Computational and Experimental Exploration of Protein Fitness Landscapes: Navigating Smooth and Rugged Terrains

被引：0

作者：

Sandhu, Mahakaran ^{[1
,2
]}

Chen, John Z. ^{[1
,3
]}

Matthews, Dana S. ^{[1
,2
]}

Spence, Matthew A. ^{[1
,2
]}

Pulsford, Sacha B. ^{[1
,2
]}

Gall, Barnabas ^{[1
,2
]}

Kaczmarski, Joe A. ^{[1
,3
]}

Nichols, James ^{[4
]}

Tokuriki, Nobuhiko ^{[5
]}

Jackson, Colin J. ^{[1
,2
,3
,4
]}

机构：

[1] Australian Natl Univ, Res Sch Chem, Canberra, ACT 2601, Australia

[2] Australian Natl Univ, ARC Ctr Excellence Innovat Peptide & Prot Sci, Res Sch Chem, Canberra, ACT 2601, Australia

[3] Australian Natl Univ, ARC Ctr Excellence Synthet Biol, Res Sch Biol, Canberra, ACT 2601, Australia

[4] Australian Natl Univ, Biol Data Sci Inst, Canberra, ACT 2601, Australia

[5] Univ British Columbia, Michael Smith Labs, Vancouver, BC V6T 1Z4, Canada

来源：

BIOCHEMISTRY | 2025年

基金：

澳大利亚研究理事会;

关键词：

ANCESTRAL SEQUENCE RECONSTRUCTION; MUTATIONAL EPISTASIS; FOURIER-TRANSFORM; EVOLUTION; PREDICTABILITY; BACTERIAL; NETWORKS; MUTANTS; BINDING; ENERGY;

D O I：

10.1021/acs.biochem.4c00673

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Proteins evolve through complex sequence spaces, with fitness landscapes serving as a conceptual framework that links sequence to function. Fitness landscapes can be smooth, where multiple similarly accessible evolutionary paths are available, or rugged, where the presence of multiple local fitness optima complicate evolution and prediction. Indeed, many proteins, especially those with complex functions or under multiple selection pressures, exist on rugged fitness landscapes. Here we discuss the theoretical framework that underpins our understanding of fitness landscapes, alongside recent work that has advanced our understanding-particularly the biophysical basis for smoothness versus ruggedness. Finally, we address the rapid advances that have been made in computational and experimental exploration and exploitation of fitness landscapes, and how these can identify efficient routes to protein optimization.

引用

页数：12