A Kepler Workflow Tool for Reproducible AMBER GPU Molecular Dynamics

Structural Analysis of Non-native Peptide-Based Catalysts Using 2D NMR-Guided MD Simulations

Proteins and enzymes generally achieve their functions by creating well-defined 3D architectures that pre-organize reactive functionalities. Mimicking this approach to supramolecular pre-organization is leading to the development of highly versatile artificial chemical environments, including new biomaterials, medicines, artificial enzymes, and enzyme-like catalysts. The use of β-turn and α-helical motifs is one approach that enables the precise placement of reactive functional groups to enable selective substrate activation and reactivity/selectivity that approaches natural enzymes. Our recent work has demonstrated that helical peptides can serve as scaffolds for pre-organizing two reactive groups to achieve enzyme-like catalysis. In this study, we used CYANA and AmberTools to develop a computational approach for determining how the structure of our peptide catalysts can lead to enhancements in reactivity. These results support our hypothesis that the bifunctional nature of the peptide enables catalysis by pre-organizing the two catalysts in reactive conformations that accelerate catalysis by proximity. We also present evidence that the low reactivity of monofunctional peptides can be attributed to interactions between the peptide-bound catalyst and the helical backbone, which are not observed in the bifunctional peptide.

Computational molecular interaction between SARS-CoV-2 main protease and theaflavin digallate using free energy perturbation and molecular dynamics

Our objective was to identify the molecule which can inhibit SARS-CoV-2 main protease and can be easily procured. Natural products may provide such molecules and can supplement the current custom chemical synthesis-based drug discovery for this objective. A combination of docking approaches, scoring functions, classical molecular dynamic simulation, binding pose metadynamics, and free energy perturbation calculations have been employed in this study. Theaflavin digallate has been observed in top-scoring compounds after the three independent virtual screening simulations of 598435 compounds (unique 27256 chemical entities). The main protease-theaflavin digallate complex interacts with critical active site residues of the main protease in molecular dynamics simulation independent of the explored computational framework, simulation time, initial structure, and force field used. Theaflavin digallate forms approximately three hydrogen bonds with Glutamate166 of main protease, primarily through hydroxyl groups in the benzene ring of benzo(7)annulen-6-one, along with other critical residues. Glu166 is the most critical amino acid for main protease dimerization, which is necessary for catalytic activity. The estimated binding free energy, calculated by Amber and Schrodinger MMGBSA module, reflects a high binding free energy between theaflavin digallate and main protease. Binding pose metadynamics simulation shows the highly persistent H-bond and a stable pose for the theaflavin digallate-main protease complex. Using method control, experimental controls, and test set, alchemical transformation studies confirm high relative binding free energy of theaflavin digallate with the main protease. Computational molecular interaction suggests that theaflavin digallate can inhibit the main protease of SARS-CoV-2.

Pre-exascale HPC approaches for molecular dynamics simulations. Covid-19 research: A use case

Exascale computing has been a dream for ages and is close to becoming a reality that will impact how molecular simulations are being performed, as well as the quantity and quality of the information derived for them. We review how the biomolecular simulations field is anticipating these new architectures, making emphasis on recent work from groups in the BioExcel Center of Excellence for High Performance Computing. We exemplified the power of these simulation strategies with the work done by the HPC simulation community to fight Covid-19 pandemics. This article is categorized under:Data Science > Computer Algorithms and ProgrammingData Science > Databases and Expert SystemsMolecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods.

A new thermophilic extradiol dioxygenase promises biodegradation of catecholic pollutants

Extradiol dioxygenases (EDOs) catalyze the meta cleavage of catechol into 2-hydroxymuconaldehyde, a critical step in the degradation of aromatic compounds in the environment. In the present work, a novel thermophilic extradiol dioxygenase from Thermomonospora curvata DSM43183 was cloned, expressed, and characterized by phylogenetic and biochemical analyses. This enzyme exhibited excellent thermo-tolerance, displaying optimal activity at 50 °C, remaining >40% activity at 70 °C. Structural modeling and molecular docking demonstrated that both active center and pocket-construction loops locate at the C-terminal domain. Site-specific mutants D285A, H205V, F301V based on a rational design were obtained to widen the entrance of substrates; resulting in significantly improved catalytic performance for all the 3 mutants. Compared to the wild-type, the mutant D285A showed remarkably improved activities with respect to the 3,4-dihydroxyphenylacetic acid, catechol, and 3-chlorocatechol, by 17.7, 6.9, and 3.7-fold, respectively. The results thus verified the effectiveness of modeling guided design; and confirmed that the C-terminal loop structure indeed plays a decisive role in determining catalytic ring-opening efficiency and substrate specificity of the enzyme. This study provided a novel thermostable dioxygenase with a broad substrate promiscuity for detoxifying environmental pollutants and provided a new thinking for further enzyme engineering of EDOs.

CAN: A new program to streamline preparation of molecular coordinate files for molecular dynamics simulations

Preparing molecular coordinate files for molecular dynamics (MD) simulations can be a very time-consuming process. Herein we present the development of a user-friendly program that drastically reduces the time required to prepare these molecular coordinate files for MD software packages such as AmberTools. Our program, known as charge atomtype naming (CAN), creates and uses a library of structures such as amino acid monomers to update the charge, atom type, and name of atoms in any molecular structure (mol2) file. We demonstrate the utility of this new program by rapidly preparing structural files for MD simulations for polypeptides ranging from small molecules to large protein structures. Both native and non-native amino acid residues are easily handled by this new program.

Making it Rain: Cloud-Based Molecular Simulations for Everyone

We present a user-friendly front-end for running molecular dynamics (MD) simulations using the OpenMM toolkit on the Google Colab framework. Our goals are (1) to highlight the usage of a cloud-computing scheme for educational purposes for a hands-on approach when learning MD simulations and (2) to exemplify how low-income research groups can perform MD simulations in the microsecond time scale. We hope this work facilitates teaching and learning of molecular simulation throughout the community.

Plant Sterol Clustering Correlates with Membrane Microdomains as Revealed by Optical and Computational Microscopy

Local inhomogeneities in lipid composition play a crucial role in the regulation of signal transduction and membrane traffic. This is particularly the case for plant plasma membrane, which is enriched in specific lipids, such as free and conjugated forms of phytosterols and typical phytosphingolipids. Nevertheless, most evidence for microdomains in cells remains indirect, and the nature of membrane inhomogeneities has been difficult to characterize. We used a new push–pull pyrene probe and fluorescence lifetime imaging microscopy (FLIM) combined with all-atom multiscale molecular dynamics simulations to provide a detailed view on the interaction between phospholipids and phytosterol and the effect of modulating cellular phytosterols on membrane-associated microdomains and phase separation formation. Our understanding of the organization principles of biomembranes is limited mainly by the challenge to measure distributions and interactions of lipids and proteins within the complex environment of living cells. Comparing phospholipids/phytosterol compositions typical of liquid-disordered (Ld) and liquid-ordered (Lo) domains, we furthermore show that phytosterols play crucial roles in membrane homeostasis. The simulation work highlights how state-of-the-art modeling alleviates some of the prior concerns and how unrefuted discoveries can be made through a computational microscope. Altogether, our results support the role of phytosterols in the lateral structuring of the PM of plant cells and suggest that they are key compounds for the formation of plant PM microdomains and the lipid-ordered phase.

Fighting viruses with computers, right now

The synergistic conjunction of various technological revolutions with the accumulated knowledge and workflows is rapidly transforming several scientific fields. Particularly, Virology can now feed from accurate physical models, polished computational tools, and massive computational power to readily integrate high-resolution structures into biological representations of unprecedented detail. That preparedness allows for the first time to get crucial information for vaccine and drug design from in-silico experiments against emerging pathogens of worldwide concern at relevant action windows. The present work reviews some of the main milestones leading to these breakthroughs in Computational Virology, providing an outlook for future developments in capacity building and accessibility to computational resources.

A Web-Based Tool for Simulating Molecular Dynamics in Cloud Environments

Molecular dynamics simulations take advantage of supercomputing environments, e.g., to solve molecular systems composed of millions of atoms. Supercomputers are increasing their computing and memory power while they are becoming more complex with the introduction of Multi-GPU environments. Despite these capabilities, the molecular dynamics simulation is not an easy process. It requires properly preparing the simulation data and configuring the entire operation, e.g., installing and managing specific software packages to take advantage of the potential of Multi-GPU supercomputers. We propose a web-based tool that facilitates the management of molecular dynamics workflows to be used in combination with a multi-GPU cloud environment. The tool allows users to perform data pipeline and run the simulation in a cloud environment, even for those who are not specialized in the development of molecular dynamics simulators or cloud management.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.

MDMS: Software Facilitating Performing Molecular Dynamics Simulations

Molecular Dynamics Made Simple (MDMS) is software that facilitates performing molecular dynamics (MD) simulations of solvated protein/protein-ligand complexes with Amber, one of the most popular MD codes. It guides users through the whole process of running MD starting with choosing a protein structure, preparing the model, parametrization of the system, establishing parameters for controlling MD, and finally running simulations. By accommodating every step required for running MD, this software ensures that the simulations performed by a user will provide as realistic insight as it is possible. Its sequential structure and a text-based interface ensure ease of use, while the flexibility required for complex cases is still preserved. MDMS also provides a very time-efficient and streamlined method to start MD simulations, which makes it a feasible tool for both novices and experienced computational chemists. © 2019 Wiley Periodicals, Inc.

Scalable Workflow-Driven Hydrologic Analysis in HydroFrame

The HydroFrame project is a community platform designed to facilitate integrated hydrologic modeling across the US. As a part of HydroFrame, we seek to design innovative workflow solutions that create pathways to enable hydrologic analysis for three target user groups: the modeler, the analyzer, and the domain science educator. We present the initial progress on the HydroFrame community platform using an automated Kepler workflow. This workflow performs end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization. We demonstrate how different modules of the workflow can be reused and repurposed for the three target user groups. The Kepler workflow ensures complete reproducibility through a built-in provenance framework that collects workflow specific parameters, software versions, and hardware system configuration. In addition, we aim to optimize the utilization of large-scale computational resources to adjust to the needs of all three user groups. Towards this goal, we present a design that leverages provenance data and machine learning techniques to predict performance and forecast failures using an automatic performance collection component of the pipeline.

Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations

Custom-designed ligand-binding proteins represent a promising class of macromolecules with exciting applications toward the design of new enzymes or the engineering of antibodies and small-molecule recruited proteins for therapeutic interventions. However, several challenges remain in designing a protein sequence such that the binding site organization results in high affinity interaction with a bound ligand. Here, we study the dynamics of explicitly solvated designed proteins through all-atom molecular dynamics (MD) simulations to gain insight into the causes that lead to the low affinity or instability of most of these designs, despite the prediction of their success by the computational design methodology. Simulations ranging from 500 to 1000 ns per replicate were conducted on 37 designed protein variants encompassing two distinct folds and a range of ligand affinities, resulting in more than 180 μs of combined sampling. The simulations provide retrospective insights into the properties affecting ligand affinity that can prove useful in guiding further steps of design optimization. Features indicate that entropic components are particularly important for affinity, which are not easily incorporated in the empirical models often used in design protocols. Additionally, we demonstrate that the application of machine learning approaches built upon the output from the simulations can help discriminate between successful and failed binders, such that MD could act as a screening step in protein design, resulting in a more efficient process.

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

Multiscale Methods in Drug Design Bridge Chemical and Biological Complexity in the Search for Cures

Drug action is inherently multiscale: it connects molecular interactions to emergent properties at cellular and larger scales. Simulation techniques at each of these different scales are already central to drug design and development, but methods capable of connecting across these scales will extend understanding of complex mechanisms and the ability to predict biological effects. Improved algorithms, ever-more-powerful computing architectures and the accelerating growth of rich datasets are driving advances in multiscale modeling methods capable of bridging chemical and biological complexity from the atom to the cell. Particularly exciting is the development of highly detailed, structure-based, physical simulations of biochemical systems, which are now able to access experimentally relevant timescales for large systems and, at the same time, achieve unprecedented accuracy. In this Perspective, we discuss how emerging data-rich, physics-based multiscale approaches are of the cusp of realizing long-promised impact in the discovery, design and development of novel therapeutics. We highlight emerging methods and applications in this growing field, and outline how different scales can be combined in practical modelling and simulation strategies.

Ensemble Docking in Drug Discovery

Ensemble docking corresponds to the generation of an "ensemble" of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.

A molecular dynamics study of the complete binding process of meropenem to New Delhi metallo-β-lactamase 1

The mechanism of substrate hydrolysis of New Delhi metallo-β-lactamase 1 (NDM-1) has been reported, but the process in which NDM-1 captures and transports the substrate into its active center remains unknown. In this study, we investigated the process of the substrate entry into the NDM-1 activity center through long unguided molecular dynamics simulations using meropenem as the substrate. A total of 550 individual simulations were performed, each of which for 200 ns, and 110 of them showed enzyme-substrate binding events. The results reveal three categories of relatively persistent and noteworthy enzyme-substrate binding configurations, which we call configurations A, B, and C. We performed binding free energy calculations of the enzyme-substrate complexes of different configurations using the molecular mechanics Poisson-Boltzmann surface area method. The role of each residue of the active site in binding the substrate was investigated using energy decomposition analysis. The simulated trajectories provide a continuous atomic-level view of the entire binding process, revealing potentially valuable regions where the enzyme and the substrate interact persistently and five possible pathways of the substrate entering into the active center, which were validated using well-tempered metadynamics. These findings provide important insights into the binding mechanism of meropenem to NDM-1, which may provide new prospects for the design of novel metallo-β-lactamase inhibitors and enzyme-resistant antibiotics.