Predicting virus Fitness: Towards a structure-based computational model

Thakur, Shivani; Planeta Kepp, Kasper; Mehra, Rukmankesh

doi:10.1016/j.jsb.2023.108042

Journal of Structural Biology

2023

DOI: 10.1016/j.jsb.2023.108042

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Predicting virus Fitness: Towards a structure-based computational model

Shivani Thakur,

Kasper Planeta Kepp,

Rukmankesh Mehra

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2024

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Cited by 3 publications

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Lernen aus Protein‐Engineering durch Dekonvolution von Multi‐Mutationalen Enzymvarianten**

Hollmann,

Sanchis,

Reetz

2024

Angewandte Chemie

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This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. As part of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi‐mutational variants was established and refined during the past 15 years. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. Later, this phenomenon was shown to be general. Molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non‐additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi‐dimensional rugged fitness pathway landscapes, which provide more mechanistic insight than traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so‐called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non‐additive effects.

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Lernen aus Protein‐Engineering durch Dekonvolution von Multi‐Mutationalen Enzymvarianten**

Hollmann,

Sanchis,

Reetz

2024

Angewandte Chemie

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Learning from Protein Engineering by Deconvolution of Multi‐Mutational Variants

Hollmann,

Sanchis,

Reetz

2024

Angew Chem Int Ed

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Fast Viral Diagnostics: FTIR-Based Identification, Strain-Typing, and Structural Characterization of SARS-CoV-2

Lahiri,

Das,

Thakur

et al. 2024

Anal. Chem.

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Predicting virus Fitness: Towards a structure-based computational model

Cited by 3 publications

References 96 publications

Lernen aus Protein‐Engineering durch Dekonvolution von Multi‐Mutationalen Enzymvarianten**

Lernen aus Protein‐Engineering durch Dekonvolution von Multi‐Mutationalen Enzymvarianten**

Learning from Protein Engineering by Deconvolution of Multi‐Mutational Variants

Fast Viral Diagnostics: FTIR-Based Identification, Strain-Typing, and Structural Characterization of SARS-CoV-2

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