Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method

Sangeet, Satyam; Sarkar, Raju; Mohanty, Saswat K; Roy, Susmita

doi:10.1021/acs.jpcb.2c04574

Cited by 6 publications

(15 citation statements)

References 52 publications

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“…Before the pandemic, ML was used extensively in biology [ 25 , 26 , 27 , 28 ], for example, to predict mutations of influenza A viruses by predicting which AA position will mutate [ 29 ] and to predict recurrent mutations in cancer [ 30 ]. ML has been used throughout the SARS-CoV-2 pandemic as a tool to assist vaccine development and predict epitope hotspots [ 31 ]; the binding affinity of antibodies to mutations in the spike RBD [ 32 ]; the binding affinity of chemical compounds as inhibitors against the M-pro protein [ 33 , 34 ]; the clinical disease severity based on the virus genome mutations [ 35 ]; the mutation rate of nucleotide substitution (e.g., A > T) [ 36 ]; the subsequent nucleotide given a sequence of the SARS-CoV-2 genome, and also given a pair of sequences to indicate the location of the changes [ 37 ]; the antibody escape mutations of the spike protein [ 38 ]; the spread of spike protein mutation, based on fold-change per country [ 39 ]; future domain-specific spike mutations [ 40 ]; anti-SARS-CoV-2 activities from molecular structure [ 41 ]; and many more [ 42 , 43 ]. In this article, we start by showing some descriptive statistics of SARS-CoV-2 mutations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Saldivar-Espinoza

Macip

Garcia-Segura

et al. 2022

IJMS

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Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model’s Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins.

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Section: Introductionmentioning

confidence: 99%

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Saldivar-Espinoza

Macip

Garcia-Segura

et al. 2022

IJMS

View full text Add to dashboard Cite

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“…To follow the evolution of SARS-CoV-2, various efforts have been made to set up an epidemiological surveillance system using information theory due to the S protein gaining many mutations. Recently, we showed how crucial it is to analyze domain-wise mutational entropy to comprehend the evolution of viruses . We described a new responsive quantity called mutational response function (MRF) that, based on its evolutionary database, accurately quantifies domain-wise average entropy-fluctuation in the spike glycoprotein sequence of SARS-CoV-2.…”

Section: Studies Of 1d Sequence Evolution Using Information Theorymentioning

confidence: 99%

“…We found that the evolutionary crossover from a particular variety to a VOC is accompanied by a significant shift in MRF, maintaining the hallmark of a dynamic phase transition. The details of the MRF description can be found elsewhere . Using viral genomic and protein sequences, our method creates a pipeline that can be used to follow the evolution of any variant.…”

Section: Studies Of 1d Sequence Evolution Using Information Theorymentioning

confidence: 99%

“…In addition to experimental methods, computational methods have drawn a great deal of interest. To comprehend the evolutionary development of SARS-CoV-2, ,,− determine the potency of natural phytochemicals as potent drugs against the virulence proteins of SARS-CoV-2, − and track the viral progression in the general population via mathematical modeling, − a variety of computational techniques have been developed. In our earlier research, we created a machine learning feedforward model that could forecast the possible mutational locations in the delta variant.…”

Section: New Cutting-edge Experimental and Computational Techniques O...mentioning

confidence: 99%

“…To comprehend the evolutionary development of SARS-CoV-2, ,,− determine the potency of natural phytochemicals as potent drugs against the virulence proteins of SARS-CoV-2, − and track the viral progression in the general population via mathematical modeling, − a variety of computational techniques have been developed. In our earlier research, we created a machine learning feedforward model that could forecast the possible mutational locations in the delta variant. We created several features to train our model, with the most crucial feature being the mutational entropy of the virus’s protein sequence.…”

Section: New Cutting-edge Experimental and Computational Techniques O...mentioning

confidence: 99%

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Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective

2023

Self Cite

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Novel coronavirus (SARS-CoV-2) enters its host cell through a surface spike protein. The viral spike protein has undergone several modifications/mutations at the genomic level, through which it modulated its structure−function and passed through several variants of concern. Recent advances in high-resolution structure determination and multiscale imaging techniques, cost-effective next-generation sequencing, and development of new computational methods (including information theory, statistical methods, machine learning, and many other artificial intelligence-based techniques) have hugely contributed to the characterization of sequence, structure, function of spike proteins, and its different variants to understand viral pathogenesis, evolutions, and transmission. Laying on the foundation of the sequence−structure−function paradigm, this review summarizes not only the important findings on structure/function but also the structural dynamics of different spike components, highlighting the effects of mutations on them. As dynamic fluctuations of three-dimensional spike structure often provide important clues for functional modulation, quantifying time-dependent fluctuations of mutational events over spike structure and its genetic/amino acidic sequence helps identify alarming functional transitions having implications for enhanced fusogenicity and pathogenicity of the virus. Although these dynamic events are more difficult to capture than quantifying a static, average property, this review encompasses those challenging aspects of characterizing the evolutionary dynamics of spike sequence and structure and their implications for functions.

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Intrinsically Disordered Regions Function as a Cervical Collar to Remotely Regulate the Nodding Dynamics of SARS-CoV-2 Prefusion Spike Heads

Sinha,

Roy

2023

J. Phys. Chem. B

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The SARS-CoV-2 prefusion spike heads (receptor binding domains, RBDs) frequently nod down and up to interact with host cell receptors. As the spike protein is a trimeric unit of significant size, to understand its large-scale structural dynamics associated with the nodding mechanism and the mutational impact on the same, we develop a topological symmetry-information-loaded coarse-grained structure-based model of a spike trimer using recent cryo-EM structural data. Our study reveals the control of two distant intrinsically disordered regions (IDRs), namely, 630 and FPPR loops, over the nodding dynamics of spike heads. We find that the order−disorder transition of IDRs becomes more evident in the variants of concern (VOCs) that are associated with the characteristic mutation, D614G, in the proximity of these IDRs. In some VOCs, the two other mutations A570D and S982A also show an integral effect. The driver mutation D614G instigates a salt-bridge disruption, altering the order−disorder dynamics of both 630 and FPPR loops and their interaction with the C-terminal domains (CTD1/CTD2). This altered connectivity in these mutants allows the two IDRs to act collectively as a "cervical collar" for the RBD, supporting various spike head postures, consistent with cryo-EM results available for specific cases. The IDRs' control over the spike structure and dynamics presents an exciting opportunity where they can be targeted as remote operational switches to artificially maneuver the nod for effective therapeutic interventions.

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Quantifying Mutational Response to Track the Evolution of SARS-CoV-2 Spike Variants: Introducing a Statistical-Mechanics-Guided Machine Learning Method

Cited by 6 publications

References 52 publications

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks

Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective

Intrinsically Disordered Regions Function as a Cervical Collar to Remotely Regulate the Nodding Dynamics of SARS-CoV-2 Prefusion Spike Heads

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