Practically useful protein-design methods combining phylogenetic and atomistic calculations

Abstract

Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscure these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.

Figure 1. Phylogenetic analysis reduces and simplifies the sequence space for protein optimization.

Eight positions in the active site of the bacterial phosphotriesterase enzyme were selected for optimization through the FuncLib method[43]. As a first step, mutations that were very rare in a multiple-sequence alignment of homologs were eliminated from consideration. This step resulted in a drastic reduction of the potentially tolerated sequence space of multipoint mutants relative to the unfiltered sequence space (numbers on the right-hand side). Furthermore, the surviving mutations were physicochemically related, suggesting that they were more likely to be tolerated (color-coding: polar amino acids in orange; charged in blue; and hydrophobic in green). Phylogenetic filtering of tolerated mutations, which is also used by other automated design methods[34,38], may reduce the risk of accumulating errors in the design of multipoint mutants.

Figure 2. A practical two-step design strategy for enzyme optimization.

A stable and high-efficiency esterase was designed first by the PROSS stability-design method[33], which introduced 19 mutations in positions remote from the active site (red spheres) and then by the FuncLib function-enhancing method, which introduced four active-site mutations[43] (the active site is surface-colored in yellow and the FuncLib mutations are marked in blue spheres). This design exhibited higher stability, bacterial expression levels and approximately 1,000-fold higher esterase activity relative to the parental enzyme. Other enzymes reported in this study exhibited nearly 4,000-fold improvement in the hydrolysis of venomous nerve agents. This first stabilization step may address the stability-threshold challenge by improving the enzyme’s tolerance for function-enhancing mutations in the second step. For clarity, only one of the two chains comprising the enzyme is shown here.

Figure 3. Designed function-enhancing mutations are unlikely to emerge through evolution.

The esterase specific activity of the parental enzyme (white flag), designed quadruple mutant (four-color flag) and each single- and double-mutant was tested experimentally (only three of the six double mutants are shown)[43]. The heights of each variant represent the log-scale esterase specific activity, revealing a highly rugged and epistatic landscape. All single-point mutations (single-color flags) exhibit higher specific activity than the parental enzyme, but all doubles (two-colored flags) are worse than the best single-point mutant (red flag). By contrast, evolutionary selection processes rely on the gradual accumulation of beneficial mutations, a process that is unlikely to lead to the designed quadruple mutant. Thus, computational protein design may expose beneficial sequences that are difficult for natural or lab evolutionary processes to discover.