Long-timescale molecular-dynamics simulations of the major urinary protein provide atomistic interpretations of the unusual thermodynamics of ligand binding

Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables

Molecular dynamics simulations of microscopic phenomena are limited by the short integration time steps which are required for numerical stability but which limit the practically achievable simulation time scales. Collective variable (CV) enhanced sampling techniques apply biases to predefined collective coordinates to promote barrier crossing, phase space exploration, and sampling of rare events. The efficacy of these techniques is contingent on the selection of good CVs correlated with the molecular motions governing the long-time dynamical evolution of the system. In this work, we introduce Girsanov Reweighting Enhanced Sampling Technique (GREST) as an adaptive sampling scheme that interleaves rounds of data-driven slow CV discovery and enhanced sampling along these coordinates. Since slow CVs are inherently dynamical quantities, a key ingredient in our approach is the use of both thermodynamic and dynamical Girsanov reweighting corrections for rigorous estimation of slow CVs from biased simulation data. We demonstrate our approach on a toy 1D 4-well potential, a simple biomolecular system alanine dipeptide, and the Trp-Leu-Ala-Leu-Leu (WLALL) pentapeptide. In each case GREST learns appropriate slow CVs and drives sampling of all thermally accessible metastable states starting from zero prior knowledge of the system. We make GREST accessible to the community via a publicly available open source Python package.

Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics

Understanding allostery in enzymes and tools to identify it offer promising alternative strategies to inhibitor development. Through a combination of equilibrium and nonequilibrium molecular dynamics simulations, we identify allosteric effects and communication pathways in two prototypical class A β-lactamases, TEM-1 and KPC-2, which are important determinants of antibiotic resistance. The nonequilibrium simulations reveal pathways of communication operating over distances of 30 Å or more. Propagation of the signal occurs through cooperative coupling of loop dynamics. Notably, 50% or more of clinically relevant amino acid substitutions map onto the identified signal transduction pathways. This suggests that clinically important variation may affect, or be driven by, differences in allosteric behavior, providing a mechanism by which amino acid substitutions may affect the relationship between spectrum of activity, catalytic turnover, and potential allosteric behavior in this clinically important enzyme family. Simulations of the type presented here will help in identifying and analyzing such differences.

Binding Thermodynamics and Interaction Patterns of Inhibitor-Major Urinary Protein-I Binding from Extensive Free-Energy Calculations: Benchmarking AMBER Force Fields

Mouse major urinary protein (MUP) plays a key role in the pheromone communication system. The one-end-closed β-barrel of MUP-I forms a small, deep, and hydrophobic central cavity, which could accommodate structurally diverse ligands. Previous computational studies employed old protein force fields and short simulation times to determine the binding thermodynamics or investigated only a small number of structurally similar ligands, which resulted in sampled regions far from the experimental structure, nonconverged sampling outcomes, and limited understanding of the possible interaction patterns that the cavity could produce. In this work, extensive end-point and alchemical free-energy calculations with advanced protein force fields were performed to determine the binding thermodynamics of a series of MUP-inhibitor systems and investigate the inter- and intramolecular interaction patterns. Three series of inhibitors with a total of 14 ligands were simulated. We independently simulated the MUP-inhibitor complexes under two advanced AMBER force fields. Our benchmark test showed that the advanced AMBER force fields including AMBER19SB and AMBER14SB provided better descriptions of the system, and the backbone root-mean-square deviation (RMSD) was significantly lowered compared with previous computational studies with old protein force fields. Surprisingly, although the latest AMBER force field AMBER19SB provided better descriptions of various observables, it neither improved the binding thermodynamics nor lowered the backbone RMSD compared with the previously proposed and widely used AMBER14SB. The older but widely used AMBER14SB actually achieved better performance in the prediction of binding affinities from the alchemical and end-point free-energy calculations. We further analyzed the protein-ligand interaction networks to identify important residues stabilizing the bound structure. Six residues including PHE38, LEU40, PHE90, ALA103, LEU105, and TYR120 were found to contribute the most significant part of protein-ligand interactions, and 10 residues were found to provide favorable interactions stabilizing the bound state. The two AMBER force fields gave extremely similar interaction networks, and the secondary structures also showed similar behavior. Thus, the intra- and intermolecular interaction networks described with the two AMBER force fields are similar. Therefore, AMBER14SB could still be the default option in free-energy calculations to achieve highly accurate binding thermodynamics and interaction patterns.

Identification of the Initial Steps in Signal Transduction in the α4β2 Nicotinic Receptor: Insights from Equilibrium and Nonequilibrium Simulations

Nicotinic acetylcholine receptors (nAChRs) modulate synaptic transmission in the nervous system. These receptors have emerged as therapeutic targets in drug discovery for treating several conditions, including Alzheimer's disease, pain, and nicotine addiction. In this in silico study, we use a combination of equilibrium and nonequilibrium molecular dynamics simulations to map dynamic and structural changes induced by nicotine in the human α4β2 nAChR. They reveal a striking pattern of communication between the extracellular binding pockets and the transmembrane domains (TMDs) and show the sequence of conformational changes associated with the initial steps in this process. We propose a general mechanism for signal transduction for Cys-loop receptors: the mechanistic steps for communication proceed firstly through loop C in the principal subunit, and are subsequently transmitted, gradually and cumulatively, to loop F of the complementary subunit, and then to the TMDs through the M2-M3 linker.

STARD6 on steroids: solution structure, multiple timescale backbone dynamics and ligand binding mechanism

START domain proteins are conserved α/β helix-grip fold that play a role in the non-vesicular and intracellular transport of lipids and sterols. The mechanism and conformational changes permitting the entry of the ligand into their buried binding sites is not well understood. Moreover, their functions and the identification of cognate ligands is still an active area of research. Here, we report the solution structure of STARD6 and the characterization of its backbone dynamics on multiple time-scales through ¹⁵N spin-relaxation and amide exchange studies. We reveal for the first time the presence of concerted fluctuations in the Ω₁ loop and the C-terminal helix on the microsecond-millisecond time-scale that allows for the opening of the binding site and ligand entry. We also report that STARD6 binds specifically testosterone. Our work represents a milestone for the study of ligand binding mechanism by other START domains and the elucidation of the biological function of STARD6.

Effect of ligand binding on the dynamics of trypsin. Comparison of different approaches

The intramolecular signal transduction induced by the binding of ligands to trypsin was investigated by molecular dynamics simulations. Ligand binding changes the residue-residue interaction energies and suppresses the mobility of loops that are in direct contact with the ligand. The reduced mobility of these loops results in the altered flexibility of the nearby loops and thereby transmits the information from ligand binding site to the remote sites. The analysis of the flexibility of all residues confirmed the coupling between loops L1 (185-188) and L2 (221-224) and the residues in the active center. The significance of S1 pocket residues for the signal transduction from the active center to the substrate-binding site was confirmed by the dynamical network and covariance matrix analyses. Gaussian network model and principal component analysis demonstrated that the active center residues had zero amplitude in the slowest fluctuations acting as hinges or anchors. Overall, our results provide a new insight into protein-ligand interactions and show how the allosteric signaling may occur.

Multiple pathways promote dynamical coupling between catalytic domains in Escherichia coli prolyl-tRNA synthetase

Aminoacyl-tRNA synthetases are multidomain enzymes that catalyze covalent attachment of amino acids to their cognate tRNA. Cross-talk between functional domains is a prerequisite for this process. In this study, we investigate the molecular mechanism of site-to-site communication in Escherichia coli prolyl-tRNA synthetase (Ec ProRS). Earlier studies have demonstrated that evolutionarily conserved and/or co-evolved residues that are engaged in correlated motion are critical for the propagation of functional conformational changes from one site to another in modular proteins. Here, molecular simulation and bioinformatics-based analysis were performed to identify dynamically coupled and evolutionarily constrained residues that form contiguous pathways of residue-residue interactions between the aminoacylation and editing domains of Ec ProRS. The results of this study suggest that multiple pathways exist between these two domains to maintain the dynamic coupling essential for enzyme function. Moreover, residues in these interaction networks are generally highly conserved. Site-directed changes of on-pathway residues have a significant impact on enzyme function and dynamics, suggesting that any perturbation along these pathways disrupts the native residue-residue interactions that are required for effective communication between the two functional domains. Free energy analysis revealed that communication between residues within a pathway and cross-talk between pathways are important for coordinating functions of different domains of Ec ProRS for efficient catalysis.

Dynamics of endoglucanase catalytic domains: implications towards thermostability

Thermostable endoglucanases play a crucial role in the production of biofuels to breakdown plant cellulose. Analyzing their structure-dynamics relationship can inform about the origins of their thermostability. Although tertiary structures of many endoglucanase proteins are available, the relationship between thermostability, structure, and dynamics is not explored fully. We have generated elastic network models for thermostable and mesostable endoglucanases with the (αβ)₈ fold in substrate bound and unbound states. The comparative analyses shed light on the relation between protein dynamics, thermostability, and substrate binding. We observed specific differences in the dynamic behavior of catalytic residues in slow modes: while both the nucleophile and the acid/base donor residues show positively correlated motions in the thermophile, their dynamics is uncoupled in the mesophile. Our proof-of-concept comparison study suggests that global dynamics can be harnessed to further our understanding of thermostability.

Characterization of the impact of alternative splicing on protein dynamics: the cases of glutathione S-transferase and ectodysplasin-A isoforms

Recent studies have shown how alternative splicing (AS), the process by which eukaryotic genes express more than one product, affects protein sequence and structure. However, little information is available on the impact of AS on protein dynamics, a property fundamental for protein function. In this work, we have addressed this issue using molecular dynamics simulations of the isoforms of two model proteins: glutathione S-transferase and ectodysplasin-A. We have found that AS does not have a noticeable impact on global or local structure fluctuations. We have also found that, quite interestingly, AS has a significant effect on the coupling between key structural elements such as surface cavities. Our results provide the first atom-level view of the impact of AS on protein dynamics, as far as we know. They can contribute to refine our present view of the relationship between AS and protein disorder and, more importantly, they reveal how AS may modify structural dynamic couplings in proteins.

Molecular dynamics simulations reveal specific interactions of post-translational palmitoyl modifications with rhodopsin in membranes

We present a detailed analysis of the behavior of the highly flexible post-translational lipid modifications of rhodopsin from multiple-microsecond all-atom molecular dynamics simulations. Rhodopsin was studied in a realistic membrane environment that includes cholesterol, as well as saturated and polyunsaturated lipids with phosphocholine and phosphoethanolamine headgroups. The simulation reveals striking differences between the palmitoylations at Cys322 and Cys323 as well as between the palmitoyl chains and the neighboring lipids. Notably the palmitoyl group at Cys322 shows considerably greater contact with helix H1 of rhodopsin, yielding frequent chain upturns with longer reorientational correlation times, and relatively low order parameters. While the palmitoylation at Cys323 makes fewer protein contacts and has increased order compared to Cys322, it nevertheless exhibits greater flexibility with smaller order parameters than the stearoyl chains of the surrounding lipids. The dynamical structure of the palmitoylations-as well as their extensive fluctuations-suggests a complex function for the post-translational modifications in rhodopsin and potentially other G protein-coupled receptors, going beyond their role as membrane anchoring elements. Rather, we propose that the palmitoylation at Cys323 has a potential role as a lipid anchor, whereas the palmitoyl-protein interaction observed for Cys322 suggests a more specific interaction that affects the stability of the dark state of rhodopsin.

Statistical mechanics and molecular dynamics in evaluating thermodynamic properties of biomolecular recognition

Molecular recognition plays a central role in biochemical processes. Although well studied, understanding the mechanisms of recognition is inherently difficult due to the range of potential interactions, the molecular rearrangement associated with binding, and the time and length scales involved. Computational methods have the potential for not only complementing experiments that have been performed, but also in guiding future ones through their predictive abilities. In this review, we discuss how molecular dynamics (MD) simulations may be used in advancing our understanding of the thermodynamics that drive biomolecular recognition. We begin with a brief review of the statistical mechanics that form a basis for these methods. This is followed by a description of some of the most commonly used methods: thermodynamic pathways employing alchemical transformations and potential of mean force calculations, along with end-point calculations for free energy differences, and harmonic and quasi-harmonic analysis for entropic calculations. Finally, a few of the fundamental findings that have resulted from these methods are discussed, such as the role of configurational entropy and solvent in intermolecular interactions, along with selected results of the model system T4 lysozyme to illustrate potential and current limitations of these methods.

Longer simulations sample larger subspaces of conformations while maintaining robust mechanisms of motion

Recent studies suggest that protein motions observed in molecular simulations are related to biochemical activities, although the computed time scales do not necessarily match those of the experimentally observed processes. The molecular origin of this conflicting observation is explored here for a test protein, cyanovirin-N (CV-N), through a series of molecular dynamics simulations that span a time range of three orders of magnitude up to 0.4 μs. Strikingly, increasing the simulation time leads to an approximately uniform amplification of the motional sizes, while maintaining the same conformational mechanics. Residue fluctuations exhibit amplitudes of 1-2 Å in the nanosecond simulations, whereas their average sizes increase by a factor of 4-5 in the microsecond regime. The mean-square displacements averaged over all residues (y) exhibit a power law dependence of the form y ∝ x(0.26) on the simulation time (x). Essential dynamics analysis of the trajectories, on the other hand, demonstrates that CV-N has robust preferences to undergo specific types of motions that already can be detected at short simulation times, provided that multiple runs are performed and carefully analyzed.

PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry measurements

Binding affinity prediction is one of the most critical components to computer-aided structure-based drug design. Despite advances in first-principle methods for predicting binding affinity, empirical scoring functions that are fast and only relatively accurate are still widely used in structure-based drug design. With the increasing availability of X-ray crystallographic structures in the Protein Data Bank and continuing application of biophysical methods such as isothermal titration calorimetry to measure thermodynamic parameters contributing to binding free energy, sufficient experimental data exists that scoring functions can now be derived by separating enthalpic (ΔH) and entropic (TΔS) contributions to binding free energy (ΔG). PHOENIX, a scoring function to predict binding affinities of protein-ligand complexes, utilizes the increasing availability of experimental data to improve binding affinity predictions by the following: model training and testing using high-resolution crystallographic data to minimize structural noise, independent models of enthalpic and entropic contributions fitted to thermodynamic parameters assumed to be thermodynamically biased to calculate binding free energy, use of shape and volume descriptors to better capture entropic contributions. A set of 42 descriptors and 112 protein-ligand complexes were used to derive functions using partial least-squares for change of enthalpy (ΔH) and change of entropy (TΔS) to calculate change of binding free energy (ΔG), resulting in a predictive r2 (r(pred)2) of 0.55 and a standard error (SE) of 1.34 kcal/mol. External validation using the 2009 version of the PDBbind "refined set" (n = 1612) resulted in a Pearson correlation coefficient (R(p)) of 0.575 and a mean error (ME) of 1.41 pK(d). Enthalpy and entropy predictions were of limited accuracy individually. However, their difference resulted in a relatively accurate binding free energy. While the development of an accurate and applicable scoring function was an objective of this study, the main focus was evaluation of the use of high-resolution X-ray crystal structures with high-quality thermodynamic parameters from isothermal titration calorimetry for scoring function development. With the increasing application of structure-based methods in molecular design, this study suggests that using high-resolution crystal structures, separating enthalpy and entropy contributions to binding free energy, and including descriptors to better capture entropic contributions may prove to be effective strategies toward rapid and accurate calculation of binding affinity.