Novel algorithms and high-performance cloud computing enable efficient fully quantum mechanical protein-ligand scoring

Ranking the binding of small molecules to protein receptors through physics-based computation remains challenging. Though inroads have been made using free energy methods, these fail when the underlying classical mechanical force fields are insufficient. In principle, a more accurate approach is provided by quantum mechanical density functional theory (DFT) scoring, but even with approximations, this has yet to become practical on drug discovery-relevant timescales and resources. Here, we describe how to overcome this barrier using algorithms for DFT calculations that scale on widely available cloud architectures, enabling full density functional theory, without approximations, to be applied to protein-ligand complexes with approximately 2500 atoms in tens of minutes. Applying this to a realistic example of 22 ligands binding to MCL1 reveals that density functional scoring outperforms classical free energy perturbation theory for this system. This raises the possibility of broadly applying fully quantum mechanical scoring to real-world drug discovery pipelines.Comment: 15 pages, 5 figures, 1 tabl

Novel algorithms and high-performance cloud computing enable efficient fully quantum mechanical protein-ligand scoring

Ranking the binding of small molecules to protein receptors through physics-based computation remains challenging. Though inroads have been made using free energy methods, these fail when the underlying classical mechanical force fields are insufficient. In principle, a more accurate approach is provided by quantum mechanical density functional theory (DFT) scoring, but even with approximations, this has yet to become practical on drug discovery-relevant timescales and resources. Here, we describe how to overcome this barrier using algorithms for DFT calculations that scale on widely available cloud architectures, enabling full density functional theory, without approximations, to be applied to protein-ligand complexes with approximately 2500 atoms in tens of minutes. Applying this to a realistic example of 22 ligands binding to MCL1 reveals that density functional scoring outperforms classical free energy perturbation theory for this system. This raises the possibility of broadly applying fully quantum mechanical scoring to real-world drug discovery pipelines

The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities

Introduction: The molecular mechanics energies combined with the Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods are popular approaches to estimate the free energy of the binding of small ligands to biological macromolecules. They are typically based on molecular dynamics simulations of the receptor-ligand complex and are therefore intermediate in both accuracy and computational effort between empirical scoring and strict alchemical perturbation methods. They have been applied to a large number of systems with varying success. Areas covered: The authors review the use of MM/PBSA and MM/GBSA methods to calculate ligand-binding affinities, with an emphasis on calibration, testing and validation, as well as attempts to improve the methods, rather than on specific applications. Expert opinion: MM/PBSA and MM/GBSA are attractive approaches owing to their modular nature and that they do not require calculations on a training set. They have been used successfully to reproduce and rationalize experimental findings and to improve the results of virtual screening and docking. However, they contain several crude and questionable approximations, for example, the lack of conformational entropy and information about the number and free energy of water molecules in the binding site. Moreover, there are many variants of the method and their performance varies strongly with the tested system. Likewise, most attempts to ameliorate the methods with more accurate approaches, for example, quantum-mechanical calculations, polarizable force fields or improved solvation have deteriorated the results

Recent Applications of Quantitative Structure-Activity Relationships in Drug Design

One of the most important challenges that face medicinal chemists today is the design of new drugs with improved properties and diminished side-effects for treating human disease such as AIDS and others. Medicinal chemists began the process by taking a lead structure and then finding analogs exhibiting the preferred biological activities. Next, they used their experience and chemical insight to eventually choose a nominee analog for further development. This process is difficult, expensive and took a long time. The conventional methods of drug discovery are now being supplemented by shortest approaches made possible by the accepting of the molecular processes involved in the original disease. In this view, the preliminary point in drug design is the molecular target which is receptor or enzyme in the body as an option of the existence of known lead structure

Computational studies of drug-binding kinetics

The drug-receptor binding kinetics are defined by the rate at which a given drug associates with and dissociates from its binding site on its macromolecular receptor. The lead optimization stage of drug discovery programs usually emphasizes optimizing the affinity (as described by the equilibrium dissociation constant, Kd) of a drug which depends on the strength of its binding to a specific target. Since affinity is optimized under equilibrium conditions, it does not always ensures higher potency in vivo. There has been a growing consensus that, in addition to Kd, kinetic parameters (kon and koff ) should be optimized to improve the chances of a good clinical outcome. However, current understanding of the physicochemical features that contribute to differences in binding kinetics is limited. Experimental methods that are used to determine kinetic parameters for drug binding and unbinding are often time consuming and labor-intensive. Therefore, robust, high-throughput in silico methods are needed to predict binding kinetic parameters and to explore the mechanistic determinants of drug-protein binding. As the experimental data on drug-binding kinetics is continuously growing and the number of crystallographic structures of ligand-receptor complexes is also increasing, methods to compute three dimensional (3D) Quantitative-Structure-Kinetics relationships (QSKRs) offer great potential for predicting kinetic rate constants for new compounds. COMparative BINding Energy(COMBINE) analysis is one example of such approach that was developed to derive target-specific scoring functions based on molecular mechanics calculations. It has been used extensively to predict properties such as binding affinity, target selectivity, and substrate specificity. In this thesis, I made the first application of COMBINE analysis to derive Quantitative Structure-Kinetics Relationships (QSKRs) for the dissociation rates. I obtained models for koff of inhibitors of HIV-1 protease and heat shock protein 90 (HSP90) with very good predictive power and identified the key ligand-receptor interactions that contribute to the variance in binding kinetics. With technological and methodological advances, the use of all-atom unbiased Molecular Dynamics (MD) simulations can allow sampling upto the millisecond timescale and investigation of the kinetic profile of drug binding and unbinding to a receptor. However, the residence times of drug-receptor complexes are usually longer than the timescales that are feasible to simulate using conventional molecular dynamics techniques. Enhanced sampling methods can allow faster sampling of protein and ligand dynamics, thereby resulting in application of MD techniques to study longer timescale processes. I have evaluated the application of Tau-Random Acceleration Molecular Dynamics (Tau-RAMD), an enhanced sampling method based on MD, to compute the relative residence times of a series of compounds binding to Haspin kinase. A good correlation (R2 = 0.86) was observed between the computed residence times and the experimental residence times of these compounds. I also performed interaction energy calculations, both at the quantum chemical level and at the molecular mechanics level, to explain the experimental observation that the residence times of kinase inhibitors can be prolonged by introducing halogen-aromatic pi interactions between halogen atoms of inhibitors and aromatic residues at the binding site of kinases. I determined different energetic contributions to this highly polar and directional halogen-bonding interaction by partitioning the total interaction energy calculated at the quantum-chemical level into its constituent energy components. It was observed that the major contribution to this interaction energy comes from the correlation energy which describes second-order intermolecular dispersion interactions and the correlation corrections to the Hartree-Fock energy. In addition, a protocol to determine diffusional kon rates of low molecular weight compounds from Brownian Dynamics (BD) simulations of protein-ligand association was established using SDA 7 software. The widely studied test case of benzamidine binding to trypsin was used to evaluate a set of parameters and a robust set of optimal parameters was determined that should be generally applicable for computing the diffusional association rate constants of a wide range of protein-ligand binding pairs. I validated this protocol on inhibitors of several targets with varying complexity such as Human Coagulation Factor Xa, Haspin kinase and N1 Neuraminidase, and the computed diffusional association rate constants were compared with the experiments. I contributed to the development of a toolbox of computational methods: KBbox (http://kbbox.h-its.org/toolbox/), which provides information about various computational methods to study molecular binding kinetics, and different computational tools that employ them. It was developed to guide researchers on the use of the different computational and simulation approaches available to compute the kinetic parameters of drug-protein binding

Molecular modeling studies on HIV-1 Reverse Transcriptase (RT) and Heat shock protein (Hsp) 90 as a potential anti-HIV-1 target.

Masters Degree. University of KwaZulu-Natal, Durban.Human immunodeficiency virus (HIV) infection is the leading cause of death globally. This dissertation addresses two HIV-1 target proteins namely, HIV-1 Reverse Transcriptase (RT) and Heat shock protein (Hsp) 90. More specifically for HIV-1 RT, a case study for the identification of potential inhibitors as anti-HIV agents was carried out. A more refined virtual screening (VS) approach was implemented, which was an improvement on work previously published by our group- “target-bound pharmacophore modeling approach”. This study generated a pharmacophore library based only on highly contributing amino acid residues (HCAAR), instead of arbitrary pharmacophores, most commonly used in the conventional approaches in literature. HCAAR were distinguished based on free binding energy (FBE) contributions, obtained using calculation from molecular dynamics (MD) simulations. Previous approaches have relied on the docking score (DS) to generate energy-based pharmacophore models. However, DS are reportedly unreliable. Thus we present a model for a per-residue energy decomposition (PRED), constructed from MD simulation ensembles generating a more trustworthy pharmacophore model which can be applied in drug discovery workflow. This approach was employed in screening for potential HIV-1 RT inhibitors using the pharmacophoric features of the compound GSK952. The complex was subjected to docking and thereafter MD simulations confirmed the stability of the system. Experimentally determined inhibitors with known HIV-RT inhibitory activity were used to validate the proposed protocol. Two potential hits ZINC46849657 and ZINC54359621 showed a significant potential with regards to FBE. Reported results obtained from this work confirm that this new approach is favourable to the future of drug design process. Hsp90 was recently discovered to play a vital role in HIV-1 replication. Thus has emerged, as a promising target for anti-HIV-1 drugs. The molecular mechanism of Hsp90 is poorly understood, thus the second study was aimed to address this issue and provide a clear insight to the inhibition mechanism of Hsp90. Reasonable continuous MD simulations were employed for both unbound and bound Hsp90 conformations, to understand the dimerization and inhibition mechanisms. Results demonstrated that coumermycin A1 (C-A1), a newly discovered Hsp90 inhibitor, binds at the CTD dimer of Hsp90 and lead to a significant separation between orthogonally opposed residues, such as Arg591.B, Lys594.A, Ser663.A, Thr653.B, Ala665.A, Thr649.B, Leu646.B and Asn669A. A Large difference in magnitudes was observed in the radius of gyration (Rg), per-residue fluctuation, root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) confirming a completely more flexible state for the unbound conformation associated with dimerization. Whereas, a less globally correlated motion in the case of the bound conformer of Hsp90 approved a reduction of the dimeric process. This undoubtedly underlines the inhibition process due to ligand binding. The detailed dynamic analyses of Hsp90 presented herein are believed to give a greater insight and understanding to the function and mechanisms of inhibition of Hsp90. The report on the inhibitor-binding mode would also be of great assistance in the design of prospective inhibitors against Hsp90 as potential HIV target

Advances in the treatment of explicit water molecules in docking and binding free energy calculations

BACKGROUND: The inclusion of direct effects mediated by water during the ligand-receptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions. OBJECTIVE: In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered. RESULTS: Here, we analyse software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules. CONCLUSIONS: Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon