| dc.description.abstract |
<p>Computational protein design aims at identifying protein mutations and conformations
with desired target properties (such as increased protein stability, switch of substrate
specificity, or novel function) from a vast combinatorial space of candidate solutions.
The development of algorithms to efficiently and accurately solve problems in protein
design has thus posed significant computational and modeling challenges. Despite the
inherent hardness of protein design, a number of computational techniques have been
previously developed and applied to a wide range of protein design problems. In many
cases, however, the available computational protein design techniques are deficient
both in computational power and modeling accuracy. Typical simplifying modeling assumptions
for computational protein design are the rigidity of the protein backbone and the
discretization of the protein side-chain conformations. Here, we present the derivation,
proofs of correctness and complexity, implementation, and application of novel algorithms
for computational protein design that, unlike previous approaches, have provably-accurate
guarantees even when backbone or continuous side-chain flexibility are incorporated
into the model. We also describe novel divide-and-conquer and dynamic programming
algorithms for improved computational efficiency that are shown to result in speed-ups
of up to several orders of magnitude as compared to previously-available techniques.
Our novel algorithms are further incorporated as part of K*, a provably-accurate ensemble-based
algorithm for protein-ligand binding prediction and protein design. The application
of our suite of protein design algorithms to a variety of problems, including enzyme
redesign and small-molecule inhibitor design, is described. Experimental validation,
performed by our collaborators, of a set of our computational predictions confirms
the feasibility and usefulness of our novel algorithms for computational protein design.</p>
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