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

Integrative Systems Approaches Towards Brain Pharmacology and Polypharmacology

Polypharmacology is considered as the future of drug discovery and emerges as the next paradigm of drug discovery. The traditional drug design is primarily based on a “one target-one drug” paradigm. In polypharmacology, drug molecules always interact with multiple targets, and therefore it imposes new challenges in developing and designing new and effective drugs that are less toxic by eliminating the unexpected drug-target interactions. Although still in its infancy, the use of polypharmacology ideas appears to already have a remarkable impact on modern drug development. The current thesis is a detailed study on various pharmacology approaches at systems level to understand polypharmacology in complex brain and neurodegnerative disorders. The research work in this thesis focuses on the design and construction of a dedicated knowledge base for human brain pharmacology. This pharmacology knowledge base, referred to as the Human Brain Pharmacome (HBP) is a unique and comprehensive resource that aggregates data and knowledge around current drug treatments that are available for major brain and neurodegenerative disorders. The HBP knowledge base provides data at a single place for building models and supporting hypotheses. The HBP also incorporates new data obtained from similarity computations over drugs and proteins structures, which was analyzed from various aspects including network pharmacology and application of in-silico computational methods for the discovery of novel multi-target drug candidates. Computational tools and machine learning models were developed to characterize protein targets for their polypharmacological profiles and to distinguish indications specific or target specific drugs from other drugs. Systems pharmacology approaches towards drug property predictions provided a highly enriched compound library that was virtually screened against an array of network pharmacology based derived protein targets by combined docking and molecular dynamics simulation workflows. The developed approaches in this work resulted in the identification of novel multi-target drug candidates that are backed up by existing experimental knowledge, and propose repositioning of existing drugs, that are undergoing further experimental validations

Multiple conformational states in retrospective virtual screening : homology models vs. crystal structures : beta-2 adrenergic receptor case study

Background: Distinguishing active from inactive compounds is one of the crucial problems of molecular docking, especially in the context of virtual screening experiments. The randomization of poses and the natural flexibility of the protein make this discrimination even harder. Some of the recent approaches to post-docking analysis use an ensemble of receptor models to mimic this naturally occurring conformational diversity. However, the optimal number of receptor conformations is yet to be determined. In this study, we compare the results of a retrospective screening of beta-2 adrenergic receptor ligands performed on both the ensemble of receptor conformations extracted from ten available crystal structures and an equal number of homology models. Additional analysis was also performed for homology models with up to 20 receptor conformations considered. Results: The docking results were encoded into the Structural Interaction Fingerprints and were automatically analyzed by support vector machine. The use of homology models in such virtual screening application was proved to be superior in comparison to crystal structures. Additionally, increasing the number of receptor conformational states led to enhanced effectiveness of active vs. inactive compounds discrimination. Conclusions: For virtual screening purposes, the use of homology models was found to be most beneficial, even in the presence of crystallographic data regarding the conformational space of the receptor. The results also showed that increasing the number of receptors considered improves the effectiveness of identifying active compounds by machine learning method

The structural properties of non-traditional drug targets present new challenges for virtual screening

Traditional drug targets have historically included signaling proteins that respond to small-molecules and enzymes that use small-molecules as substrates. Increasing attention is now being directed towards other types of protein targets, in particular those that exert their function by interacting with nucleic acids or other proteins rather than small-molecule ligands. Here, we systematically compare existing examples of inhibitors of protein–protein interactions to inhibitors of traditional drug targets. While both sets of inhibitors bind with similar potency, we find that the inhibitors of protein–protein interactions typically bury a smaller fraction of their surface area upon binding to their protein targets. The fact that an average atom is less buried suggests that more atoms are needed to achieve a given potency, explaining the observation that ligand efficiency is typically poor for inhibitors of protein– protein interactions. We then carried out a series of docking experiments, and found a further consequence of these relatively exposed binding modes is that structure-based virtual screening may be more difficult: such binding modes do not provide sufficient clues to pick out active compounds from decoy compounds. Collectively, these results suggest that the challenges associated with such non-traditional drug targets may not lie with identifying compounds that potently bind to the target protein surface, but rather with identifying compounds that bind in a sufficiently buried manner to achieve good ligand efficiency, and thus good oral bioavailability. While the number of available crystal structures of distinct protein interaction sites bound to small-molecule inhibitors is relatively small at present (only 21 such complexes were included in this study), these are sufficient to draw conclusions based on the current state of the field; as additional data accumulate it will be exciting to refine the viewpoint presented here. Even with this limited perspective however, we anticipate that these insights, together with new methods for exploring protein conformational fluctuations, may prove useful for identifying the “low-hanging fruit” amongst non-traditional targets for therapeutic intervention

On the mechanisms of protein interactions : predicting their affinity from unbound tertiary structures

Motivation: The characterization of the protein–protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein–protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures. Results: We present a new approach that relies on the unbound protein structures and protein docking to predict protein–protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol. Availability and implementation: The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock

Automated Docking Screens: A Feasibility Study

Molecular docking is themost practical approach to leverage protein structure for ligand discovery, but the technique retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCKBlaster, to investigate the feasibility of full automation. The method requires a PDB code, sometimes with a ligand structure, and from that alone can launch a full screen of large libraries. A critical feature is self-assessment, which estimates the anticipated reliability of the automated screening results using pose fidelity and enrichment. Against common benchmarks, DOCKBlaster recapitulates the crystal ligand pose within 2 A ̊ rmsd 50-60 % of the time; inferior to an expert, but respectrable. Half the time the ligand also ranked among the top 5 % of 100 physically matched decoys chosen on the fly. Further tests were undertaken culminating in a study of 7755 eligible PDB structures. In 1398 cases, the redocked ligand ranked in the top 5 % of 100 property-matched decoys while also posing within 2 A ̊ rmsd, suggesting that unsupervised prospective docking is viable. DOCK Blaster is available a