Detection of Putative Ligand Dissociation Pathways in Proteins Using Site-Identification by Ligand Competitive Saturation.

Drug efficacy often correlates better with dissociation kinetics than binding affinity alone. To study binding kinetics computationally, it is necessary to identify all of the possible ligand dissociation pathways. The site identification by ligand competitive saturation (SILCS) method involves the precomputation of a set of maps (FragMaps), which describe the free energy landscapes of typical chemical functionalities in and around a target protein or RNA.

Modeling Ligand Binding Site Water Networks with Site Identification by Ligand Competitive Saturation: Impact on Ligand Binding Orientations and Relative Binding Affinities.

Appropriate treatment of water contributions to protein-ligand interactions is a very challenging problem in the context of adequately determining the number of waters to investigate and undertaking conformational sampling of the ligands, the waters, and the surrounding protein. In the present study, an extension of the Site Identification by Ligand Competitive Saturation-Monte Carlo (SILCS-MC) docking approach is presented that enables the determination of the location of water molecules in the binding pocket and their impact on the predicted ligand binding orientation and affinities. The approach, termed SILCS-WATER, involves MC sampling of the ligand along with explicit water molecules in a binding site followed by selection of a subset of waters within specified energetic and distance cutoffs that contribute to ligand binding and orientation.

Isolation of phytoconstituents from an extract of with cytotoxicity and antioxidant activities and evaluation of their potential to bind to aldose reductase (AKR1B1).

The study on (Orange Jasmine) stem bark extract found it to have antioxidant and cytotoxic proper-ties. The structures of the isolated phytoconstituents were determined using NMR spectroscopy. Compounds were evaluated for their potential to be aldose reductase inhibitors using molecular docking and dynamics (MD) simulations.

Balancing Group 1 Monoatomic Ion-Polar Compound Interactions in the Polarizable Drude Force Field: Application in Protein and Nucleic Acid Systems.

An accurate force field (FF) is the foundation of reliable results from molecular dynamics (MD) simulations. In our recently published work, we developed a protocol to generate atom pair-specific Lennard-Jones (known as NBFIX in CHARMM) and through-space Thole dipole screening (NBTHOLE) parameters in the context of the Drude polarizable FF based on readily accessible quantum mechanical (QM) data to fit condensed phase experimental thermodynamic benchmarks, including the osmotic pressure, diffusion coefficient, ionic conductivity, and solvation free energy, when available. In the present work, the developed protocol is applied to generate NBFIX and NBTHOLE parameters for interactions between monatomic ions (specifically Li, Na, K, Rb, Cs, and Cl) and common functional groups found in proteins and nucleic acids.

Revised 4-Point Water Model for the Classical Drude Oscillator Polarizable Force Field: SWM4-HLJ.

In this work the 4-point polarizable SWM4 Drude water model is reparametrized. Multiple models were developed using different strategies toward reproduction of specific target data. Results indicate that no individual model can reproduce all the selected target data in the context of the present form of the potential energy function.

Refinement of the Drude Polarizable Force Field for Hexose Monosaccharides: Capturing Ring Conformational Dynamics with Enhanced Accuracy.

We present a revised version of the Drude polarizable carbohydrate force field (FF), focusing on refining the ring and exocyclic torsional parameters for hexopyranose monosaccharides. This refinement addresses the previously observed discrepancies between calculated and experimental NMR coupling values, particularly in describing ring dynamics and exocyclic rotamer populations within major hexose monosaccharides and their anomers. Specifically, α-MAN, β-MAN, α-GLC, β-GLC, α-GAL, β-GAL, α-ALT, β-ALT, α-IDO, and β-IDO were targeted for optimization.

Exploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation.

G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites.

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.

Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures.

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms.

In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule.

FFParam-v2.0: A Comprehensive Tool for CHARMM Additive and Drude Polarizable Force-Field Parameter Optimization and Validation.

Developing production quality CHARMM force-field (FF) parameters is a very detailed process involving a variety of calculations, many of which are specific for the molecule of interest. The first version of FFParam was developed as a standalone Python package designed for the optimization of electrostatic and bonded parameters of the CHARMM additive and polarizable Drude FFs by using quantum mechanical (QM) target data. The new version of FFParam has multiple new capabilities for FF parameter optimization and validation, with an emphasis on the ability to use condensed-phase target data in optimization.

Identifying and Assessing Putative Allosteric Sites and Modulators for CXCR4 Predicted through Network Modeling and Site Identification by Ligand Competitive Saturation.

The chemokine receptor CXCR4 is a critical target for the treatment of several cancer types and HIV-1 infections. While orthosteric and allosteric modulators have been developed targeting its extracellular or transmembrane regions, the intramembrane region of CXCR4 may also include allosteric binding sites suitable for the development of allosteric drugs. To investigate this, we apply the Gaussian Network Model (GNM) to the monomeric and dimeric forms of CXCR4 to identify residues essential for its local and global motions located in the hinge regions of the protein.

Balancing Group I Monatomic Ion-Polar Compound Interactions for Condensed Phase Simulation in the Polarizable Drude Force Field.

Molecular dynamics (MD) simulations are a commonly used method for investigating molecular behavior at the atomic level. Achieving reliable MD simulation results necessitates the use of an accurate force field. In the present work, we present a protocol to enhance the quality of group 1 monatomic ions (specifically Li, Na, K, Rb, and Cs) with respect to their interactions with common polar model compounds in biomolecules in condensed phases in the context of the Drude polarizable force field.

Biomolecular dynamics in the 21st century.

The relevance of motions in biological macromolecules has been clear since the early structural analyses of proteins by X-ray crystallography. Computer simulations have been applied to provide a deeper understanding of the dynamics of biological macromolecules since 1976, and are now a standard tool in many labs working on the structure and function of biomolecules. In this mini-review we highlight some areas of current interest and active development for simulations, in particular all-atom molecular dynamics simulations.

Dendritic Cell-Mediated Cross-Priming by a Bispecific Neutralizing Antibody Boosts Cytotoxic T Cell Responses and Protects Mice against SARS-CoV-2.

Administration of neutralizing antibodies (nAbs) has proved to be effective by providing immediate protection against SARS-CoV-2. However, dual strategies combining virus neutralization and immune response stimulation to enhance specific cytotoxic T cell responses, such as dendritic cell (DC) cross-priming, represent a promising field but have not yet been explored. Here, a broadly nAb, TN , are first generated by grafting an anti-RBD biparatopic tandem nanobody onto a trimerbody scaffold.

Non-β Lactam Inhibitors of the Serine β-Lactamase blaCTX-M15 in Drug-Resistant .

Antibiotic resistance by bacterial pathogens against widely used β-lactam drugs is a major concern to public health worldwide, resulting in high healthcare cost. The present study aimed to extend previous research by investigating the potential activity of reported compounds against the β-lactamase protein. 74 compounds from computational screening reported in our previous study against β-lactamase CMY-10 were subjected to docking studies against blaCTX-M15.

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules.

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules.

Nitro-benzylideneoxymorphone, a bifunctional mu and delta opioid receptor ligand with high mu opioid receptor efficacy.

There is a major societal need for analgesics with less tolerance, dependence, and abuse liability. Preclinical rodent studies suggest that bifunctional ligands with both mu (MOPr) and delta (DOPr) opioid peptide receptor activity may produce analgesia with reduced tolerance and other side effects. This study explores the structure-activity relationships (SAR) of our previously reported MOPr/DOPr lead, benzylideneoxymorphone (BOM) with C7-methylene-substituted analogs.

Identification of a novel transport system in Borrelia burgdorferi that links the inner and outer membranes.

Borrelia burgdorferi, the spirochete that causes Lyme disease, is a diderm organism that is similar to Gram-negative organisms in that it contains both an inner and outer membrane. Unlike typical Gram-negative organisms, however, B. burgdorferi lacks lipopolysaccharide (LPS).

Influence of Mg Distribution on the Stability of Folded States of the Twister Ribozyme Revealed Using Grand Canonical Monte Carlo and Generative Deep Learning Enhanced Sampling.

Metal ions, particularly magnesium ions (Mg), play a role in stabilizing the tertiary structures of RNA molecules. Theoretical models and experimental techniques show that metal ions can change RNA dynamics and how it transitions through different stages of folding. However, the specific ways in which metal ions contribute to the formation and stabilization of RNA's tertiary structure are not fully understood at the atomic level.

Integrated Covalent Drug Design Workflow Using Site Identification by Ligand Competitive Saturation.

Covalent drug design is an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium, while irreversible covalent drugs increase the drug-target interaction duration by forming a covalent bond with targeted residues and thus may offer a more effective therapeutic approach. To facilitate the design of this class of ligands, computational methods can be used to help identify reactive nucleophilic residues, frequently cysteines, on a target protein for covalent binding, to test various warhead groups for their potential reactivities, and to predict noncovalent contributions to binding that can facilitate drug-target interactions that are important for binding specificity.

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.

The Grand Canonical Monte Carlo (GCMC) ensemble defined by the excess chemical potential, μ , volume, and temperature, in the context of molecular simulations allows for variations in the number of particles in the system. In practice, GCMC simulations have been widely applied for the sampling of rare gasses and water, but limited in the context of larger molecules. To overcome this limitation, the oscillating μ GCMC method was introduced and shown to be of utility for sampling small solutes, such as formamide, propane, and benzene, as well as for ionic species such as monocations, acetate, and methylammonium.

Drude Polarizable Lipid Force Field with Explicit Treatment of Long-Range Dispersion: Parametrization and Validation for Saturated and Monounsaturated Zwitterionic Lipids.

Accurate empirical force fields of lipid molecules are a critical component of molecular dynamics simulation studies aimed at investigating properties of monolayers, bilayers, micelles, vesicles, and liposomes, as well as heterogeneous systems, such as protein-membrane complexes, bacterial cell walls, and more. While the majority of lipid force field-based simulations have been performed using pairwise-additive nonpolarizable models, advances have been made in the development of the polarizable force field based on the classical Drude oscillator model. In the present study, we undertake further optimization of the Drude lipid force field, termed Drude2023, including improved treatment of the phosphate and glycerol linker region of PC and PE headgroups, additional optimization of the alkene group in monounsaturated lipids, and inclusion of long-range Lennard-Jones interactions using the particle-mesh Ewald method.