Viewing Protein Fitness Landscapes Through a Next-Gen Lens

Abstract

High-throughput sequencing has enabled many powerful approaches in biological research. Here, we review sequencing approaches to measure frequency changes within engineered mutational libraries subject to selection. These analyses can provide direct estimates of biochemical and fitness effects for all individual mutations across entire genes (and likely compact genomes in the near future) in genetically tractable systems such as microbes, viruses, and mammalian cells. The effects of mutations on experimental fitness can be assessed using sequencing to monitor time-dependent changes in mutant frequency during bulk competitions. The impact of mutations on biochemical functions can be determined using reporters or other means of separating variants based on individual activities (e.g., binding affinity for a partner molecule can be interrogated using surface display of libraries of mutant proteins and isolation of bound and unbound populations). The comprehensive investigation of mutant effects on both biochemical function and experimental fitness provide promising new avenues to investigate the connections between biochemistry, cell physiology, and evolution. We summarize recent findings from systematic mutational analyses; describe how they relate to a field rich in both theory and experimentation; and highlight how they may contribute to ongoing and future research into protein structure-function relationships, systems-level descriptions of cell physiology, and population-genetic inferences on the relative contributions of selection and drift.

Systematic Mutant Analyses Elucidate General and Client-Specific Aspects of Hsp90 Function

“…1B). The correlation coefficient from these biological replicates (R 2 =0.92) is consistent with expected variation based on sequencing depth (Boucher et al, 2014) (average confidence interval of 9%, Table S1). Mutations with strongly deleterious effects exhibited greater variation between replicates consistent with expected uncertainty in estimating the frequency of mutations that deplete rapidly.…”

“…Libraries of Hsp82 (yeast Hsp90) point mutants were generated in p414ADHΔTER, which expresses Hsp82 at a reduced level relative to wild-type strains and provides a sensitive readout of the functional impacts of mutations (Jiang et al, 2013). Confidence intervals (95%) were calculated for the growth effects of each amino acid change (Table S1) based on the standard error ( S p ) from sampling depth in sequencing (Boucher et al, 2014) using the equation below.…”

“…Environmental conditions can have a dramatic impact on the fitness effects of Hsp90 mutations (Hietpas, 2013), and the conditions experienced in evolution likely impose pressures that cannot be fully captured by laboratory experiments. In addition, the strength of purifying selection in large natural populations over evolutionary timescales is far more stringent than can be distinguished by experimental evolution studies (Boucher et al, 2014; Ohta, 1973). The nearly-neutral window in natural evolution is inversely related to effective population size, and for yeast it has been estimated that a fitness defects of 0.0001% would be subject to purifying natural selection (Tsai et al, 2008).…”

“…Using a mutational scanning approach (Boucher et al, 2014; Fowler and Fields, 2014) that we refer to as EMPIRIC (Hietpas et al, 2011), we analyzed the effects of each amino acid change in an otherwise wild-type Hsp90 sequence background on the growth rate of engineered budding yeast. This screen probes the impacts of Hsp90 mutants integrated over multiple clients that contribute to yeast growth.…”

Experimental Estimation of the Effects of All Amino-Acid Mutations to HIV’s Envelope Protein on Viral Replication in Cell Culture

“…It is now possible to make massively parallel experimental measurements of the effects of protein mutations using deep mutational scanning [23–25]. These experiments involve creating large libraries of mutants of a gene, subjecting them to bulk functional selections, and quantifying the effect of each mutation by using deep sequencing to assess its frequency pre- and post-selection.…”