Molecular Dynamics Simulations of Macromolecular Crystals

Functional protein dynamics in a crystal

Proteins are molecular machines and to understand how they work, we need to understand how they move. New pump-probe time-resolved X-ray diffraction methods open up ways to initiate and observe protein motions with atomistic detail in crystals on biologically relevant timescales. However, practical limitations of these experiments demands parallel development of effective molecular dynamics approaches to accelerate progress and extract meaning. Here, we establish robust and accurate methods for simulating dynamics in protein crystals, a nontrivial process requiring careful attention to equilibration, environmental composition, and choice of force fields. With more than seven milliseconds of sampling of a single chain, we identify critical factors controlling agreement between simulation and experiments and show that simulated motions recapitulate ligand-induced conformational changes. This work enables a virtuous cycle between simulation and experiments for visualizing and understanding the basic functional motions of proteins.

Functional Protein Dynamics in a Crystal

Proteins are molecular machines and to understand how they work, we need to understand how they move. New pump-probe time-resolved X-ray diffraction methods open up ways to initiate and observe protein motions with atomistic detail in crystals on biologically relevant timescales. However, practical limitations of these experiments demands parallel development of effective molecular dynamics approaches to accelerate progress and extract meaning. Here, we establish robust and accurate methods for simulating dynamics in protein crystals, a nontrivial process requiring careful attention to equilibration, environmental composition, and choice of force fields. With more than seven milliseconds of sampling of a single chain, we identify critical factors controlling agreement between simulation and experiments and show that simulated motions recapitulate ligand-induced conformational changes. This work enables a virtuous cycle between simulation and experiments for visualizing and understanding the basic functional motions of proteins.

Network pharmacology and in vitro experimental verification unveil glycyrrhizin from glycyrrhiza glabra alleviates acute pancreatitis via modulation of MAPK and STAT3 signaling pathways

Acute pancreatitis (AP) is a severe gastrointestinal inflammatory disease with increasing mortality and morbidity. Glycyrrhiza glabra, commonly known as Liquorice, is a widely used plant containing bioactive compounds like Glycyrrhizin, which possesses diverse medicinal properties such as anti-inflammatory, antioxidant, antiviral, and anticancer activities. The objective of this study is to investigate the active components, relevant targets, and underlying mechanisms of the traditional Chinese medicine Glycyrrhiza glabra in the treatment of AP. Utilizing various computational biology methods, we explored the potential targets and molecular mechanisms through Glycyrrhizin supplementation. Computational results indicated that Glycyrrhizin shows promising pharmacological potential, particularly with mitogen-activated protein kinase 3 (MAPK3) protein (degree: 70), forming stable complexes with Glycyrrhizin through ionic and hydrogen bonding interactions, with a binding free energy (ΔGbind) of -33.01 ± 0.08 kcal/mol. Through in vitro experiments, we validated that Glycyrrhizin improves primary pancreatic acinar cell injury by inhibiting the MAPK/STAT3/AKT signaling pathway. Overall, MAPK3 emerges as a reliable target for Glycyrrhizin's therapeutic effects in AP treatment. This study provides novel insights into the active components and potential targets and molecular mechanisms of natural products.

Computational simulations on the taste mechanism of steviol glycosides based on their interactions with receptor proteins

Steviol glycoside (SG) is a potential natural sugar substitute. The taste of various SG structures differ significantly, while their mechanism has not been thoroughly investigated. To investigate the taste mechanism, molecular docking simulations of SGs with sweet taste receptor TAS1R2 and bitter taste receptor TAS2R4 were conducted. The result suggested that four flexible coils (regions) in TAS1R2 constructed a geometry open pocket in space responsible for the binding of sweeteners. Amino acids that form hydrogen bonds with sweeteners are located in different receptor regions. In bitterness simulation, fewer hydrogen bonds were formed with the increased size of SG molecules. Particularly, there was no interaction between RM and TAS2R4 due to its size, which explains the non-bitterness of RM. Molecular dynamics simulations further indicated that the number of hydrogen bonds between SGs and TAS1R2 was maintained during a simulation time of 50 ns, while sucrose was gradually released from the binding site, leading to the break of interaction. Conclusively, the high sweetness intensity of SG can be attributed to its durative concurrent interaction with the receptor's binding site, and such behavior was determined by the structure feature of SG.

Technologies of targeting histone deacetylase in drug discovery: Current progress and emerging prospects

Histone deacetylases (HDACs) catalyze the hydrolysis of acetyl-l-lysine side chains in histones and non-histones, which are key to epigenetic regulation in humans. Targeting HDACs has emerged as a promising strategy for treating various types of cancer, including myeloma and hematologic malignancies. At present, numerous small molecule inhibitors targeting HDACs are actively being investigated in clinical trials. Despite their potential efficacy in cancer treatment, HDAC inhibitors suffer from multi-directional selectivity and preclinical resistance issues. Hence, developing novel inhibitors based on cutting-edge medicinal chemistry techniques is essential to overcome these limitations and improve clinical outcomes. This manuscript presents an extensive overview of the properties and biological functions of HDACs in cancer, provides an overview of the current state of development and limitations of clinical HDAC inhibitors, and analyzes a range of innovative medicinal chemistry techniques that are applied. These techniques include selective inhibitors, dual-target inhibitors, proteolysis targeting chimeras, and protein-protein interaction inhibitors.

Interaction between hydrophobic chitosan derivative and asphaltene in heavy oil to reduce viscosity of heavy oil

The high viscosity of heavy oil made it difficult to exploit and transport heavy oil in pipeline. In this research, N-[(2-hydroxy-3-trimethylammonium) propyl] O-stearoyl chitosan tetraphenylboride (sc-CTS-st) was synthesized from chitosan, 2, 3-epoxy-propyl trimethyl ammonium chloride, sodium tetraphenylboron and stearyl chloride. sc-CTS-st contains long chain saturated aliphatic hydrocarbon, hydroxyl group and benzene ring, which could be dissolved in heavy oil fully and interacted with asphaltene. At 50 °C, the viscosity of heavy oil could be reduced to 13,800 mPa·s at most, with a viscosity reduction rate of 57.54 %. SEM and XRD showed that sc-CTS-st could affect the supramolecular accumulation structure of asphaltenes. Using FT-IR, sc-CTS-st could interact with asphaltene in the form of hydrogen bonds using the polar parts of the molecule, thereby weakening the self-association between asphaltene molecules. Molecular simulation was used to demonstrate the interaction mechanism between chitosan derivatives and asphaltenes. sc-CTS-st interacted with asphaltene through chemical bonding and influenced the self-association of asphaltene molecules. In addition, the non-polar portion of sc-CTS-st molecules could form a coating on the outside of the asphaltenes stacking structure, thus shielding or reducing the polarity of the stacking structure surface.

Molecular-dynamics simulations of macromolecular diffraction, part II: Analysis of protein crystal simulations

Molecular-dynamics (MD) simulations have contributed substantially to our understanding of protein structure and dynamics, yielding insights into many biological processes including protein folding, drug binding, and mechanisms of protein-protein interactions. Much of what is known about protein structure comes from macromolecular crystallography (MX) experiments. MD simulations of protein crystals are useful in the study of MX because the simulations can be analyzed to calculate almost any crystallographic observable of interest, from atomic coordinates to structure factors and densities, B-factors, multiple conformations and their populations/occupancies, and diffuse scattering intensities. Computing resources and software to support crystalline MD simulations are now readily available to many researchers studying protein structure and dynamics and who may be interested in advanced interpretation of MX data, including diffuse scattering. In this work, we outline methods of analyzing MD simulations of protein crystals and provide accompanying Jupyter notebooks as practical resources for researchers wishing to perform similar analyses on their own systems of interest.

Molecular-dynamics simulations of macromolecular diffraction, part I: Preparation of protein crystal simulations

Molecular-dynamics (MD) simulations of protein crystals enable the prediction of structural and dynamical features of both the protein and the solvent components of macromolecular crystals, which can be validated against diffraction data from X-ray crystallographic experiments. The simulations have been useful for studying and predicting both Bragg and diffuse scattering in protein crystallography; however, the preparation is not yet automated and includes choices and tradeoffs that can impact the results. Here we examine some of the intricacies and consequences of the choices involved in setting up MD simulations of protein crystals for the study of diffraction data, and provide a recipe for preparing the simulations, packaged in an accompanying Jupyter notebook. This article and the accompanying notebook are intended to serve as practical resources for researchers wishing to put these models to work.

LAWS: Local alignment for water sites-Tracking ordered water in simulations

Accurate modeling of protein-water interactions in molecular dynamics (MD) simulations is important for understanding the molecular basis of protein function. Data from x-ray crystallography can be useful in assessing the accuracy of MD simulations, in particular, the locations of crystallographic water sites (CWS) coordinated by the protein. Such a comparison requires special methodological considerations that take into account the dynamic nature of proteins. However, existing methods for analyzing CWS in MD simulations rely on global alignment of the protein onto the crystal structure, which introduces substantial errors in the case of significant structural deviations. Here, we propose a method called local alignment for water sites (LAWS), which is based on multilateration-an algorithm widely used in GPS tracking. LAWS considers the contacts formed by CWS and protein atoms in the crystal structure and uses these interaction distances to track CWS in a simulation. We apply our method to simulations of a protein crystal and to simulations of the same protein in solution. Compared with existing methods, LAWS defines CWS characterized by more prominent water density peaks and a less-perturbed protein environment. In the crystal, we find that all high-confidence crystallographic waters are preserved. Using LAWS, we demonstrate the importance of crystal packing for the stability of CWS in the unit cell. Simulations of the protein in solution and in the crystal share a common set of preserved CWS that are located in pockets and coordinated by residues of the same domain, which suggests that the LAWS algorithm will also be useful in studying ordered waters and water networks in general.

Emerging Time-Resolved X-Ray Diffraction Approaches for Protein Dynamics

Proteins guide the flows of information, energy, and matter that make life possible by accelerating transport and chemical reactions, by allosterically modulating these reactions, and by forming dynamic supramolecular assemblies. In these roles, conformational change underlies functional transitions. Time-resolved X-ray diffraction methods characterize these transitions either by directly triggering sequences of functionally important motions or, more broadly, by capturing the motions of which proteins are capable. To date, most successful have been experiments in which conformational change is triggered in light-dependent proteins. In this review, I emphasize emerging techniques that probe the dynamic basis of function in proteins lacking natively light-dependent transitions and speculate about extensions and further possibilities. In addition, I review how the weaker and more distributed signals in these data push the limits of the capabilities of analytical methods. Taken together, these new methods are beginning to establish a powerful paradigm for the study of the physics of protein function.

Torsion angles to map and visualize the conformational space of a protein

Present understanding of protein structure dynamics trails behind that of static structures. A torsion-angle based approach, called representation of protein entities (RoPE), derives an interpretable conformational space which correlates with data collection temperature, resolution and reaction coordinate. For more complex systems, atomic coordinates fail to separate functional conformational states, which are still preserved by torsion angle-derived space. This indicates that torsion angles are often a more sensitive and biologically relevant descriptor for protein conformational dynamics than atomic coordinates. This article is protected by copyright. All rights reserved.

Integrated Assessment of the Structure and Dynamics of Solid Proteins

Understanding macromolecular function, interactions, and stability hinges on detailed assessment of conformational ensembles. For solid proteins, accurate elucidation of the spatial aspects of dynamics at physiological temperatures is limited by the qualitative character or low abundance of solid-state nuclear magnetic resonance internuclear distance information. Here, we demonstrate access to abundant proton-proton internuclear distances for integrated structural biology and chemistry with unprecedented accuracy. Apart from highest-resolution single-state structures, the exact distances enable molecular dynamics (MD) ensemble simulations orchestrated by a dense network of experimental interproton distance boundaries gathered in the context of their physical lattices. This direct embedding of experimental ensemble distances into MD will provide access to representative, atomic-level spatial details of conformational dynamics in supramolecular assemblies, crystalline and lipid-embedded proteins, and beyond.

Integrating experimental data with molecular simulations to investigate RNA structural dynamics

Conformational dynamics is crucial for ribonucleic acid (RNA) function. Techniques such as nuclear magnetic resonance, cryo-electron microscopy, small- and wide-angle X-ray scattering, chemical probing, single-molecule Förster resonance energy transfer, or even thermal or mechanical denaturation experiments probe RNA dynamics at different time and space resolutions. Their combination with accurate atomistic molecular dynamics (MD) simulations paves the way for quantitative and detailed studies of RNA dynamics. First, experiments provide a quantitative validation tool for MD simulations. Second, available data can be used to refine simulated structural ensembles to match experiments. Finally, comparison with experiments allows for improving MD force fields that are transferable to new systems for which data is not available. Here we review the recent literature and provide our perspective on this field.

Simulating diffraction photographs based on molecular dynamics trajectories of a protein crystal: a new option to examine structure-solving strategies in protein crystallography

A molecular dynamics (MD)-based pipeline has been designed and implemented to emulate the entire process of collecting diffraction photographs and calculating crystallographic structures of proteins from them. Using a structure of lysozyme solved in-house, a supercell comprising 125 (5 × 5 × 5) crystal unit cells containing a total of 1000 protein molecules and explicit interstitial solvent was constructed. For this system, two 300 ns MD trajectories at 298 and 250 K were recorded. A series of snapshots from these trajectories were then used to simulate a fully realistic set of diffraction photographs, which were further fed into the standard pipeline for structure determination. The resulting structures show very good agreement with the underlying MD model not only in terms of coordinates but also in terms of B factors; they are also consistent with the original experimental structure. The developed methodology should find a range of applications, such as optimizing refinement protocols to solve crystal structures and extracting dynamics information from diffraction data or diffuse scattering.

Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes

There is long-standing interest in accurately modeling the structural features of peptides bound and presented by class I MHC proteins. This interest has grown with the advent of rapid genome sequencing and the prospect of personalized, peptide-based cancer vaccines, as well as the development of molecular and cellular therapeutics based on T cell receptor recognition of peptide-MHC. However, while the speed and accessibility of peptide-MHC modeling has improved substantially over the years, improvements in accuracy have been modest. Accuracy is crucial in peptide-MHC modeling, as T cell receptors are highly sensitive to peptide conformation and capturing fine details is therefore necessary for useful models. Studying nonameric peptides presented by the common class I MHC protein HLA-A*02:01, here we addressed a key question common to modern modeling efforts: from a set of models (or decoys) generated through conformational sampling, which is best? We found that the common strategy of decoy selection by lowest energy can lead to substantial errors in predicted structures. We therefore adopted a data-driven approach and trained functions capable of predicting near native decoys with exceptionally high accuracy. Although our implementation is limited to nonamer/HLA-A*02:01 complexes, our results serve as an important proof of concept from which improvements can be made and, given the significance of HLA-A*02:01 and its preference for nonameric peptides, should have immediate utility in select immunotherapeutic and other efforts for which structural information would be advantageous.

Hydration and its Hydrogen Bonding State on a Protein Surface in the Crystalline State as Revealed by Molecular Dynamics Simulation

Protein hydration is crucial for the stability and molecular recognition of a protein. Water molecules form a hydration water network on a protein surface via hydrogen bonds. This study examined the hydration structure and hydrogen bonding state of a protein, staphylococcal nuclease, at various hydration levels in its crystalline state by all-atom molecular dynamics (MD) simulation. Hydrophilic residues were more hydrated than hydrophobic residues. As the water content increases, both types of residues were uniformly more hydrated. The number of hydrogen bonds per single water asymptotically approaches 4, the same as bulk water. The distances and angles of hydrogen bonds in hydration water in the protein crystal were almost the same as those in the tetrahedral structure of bulk water regardless of the hydration level. The hydrogen bond structure of hydration water observed by MD simulations of the protein crystalline state was compared to the Hydrogen and Hydration Database for Biomolecule from experimental protein crystals.

Computer-aided understanding and engineering of enzymatic selectivity

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

Targeting Bromodomain and Extraterminal Proteins for Drug Discovery: From Current Progress to Technological Development

Bromodomain and extraterminal (BET) proteins bind acetylated lysine residues in histones and nonhistone proteins via tandem bromodomains and regulate chromatin dynamics, cellular processes, and disease procession. Thus targeting BET proteins is a promising strategy for treating various diseases, especially malignant tumors and chronic inflammation. Many pan-BET small-molecule inhibitors have been described, and some of them are in clinical evaluation. Nevertheless, the limited clinical efficacy of the current BET inhibitors is also evident and has inspired the development of new technologies to improve their clinical outcomes and minimize unwanted side effects. In this Review, we summarize the latest protein characteristics and biological functions of BRD4 as an example of BET proteins, analyze the clinical development status and preclinical resistance mechanisms, and discuss recent advances in BRD4-selective inhibitors, dual-target BET inhibitors, proteolysis targeting chimera degraders, and protein-protein interaction inhibitors.

Persistent Protein Motions in a Rugged Energy Landscape Revealed by Normal Mode Ensemble Analysis

Proteins are allosteric machines that couple motions at distinct, often distant, sites to control biological function. Low-frequency structural vibrations are a mechanism of this long-distance connection and are often used computationally to predict correlations, but experimentally identifying the vibrations associated with specific motions has proved challenging. Spectroscopy is an ideal tool to explore these excitations, but measurements have been largely unable to identify important frequency bands. The result is at odds with some previous calculations and raises the question what methods could successfully characterize protein structural vibrations. Here we show the lack of spectral structure arises in part from the variations in protein structure as the protein samples the energy landscape. However, by averaging over the energy landscape as sampled using an aggregate 18.5 μs of all-atom molecular dynamics simulation of hen egg white lysozyme and normal-mode analyses, we find vibrations with large overlap with functional displacements are surprisingly concentrated in narrow frequency bands. These bands are not apparent in either the ensemble averaged vibrational density of states or isotropic absorption. However, in the case of the ensemble averaged anisotropic absorption, there is persistent spectral structure and overlap between this structure and the functional displacement frequency bands. We systematically lay out heuristics for calculating the spectra robustly, including the need for statistical sampling of the protein and inclusion of adequate water in the spectral calculation. The results show the congested spectrum of these complex molecules obscures important frequency bands associated with function and reveal a method to overcome this congestion by combining structurally sensitive spectroscopy with robust normal mode ensemble analysis.

Advances in methods for atomic resolution macromolecular structure determination

Recent technical advances have dramatically increased the power and scope of structural biology. New developments in high-resolution cryo-electron microscopy, serial X-ray crystallography, and electron diffraction have been especially transformative. Here we highlight some of the latest advances and current challenges at the frontiers of atomic resolution methods for elucidating the structures and dynamical properties of macromolecules and their complexes.

Diffuse X-ray scattering from correlated motions in a protein crystal

Protein dynamics are integral to biological function, yet few techniques are sensitive to collective atomic motions. A long-standing goal of X-ray crystallography has been to combine structural information from Bragg diffraction with dynamic information contained in the diffuse scattering background. However, the origin of macromolecular diffuse scattering has been poorly understood, limiting its applicability. We present a finely sampled diffuse scattering map from triclinic lysozyme with unprecedented accuracy and detail, clearly resolving both the inter- and intramolecular correlations. These correlations are studied theoretically using both all-atom molecular dynamics and simple vibrational models. Although lattice dynamics reproduce most of the diffuse pattern, protein internal dynamics, which include hinge-bending motions, are needed to explain the short-ranged correlations revealed by Patterson analysis. These insights lay the groundwork for animating crystal structures with biochemically relevant motions.

Protein motion in crystals causes diffuse X-ray scattering, which so far has been very challenging to measure and interpret. Here the authors present a finely sampled diffuse scattering map from triclinic lysozyme, which allows them to resolve inter- and intramolecular correlations and they further analyze the maps using all-atom molecular dynamics simulations and simple vibrational models, revealing the contribution of internal protein motion.