Abstract
The rapid evolution of HIV is constrained by interactions between mutations which affect viral fitness. In this work, we explore the role of epistasis in determining the mutational fitness landscape of HIV for multiple drug target proteins, including Protease, Reverse Transcriptase, and Integrase. Epistatic interactions between residues modulate the mutation patterns involved in drug resistance, with unambiguous signatures of epistasis best seen in the comparison of the Potts model predicted and experimental HIV sequence "prevalences" expressed as higher-order marginals (beyond triplets) of the sequence probability distribution. In contrast, experimental measures of fitness such as viral replicative capacities generally probe fitness effects of point mutations in a single background, providing weak evidence for epistasis in viral systems. The detectable effects of epistasis are obscured by higher evolutionary conservation at sites. While double mutant cycles in principle, provide one of the best ways to probe epistatic interactions experimentally without reference to a particular background, we show that the analysis is complicated by the small dynamic range of measurements. Overall, we show that global pairwise interaction Potts models are necessary for predicting the mutational landscape of viral proteins.
Highlights
A major challenge in biological research, clinical medicine, and biotechnology is how to decipher and exploit the effects of mutations [1]
We explore the limits to detecting epistasis and the role of epistatic interactions between sites in modulating the fitness landscape of HIV with many mutations, focusing on the drug target proteins, protease (PR), reverse transcriptase (RT), and integrase (IN), as well as the emerging target protein of capsid (CA)
A central quantity known as the “statistical” energy of a sequence E(S) (Eq 2, Methods) is commonly interpreted to be proportional to fitness; the model predicts that sequences will appear in the dataset with probability P(S) / e−E(S), such that sequences with favorable statistical energies are more prevalent in the multiple sequence alignment (MSA)
Summary
A major challenge in biological research, clinical medicine, and biotechnology is how to decipher and exploit the effects of mutations [1]. In efforts ranging from the identification of genetic variations underlying disease-causing mutations, to the understanding of the genotype-phenotype mapping, to development of modified proteins with useful properties, there is a need to rapidly assess the functional effects of mutations. Experimental techniques to assess the effect of multiple mutations on phenotype have been effective [2,3,4,5], but functional assays to test all possible combinations are not possible due to the vast size of the mutational landscape. Recent advances in biotechnology have enabled deep mutational scans [6] and multiplexed assays [7] for a more complete description of the mutational landscapes of a few proteins, but remain resource intensive and limited in scalability. The measured phenotypes depend on the type of experiment and are susceptible to changes in experimental conditions
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