Modeling of Halogen–Protein Interactions in Co-Solvent Molecular Dynamics Simulations

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.

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.

EXPRORER: Rational Cosolvent Set Construction Method for Cosolvent Molecular Dynamics Using Large-Scale Computation

Cosolvent molecular dynamics (CMD) simulations involve an MD simulation of a protein in the presence of explicit water molecules mixed with cosolvent molecules to perform hotspot detection, binding site identification, and binding energy estimation, while other existing methods (e.g., MixMD, SILCS, and MDmix) utilize small molecules that represent functional groups of compounds. However, the cosolvent selections employed in these methods differ and there are only a few cosolvents that are commonly used in these methods. In this study, we proposed a systematic method for constructing a set of cosolvents for drug discovery, termed the EXtended PRObes set construction by REpresentative Retrieval (EXPRORER). First, we extracted typical substructures from FDA-approved drugs, generated 138 cosolvent structures, and for each cosolvent molecule, we conducted CMD simulations to generate a spatial probability distribution map of cosolvent atoms (PMAP). Analyses of PMAP similarity revealed that a cosolvent pair with a PMAP similarity greater than 0.70-0.75 shared similar structural features. We present a method for the construction of a cosolvent subset that satisfies a similarity threshold for all cosolvents, and we tested the constructed sets for four proteins. To our knowledge, this is the first study to include a systematic proposal for cosolvent set construction, and thus, the EXPRORER cosolvents will provide deeper insights into ligand binding sites of various proteins.

Straightforward Incorporation of Multiple Ligand Types into Molecular Dynamics Simulations for Efficient Binding Site Detection and Characterization

Binding site identification and characterization is an important initial step in structure-based drug design. To account for the effects of protein flexibility and solvation, several cosolvent molecular dynamics (MD) simulation methods that incorporate small organic molecules into the protein's solvent box to probe for binding sites have been developed. However, most of these methods are highly inefficient, as they allow for the use of only one probe type at a time, which means that multiple sets of simulations have to be performed to map different types of binding sites. The high probe concentrations used in some of these methods also necessitate the use of artificial repulsive forces to prevent the probes from aggregating. Here, we present multiple-ligand-mapping MD (mLMMD), a method that incorporates multiple types of probes for simultaneous and efficient mapping of different types of binding sites without the need for introduction of artificial forces that may cause unintended mapping artifacts. We validate the method on a diverse set of 10 proteins and show that the mLMMD probes are able to reliably identify hydrophobic, hydrogen-bonding, charged, and cryptic binding sites in all of the test cases. Our results also highlight the potential utility of mLMMD for virtual screening and rational drug design.

Hybrid Molecular Dynamics for Elucidating Cooperativity Between Halogen Bond and Water Molecules During the Interaction of p53-Y220C and the PhiKan5196 Complex

The cooperativity between hydrogen and halogen bonds plays an important role in rational drug design. However, mimicking the dynamic cooperation between these bonds is a challenging issue, which has impeded the development of the halogen bond force field. In this study, the Y220C–PhiKan5196 complex of p53 protein was adopted as a model, and the functions of three water molecules that formed hydrogen bonds with halogen atoms were analyzed by the simulation method governed by the hybrid quantum mechanical/molecular mechanical molecular dynamics. A comparison with the water-free model revealed that the strength of the halogen bond in the complex was consistently stronger. This confirmed that the water molecules formed weak hydrogen bonds with the halogen atom and cooperated with the halogen atom to enhance the halogen bond. Further, it was discovered that the roles of the three water molecules were not the same. Therefore, the results obtained herein can facilitate a rational drug design. Further, this work emphasizes on the fact that, in addition to protein pockets and ligands, the role of voids should also be considered with regard to the water molecules surrounding them.

Optimization and Evaluation of Site-Identification by Ligand Competitive Saturation (SILCS) as a Tool for Target-Based Ligand Optimization

Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-identification by ligand competitive saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's predictive index, correct relative binding affinity, and root-mean-square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69% of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75% relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76% are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76% correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.