Amino-acid site variability among natural and designed proteins

Abstract

Computational protein design attempts to create protein sequences that fold stably into pre-specified structures. Here we compare alignments of designed proteins to alignments of natural proteins and assess how closely designed sequences recapitulate patterns of sequence variation found in natural protein sequences. We design proteins using RosettaDesign, and we evaluate both fixed-backbone designs and variable-backbone designs with different amounts of backbone flexibility. We find that proteins designed with a fixed backbone tend to underestimate the amount of site variability observed in natural proteins while proteins designed with an intermediate amount of backbone flexibility result in more realistic site variability. Further, the correlation between solvent exposure and site variability in designed proteins is lower than that in natural proteins. This finding suggests that site variability is too uniform across different solvent exposure states (i.e., buried residues are too variable or exposed residues too conserved). When comparing the amino acid frequencies in the designed proteins with those in natural proteins we find that in the designed proteins hydrophobic residues are underrepresented in the core. From these results we conclude that intermediate backbone flexibility during design results in more accurate protein design and that either scoring functions or backbone sampling methods require further improvement to accurately replicate structural constraints on site variability.

Predicting Evolutionary Site Variability from Structure in Viral Proteins: Buriedness, Packing, Flexibility, and Design

“…As an alternative to predicting evolutionary variation from simple structural measures such as contact density or backbone flexibility, one can also predict evolutionary variation via a protein-design approach (Dokholyan and Shakhnovich 2001; Ollikainen and Kortemme 2013; Jackson et al 2013). In this case, one takes the protein structure of interest, replaces all residue side chains with randomly chosen alternatives, and uses a coarse-grained or atom-level energy function to assess which side-chain choices are consistent with the backbone conformation of the focal structure.…”

“…In this case, one takes the protein structure of interest, replaces all residue side chains with randomly chosen alternatives, and uses a coarse-grained or atom-level energy function to assess which side-chain choices are consistent with the backbone conformation of the focal structure. We have recently used this approach to compare natural and designed sequence variability in cellular proteins (Jackson et al 2013), and we have found that (i) flexible-backbone design, where small backbone movements are allowed during the design phase, outperformed fixed-backbone design, and (ii) intermediate backbone flexibility, obtained via an intermediate design temperature, produced the highest congruence between designed and natural sequences. Similarly, Dokholyan and Shakhnovich (2001) had previously found that an intermediate temperature parameter gave the best agreement between designed and natural sequences in their model.…”

“…We refer to the resulting quantity as the designed entropy . We chose a design temperature of T = 0.6, which was near the optimal range in our previous work (Jackson et al 2013). …”

“…The temperature parameter in Backrub was set to 0.6, allowing for an intermediate amount of flexibility. We had previously found in a different data set that intermediate flexibility gave the highest congruence between designed and observed site variability (Jackson et al 2013). …”

“…Designed entropy was calculated as described (Jackson et al 2013). In brief, proteins were designed using Rosetta Design (Version 39284) (Leaver-Fay et al 2011) using a flexible-backbone approach.…”