Abstract
Optimizing amino acid conformation and identity is a central problem in computational protein design. Protein design algorithms must allow realistic protein flexibility to occur during this optimization, or they may fail to find the best sequence with the lowest energy. Most design algorithms implement side-chain flexibility by allowing the side chains to move between a small set of discrete, low-energy states, which we call rigid rotamers. In this work we show that allowing continuous side-chain flexibility (which we call continuous rotamers) greatly improves protein flexibility modeling. We present a large-scale study that compares the sequences and best energy conformations in 69 protein-core redesigns using a rigid-rotamer model versus a continuous-rotamer model. We show that in nearly all of our redesigns the sequence found by the continuous-rotamer model is different and has a lower energy than the one found by the rigid-rotamer model. Moreover, the sequences found by the continuous-rotamer model are more similar to the native sequences. We then show that the seemingly easy solution of sampling more rigid rotamers within the continuous region is not a practical alternative to a continuous-rotamer model: at computationally feasible resolutions, using more rigid rotamers was never better than a continuous-rotamer model and almost always resulted in higher energies. Finally, we present a new protein design algorithm based on the dead-end elimination (DEE) algorithm, which we call iMinDEE, that makes the use of continuous rotamers feasible in larger systems. iMinDEE guarantees finding the optimal answer while pruning the search space with close to the same efficiency of DEE. Availability: Software is available under the Lesser GNU Public License v3. Contact the authors for source code.
Highlights
Computational structure-based protein redesign is a promising field with applications for drug design [1], biosynthesis [2], protein:peptide design [3], and predicting drug resistance [4]
Computational protein design is a promising field with many biomedical applications, such as drug design, or the redesign of new enzymes to perform nonnatural chemical reactions
In this work we present a large-scale study of 69 protein redesigns that shows the necessity of modeling more realistic protein flexibility
Summary
Computational structure-based protein redesign is a promising field with applications for drug design [1], biosynthesis [2], protein:peptide design [3], and predicting drug resistance [4]. The goal of a structure-based protein redesign algorithm is to search over protein conformations and find the global minimum energy conformation, or GMEC, with respect to a given protein design model. The protein design model defines both the input to the algorithm and how the redesigned protein can move (the flexible space). As input the algorithm takes one or several starting protein structures, an energy function to score the designed proteins, and whether the design search allows amino acid type mutations (a mutation search). The protein design algorithm searches protein conformations from multiple sequences to find the amino acid sequence of the GMEC. Most protein design models limit the flexible space during the search in the interest of computational feasibility. A common protein design model assumes a fixed backbone and only allows the side chains to move among a set of discrete conformations called rotamers. Rotamers are determined using theoretical calculations and the empirical observation that the side chains of amino acids in protein structures avoid most of the available conformational space and appear frequently as clusters in x-angle space [5] (Figure 1A)
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