Simulating molecular docking with haptics

Intermolecular binding underlies various metabolic and regulatory processes of the cell, and the therapeutic and pharmacological properties of drugs. Molecular docking systems model and simulate these interactions in silico and allow the study of the binding process. In molecular docking, haptics enables the user to sense the interaction forces and intervene cognitively in the docking process. Haptics-assisted docking systems provide an immersive virtual docking environment where the user can interact with the molecules, feel the interaction forces using their sense of touch, identify visually the binding site, and guide the molecules to their binding pose. Despite a forty-year research e�ort however, the docking community has been slow to adopt this technology. Proprietary, unreleased software, expensive haptic hardware and limits on processing power are the main reasons for this. Another signi�cant factor is the size of the molecules simulated, limited to small molecules. The focus of the research described in this thesis is the development of an interactive haptics-assisted docking application that addresses the above issues, and enables the rigid docking of very large biomolecules and the study of the underlying interactions. Novel methods for computing the interaction forces of binding on the CPU and GPU, in real-time, have been developed. The force calculation methods proposed here overcome several computational limitations of previous approaches, such as precomputed force grids, and could potentially be used to model molecular exibility at haptic refresh rates. Methods for force scaling, multipoint collision response, and haptic navigation are also reported that address newfound issues, particular to the interactive docking of large systems, e.g. force stability at molecular collision. The i ii result is a haptics-assisted docking application, Haptimol RD, that runs on relatively inexpensive consumer level hardware, (i.e. there is no need for specialized/proprietary hardware)

Mechanistic insights and in silico studies on selected G protein-coupled receptors implicated in HIV and neurological disorders.

Doctoral Degree. University of KwaZulu-Natal, Durban.G protein-coupled receptors (GPCRs) are the largest membrane protein receptor superfamily involved in a wide range of physiological processes. GPCRs form the major class of drug targets for a diverse array of pathophysiological conditions. Consequently, GPCRs are recognised as drug targets for the treatment of various diseases, including neurological disorders, cardiovascular conditions, oncology, diabetes, and HIV. The recent advancement in GPCR structure resolutions has provided novel avenues to understand their molecular basis of signal transduction, ligand recognition and ligand-receptor interactions. These advances provide a framework for the structure-based discovery of new drugs in targeting GPCRs implicated in the pathogenesis of various human diseases. In this thesis, the interactions of inhibitors at two dopamine receptor subtypes and C-C chemokine receptor 5 (CCR5) of the Class A GPCR family were investigated. Dopamine receptors and CCR5 are validated GPCR targets implicated in neurological disorders and HIV disease, respectively. The lack of structural information on these receptors limited our comprehension of their antagonists’ structural dynamics and binding mechanisms. The recently solved crystal structures for these receptors have necessitated further investigations in their ligand-receptor interactions to obtain novel insights that may assist drug discovery towards these receptors. This thesis comprehensively investigated the binding profiles of atypical antipsychotics (class I and class II) at the first crystal structure of the D2 dopamine receptor (D2DR). The class I antipsychotics exhibited binding poses and dynamics different from the class II antipsychotics with disparate interaction mechanistic at D2DR active site. The class II antipsychotics were remarkably observed to establish a recurrent and vital interaction with Asp114 via strong hydrogen bond interactions. Furthermore, compared to class I antipsychotics, the class II antipsychotics were found to engage favourably with the deep hydrophobic pocket of D2DR. In addition, the structural basis and atomistic binding mechanistic of the preferential selective inhibition at D3DR over D2DR were explored. This study investigated two small molecules (R-VK4-40 and Y-QA31) with substantial selectivity (> 180-fold) for D3DR over D2DR. The selective antagonists adopted shallow binding modes at D3DR while demonstrating a deep hydrophobic pocket binding at D2DR. Also, the vital roles and contribution of critical residues to the selective binding of R-VK4-40 and Y-QA31were identified in D3DR. Structural and binding free energy analyses further discovered distinct stabilising effects of the selective antagonists on the secondary architecture and binding profiles of D3DR relative to D2DR. Furthermore, the atomistic molecular interaction mechanism of how slight structural modification between novel derivatives of 1-heteroaryl-1,3-propanediamine (Compd-21 and - 34) and Maraviroc significantly affects their binding profiles toward CCR5 were elucidated. This study utilised explicit lipid bilayer molecular dynamics (MD) simulations and advanced analyses to explore these inhibitory disparities. The thiophene moiety substitution common to Compd-21 and -34 was found to enhance their CCR5-inhibitory activities due to complementary high-affinity interactions with residues critical for the gp120 V3 loop binding. The study further highlights the structural modifications that may improve inhibitor competitiveness with the gp120 V3 loop. Finally, structure-based virtual screening of antiviral chemical database was performed to identify potential compounds as HIV-1 entry inhibitors targeting CCR5. The identified compounds made pertinent interactions with CCR5 residues critical for the HIV-1 gp120-V3 loop binding. Their predicted in silico physicochemical and pharmacokinetic descriptors were within the acceptable range for drug-likeness. Further structural optimisations and biochemical testing of the proposed compounds may assist in the discovery of novel HIV-1 therapy. The studies presented in this thesis provide novel mechanistic and in silico perspective on the ligand-receptor interactions of GPCRs. The findings highlighted in this thesis may assist in further research towards the identification of novel drug molecules towards CCR5 and D2-like dopamine receptor subtypes.List of thesis publications on page vi-vii. Research Output on page viii-ix

Continuous Perception for Immersive Interaction and Computation in Molecular Sciences

Chemistry aims to understand the structure and reactions of molecules, which involve phenomena occurring at microscopic scales. However, scientists perceive the world at macroscopic scales, making it difficult to study complex molecular objects. Graphical representations, such as structural formulas, were developed to bridge this gap and aid in understanding. The advent of Quantum Mechanics further increased the complexity of the representation of microscopic objects. This dichotomy between conceptual representation and predictive quantification forms the foundation of Chemistry, now further explored with the rise of Artificial Intelligence. Recent advancements in computational sciences, increased computational power, and developments in Machine-Learning (ML) raise questions about the traditional scientific method. Computational scientists, who have relied on approximations based on fundamental rules, now face the possibility of accurately simulating nature without strictly adhering to its laws. This shift challenges the association between progress in understanding a phenomenon and the ability to predict it. Deep learning models can not only make predictions but also create new data. While these techniques find applications in fields like Natural Language Processing, they suffer from limitations and lack true intelligence or awareness of physical laws. The thesis aims to create mathematical descriptors for atom types, bond types, and angle types in ML procedures, ensuring the retention of their chemical meaning. The goal is to make quantitative predictions while interpreting changes in descriptors as chemical changes. To achieve this, the thesis develops a software called Proxima for Molecular Perception, which automatically perceives features from molecules. Proxima treats strongly coupled electrons as covalent bonds and lone pairs, while delocalized electrons are modeled using a Tight-Binding model. The resulting Molecular Graph captures the weak interactions between these units. Overall, this thesis explores the intersection of computational chemistry and Machine-Learning to enhance our understanding and predictive capabilities in the field of Chemistry by building the so-called Virtual Laboratory, a virtual environment with automatic access to structural databases to test chemical ideas on the fly (pre-processing) and explore the output of computational software (post-processing).  &nbsp

OPTIMIZATION AND APPLICATION OF COMPUTATIONAL METHODS FOR THE DESIGN OF PROTEIN-PROTEIN INTERACTIONS MODULATORS

In the wide field of PPIs, this PhD project has been focused on the optimization and application of computational methods for the design of PPIs modulators, with a particular interest toward peptide modulators targeting PPIs involving helical motifs. In this contest, the first part of the project has been aimed to define the rationales behind the helical secondary structure stabilization and the helical screw sense selectivity exerted by chiral C\u3b1-tetrasubstituted amino acids (cCTAAs) through REMD simulations and QTAIM analyses, and the mechanisms responsible of the helical screw sense inversion through PNEB simulations. In detail, it has been found that the helical motif is stabilized by two complementary mechanisms: the first depends on the steric hindrance exerted by the cCTAA in an area parallel to the peptide helix axis and downstream of the cCTAA itself, whereas the second consists in the strengthening of the helical H-bond network thanks to peculiar C-H\ub7\ub7\ub7O=C interactions. Analogously, P-helical screw sense selectivity is ascribable to the cCTAA steric hindrance parallel to the peptide helix axis, without particular preferences for the region downstream and upstream of the cCTAA, together with quite strong noncovalent interactions, consisting of classical N \u2013 H\ub7\ub7\ub7O=C H-bonds and weak C \u2013 H\ub7\ub7\ub7O=C interactions. Furthermore, PNEB simulations performed on achiral peptides of different lengths suggest that the helical screw sense inversion requires the formation of \u3b3-turns, although a preferential screw sense inversion direction was not found. Therefore, the knowledge gained from these studies could be helpful in designing stable helical peptides, having a preferential screw sense and that can be in principle activated in situ by inducing a conformational switch from P to M helix or vice versa. Conversely, the second part of the project has been focused on the optimization of an MMGBSA based method, called Nwat-MMGBSA, aimed to improve the correlation between predicted binding energies of PPI complexes and experimental data. This approach, consisting in the inclusion, as part of the receptor, of hydration shells around the ligand during the MMGBSA calculations, was initially tested on classical receptor-ligand complexes and, then, automatized, optimized and tested on PPI complexes. This approach turned out to be good for the evaluation of PPI modulators activities, from different points of view. First of all, when water played a significant role in mediating protein-ligand interactions, the application of Nwat-MMGBSA improved the correlation between predicted and experimental data. On the other hand, if the solvent does not explicitly participate to the interaction, it did not give detrimental results compared to those obtained with the standard approach. In addition, the protocol proved to be robust and reproducible, giving equivalent results by using different setups. Furthermore, although an optimal number of water molecules to include in the hydration shell could not be found, in the case of PPI interactions inhibited by small molecules the inclusion of 50 \u2013 60 water molecules appears to be a good choice. A non-negligible advantage of this approach is represented by the possibility to automatize it, making it applicable for drug design/discovery purposes. Therefore, although further evaluations are needed, most of all on larger datasets, the knowledge coming from the combination of both parts of the project can be exploited for the design of stable non-natural peptides targeting PPIs

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD