Molecular Docking: Metamorphosis in Drug Discovery

Molecular docking is recognized a part of computer-aided drug design that is mostly used in medicinal chemistry. It has proven to be an effective, quick, and low-cost technique in both scientific and corporate contexts. It helps in rationalizing the ligands activity towards a target to perform structure-based drug design (SBDD). Docking assists the revealing of novel compound of therapeutic interest, forecasting ligand-protein interaction at a molecular basis and delineating structure activity relationships (SARs). Molecular docking acts as a boon to identify promising agents in emergence of diseases which endangering the human health. In this chapter, we engrossed on the techniques, types, opportunities, challenges and success stories of molecular docking in drug development

Computational Modeling of (De)-Solvation Effects and Protein Flexibility in Protein-Ligand Binding using Molecular Dynamics Simulations

Water is a crucial participant in virtually all cellular functions. Evidently, water molecules in the binding site contribute significantly to the strength of intermolecular interactions in the aqueous phase by mediating protein-ligand interactions, solvating and de-solvating both ligand and protein upon protein-ligand dissociation and association. Recently many published studies use water distributions in the binding site to retrospectively explain and rationalize unexpected trends in structure-activity relationships (SAR). However, traditional approaches cannot quantitatively predict the thermodynamic properties of water molecules in the binding sites and its associated contribution to the binding free energy of a ligand. We have developed and validated a computational method named WATsite to exploit high-resolution solvation maps and thermodynamic profiles to elucidate the water molecules’ potential contribution to protein-ligand and protein-protein binding. We have also demonstrated the utility of the computational method WATsite to help direct medicinal chemistry efforts by using explicit water de-solvation. In addition, protein conformational change is typically involved in the ligand-binding process which may completely change the position and thermodynamic properties of the water molecules in the binding site before or upon ligand binding. We have shown the interplay between protein flexibility and solvent reorganization, and we provide a quantitative estimation of the influence of protein flexibility on desolvation free energy and, therefore, protein-ligand binding. Different ligands binding to the same target protein can induce different conformational adaptations. In order to apply WATsite to an ensemble of different protein conformations, a more efficient implementation of WATsite based on GPU-acceleration and system truncation has been developed. Lastly, by extending the simulation protocol from pure water to mixed water-organic probes simulations, accurate modeling of halogen atom-protein interactions has been achieved

Understanding virtual solvent through large-scale ligand discovery

Predicting new ligands and their binding poses for a protein target relies on an understanding of the physical forces that exist between the water-submerged protein and ligand. The relative favorability of these molecular and atomic interactions between the protein and ligand compared with their interactions with water determine the binding affinity, which in turn can be converted into a binding free energy. Protein-ligand binding energetics are, with varying levels of success, encoded into scoring functions, which at their best, can only partially emulate the true binding affinity of a protein-ligand binding event. In the context of virtually screening millions or hundreds of millions of drug-like ligands, molecular docking algorithms take advantage of scoring functions to rank the binding energies of these molecules relative to one another to help prioritize the most promising ligands.The focus of this dissertation is the balance between scoring function energy terms with an emphasis on water energetics, specifically the desolvation of the protein upon ligand binding. It is thought that our limited understanding of water is largely responsible for our limitations in discovering and designing drugs. This is due to the large number of roles that water can play, as well as its significant, and even dominant, contribution to protein-ligand binding energetics, which in the realm of molecular docking, is typically under-modeled or completely neglected. First, I focus on the incorporation of receptor desolvation into the standard DOCK3.7 scoring function to more accurately model protein-ligand binding interactions by including further contributions of water. This is the original implementation of Grid Inhomogeneous Solvation Theory applied to the model cavity, cytochrome c peroxidate, and spearheaded by Trent Balius and Marcus Fischer. Second, I discuss an extension of GIST in DOCK3.7, a new implementation that relies on pre-computed Gaussian-weighted GIST receptor desolvation enthalpies. This results in negligible slowdown of the standard DOCK3.7 scoring function, similar performance to the original implementation of GIST, and the identification of new ligands for the drug-like model system, AmpC β-lactamase. The work on receptor desolvation contained within these two chapters inspires the name of this thesis, and were started in my rotation and have continued until the end. Third, I focus on the use of property-matched and property-unmatched decoys for use in retrospective enrichment calculations prior to running a large-scale molecular docking virtual screen. Decoy molecules share the same physical properties as ligands that bind a protein but are topologically dissimilar to ensure that they do not actually bind the protein. What we found was that charge mismatching between ligands and decoys could bias one’s docking setup towards artifactually strong performance. Chapter 3 focuses on how we both decreased and increased the property space of decoys relative to ligands to safeguard against these docking setup biases. Fourth, I employ this knowledge of protein-ligand binding affinities to identify novel selective melatonin receptor ligands that are active in in vivo circadian rhythm assays. Finally, I discuss my current project on the CB1 cannabinoid receptor in the context of analgesia, followed by future directions

Modeling the Binding of Inhibitors/Drugs to the Human Serotonin Transporter

Human serotonin transporter (hSERT), a membrane protein from the neurotransmitter sodium symporter family, is implicated in depression disorder and has been the primary target of antidepressant discovery research for several decades. Since the currently available antidepressants may cause adverse effects and have several limitations, novel drugs are highly desired. However, the efforts to develop better therapeutics are hampered by the lack of a crystal structure of hSERT. Knowledge of the binding site of the drug and its orientation in the protein is crucial in structure-based drug discovery. We employed a novel computational protocol comprised of active site detection, docking, scoring, molecular dynamics simulations, and absolute binding free energy (ABFE) calculations to elucidate the binding site and the binding mode of a dual hSERT/5HT-1A blocker SSA-426 and our in-house hSERT inhibitor DJLDU-3-79 in hSERT. Through this approach, we propose that both of these inhibitors bind in the S1 pocket of hSERT and in a similar orientation. This disproves the earlier hypothesis that both these inhibitors bind in the S2 site; however, we are in agreement with the earlier hypothesis that both of the ligands orient similarly. Further, we resolved the ambiguity in binding energies and binding trends of the tricyclic antidepressant drugs clomipramine, imipramine, and desipramine with leucine transporter (LeuT) (a bacterial homologue of hSERT) through relative binding free energy (RBFE) calculations. Based on our RBFE results, we proposed that clomipramine should have the highest affinity for LeuT, followed by imipramine and desipramine. Finally, to achieve accuracy in binding energy estimations and to perform all CHARMM simulations, we developed CHARMM general force field parameters (CGenFF) for fifteen monoamine transporter ligands

Computer-Aided Design of Ligands at Multiple Protein Targets for Multifactorial Diseases

Today, drug discovery predominately focuses on the design of ligands with high selectivity towards a specific biological target. A significant limitation in the case of multi-factorial diseases (e.g. neurodegenerative disorders) is that effective therapy may require multi-target drugs addressing the complexity of multi-factorial pathologies. Here, single- and multi-target ligand design was investigated to discover novel compounds active at multiple proteins/multiple binding sites including allosteric ligands. Calpain-1, a challenging target, was selected to develop and evaluate computational approaches to the discovery of novel ligands. Current selective calpain-1 inhibitors are reported to bind to an allosteric site and their mode of action has remained elusive. To elucidate this, a structure-based virtual screening protocol was implemented to find chemically novel compounds with improved selectivity and a reduced side-effects profile. To develop methods for the discovery of multi-target ligands, a multi-target design approach, which could be beneficial in the treatment of Lung Carcinoma and Neurodegenerative diseases, was investigated. A novel ensemble of proteins was targeted to elevate intracellular cAMP, deemed to be beneficial in these diseases resulting in the discovery of ligands with high binding affinity at three targets, PDE10A, A1 and A2A receptors. In tandem, functional activity at the A2A receptor and PDE10A was investigated, resulting in the discovery of novel compounds, which exhibited anti-proliferative effects in lung carcinoma cell-lines correlating with the co-expression of the two targets and increased cAMP levels. Critically, the dynamics of one amino acid residue, Val84, was identified as a novel conformational descriptor of A2A receptor activation. Overall, novel single- and multi-target ligand design approaches are presented in this work, which could be applicable to a wide range of ligand design problems, across (multi-factorial) disease areas and target families. The findings may facilitate improved design of allosteric calpain-1 inhibitors using the PEF(S) domain, and encourage investigating the therapeutic benefits of dual ligands at the A2A receptor and PDE10A against lung cancer in vivo.IDB Cambridge International Scholarship and ERC Starting Grant (No. 336159