Adaptation in protein fitness landscapes is facilitated by indirect paths

By Nicholas C. Wu, Lei Dai, Anders C. Olson, James O. Lloyd-Smith, Ren Sun. 2016

266 citations recorded by the source corpus.

The structure of fitness landscapes is critical for understanding adaptive protein evolution (e.g. antimicrobial resistance, affinity maturation, etc.). Due to limited throughput in fitness measurements, previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, however, the dimensionality of protein sequence space is higher (20L, L being the length of the relevant sequence) and there may be higher-order interactions among more than two sites. To study how these features impact the course of protein evolution, we experimentally characterized the fitness landscape of four sites in the IgG-binding domain of protein G, containing 204 = 160,000 variants. We found that the fitness landscape was rugged and direct paths of adaptation were often constrained by pairwise epistasis. However, while direct paths were blocked by reciprocal sign epistasis, we found systematic evidence that such evolutionary traps could be circumvented by “extra-dimensional bypass”. Extra dimensions in sequence space – with a different amino acid at the site of interest or an additional interacting site – open up indirect paths of adaptation via gain and subsequent loss of mutations. These indirect paths alleviate the constraint on reaching high fitness genotypes via selectively accessible trajectories, suggesting that the heretofore neglected dimensions of sequence space may completely change our views on how proteins evolve.

Subfields: Genetics, Molecular Biology, Sociology and Political Science

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Citations in the corpus

ProGen: Language Modeling for Protein Generation (2020, methodology): “We examine the empirical fitness landscape of protein G domain B1 (GB1) binding to an antibody [38].”
Machine learning in protein engineering (2018, background): “…(Haddox et al., 2016), inﬂuenza nucleoprotein (Ashenberg et al., 2017), inﬂuenza hemagglutinin (Doud & Bloom, 2016), PhoQ (Podgornaia & Laub, 2015), GB1 (Wu et al., 2016), the DNA-binding domain of a steroid hormone receptor (Starr et al., 2017), and Gal4 transcription factor (Kitzman et al.,…”
Machine learning-assisted directed protein evolution with combinatorial libraries (2019, methodology): “We first investigate the benefits of in silico screening by machine learning using the dataset collected by Wu and coworkers (16), who studied the effects on antibody binding of mutations at four positions in human GB1 binding protein (theoretical library size 204 = 160,000 variants).”
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design (2019, methodology): “For both PhoQ and GB1, we initiated the simulation by selecting an initial batch of sequences consisting of all the single mutants plus 100 randomly-selected sequences.”
FLIP: Benchmark tasks in fitness landscape inference for proteins (2021, methodology): “We use the GB1 landscape [41], which has become a gold standard for investigating epistatic interactions [10].”
Guided Generative Protein Design using Regularized Transformers (2022, background): “• GB1 Dataset Wu et al. [2016] carried out a saturation mutagenesis study targeted four sites and generated all 20 4 possible mutants to explore the local ﬁtness landscape of GB1, an immunoglobulin-binding protein.”
Inferring the shape of global epistasis (2018, methodology): “(38) measured the binding of GB1 variants mutated to all possible combinations of amino acids at four sites specifically chosen because they were suspected to exhibit idiosyncratic epistatic interactions.”