A message passing approach to Side Chain Positioning with applications in protein docking refinement

Mirzaei

Mamonov

et al. 2015

J. Chem. Inf. Model.

Self Cite

We study the impact of optimizing side-chain positions in the interface region between two proteins during the process of binding. Mathematically, the problem is similar to side-chain prediction, extensively explored in the process of protein structure prediction. The protein-protein docking application, however, has a number of characteristics that necessitate different algorithmic and implementation choices. In this work, we implement a distributed approximate algorithm that can be implemented on multi-processor architectures and enables trading off accuracy with running speed. We report computational results on benchmarks of enzyme-inhibitor and other types of complexes, establishing that the side-chain flexibility our algorithm introduces substantially improves the performance of docking protocols. Further, we establish that the inclusion of unbound side-chain conformers in the side-chain positioning problem is critical in these performance improvements.

Section: Methodsmentioning

confidence: 99%

The Impact of Side-Chain Packing on Protein Docking Refinement

Mirzaei

Mamonov

et al. 2015

J. Chem. Inf. Model.

Self Cite

“…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]. …”

Section: Related Work and Key Contributionsmentioning

confidence: 99%

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

Feng

53rd IEEE Conference on Decision and Control

Vakili

et al. 2014

Self Cite

We propose a new stochastic global optimization method targeting protein docking problems. The method is based on finding a general convex polynomial underestimator to the binding energy function in a permissive subspace that possesses a funnel-like structure. We use Principal Component Analysis (PCA) to determine such permissive subspaces. The problem of finding the general convex polynomial underestimator is reduced into the problem of ensuring that a certain polynomial is a Sum-of-Squares (SOS), which can be done via semi-definite programming. The underestimator is then used to bias sampling of the energy function in order to recover a deep minimum. We show that the proposed method significantly improves the quality of docked conformations compared to existing methods.

“…We call (2) the edge formulation of MWIS. In our earlier work [9], an algorithm was introduced for solving (2) that relied on solving its Linear Programming (LP) relaxation. In particular, the LP relaxation of (2) is formed by relaxing the integer constraints x i ∈ {0, 1} as 0 ≤ x i ≤ 1.…”

Section: Side-chain Positioning Formulationmentioning

confidence: 99%

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

52nd IEEE Conference on Decision and Control

Kozakov

Vakili

et al. 2013

Self Cite

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.