Mechanistic Models of Protein Evolution

Pollock, David D.; Pollard, Stephen T.; Shortt, Jonathan A.; Goldstein, Richard A.

doi:10.1007/978-3-319-61569-1_15

Cited by 5 publications

(5 citation statements)

References 57 publications

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“…We sought to determine if simulated trajectories using this simple fitness model could capture observed evolutionary trajectories, despite not considering all factors that contribute to DHFR fitness. We simulated the evolutionary trajectories to the quadruple mutants described above for the genes Pf DHFR and Pv DHFR using an evolutionary model, adapted from previous studies ( Eccleston et al, 2021 ; Pollock et al, 2017 ; Pollock et al, 2012 ). In this model, selection acts to reduce the binding affinity between target protein and the antimalarial drug with which the mutations have been associated with resistance.…”

Section: Resultsmentioning

confidence: 99%

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

Eccleston,

Manko,

Campino

et al. 2023

eLife

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Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf ) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterized using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.

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Section: Resultsmentioning

confidence: 99%

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

Eccleston,

Manko,

Campino

et al. 2023

eLife

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“…We simulated the evolutionary trajectories to quadruple mutant N51I,C59R,S108N,I164L, using an evolutionary model, adapted from previous studies [42][43][44], in which selection acts to reduce the binding affinity between PfDHFR and pyrimethamine. Briefly, starting from the wild-type protein, we randomly sample a value from the Flex ddG distributions for each of the four single mutations and calculate the fitness of the mutated protein (Eq.…”

Section: A Thermodynamic Evolutionary Model Can Predict the Most Like...mentioning

confidence: 99%

“…We simulated the evolutionary trajectories to the quadruple mutants described above for the genes Pf DHFR and PvDHFR using an evolutionary model, adapted from previous studies (Eccleston et al, 2021;Pollock et al, 2012Pollock et al, , 2017. In this model, selection acts to reduce the binding affinity between target protein and the antimalarial drug with which the mutations have been associated with resistance.…”

Section: A Thermodynamic Evolutionary Model Predicts the Most Likely ...mentioning

confidence: 99%

A computational method for predicting the most likely evolutionary trajectories in the step-wise accumulation of resistance mutations

Eccleston

Mańko

Campino

et al. 2022

Preprint

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Pathogen evolution of drug resistance often occurs in a stepwise manner via accumulation of multiple mutations, which in combination have a non-additive impact on fitness, a phenomenon known as epistasis. Epistasis complicates the sequence-structure-function relationship and undermines our ability to predict evolution. We present a computational method to predict evolutionary trajectories that accounts for epistasis, using the Rosetta Flex ddG protocol to estimate drug binding free energy changes upon mutation and an evolutionary model based in thermodynamics and statistical mechanics. We apply this method to predict evolutionary trajectories to known multiple mutations associated with resistant phenotypes in malaria. Resistance to the combination drug sulfadoxine-pyrimethamine (SP) in malaria-causing species Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has arisen via the accumulation of multiple point mutations in the DHFR and DHPS genes. Four known PfDHFR pyrimethamine resistance mutations are highly prevalent in field-isolates and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to the highly resistant quadruple mutation N51I,C59R,S108N,I164L. We simulated the possible evolutionary trajectories to this quadruple PfDHFR mutation as well as the homologous PvDHFR mutations. In both cases, our most probable pathways agreed well with those determined experimentally. We also applied this method to predict the most likely evolutionary pathways to observed multiple mutations associated with sulfadoxine resistance in PfDHPS and PvDHPS. This novel method can be applied to any drug-target system where the drug acts by binding to the target.

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“…It seems possible that selection for one might drive the other, or vice versa, but how they influence each other is unknown. We chose to investigate this by simulating protein evolution using a mechanistic model based in thermodynamics and statistical mechanics that has been shown to be able to reproduce many important features of protein evolution such as epistasis and coevolution (12,25). We evolved a protein under different levels of selection for cooperativity to explore how and why epistasis differs between cooperative and noncooperative sequences.…”

Section: Significancementioning

confidence: 99%

Selection for cooperativity causes epistasis predominately between native contacts and enables epistasis-based structure reconstruction

Eccleston

Pollock

Goldstein

2021

Proc. Natl. Acad. Sci. U.S.A.

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Epistasis and cooperativity of folding both result from networks of energetic interactions in proteins. Epistasis results from energetic interactions among mutants, whereas cooperativity results from energetic interactions during folding that reduce the presence of intermediate states. The two concepts seem intuitively related, but it is unknown how they are related, particularly in terms of selection. To investigate their relationship, we simulated protein evolution under selection for cooperativity and separately under selection for epistasis. Strong selection for cooperativity created strong epistasis between contacts in the native structure but weakened epistasis between nonnative contacts. In contrast, selection for epistasis increased epistasis in both native and nonnative contacts and reduced cooperativity. Because epistasis can be used to predict protein structure only if it preferentially occurs in native contacts, this result indicates that selection for cooperativity may be key for predicting structure using epistasis. To evaluate this inference, we simulated the evolution of guanine nucleotide-binding protein (GB1) with and without cooperativity. With cooperativity, strong epistatic interactions clearly map out the native GB1 structure, while allowing the presence of intermediate states (low cooperativity) obscured the structure. This indicates that using epistasis measurements to reconstruct protein structure may be inappropriate for proteins with stable intermediates.

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Mechanistic Models of Protein Evolution

Cited by 5 publications

References 57 publications

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations

A computational method for predicting the most likely evolutionary trajectories in the step-wise accumulation of resistance mutations

Selection for cooperativity causes epistasis predominately between native contacts and enables epistasis-based structure reconstruction

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