MPL resolves genetic linkage in fitness inference from complex evolutionary histories

Sohail, Muhammad Saqib; Louie, Raymond H. Y.; McKay, Matthew R.; Barton, John P.

doi:10.1038/s41587-020-0737-3

Cited by 35 publications

(53 citation statements)

References 77 publications

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“…Hence, based on our current understanding, T cell escape by genetic variation may not be a significant factor for SARS-CoV-2. This is in contrast to other viruses that are highly mutable, such as HIV and hepatitis C virus, for which more elaborate computational methods have been developed to facilitate robust T cell epitope identification and to aid vaccine design [ [195] , [196] , [197] , [198] , [199] , [200] , [201] , [202] , [203] , [204] , [205] , [206] , [207] ].…”

Section: Summary and Perspectivesmentioning

confidence: 99%

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Sohail

Ahmed

Quadeer

et al. 2021

Advanced Drug Delivery Reviews

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Section: Summary and Perspectivesmentioning

confidence: 99%

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Sohail

Ahmed

Quadeer

et al. 2021

Advanced Drug Delivery Reviews

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“…The significance of incorporating interactions between mutations at different residues has been demonstrated in multiple previous studies for inferring protein fitness landscapes (Sohail et al, 2021;Hart and Ferguson, 2015;Ferguson et al, 2013;Barton et al, 2016;Flynn et al, 2017;Louie et al, 2018;Quadeer et al, 2019aQuadeer et al, , 2020 and for determining networks of residues important for mediating protein structure and function (Dahirel et al, 2011;Quadeer et al, 2014Quadeer et al, , 2018Ahmed et al, 2019;Gaiha et al, 2019). However, residue-residue interactions, represented in the maximum entropy fitness landscape model by the coupling parameters (Equation 3 in STAR Methods), seem to be especially important for HCV E2 1b.…”

Section: Discussionmentioning

confidence: 97%

Evolutionary modeling reveals enhanced mutational flexibility of HCV subtype 1b compared with 1a

Zhang

Quadeer²,

McKay³

2022

iScience

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Hepatitis C virus (HCV) is a leading cause of liver-associated disease and liver cancer. Of the major HCV subtypes, patients infected with subtype 1b have been associated with having a higher risk of developing chronic infection and hepatocellular carcinoma. However, underlying reasons for this increased disease severity remain unknown. Here, we provide an evolutionary rationale, based on a comparative study of fitness landscape and in-host evolutionary models of the E2 glycoprotein of HCV subtypes 1a and 1b. Our analysis demonstrates that a higher chronicity rate of 1b may be attributed to lower fitness constraints, enabling 1b viruses to more easily escape antibody responses. More generally, our results suggest that differences in evolutionary constraints between HCV subtypes may be an important factor in mediating distinct disease outcomes. Our analysis also identifies antibodies that appear escape-resistant against both subtypes 1a and 1b, providing directions for designing HCV vaccines having cross-subtype protection.Despite these observations, it remains unclear as to why subtype 1b infections may lead to more severe disease outcomes. This may be attributed to various reasons, including differences in viral phenotype, host-specific immune factors, demographics, transmission route (injectable drug use in the case of 1a

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“…Our findings are also corroborated by extensive in silico Directed Evolution experiments, where the modification of these two parameters can easily be taken into consideration in limits that would otherwise be experimentally inaccessible. AMaLa, of course, is a first attempt at modeling evolutionary trajectories under fixed selection pressure, although some interesting attempts have been recently published in [62] in the somehow different context of the inference of genetic linkage in population genetics. In particular, the way in which we model the mutation step could be made more accurate by taking into account codon biases and more realistic transition probabilities (the first attempt in this direction has been proposed in [46]).…”

Section: Discussionmentioning

confidence: 99%

AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape

Sesta

Uguzzoni

Fernández-de-Cossio-Díaz

et al. 2021

IJMS

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We present Annealed Mutational approximated Landscape (AMaLa), a new method to infer fitness landscapes from Directed Evolution experiments sequencing data. Such experiments typically start from a single wild-type sequence, which undergoes Darwinian in vitro evolution via multiple rounds of mutation and selection for a target phenotype. In the last years, Directed Evolution is emerging as a powerful instrument to probe fitness landscapes under controlled experimental conditions and as a relevant testing ground to develop accurate statistical models and inference algorithms (thanks to high-throughput screening and sequencing). Fitness landscape modeling either uses the enrichment of variants abundances as input, thus requiring the observation of the same variants at different rounds or assuming the last sequenced round as being sampled from an equilibrium distribution. AMaLa aims at effectively leveraging the information encoded in the whole time evolution. To do so, while assuming statistical sampling independence between sequenced rounds, the possible trajectories in sequence space are gauged with a time-dependent statistical weight consisting of two contributions: (i) an energy term accounting for the selection process and (ii) a generalized Jukes–Cantor model for the purely mutational step. This simple scheme enables accurately describing the Directed Evolution dynamics and inferring a fitness landscape that correctly reproduces the measures of the phenotype under selection (e.g., antibiotic drug resistance), notably outperforming widely used inference strategies. In addition, we assess the reliability of AMaLa by showing how the inferred statistical model could be used to predict relevant structural properties of the wild-type sequence.

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MPL resolves genetic linkage in fitness inference from complex evolutionary histories

Cited by 35 publications

References 77 publications

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

In silico T cell epitope identification for SARS-CoV-2: Progress and perspectives

Evolutionary modeling reveals enhanced mutational flexibility of HCV subtype 1b compared with 1a

AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape

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