Protein-protein docking by generalized Fourier transforms on 5D rotational manifolds

TL;DR

This paper introduces an efficient Fourier transform-based algorithm for protein-protein docking that expands sampling to 5 rotational coordinates, achieving at least tenfold speedup while maintaining accuracy.

Contribution

The authors develop a novel method treating the search space as a product manifold, enabling efficient FFT on 5D rotational space and improving docking speed and accuracy.

Findings

At least tenfold speedup over previous methods

Effective sampling of complex energy functions

Cluster-based prediction improves docking accuracy

Abstract

Energy evaluation using fast Fourier transforms enables sampling billions of putative complex structures and hence revolutionized rigid protein-protein docking. However, in current methods efficient acceleration is achieved only in either the translational or the rotational subspace. Developing an efficient and accurate docking method that expands FFT based sampling to 5 rotational coordinates is an extensively studied but still unsolved problem. The algorithm presented here retains the accuracy of earlier methods but yields at least tenfold speedup. The improvement is due to two innovations. First, the search space is treated as the product manifold $\mathbf{SO(3)x(SO(3)\setminus S^1)}$, where $\mathbf{SO(3)}$ is the rotation group representing the space of the rotating ligand, and $\mathbf{(SO(3)\setminus S^1)}$ is the space spanned by the two Euler angles that define the orientation of the vector from the center of the fixed receptor toward the center of the ligand. This representation enables the use of efficient FFT methods developed for $\mathbf{SO(3)}$. Second, we select the centers of highly populated clusters of docked structures, rather than the lowest energy conformations, as predictions of the complex, and hence there is no need for very high accuracy in energy evaluation. Therefore it is sufficient to use a limited number of spherical basis functions in the Fourier space, which increases the efficiency of sampling while retaining the accuracy of docking results. A major advantage of the method is that, in contrast to classical approaches, increasing the number of correlation function terms is computationally inexpensive, which enables using complex energy functions for scoring.