Fragment dissolved molecular dynamics: a systematic and efficient method to locate binding sites

Predicting fragment binding modes using customized Lennard-Jones potentials in short molecular dynamics simulations

Reliable in silico prediction of fragment binding modes remains a challenge in current drug design research. Due to their small size and generally low binding affinity, fragments can potentially interact with their target proteins in different ways. In the current study, we propose a workflow aimed at predicting favorable fragment binding sites and binding poses through multiple short molecular dynamics simulations. Tailored Lennard-Jones potentials enable the simulation of systems with high concentrations of identical fragment molecules surrounding their respective target proteins. In the present study, descriptors and binding free energy calculations were implemented to filter out the desired fragment position. The proposed method was tested for its performance using four epigenetic target proteins and their respective fragment binders and showed high accuracy in identifying the binding sites as well as predicting the native binding modes. The approach presented here represents an alternative method for the prediction of fragment binding modes and may be useful in fragment-based drug discovery when the corresponding experimental structural data are limited.

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. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

Shedding Light on Dark Chemical Matter: The Discovery of a SARS-CoV-2 Mpro Main Protease Inhibitor through Intensive Virtual Screening and In Vitro Evaluation

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds.

The Discovery of inhibitors of the SARS-CoV-2 S protein through computational drug repurposing

SARS-CoV-2 must bind its principal receptor, ACE2, on the target cell to initiate infection. This interaction is largely driven by the receptor binding domain (RBD) of the viral Spike (S) protein. Accordingly, antiviral compounds that can block RBD/ACE2 interactions can constitute promising antiviral agents. To identify such molecules, we performed a virtual screening of the Selleck FDA approved drugs and the Selleck database of Natural Products using a multistep computational procedure. An initial set of candidates was identified from an ensemble docking process using representative structures determined from the analysis of four 3 μ s molecular dynamics trajectories of the RBD/ACE2 complex. Two procedures were used to construct an initial set of candidates including a standard and a pharmacophore guided docking procedure. The initial set was subsequently subjected to a multistep sieving process to reduce the number of candidates to be tested experimentally, using increasingly demanding computational procedures, including the calculation of the binding free energy computed using the MMPBSA and MMGBSA methods. After the sieving process, a final list of 10 candidates was proposed, compounds which were subsequently purchased and tested ex-vivo. The results identified estradiol cypionate and telmisartan as inhibitors of SARS-CoV-2 entry into cells. Our findings demonstrate that the methodology presented here enables the discovery of inhibitors targeting viruses for which high-resolution structures are available.

Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design

Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.