Abstract:
Motivation: When
proteins mutate or bind to ligands, their backbones often move
significantly, especially in loop regions. Computational protein
design algorithms must model these motions in order to accurately
optimize protein stability and binding affinity. However, methods for
backbone conformational search in design have been much more limited
than for sidechain conformational search. This is especially true for
combinatorial protein design algorithms, which aim to search
a large sequence space efficiently and thus cannot rely on temporal
simulation of each candidate sequence. Results:
We alleviate this difficulty with a new parameterization of backbone
conformational space, which represents all degrees of freedom of a
specified segment of protein chain that maintain valid bonding
geometry (by maintaining the original bond lengths and angles and
ω dihedrals). In order to search this space, we present an
efficient algorithm, CATS, for computing atomic coordinates as a
function of our new continuous backbone internal coordinates. CATS
generalizes the iMinDEE and EPIC protein design algorithms, which
model continuous flexibility in sidechain dihedrals, to model
continuous, appropriately localized flexibility in the backbone
dihedrals φ and ψ as well. We show using 81
test cases based on 29 different protein structures that CATS finds
sequences and conformations that are significantly lower in energy
than methods with less or no backbone flexibility do. In particular,
we show that CATS can model the viability of an antibody mutation
known experimentally to increase affinity, but that appears sterically
infeasible when modeled with less or no backbone
flexibility.
Supplementary
information:Supplementary
data are available at Bioinformatics
online, or here, on our lab website.