Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design

Abstract

The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.

Nitrogenase resurrection and the evolution of a singular enzymatic mechanism

“…The use of phylogenetic information as a guide for experimental investigations provides a way to efficiently traverse functional sequence space already “vetted” by natural selection ( Fig 2D ). These strategies can integrate the effects of epistatic interactions that are challenging to predict a priori and are thus more likely to produce functional variants than rationally designed or random strategies that can be hindered by local adaptive valleys (Castle, Grierson, & Gorochowski, 2021; Clifton, Kozome, & Laurino, 2022; Hochberg & Thornton, 2017; Spence, Kaczmarski, Saunders, & Jackson, 2021).…”

Machine Learning-Guided Protein Engineering

“…178 This tool exhaustively models each combination of mutants and ranks them by energy. Evolutionary information can also be captured by ML-based models 179 and used to suggest promising substitutions, e.g., amino acids with conditional likelihoods higher than the wild-type. 180 Mathematical optimization methods can generate promising protein sequence candidates in silico by iteratively producing new designs based on available ML scoring data (Figure 3).…”

Mitochondrial Cardiomyopathy: The Roles of mt-tRNA Mutations

“…The heteroplasmic m.A4295G mutation is located directly 3′ end immediately to the anticodon stem of the tRNA Ile , which is very conserved in various species [ 105 ]. Notably, the m.A4295G mutation introduced an m 1 G37 modification of tRNA Ile, which was catalyzed by methyltransferase 5 (TRMT5) [ 106 ]. Simulations of molecular dynamics suggested that the m.A4295G mutation altered the structure and function of tRNA Ile , as evidenced by enhanced Tm , structural alternations, and instability of mutated tRNA.…”