Computationally Mapping Sequence Space To Understand Evolutionary Protein Engineering

Abstract

Evolutionary protein engineering has been dramatically successful, producing a wide variety of new proteins with altered stability, binding affinity, and enzymatic activity. However, the success of such procedures is often unreliable, and the impact of the choice of protein, engineering goal, and evolutionary procedure is not well understood. We have created a framework for understanding aspects of the protein engineering process by computationally mapping regions of feasible sequence space for three small proteins using structure-based design protocols. We then tested the ability of different evolutionary search strategies to explore these sequence spaces. The results point to a non-intuitive relationship between the error-prone PCR mutation rate and the number of rounds of replication. The evolutionary relationships among feasible sequences reveal hub-like sequences that serve as particularly fruitful starting sequences for evolutionary search. Moreover, genetic recombination procedures were examined, and tradeoffs relating sequence diversity and search efficiency were identified. This framework allows us to consider the impact of protein structure on the allowed sequence space and therefore on the challenges that each protein presents to error-prone PCR and genetic recombination procedures.

Applying Physics-Based Scoring to Calculate Free Energies of Binding for Single Amino Acid Mutations in Protein-Protein Complexes

“…We performed MM-GBSA scoring of mutations for the 19 targets in the Kortemme and Baker test set [ 13 ], as described in the Materials and Methods. The MM-GBSA score for a protein-protein complex is calculated by scoring three protein structures (one binding partner alone, the other binding partner alone, and the complex) using the OPLS2005 force field and an implicit solvent model for water.…”

“…For our test set we used the proteins and mutations from Kortemme and Baker [13], since this is a well-known dataset in the protein design field. Importantly, we did not use any training set to modify our force field or solvent model during this work.…”

“…Various computational methods have been used to predict the impact of single amino acid mutations, and in particular these methods are successful at distinguishing protein “hotspots” (positions where mutations have a large impact on protein stability or affinity) from other positions where mutations have little to no impact on these properties [ 13 , 14 , 15 , 16 ]. Predicting both types of mutations can be useful because even when a single mutation has no direct impact on the protein function, it may modulate pharmacokinetic and ADME properties or increase the efficiency of manufacturing the protein.…”

“…In this work, we present results for computational affinity predictions of single mutations at protein-protein interfaces using the MM-GBSA approach [ 24 , 25 ] in BioLuminate (version 1.0, Schrödinger, LLC, New York, NY, 2012), which incorporates the OPLS2005 force field [ 26 , 27 ], VSGB solvent model [ 28 ], and rotamer search algorithms from Prime (version 3.1, Schrödinger, LLC, New York, NY, 2012). For our test set we used the proteins and mutations from Kortemme and Baker [ 13 ], since this is a well-known dataset in the protein design field. Importantly, we did not use any training set to modify our force field or solvent model during this work.…”

Metabolic and evolutionary engineering research in Turkey and beyond

“…Thus, the terms 'directed evolution' and 'evolutionary engineering' are used in the fields of protein and metabolic/cellular engineering [105][106][107][108]. Other similar terms such as evolutionary molecular engineering [109][110][111], directed enzyme evolution [112], evolutionary protein engineering [113,114], and evolutionary metabolic engineering [104] have also been used by different research groups. I refer to studies on whole cells as 'evolutionary engineering' and on proteins as 'evolutionary protein engineering'.…”