A new distributed algorithm for side-chain positioning in the process of protein docking

Abstract

Side-chain positioning (SCP) is an important component of computational protein docking methods. Existing SCP methods and available software have been designed for protein folding applications where side-chain positioning is also important. As a result they do not take into account significant special structure that SCP for docking exhibits. We propose a new algorithm which poses SCP as a Maximum Weighted Independent Set (MWIS) problem on an appropriately constructed graph. We develop an approximate algorithm which solves a relaxation of the MWIS and then rounds the solution to obtain a high-quality feasible solution to the problem. The algorithm is fully distributed and can be executed on a large network of processing nodes requiring only local information and message-passing between neighboring nodes. Motivated by the special structure in docking, we establish optimality guarantees for a certain class of graphs. Our results on a benchmark set of enzyme-inhibitor protein complexes show that our predictions are close to the native structure and are comparable to the ones obtained by a state-of-the-art method. The results are substantially improved if rotamers from unbound protein structures are included in the search. We also establish that the use of our SCP algorithm substantially improves docking results.

A Subspace Semi-Definite programming-based Underestimation (SSDU) method for stochastic global optimization in protein docking

“…The refinement algorithms we compare in this paper are two existing methods: an MCM-based algorithm (MCM) [12] and SDU [3] against two proposed methods: SSDU with quadratic underestimator (SSDU2) and SSDU with degree four underestimator (SSDU4). As part of our implementation, we have added a local minimization subroutine to all algorithms in order to resolve the steric clashes at the protein interfaces and thus obtain more reliable energy evaluation.…”

“…Specifically, for each sample conformation we first run a rigid-body energy minimization algorithm [1] which locally minimizes the position and orientation of the ligand with respect to the receptor. Then we run a side-chain positioning (SCP) algorithm [12] that solves a combinatorial optimization problem in order to re-position the amino acid residues at the interface of the receptor-ligand interaction. The SCP algorithm models the flexibility of the protein structures upon binding.…”

“…In this fashion, the algorithm iteratively performs rigid-body and flexible motions of the ligand towards the receptor for each input conformation. Similar approaches which consider the flexibility of protein interfaces and employ a powerful local minimization step in each iteration of an MCM-based search have been explored in our earlier work [11], [12]. …”

The Impact of Side-Chain Packing on Protein Docking Refinement

“…In particular, we introduce constraints on the cliques of the graph that are redundant but make the linear programming relaxation of the integer programming problem tighter. We have developed a gradient projection (GP) method (see ref 24) for solving the (linear programming) dual problem of this relaxation. Our algorithm involves message passing only among adjacent nodes of the graph and uses local information.…”

“…We have established that our algorithm obtains an optimal solution to SCP for a special class of problem instances motivated by the structure of SCP arising in docking. 24 In contrast to the aforementioned related work in the context of folding, our method is based on an approximate algorithm and forgoes optimality since state-of-the-art interaction energy models are also approximate. However, our method is fully distributed and requires only message passing between neighboring nodes of the graphical model of the SCP problem, which will be illustrated in Figure 1.…”