Assessing and enhancing foldability in designed proteins

Abstract

Recent advances in protein-design methodology have led to a dramatic increase in its reliability and scale. With these advances, dozens and even thousands of designed proteins are automatically generated and screened. Nevertheless, the success rate, particularly in design of functional proteins, is low and fundamental goals such as reliable de novo design of efficient enzymes remain beyond reach. Experimental analyses have consistently indicated that a major cause of design failure is inaccuracy and misfolding relative to the design model. To address this challenge, we describe complementary methods to diagnose and ameliorate suboptimal regions in designed proteins: first, we develop a Rosetta atomistic computational mutation scanning approach to detect energetically suboptimal positions in designs; second, we demonstrate that the AlphaFold2 ab initio structure prediction method flags regions that may misfold in designed enzymes and binders; and third, we focus FuncLib design calculations on suboptimal positions in a previously designed low-efficiency enzyme, thereby improving its catalytic efficiency by 330 fold. Thus, foldability analysis and enhancement may dramatically increase the success rate in design of functional proteins.

Increasing Computational Protein Design Literacy through Cohort-Based Learning for Undergraduate Students

“…To better understand the pitfalls of current nanoparticle design methods, we aimed to computationally distinguish between successful and unsuccessful protein nanoparticle designs. While several published efforts to stabilize monomeric proteins demonstrated high success rates based on approaches using phylogenetic analysis, structure-based rational design, or sequence-based design, − none of these approaches addressed the stability of interfaces between multiple interacting proteins and its effect on design success . We thus sought to test a recently developed computational pipeline p rotein S train U nsatisfactoriness and F rustration find ER (pSUFER)for local sequence optimality evaluation on previously designed protein assemblies of known assembly phenotype. ,, pSUFER is a Rosetta software suite-based protocol that estimates the effect of point mutations at a particular position in a protein.…”

“…While several published efforts to stabilize monomeric proteins demonstrated high success rates based on approaches using phylogenetic analysis, structure-based rational design, or sequence-based design, − none of these approaches addressed the stability of interfaces between multiple interacting proteins and its effect on design success . We thus sought to test a recently developed computational pipeline p rotein S train U nsatisfactoriness and F rustration find ER (pSUFER)for local sequence optimality evaluation on previously designed protein assemblies of known assembly phenotype. ,, pSUFER is a Rosetta software suite-based protocol that estimates the effect of point mutations at a particular position in a protein. We tested the pSUFER protocol on a set of 809 designed protein nanoparticle models in order to (1) determine the effect of point mutations at the previously designed protein–protein interfaces on free energy; and (2) to identify key residue positions predicted to weaken/strengthen the protein–protein interfaces.…”

Increasing computational protein design literacy through cohort-based learning for undergraduate students

“…34,41,42 To better understand the pitfalls of current nanoparticle design methods, we aimed to computationally distinguish between successful and unsuccessful protein nanoparticle designs. While several published efforts to stabilize monomeric proteins demonstrated high success rates based on approaches using phylogenetic analysis, structure-based rational design, or sequence-based design, [43][44][45][46][47][48][49] none of these approaches addressed the stability of interfaces between multiple interacting proteins and its effect on design success. 50 We thus sought to test a recently developed computational pipeline-protein Strain Unsatisfactoriness and Frustration findER (pSUFER)-for local sequence optimality evaluation on previously designed protein assemblies of known assembly phenotype.…”

“…50 We thus sought to test a recently developed computational pipeline— p rotein S train U nsatisfactoriness and F rustration find ER (pSUFER)—for local sequence optimality evaluation on previously designed protein assemblies of known assembly phenotype. 49,51,52 pSUFER is a Rosetta software suite-based protocol that estimates the effect of point mutations at a particular position in a protein. We tested the pSUFER protocol on a set of 809 designed protein nanoparticle models in order to: (1) determine the effect of point mutations at the previously designed protein-protein interfaces on free energy; and (2) to identify key residue positions predicted to weaken/strengthen the protein-protein interfaces.…”

A Method for Assessing the Robustness of Protein Structures by Randomizing Packing Interactions

“…It is also not intended to improve sequences. Other methods that use evolutionary information ( Goldenzweig et al, 2016 ) or directed evolution ( Ben-David et al, 2019 ) can be used to improve the sequences of designed proteins ( Listov et al, 2021 ).…”