Computational Feasibility of an Exhaustive Search of Side‐Chain Conformations in Protein‐Protein Docking

Abstract

Protein-protein docking procedures typically perform the global scan of the proteins relative positions, followed by the local refinement of the putative matches. Because of the size of the search space, the global scan is usually implemented as rigid-body search, using computationally inexpensive intermolecular energy approximations. An adequate refinement has to take into account structural flexibility. Since the refinement performs conformational search of the interacting proteins, it is extremely computationally challenging, given the enormous amount of the internal degrees of freedom. Different approaches limit the search space by restricting the search to the side chains, rotameric states, coarse-grained structure representation, principal normal modes, and so on. Still, even with the approximations, the refinement presents an extreme computational challenge due to the very large number of the remaining degrees of freedom. Given the complexity of the search space, the advantage of the exhaustive search is obvious. The obstacle to such search is computational feasibility. However, the growing computational power of modern computers, especially due to the increasing utilization of Graphics Processing Unit (GPU) with large amount of specialized computing cores, extends the ranges of applicability of the brute-force search methods. This proof-of-concept study demonstrates computational feasibility of an exhaustive search of side-chain conformations in protein pocking. The procedure, implemented on the GPU architecture, was used to generate the optimal conformations in a large representative set of protein-protein complexes. © 2018 Wiley Periodicals, Inc.

AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information

“…Although the experimental methods achieved the highest accuracy, these methods often need high cost of time, labor, and specific experimental conditions. Molecular docking based on protein structures is a common computational method for predicting antibody-antigen interactions ( 22 ). However, due to the difficulty in obtaining accurate structures of both antibody and antigen from sequences, predicting the interactions remains a difficult task.…”

“…Molecular docking is a classical method for predicting antibody-antigen binding mode and relative positions. However, molecular docking is often computationally expensive, especially when dealing with flexible molecules such as antibodies ( 22 ). The emergence of epitope or paratope prediction tools based on structural or sequence features, such as PECAN ( 23 ), BepiPred2.0 ( 24 ), and Epipred ( 25 ), greatly reduces the search space of docking.…”

Docking-based long timescale simulation of cell-size protein systems at atomic resolution

“…Our approach was to dramatically speed-up the sampling of the intermolecular energy landscape by skipping the low-probability (high-energy) states, focusing only on the set of high-probability (low-energy) states corresponding to the energy minima. The “minima hopping” paradigm has been widely used since the early days of molecular modeling for the sampling of the energy landscapes of biomolecules - such as conformational analysis of biopolymers, 37 rotamer libraries, 38 and refinement of protein-protein interfaces, 39 providing extraordinary savings of computing time by avoiding travel in low-probability areas of the landscape. Markov State Models (MSM), have been used to study protein folding, dynamics, 40 and association 41 by representing the energy landscape by a set of the energy minima and the probabilities of transition between them.…”

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

“…This algorithm can be divided into two subgroups: stochastic and systematic. Systematic search includes matching algorithms (MA), incremental construction (IC), exhaustive search, conformation ensemble, and fragment-based search, whereas stochastic methods in searching contain Monte Carlo (MC), swarm optimization (SO), evolutionary algorithm (EA), etc. , Finally, these algorithms compute the predicted binding free energy, which is then used with the scoring function to decide which compounds are more likely to bind to target proteins to form stable complexes during molecular docking . There are many current classical scoring functions, and they are all based on the idea that binding affinity and the structure of drug–target complexes have a functional relationship.…”