(T)Race back in Space - Recovering protein evolvability from multifunctional ancestors

Basic data for this project

Description

Sophisticated models exist for the evolutionary pathways by which proteins have evolved in Nature over billions of years to form an impressive diversity of structure and to carry out many functions with unrivalled efficiency. Directed protein evolution in the test tube can emulate natural evolution, but is often limited by low hit rates and small improvements during evolution cycles. Burdened by their evolutionary history, proteins often show low evolvability, due to loss-of-function and/or structure or epistatic ratchets (ie deleterious or permissive mutations that only become beneficial upon occurrence of further mutations). Given the danger of getting lost or stuck in sequence space, the question arises how natural and forced evolution avoids dead ends. We will create a mathematical model to simulate the effects of mutational robustness on evolvability using parameters that can be adjusted to experimentally determined values for neutrality and accessibility of novel phenotypes. Based on experimental measurements of the dynamics of fitness landscapes of a protein population during evolution cycles we aim at insight into the fundamental as well as an effective basis for protein engineering by evolution. Investigations of duplicated specialized proteins (paralogs) and their ancestral precursors showed the latter often to be promiscuous (i.e. to have multiple functions) and more highly evolvable, suggesting that evolution may proceed by shifting the relative balance of two functions along paths of successive point mutations. We probe the evolution dynamics of promiscuous members of the metallo-ß-lactamase and alkaline phosphatase superfamilies. Ancestral sequences will be computed from alignments and databases as starting points for evolution to give a dynamic experimental picture of fitness of a protein population in response to the ancestor sequence and to selective pressure. Such distribution functions will be gathered by ultrahigh-throughput screening of >10^6 clones in picolitre droplet compartments during in vitro and in vivo evolution cycles, followed by systematic characterization of multiple fitness traits such as expression level, stability or reaction kinetics for multiple activities. These approaches will enable a dynamic picture of protein evolution at various levels from transition state recognition, protein biophysics to population genetics.