Accelerated flexible protein-ligand docking using Hamiltonian replica exchange with a repulsive biasing potential

Docking Foundations: From Rigid to Flexible Docking

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.

Approaches to Backbone Flexibility in Protein-Protein Docking

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations

Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).

Induced fit with replica exchange improves protein complex structure prediction

Despite the progress in prediction of protein complexes over the last decade, recent blind protein complex structure prediction challenges revealed limited success rates (less than 20% models with DockQ score > 0.4) on targets that exhibit significant conformational change upon binding. To overcome limitations in capturing backbone motions, we developed a new, aggressive sampling method that incorporates temperature replica exchange Monte Carlo (T-REMC) and conformational sampling techniques within docking protocols in Rosetta. Our method, ReplicaDock 2.0, mimics induced-fit mechanism of protein binding to sample backbone motions across putative interface residues on-the-fly, thereby recapitulating binding-partner induced conformational changes. Furthermore, ReplicaDock 2.0 clocks in at 150-500 CPU hours per target (protein-size dependent); a runtime that is significantly faster than Molecular Dynamics based approaches. For a benchmark set of 88 proteins with moderate to high flexibility (unbound-to-bound iRMSD over 1.2 Å), ReplicaDock 2.0 successfully docks 61% of moderately flexible complexes and 35% of highly flexible complexes. Additionally, we demonstrate that by biasing backbone sampling particularly towards residues comprising flexible loops or hinge domains, highly flexible targets can be predicted to under 2 Å accuracy. This indicates that additional gains are possible when mobile protein segments are known.

Advances to tackle backbone flexibility in protein docking

Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the 'difficult' targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.

Efficient Refinement and Free Energy Scoring of Predicted Protein-Protein Complexes Using Replica Exchange with Repulsive Scaling

Accurate prediction and evaluation of protein-protein complex structures are of major importance to understand the cellular interactome. Typically, putative complexes are predicted based on docking methods, and simple force field or knowledge-based scoring functions are applied to evaluate single complex structures. We have extended a replica-exchange-based scheme employing different levels of a repulsive biasing between partners in each replica simulation (RS-REMD) to simultaneously refine and score protein-protein complexes. The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii (repulsive scaling (RS)-REMD) without affecting interactions within each protein or with the solvent. The method provides a free energy score that correlates quite well with experimental binding free energies on a set of 36 complexes with correlation coefficients of 0.77 and 0.55 in explicit and implicit solvent simulations, respectively. For a large set of docked decoy complexes, significant improvement of docked complexes was found in many cases with the starting structure in the vicinity (within 20 Å) of the native complex. In the majority of cases (14 out of 20 in explicit solvent), near native docking solutions were identified as the best scoring complexes. The approach is computational demanding but may offer a route for refinement and realistic ranking of predicted protein-protein docking geometries.

Prediction of protein-protein complexes using replica exchange with repulsive scaling

The realistic prediction of protein-protein complex structures is import to ultimately model the interaction of all proteins in a cell and for the design of new protein-protein interactions. In principle, molecular dynamics (MD) simulations allow one to follow the association process under realistic conditions including full partner flexibility and surrounding solvent. However, due to the many local binding energy minima at the surface of protein partners, MD simulations are frequently trapped for long times in transient association states. We have designed a replica-exchange based scheme employing different levels of a repulsive biasing between partners in each replica simulation. The bias acts only on intermolecular interactions based on an increase in effective pairwise van der Waals radii (repulsive scaling (RS)-REMD) without affecting interactions within each protein or with the solvent. For a set of five protein test cases (out of six) the RS-REMD technique allowed the sampling of near-native complex structures even when starting from the opposide site with respect to the native binding site for one partner. Using the same start structures and same computational demand regular MD simulations sampled near native complex structures only for one case. The method showed also improved results for the refinement of docked structures in the vicinity of the native binding geometry compared to regular MD refinement.

Enhanced sampling and free energy calculations for protein simulations

Molecular dynamics simulation is a powerful computational technique to study biomolecular systems, which complements experiments by providing insights into the structural dynamics relevant to biological functions at atomic scale. It can also be used to calculate the free energy landscapes of the conformational transitions to better understand the functions of the biomolecules. However, the sampling of biomolecular configurations is limited by the free energy barriers that need to be overcome, leading to considerable gaps between the timescales reached by MD simulation and those governing biological processes. To address this issue, many enhanced sampling methodologies have been developed to increase the sampling efficiency of molecular dynamics simulations and free energy calculations. Usually, enhanced sampling algorithms can be classified into methods based on collective variables (CV-based) and approaches which do not require predefined CVs (CV-free). In this chapter, the theoretical basis of free energy estimation is briefly reviewed first, followed by the reviews of the most common CV-based and CV-free methods including the presentation of some examples and recent developments. Finally, the combination of different enhanced sampling methods is discussed.

Free-energy landscape of molecular interactions between endothelin 1 and human endothelin type B receptor: fly-casting mechanism

The free-energy landscape of interaction between a medium-sized peptide, endothelin 1 (ET1), and its receptor, human endothelin type B receptor (hETB), was computed using multidimensional virtual-system coupled molecular dynamics, which controls the system's motions by introducing multiple reaction coordinates. The hETB embedded in lipid bilayer was immersed in explicit solvent. All molecules were expressed as all-atom models. The resultant free-energy landscape had five ranges with decreasing ET1-hETB distance: completely dissociative, outside-gate, gate, binding pocket, and genuine-bound ranges. In the completely dissociative range, no ET1-hETB interaction appeared. In the outside-gate range, an ET1-hETB attractive interaction was the fly-casting mechanism. In the gate range, the ET1 orientational variety decreased rapidly. In the binding pocket range, ET1 was in a narrow pathway with a steep free-energy slope. In the genuine-bound range, ET1 was in a stable free-energy basin. A G-protein-coupled receptor (GPCR) might capture its ligand from a distant place.

Binding Modes and Metabolism of Caffeine

A correct estimate of ligand binding modes and a ratio of their occupancies is crucial for calculations of binding free energies. The newly developed method BLUES combines molecular dynamics with nonequilibrium candidate Monte Carlo. Nonequilibrium candidate Monte Carlo generates a plethora of possible binding modes and molecular dynamics enables the system to relax. We used BLUES to investigate binding modes of caffeine in the active site of its metabolizing enzyme Cytochrome P450 1A2 with the aim of elucidating metabolite-formation profiles at different concentrations. Because the activation energies of all sites of metabolism do not show a clear preference for one metabolite over the others, the orientations in the active site must play a key role. In simulations with caffeine located in a spacious pocket above the I-helix, it points N3 and N1 to the heme iron, whereas in simulations where caffeine is in close proximity to the heme N7 and C8 are preferably oriented toward the heme iron. We propose a mechanism where at low caffeine concentrations caffeine binds to the upper part of the active site, leading to formation of the main metabolite paraxanthine. On the other hand, at high concentrations two molecules are located in the active site, forcing one molecule into close proximity to the heme and yielding metabolites theophylline and trimethyluretic acid. Our results offer an explanation of previously published experimental results.

Gaussian accelerated molecular dynamics for elucidation of drug pathways

INTRODUCTION

Understanding pathways and mechanisms of drug binding to receptors is important for rational drug design. Remarkable advances in supercomputing and methodological developments have opened a new era for application of computer simulations in predicting drug-receptor interactions at an atomistic level. Gaussian accelerated molecular dynamics (GaMD) is a computational enhanced sampling technique that works by adding a harmonic boost potential to reduce energy barriers. GaMD enables free energy calculations without the requirement of predefined collective variables. GaMD has proven useful in biomolecular simulations, in particular, the prediction of drug-receptor interactions. Areas covered: Herein, the authors review recent GaMD simulation studies that elucidated pathways of drug binding to proteins including the G-protein-coupled receptors and HIV protease. Expert opinion: GaMD is advantageous for enhanced simulations of, amongst many biological processes, drug binding to target receptors. Compared with conventional molecular dynamics, GaMD speeds up biomolecular simulations by orders of magnitude. GaMD enables routine drug binding simulations using personal computers with GPUs or common computing clusters. GaMD and, more broadly, enhanced sampling simulations are expected to dramatically increase our capabilities to determine the mechanisms of drug binding to a wide range of receptors in the near future. This will greatly facilitate computer-aided drug design.

Multiscale Simulation of Receptor-Drug Association Kinetics: Application to Neuraminidase Inhibitors

A detailed understanding of the drug-receptor association process is of fundamental importance for drug design. Due to the long time scales of typical binding kinetics, the atomistic simulation of the ligand traveling from bulk solution into the binding site is still computationally challenging. In this work, we apply a multiscale approach of combined Molecular Dynamics (MD) and Brownian Dynamics (BD) simulations to investigate association pathway ensembles for the two prominent H1N1 neuraminidase inhibitors oseltamivir and zanamivir. Including knowledge of the approximate binding site location allows for the selective confinement of detailed but expensive MD simulations and application of less demanding BD simulations for the diffusion controlled part of the association pathway. We evaluate a binding criterion based on the residence time of the inhibitor in the binding pocket and compare it to geometric criteria that require prior knowledge about the binding mechanism. The method ranks the association rates of both inhibitors in qualitative agreement with experiment and yields reasonable absolute values depending, however, on the reaction criteria. The simulated association pathway ensembles reveal that, first, ligands are oriented in the electrostatic field of the receptor. Subsequently, a salt bridge is formed between the inhibitor's carboxyl group and neuraminidase residue Arg368, followed by adopting the native binding mode. Unexpectedly, despite oseltamivir's higher overall association rate, the rate into the intermediate salt-bridge state was found to be higher for zanamivir. The present methodology is intrinsically parallelizable and, although computationally demanding, allows systematic binding rate calculation on selected sets of potential drug molecules.