Exploring the Limits of the Generalized CHARMM and AMBER Force Fields through Predictions of Hydration Free Energy of Small Molecules.

Accurate force field parameters, potential energy functions, and receptor-ligand models are essential for modeling the solvation and binding of drug-like molecules to a receptor. A large and ever-growing chemical space of medicinally relevant scaffolds has also required these factors, especially force field parameters, to be highly transferable. Generalized force fields such as the CHARMM General Force Field (CGenFF) and the generalized AMBER force field (GAFF) have accomplished this feat along with other contemporaneous ones like OPLS. Here, we analyze the limits in the parametrization of drug-like small molecules by CGenFF and GAFF in terms of the various functional groups represented within them. Specifically, we link the presence of specific functional groups to the error in the absolute hydration free energy of over 600 small molecules, predicted by alchemical free energy methods implemented in the CHARMM program. Our investigation reveals that molecules with (i) a nitro group in CGenFF and GAFF are, respectively, over- or undersolubilized in aqueous medium, (ii) amine groups are undersolubilized more so in CGenFF than in GAFF, and (iii) carboxyl groups are more oversolubilized in GAFF than in CGenFF. We present our analyses of the potential factors underlying these trends. We also showcase the use of a machine-learning-based approach combined with the SHapley Additive exPlanations framework to attribute these trends to specific functional groups, which can be easily adopted to explore the limits of other general force fields.

pyCHARMM: Embedding CHARMM Functionality in a Python Framework.

CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.

Total free energy analysis of fully hydrated proteins.

The total free energy of a hydrated biomolecule and its corresponding decomposition of energy and entropy provides detailed information about regions of thermodynamic stability or instability. The free energies of four hydrated globular proteins with different net charges are calculated from a molecular dynamics simulation, with the energy coming from the system Hamiltonian and entropy using multiscale cell correlation. Water is found to be most stable around anionic residues, intermediate around cationic and polar residues, and least stable near hydrophobic residues, especially when more buried, with stability displaying moderate entropy-enthalpy compensation. Conversely, anionic residues in the proteins are energetically destabilized relative to singly solvated amino acids, while trends for other residues are less clear-cut. Almost all residues lose intraresidue entropy when in the protein, enthalpy changes are negative on average but may be positive or negative, and the resulting overall stability is moderate for some proteins and negligible for others. The free energy of water around single amino acids is found to closely match existing hydrophobicity scales. Regarding the effect of secondary structure, water is slightly more stable around loops, of intermediate stability around ß strands and turns, and least stable around helices. An interesting asymmetry observed is that cationic residues stabilize a residue when bonded to its N-terminal side but destabilize it when on the C-terminal side, with a weaker reversed trend for anionic residues.

COVID-19: An Accelerator for Global Plastic Consumption and Its Implications.

Plastic has been ingrained in our society. Repercussions on the usage of nonbiodegradable plastics and their problems have been recently realized. Despite its detrimental environmental impact, the COVID-19 epidemic has compelled worldwide citizens to increase their plastic use due to affordability and availability. The volume of hospital solid waste, particularly plastics, is overgrowing due to an unexpected increase in medical waste, culminating in the global waste management catastrophe. Henceforth, adopting good waste management practices along with appropriate technologies and viewing the current issue from a fresh perspective would be an opportunity in this current scenario. Accordingly, this review study will focus on the plastic waste scenario before and during the COVID-19 epidemic. This review also disseminates alternative disposal options and recommends practical solutions to lessen human reliance on traditional plastics. Further, the responsibilities of various legislative and regulatory authorities at the local, regional, and worldwide levels are addressed.

Free Gangliosides Can Alter Amyloid-ß Aggregation.

A recently proposed lipid-chaperone hypothesis suggests that free lipid molecules, not bound to membranes, affect the aggregation of amyloidogenic peptides such as amyloid-ß (Aß) peptides, whose aggregates are the hallmarks of Alzheimer's disease. Here, we combine experiments with all-atom molecular dynamics simulations in explicit solvent to explore the effects of neuronal ganglioside GM1, abundant in mammalian brains, on the aggregation of two principal isoforms of Aß, Aß40 and Aß42. Our simulations show that free GM1 forms stable, highly water-soluble complexes with both isoforms, and nuclear magnetic resonance experiments support the formation of well-ordered, structurally compact GM1+Aß complexes. By simulation, we also show that Aß40 monomers display a preference for binding to GM1-containing hetero-oligomers over GM1-lacking homo-oligomers, while Aß42 monomers have the opposite preference. These observations explain why GM1 dose-dependently inhibits Aß40 aggregation but has no effect on Aß42 aggregation, as assessed by thioflavin T fluorescence.

Energy-entropy method using multiscale cell correlation to calculate binding free energies in the SAMPL8 host-guest challenge.

Free energy drives a wide range of molecular processes such as solvation, binding, chemical reactions and conformational change. Given the central importance of binding, a wide range of methods exist to calculate it, whether based on scoring functions, machine-learning, classical or electronic structure methods, alchemy, or explicit evaluation of energy and entropy. Here we present a new energy-entropy (EE) method to calculate the host-guest binding free energy directly from molecular dynamics (MD) simulation. Entropy is evaluated using Multiscale Cell Correlation (MCC) which uses force and torque covariance and contacts at two different length scales. The method is tested on a series of seven host-guest complexes in the SAMPL8 (Statistical Assessment of the Modeling of Proteins and Ligands) "Drugs of Abuse" Blind Challenge. The EE-MCC binding free energies are found to agree with experiment with an average error of 0.9 kcal mol-1. MCC makes clear the origin of the entropy changes, showing that the large loss of positional, orientational, and to a lesser extent conformational entropy of each binding guest is compensated for by a gain in orientational entropy of water released to bulk, combined with smaller decreases in vibrational entropy of the host, guest and contacting water.

Entropy of Proteins Using Multiscale Cell Correlation.

A new multiscale method is presented to calculate the entropy of proteins from molecular dynamics simulations. Termed Multiscale Cell Correlation (MCC), the method decomposes the protein into sets of rigid-body units based on their covalent-bond connectivity at three levels of hierarchy: molecule, residue, and united atom. It evaluates the vibrational and topographical entropy from forces, torques, and dihedrals at each level, taking into account correlations between sets of constituent units that together make up a larger unit at the coarser length scale. MCC gives entropies in close agreement with normal-mode analysis and smaller than those using quasiharmonic analysis as well as providing much faster convergence. Moreover, MCC provides an insightful decomposition of entropy at each length scale and for each type of amino acid according to their solvent exposure and whether they are terminal residues. While the residue entropy depends weakly on solvent exposure, there is greater variation in entropy components for larger, more polar amino acids, which have increased conformational entropy but reduced vibrational entropy with greater solvent exposure.

Effects of microplastics and nanoplastics on marine environment and human health.

Microplastics (MPs) with an average size of less than 5 mm, along with nanoplastics (NPs) of an average size of fewer than 0.1 µm are the result of huge plastic waste fragmentation or straight environmental emissions. Pollution from micro- and nanoplastics is a worldwide paradigm that raises environmental and human health concerns. They may also comprise very harmful chemicals that are implemented in plants and animals when MPs/NPs are used that may lead to higher accumulation of these compounds in food chains. In addition, higher surface area-to-volume ratio, characteristic of MPs/NPs can contribute to their potentially harmful impact as other pollutants, like continuous organic contaminants, can also be bio-accumulated and adsorbed. A complex issue correlated with MPs/NPs is their ability to absorb and interact with other common pollutants in the environment, such as metals, pharmaceuticals, and other contaminants. Thus, MPs/NPs can directly influence on destiny and toxicity of these substances to the environment and organisms. In this review, first, we introduce possible sources and formation, their destinies, and environmental impact of MPs/NPs and then explain feasible paths of all these particles entering the human body. Then, the review highlights the effect of MPs/NPs on human health. Finally, it provides a brief summary of the potential as well as the neurological toxicity of MPs/NPs.

Capturing the Effects of Explicit Waters in Implicit Electrostatics Modeling: Qualitative Justification of Gaussian-Based Dielectric Models in DelPhi.

Our group has implemented a smooth Gaussian-based dielectric function in DelPhi (J. Chem. Theory Comput. 2013, 9 (4), 2126-2136) which models the solute as an object with inhomogeneous dielectric permittivity and provides a smooth transition of dielectric permittivity from surface-bound water to bulk solvent. Although it is well-understood that the protein hydrophobic core is less polarizable than the hydrophilic protein surface, less attention is paid to the polarizability of water molecules inside the solute and on its surface. Here, we apply explicit water simulations to study the behavior of water molecules buried inside a protein and on the surface of that protein and contrast it with the behavior of the bulk water. We selected a protein that is experimentally shown to have five cavities, most of which are occupied by water molecules. We demonstrate through molecular dynamics (MD) simulations that the behavior of water in the cavity is drastically different from that in the bulk. These observations were made by comparing the mean residence times, dipole orientation relaxation times, and average dipole moment fluctuations. We also show that the bulk region has a nonuniform distribution of these tempo-spatial properties. From the perspective of continuum electrostatics, we argue that the dielectric "constant" in water-filled cavities of proteins and the space close to the molecular surface should differ from that assigned to the bulk water. This provides support for the Gaussian-based smooth dielectric model for solving electrostatics in the Poisson-Boltzmann equation framework. Furthermore, we demonstrate that using a well-parametrized Gaussian-based model with a single energy-minimized configuration of a protein can also reproduce its ensemble-averaged polar solvation energy. Thus, we argue that the Gaussian-based smooth dielectric model not only captures accurate physics but also provides an efficient way of computing ensemble-averaged quantities.

Synthesis and characterisation of starch/agar nanocomposite films for food packaging application.

In the present work cassava starch/agar Ag and ZnO nanocomposite films were prepared by the solution casting method. The structural, physical and antimicrobial properties of the nanocomposite films were studied as a function of the concentration of Ag and ZnO nanoparticles. The results of the thermogravimetric analysis showed 8-15% degradation of both the nanocomposite films at 150°C endorsing the thermal stability of the films. Scanning electron microscopic analysis reveals the uniform blending of Ag and ZnO nanoparticles with a starch/agar matrix with tiny waves like appearance on the surface. The incorporation of Ag and ZnO nanoparticles in the film was found to reduce the moisture content, water solubility and water vapour permeability with increase in the concentration of Ag and ZnO nanoparticles. The growth kinetics study of Pseudomonas aeruginosa and Staphylococcus aureus in the presence of Ag and ZnO blended nanocomposite films showed promising results especially against Gram-negative P. aeruginosa. Thus, the film synthesised in the present study bears the potential to be used as active packaging material to prevent food from bacterial contamination and spoilage.

BION-2: Predicting Positions of Non-Specifically Bound Ions on Protein Surface by a Gaussian-Based Treatment of Electrostatics.

Ions play significant roles in biological processes-they may specifically bind to a protein site or bind non-specifically on its surface. Although the role of specifically bound ions ranges from actively providing structural compactness via coordination of charge-charge interactions to numerous enzymatic activities, non-specifically surface-bound ions are also crucial to maintaining a protein's stability, responding to pH and ion concentration changes, and contributing to other biological processes. However, the experimental determination of the positions of non-specifically bound ions is not trivial, since they may have a low residential time and experience significant thermal fluctuation of their positions. Here, we report a new release of a computational method, the BION-2 method, that predicts the positions of non-specifically surface-bound ions. The BION-2 utilizes the Gaussian-based treatment of ions within the framework of the modified Poisson-Boltzmann equation, which does not require a sharp boundary between the protein and water phase. Thus, the predictions are done by the balance of the energy of interaction between the protein charges and the corresponding ions and the de-solvation penalty of the ions as they approach the protein. The BION-2 is tested against experimentally determined ion's positions and it is demonstrated that it outperforms the old BION and other available tools.

Modeling Electrostatic Force in Protein-Protein Recognition.

Electrostatic interactions are important for understanding molecular interactions, since they are long-range interactions and can guide binding partners to their correct binding positions. To investigate the role of electrostatic forces in molecular recognition, we calculated electrostatic forces between binding partners separated at various distances. The investigation was done on a large set of 275 protein complexes using recently developed DelPhiForce tool and in parallel, evaluating the total electrostatic force via electrostatic association energy. To accomplish the goal, we developed a method to find an appropriate direction to move one chain of protein complex away from its bound position and then calculate the corresponding electrostatic force as a function of separation distance. It is demonstrated that at large distances between the partners, the electrostatic force (magnitude and direction) is consistent among the protocols used and the main factors contributing to it are the net charge of the partners and their interfaces. However, at short distances, where partners form specific pair-wise interactions or de-solvation penalty becomes significant, the outcome depends on the precise balance of these factors. Based on the electrostatic force profile (force as a function of distance), we group the cases into four distinctive categories, among which the most intriguing is the case termed "soft landing." In this case, the electrostatic force at large distances is favorable assisting the partners to come together, while at short distance it opposes binding, and thus slows down the approach of the partners toward their physical binding.

DelPhi Suite: New Developments and Review of Functionalities.

Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object-oriented package supporting various levels of multiprocessing and memory distribution. It is demonstrated that multiprocessing results in significant improvement of computational time. Furthermore, for computations requiring large grid size (large macromolecular assemblages), the approach of memory distribution is shown to reduce the requirement of RAM and thus permitting large-scale modeling to be done on Linux clusters with moderate architecture. The new release comes with new features, whose functionalities and applications are described as well. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

The HRP3 PWWP domain recognizes the minor groove of double-stranded DNA and recruits HRP3 to chromatin.

HDGF-related protein 3 (HRP3, also known as HDGFL3) belongs to the family of HDGF-related proteins (HRPs) and plays an essential role in hepatocellular carcinoma pathogenesis. All HRPs have a PWWP domain at the N-terminus that binds both histone and DNA substrates. Despite previous advances in PWWP domains, the molecular basis by which HRP3 interacts with chromatin is unclear. In this study, we solved the crystal structures of the HRP3 PWWP domain in complex with various double-stranded DNAs with/without bound histone peptides. We found that HRP3 PWWP bound to the phosphate backbone of the DNA minor groove and showed a preference for DNA molecules bearing a narrow minor groove width. In addition, HRP3 PWWP preferentially bound to histone peptides bearing the H3K36me3/2 modification. HRP3 PWWP uses two adjacent surfaces to bind both DNA and histone substrates simultaneously, enabling us to generate a model illustrating the recruitment of PWWP to H3K36me3-containing nucleosomes. Cell-based analysis indicated that both DNA and histone binding by the HRP3 PWWP domain is important for HRP3 recruitment to chromatin in vivo. Our work establishes that HRP3 PWWP is a new family of minor groove-specific DNA-binding proteins, which improves our understanding of HRP3 and other PWWP domain-containing proteins.

A grid-based algorithm in conjunction with a gaussian-based model of atoms for describing molecular geometry.

A novel grid-based method is presented, which in conjunction with a smooth Gaussian-based model of atoms, is used to compute molecular volume (MV) and surface area (MSA). The MV and MSA are essential for computing nonpolar component of free energies. The objective of our grid-based approach is to identify solute atom pairs that share overlapping volumes in space. Once completed, this information is used to construct a rooted tree using depth-first method to yield the final volume and SA by using the formulations of the Gaussian model described by Grant and Pickup (J. Phys Chem, 1995, 99, 3503). The method is designed to function uninterruptedly with the grid-based finite-difference method implemented in Delphi, a popular and open-source package used for solving the Poisson-Boltzmann equation (PBE). We demonstrate the time efficacy of the method while also validating its performance in terms of the effect of grid-resolution, positioning of the solute within the grid-map and accuracy in identification of overlapping atom pairs. We also explore and discuss different aspects of the Gaussian model with key emphasis on its physical meaningfulness. This development and its future release with the Delphi package are intended to provide a physically meaningful, fast, robust and comprehensive tool for MM/PBSA based free energy calculations. © 2019 Wiley Periodicals, Inc.

Gaussian-Based Smooth Dielectric Function: A Surface-Free Approach for Modeling Macromolecular Binding in Solvents.

Conventional modeling techniques to model macromolecular solvation and its effect on binding in the framework of Poisson-Boltzmann based implicit solvent models make use of a geometrically defined surface to depict the separation of macromolecular interior (low dielectric constant) from the solvent phase (high dielectric constant). Though this simplification saves time and computational resources without significantly compromising the accuracy of free energy calculations, it bypasses some of the key physio-chemical properties of the solute-solvent interface, e.g., the altered flexibility of water molecules and that of side chains at the interface, which results in dielectric properties different from both bulk water and macromolecular interior, respectively. Here we present a Gaussian-based smooth dielectric model, an inhomogeneous dielectric distribution model that mimics the effect of macromolecular flexibility and captures the altered properties of surface bound water molecules. Thus, the model delivers a smooth transition of dielectric properties from the macromolecular interior to the solvent phase, eliminating any unphysical surface separating the two phases. Using various examples of macromolecular binding, we demonstrate its utility and illustrate the comparison with the conventional 2-dielectric model. We also showcase some additional abilities of this model, viz. to account for the effect of electrolytes in the solution and to render the distribution profile of water across a lipid membrane.

Reproducing the Ensemble Average Polar Solvation Energy of a Protein from a Single Structure: Gaussian-Based Smooth Dielectric Function for Macromolecular Modeling.

Typically, the ensemble average polar component of solvation energy (ΔGpolarsolv) of a macromolecule is computed using molecular dynamics (MD) or Monte Carlo (MC) simulations to generate conformational ensemble and then single/rigid conformation solvation energy calculation is performed on each snapshot. The primary objective of this work is to demonstrate that Poisson-Boltzmann (PB)-based approach using a Gaussian-based smooth dielectric function for macromolecular modeling previously developed by us (Li et al. J. Chem. Theory Comput. 2013, 9 (4), 2126-2136) can reproduce that ensemble average (ΔGpolarsolv) of a protein from a single structure. We show that the Gaussian-based dielectric model reproduces the ensemble average ΔGpolarsolv(⟨ΔGpolarsolv⟩) from an energy-minimized structure of a protein regardless of the minimization environment (structure minimized in vacuo, implicit or explicit waters, or crystal structure); the best case, however, is when it is paired with an in vacuo-minimized structure. In other minimization environments (implicit or explicit waters or crystal structure), the traditional two-dielectric model can still be selected with which the model produces correct solvation energies. Our observations from this work reflect how the ability to appropriately mimic the motion of residues, especially the salt bridge residues, influences a dielectric model's ability to reproduce the ensemble average value of polar solvation free energy from a single in vacuo-minimized structure.

DelPhiForce web server: electrostatic forces and energy calculations and visualization.

SUMMARY: Electrostatic force is an essential component of the total force acting between atoms and macromolecules. Therefore, accurate calculations of electrostatic forces are crucial for revealing the mechanisms of many biological processes. We developed a DelPhiForce web server to calculate and visualize the electrostatic forces at molecular level. DelPhiForce web server enables modeling of electrostatic forces on individual atoms, residues, domains and molecules, and generates an output that can be visualized by VMD software. Here we demonstrate the usage of the server for various biological problems including protein-cofactor, domain-domain, protein-protein, protein-DNA and protein-RNA interactions. AVAILABILITY AND IMPLEMENTATION: The DelPhiForce web server is available at: http://compbio.clemson.edu/delphi-force. CONTACT: delphi@clemson.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Treating ion distribution with Gaussian-based smooth dielectric function in DelPhi.

The standard treatment of ions in the framework of the Poisson-Boltzmann equation relies on molecular surfaces, which are commonly constructed along with the Stern layer. The molecular surface determines where ions can be present. In the Gaussian-based smooth dielectric function in DelPhi, smooth boundaries between the solute and solvent take the place of molecular surface. Therefore, this invokes the question of how to model mobile ions in the water phase without a definite solute-solvent boundary. This article reports a natural extension of the Gaussian-based smooth dielectric function approach that treats mobile ions via Boltzmann distribution with an added desolvation penalty. Thus, ion concentration near macromolecules is governed by the local electrostatic potential and the desolvation penalty (from being partially desolvated). The approach is tested against the experimental salt dependence of binding free energy on 7 protein-protein complexes and 12 DNA-protein complexes, resulting in Pearson correlations of 0.95 and 0.88, respectively. © 2017 Wiley Periodicals, Inc.

A New DelPhi Feature for Modeling Electrostatic Potential around Proteins: Role of Bound Ions and Implications for Zeta-Potential.

A new feature of the popular software DelPhi is developed and reported, allowing for computing the surface averaged electrostatic potential (SAEP) of macromolecules. The user is given the option to specify the distance from the van der Waals surface where the electrostatic potential will be outputted. In conjunction with DelPhiPKa and the BION server, the user can adjust the charges of titratable groups according to specific pH values, and add explicit ions bound to the macromolecular surface. This approach is applied to a set of four proteins with "experimentally" delivered zeta (ζ)-potentials at different pH values and salt concentrations. It has been demonstrated that the protocol is capable of predicting ζ-potentials in the case of proteins with relatively large net charges. This protocol has been less successful for proteins with low net charges. The work demonstrates that in the case of proteins with large net charges, the electrostatic potential should be collected at distances about 4 Šaway from the vdW surface and explicit ions should be added at a binding energy cutoff larger than 1-2kT, in order to accurately predict ζ-potentials. The low salt conditions substantiate this effect of ions on SAEP.