profiling of binding energetics and stability for prediction and comprehensive analysis of the structure, dynamics, and binding of the SARS-CoV-2 Omicron BA.2.86 spike variant with ACE2 host receptor and distinct classes of antibodies. We adapted several AlphaFold2 approaches to predict both the structure and conformational ensembles of the Omicron BA.2.86 spike protein in the complex with the host receptor. The results showed that the AlphaFold2-predicted structural ensemble of the BA.2.86 spike protein complex with ACE2 can accurately capture the main conformational states of the Omicron variant. Complementary to AlphaFold2 structural predictions, microsecond molecular dynamics simulations reveal the details of the conformational landscape and produced equilibrium ensembles of the BA.2.86 structures that are used to perform mutational scanning of spike residues and characterize structural stability and binding energy hotspots. The ensemble-based mutational profiling of the receptor binding domain residues in the BA.2 and BA.2.86 spike complexes with ACE2 revealed a group of conserved hydrophobic hotspots and critical variant-specific contributions of the BA.2.86 convergent mutational hotspots R403K, F486P, and R493Q. To examine the immune evasion properties of BA.2.86 in atomistic detail, we performed structure-based mutational profiling of the spike protein binding interfaces with distinct classes of antibodies that displayed significantly reduced neutralization against the BA.2.86 variant. The results revealed the molecular basis of compensatory functional effects of the binding hotspots, showing that BA.2.86 lineage may have evolved to outcompete other Omicron subvariants by improving immune evasion while preserving binding affinity with ACE2 via through a compensatory effect of R493Q and F486P convergent mutational hotspots. This study demonstrated that an integrative approach combining AlphaFold2 predictions with complementary atomistic molecular dynamics simulations and robust ensemble-based mutational profiling of spike residues can enable accurate and comprehensive characterization of structure, dynamics, and binding mechanisms of newly emerging Omicron variants.

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Section: Methodsmentioning

confidence: 99%

Section: Ai-based Structural Modeling and Statisticalmentioning

confidence: 99%

AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape

Raisinghani,

Alshahrani,

Gupta

et al. 2024

J. Chem. Inf. Model.

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“…Figure 1 presents the interaction between the Spike glycoprotein and the ACE2 receptor. ACE2 is a homologous molecule of angiotensin-converting enzyme (ACE), a carboxypeptidase that degrades angiotensin II peptide on the cell membrane surface [ 5 ]. The receptor binding domain (RBD) of the new coronavirus spike protein is composed of 192 amino acid residues.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The Spike glycoprotein predominantly binds to the peptidase domain (PD) of the host cell receptor ACE2 via RBD. There is a receptor binding sequence (RBM) on the RBD, which will specifically recognize the PD located in the extracellular domain of ACE2, but will not affect ACE2 function [5]. In recent years, machine learning (ML) technology has played an important supporting role in protein-directed evolution work, and thousands of proteins have been In recent years, machine learning (ML) technology has played an important supporting role in protein-directed evolution work, and thousands of proteins have been successfully designed using various software tools [6].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Natural Evolution in the RBD Region of the Spike Glycoprotein of SARS-CoV-2 by Machine Learning

Liu,

He,

Jia

et al. 2024

Viruses

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Machine learning (ML) is a key focus in predicting protein mutations and aiding directed evolution. Research on potential virus variants is crucial for vaccine development. In this study, the machine learning software PyPEF was employed to conduct mutation analysis within the receptor-binding domain (RBD) of the Spike glycoprotein of SARS-CoV-2. Over 48,960,000 variants were predicted. Eight prospective variants that could surface in the future underwent modeling and molecular dynamics simulations. The study forecasts that the latest variant, ISOY2P5O1, may potentially emerge around 17 November 2023, with an approximate window of uncertainty of ±22 days. The ISOY8P5O2 variant displayed an increased binding capacity in the dry assay, with a total predicted binding energy of −110.306 kcal/mol. This represents an 8.25% enhancement in total binding energy compared to the original SARS-CoV-2 strain discovered in Wuhan (−101.892 kcal/mol). Reverse research confirmed the structural significance of mutation sites using ML models, particularly in the context of protein folding. The study validated regression methods (SVR, RF, and PLS) with different data structures. This study investigates the effectiveness of the “ML-Guided Design Correctly Predicts Combinatorial Effects Strategy” compared to the “ML-Guided Design Correctly Predicts Natural Evolution Prediction Strategy”. To enhance machine learning, we created a timestamping algorithm and two auxiliary programs using advanced techniques to rapidly process extensive data, surpassing batch sequencing capabilities. This study not only advances machine learning in guiding protein evolution but also holds potential for forecasting future viruses and vaccine development.

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Predicting virus Fitness: Towards a structure-based computational model

Thakur,

Planeta Kepp,

Mehra

2023

Journal of Structural Biology

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Modelling SARS-CoV-2 spike-protein mutation effects on ACE2 binding

Cited by 6 publications

References 72 publications

Predicting Natural Evolution in the RBD Region of the Spike Glycoprotein of SARS-CoV-2 by Machine Learning

Predicting virus Fitness: Towards a structure-based computational model

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