Development and application of modelling techniques in drug design

Structure-based drug design is a creative process that displays several features that make it closer to human reasoning than to machine automation. However, very often the user intervention is limited to the preparation of the input and analysis of the output of a computer simulation. In some cases, allowing human intervention directly in the process could improve the quality of the results by applying the researcher intuition directly into the simulation. Haptic technology has proven to be a useful method to interact in realtime with a virtual environment, enriching the user's experience and allowing for a more natural and direct interaction with three-dimensional systems. Reported in this thesis is the design and implementation of a user-driven computer program for structure-based drug design based on haptic technology and char acterised by a trimodal feedback system and its application alongside more traditional approaches to drug design projects in the anticancer and antiviral area. The software proved to be very useful in several projects, validating the applicability of haptic technology to drug design. The results were in good agreement with those obtained by traditional techniques. Moreover the approach resulted in the identification of novel HCV inhibitors and a putative inhibitor of the dimerisation of EGFR which resulted active in vitro tests

A computational framework for structure-based drug discovery with GPU acceleration.

Li, Hongjian.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 132-156).Abstracts in English and Chinese.Abstract --- p.iAbstract in Chinese --- p.iiiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.2Chapter 1.2 --- Objective --- p.2Chapter 1.3 --- Method --- p.3Chapter 1.4 --- Outline --- p.4Chapter 2 --- Background --- p.7Chapter 2.1 --- Overview of the Pharmaceutical Industry --- p.7Chapter 2.2 --- The Process of Modern Drug Discovery --- p.10Chapter 2.2.1 --- Development of an Innovative Idea --- p.10Chapter 2.2.2 --- Establishment of a Project Team --- p.11Chapter 2.2.3 --- Target Identification --- p.11Chapter 2.2.4 --- Hit Identification --- p.12Chapter 2.2.5 --- Lead Identification --- p.13Chapter 2.2.6 --- Lead Optimization --- p.14Chapter 2.2.7 --- Clinical Trials --- p.14Chapter 2.3 --- Drug Discovery via Computational Means --- p.15Chapter 2.3.1 --- Structure-Based Virtual Screening --- p.16Chapter 2.3.2 --- Computational Synthesis of Potent Ligands --- p.20Chapter 2.3.3 --- General-Purpose Computing on GPU --- p.23Chapter 3 --- Approximate Matching of DNA Patterns --- p.26Chapter 3.1 --- Problem Definition --- p.27Chapter 3.2 --- Motivation --- p.28Chapter 3.3 --- Background --- p.30Chapter 3.4 --- Method --- p.32Chapter 3.4.1 --- Binary Representation --- p.32Chapter 3.4.2 --- Agrep Algorithm --- p.32Chapter 3.4.3 --- CUDA Implementation --- p.34Chapter 3.5 --- Experiments and Results --- p.39Chapter 3.6 --- Discussion --- p.44Chapter 3.7 --- Availability --- p.45Chapter 3.8 --- Conclusion --- p.47Chapter 4 --- Structure-Based Virtual Screening --- p.50Chapter 4.1 --- Problem Definition --- p.51Chapter 4.2 --- Motivation --- p.52Chapter 4.3 --- Medicinal Background --- p.52Chapter 4.4 --- Computational Background --- p.59Chapter 4.4.1 --- Scoring Function --- p.59Chapter 4.4.2 --- Optimization Algorithm --- p.65Chapter 4.5 --- Method --- p.68Chapter 4.5.1 --- Scoring Function --- p.69Chapter 4.5.2 --- Inactive Torsions --- p.72Chapter 4.5.3 --- Optimization Algorithm --- p.73Chapter 4.5.4 --- C++ Implementation Tricks --- p.74Chapter 4.6 --- Data --- p.75Chapter 4.6.1 --- Proteins --- p.75Chapter 4.6.2 --- Ligands --- p.76Chapter 4.7 --- Experiments and Results --- p.77Chapter 4.7.1 --- Program Validation --- p.77Chapter 4.7.2 --- Virtual Screening --- p.81Chapter 4.8 --- Discussion --- p.89Chapter 4.9 --- Availability --- p.90Chapter 4.10 --- Conclusion --- p.91Chapter 5 --- Computational Synthesis of Ligands --- p.92Chapter 5.1 --- Problem Definition --- p.93Chapter 5.2 --- Motivation --- p.93Chapter 5.3 --- Background --- p.94Chapter 5.4 --- Method --- p.97Chapter 5.4.1 --- Selection --- p.99Chapter 5.4.2 --- Mutation --- p.102Chapter 5.4.3 --- Crossover --- p.102Chapter 5.4.4 --- Split --- p.103Chapter 5.4.5 --- Merging --- p.104Chapter 5.4.6 --- Drug Likeness Testing --- p.104Chapter 5.5 --- Data --- p.105Chapter 5.5.1 --- Proteins --- p.105Chapter 5.5.2 --- Initial Ligands --- p.107Chapter 5.5.3 --- Fragments --- p.107Chapter 5.6 --- Experiments and Results --- p.109Chapter 5.6.1 --- Binding Conformation --- p.112Chapter 5.6.2 --- Free Energy and Molecule Weight --- p.115Chapter 5.6.3 --- Execution Time --- p.116Chapter 5.6.4 --- Support for Phosphorus --- p.116Chapter 5.7 --- Discussion --- p.120Chapter 5.8 --- Availability --- p.123Chapter 5.9 --- Conclusion --- p.123Chapter 5.10 --- Personal Contribution --- p.124Chapter 6 --- Conclusion --- p.125Chapter 6.1 --- Conclusion --- p.125Chapter 6.2 --- Future Work --- p.128Chapter A --- Publications --- p.130Chapter A.1 --- Conference Papers --- p.130Chapter A.2 --- Journal Papers --- p.131Bibliography --- p.13

Computer-Aided Drug Design of Neuraminidase Inhibitors and MCL-1 Specific Drugs

Ph.DDOCTOR OF PHILOSOPH

Molecular dynamics based methods for the computation of standard binding free energies and binding selectivity of inhibitors of proteins of pharmaceutical interest

The field of Computer Aided Drug Design (CADD) has experienced substantial developments over the last few decades thanks to a rapid growth incomputing power. In particular, Molecular Dynamics (MD) simulations and associated techniques have earned increased attention within the pharmaceutical sector thanks to their rising accuracy and diminishing cost. However, there are still limitations in the usage of these methods, due to thedifficulty of sampling the rugged energy landscapes of protein-ligand complexes. The main theme of this work is to address the sampling problem of MD methods for predicting the binding free energies of different biomolecular complexes. This work starts using MD simulations as a sampling technique for a relative free energy calculation protocol using the Sire Open Molecular Dynamics (SOMD) software. This protocol was then integrated in a ligand design workflow to optimize the binding selectivity of cyclophilin (Cyps) inhibitors. Cyps are proteins known to play a vital role in various diseases, such as cancer, Alzheimer and viral infections. Most Cyp inhibitors to date,however, are cyclic peptides that have potency in the nanomolar range but produce severe side effects, are complex to synthesize and display complex pharmacokinetic profiles. Thus, there is a need for new selective smallmolecules targeting specific Cyps isoforms, in order to gain new insightsfor the inhibition of these therapeutically vital proteins. The computational workflow was able to suggest auspicious designs that they will be synthesized and characterized using biophysical techniques from Alison Hulme’s lab. Following, MD simulation methods were employed for the more challenging task of predicting the absolute free energies of binding of protein-ligand complexes. For this purpose, an Alchemical Free Energy (AFE) protocol was generated and its efficiency was evaluated in the Statistical Assessment of Modelling of Proteins and Ligands (SAMPL6) challenge. SAMPL challenges involve a series of blinded predictions of standard binding freeenergies for toy host-guest molecules. The results obtained from our protocol were ranked among the top submissions in terms of accuracy and correlation with experimental data. Encouraged by these results, we wanted to compare the efficiency of the AFE protocol versus a Markov State Modelling (MSM) protocol for the calculation of the standard binding free energy of a ligand to the intrinsically disordered protein c-Myc. The oncoprotein c-Myc is overexpressed in over 70% of human cancers and its inhibition has been considered the holygrail in cancer therapy. Due to its structural elasticity it is difficult to perform structure-based drug design methods for the discovery of novel compounds. The results showed that MSM can describe accurately the binding process of the ligand to the oncoprotein c-Myc, but the binding free energies were similar with the ones of the AFE protocol. Finally, an adaptive sampling protocol was established for the computation of the standard binding free energy and binding selectivity of lead-like ligands for the flexible protein MDM2. MDM2 is a vital protein that acts as an inhibitory mechanism of the transcription factor p53. p53 plays animportant role in the regulation of cellular processes and suppression of tumor development. For this reason, it is important to develop methods for the discovery of novel ligands that could inhibit the MDM2-p53 interaction through binding to the MDM2 protein. The results of the adaptive sampling study were encouraging as the protocol was able to predict binding selectivity trends for the MDM2-ligand complexes approximately six times faster than the original AFE protocol

Using Molecular Dynamics to Elucidate the Mechanism of Cyclophilin

Cyclophilins are ubiquitous enzymes that are involved in protein folding, signal transduction, viral proliferation, oncogenesis, and regulation of the immune system. Cyclophilin A is the prototype of the cyclophilin family. We use molecular dynamics to describe the catalytic mechanism of cyclophilin A in full atomistic detail by sampling critical points along the reaction coordinate, and use accelerated molecular dynamics to sample cis-trans interconversions. At these critical points, we analyze the conformational space sampled by the active site, flexibility of the enzyme backbone, and modulation of binding interactions.We use Kramer’s rate theory to determine how diffusion and free energy contribute to lowering the activation energy of prolyl isomerization. We also find preferential binding modes of several cyclophiln A inhibitors, and compare the conformational space sampled by inhibited cyclophilin A to the conformational space sampled during wild-type interactions. We also analyze the mechanism of the next family member cyclophilin B in order to probe differences in enzyme dynamics and intermolecular interactions that could possibly be exploited in isoform-specific drug design. Our results indicate that cyclophilin proceeds by a conformational selection binding mechanism that manipulates substrate sterics, electrostatic interactions, and multiple reaction timescales in order to speed up reaction rate. Conformational space sampled by cyclophilin when inhibited and when undergoing wild-type interactions share significant similarity. Cyclophilins A and B do have notable differences in enzyme dynamics, due to variation in intramolecular interactions that arise from variation in primary structures. This work demonstrates how computational methods can be used to clarify catalytic mechanisms

Theoretical and computational modeling of rna-ligand interactions

Ribonucleic acid (RNA) is a polymeric nucleic acid that plays a variety of critical roles in gene expression and regulation at the level of transcription and translation. Recently, there has been an enormous interest in the development of therapeutic strategies that target RNA molecules. Instead of modifying the product of gene expression, i.e., proteins, RNAtargeted therapeutics aims to modulate the relevant key RNA elements in the disease-related cellular pathways. Such approaches have two significant advantages. First, diseases with related proteins that are difficult or unable to be drugged become druggable by targeting the corresponding messenger RNAs (mRNAs) that encode the amino acid sequences. Second, besides coding mRNAs, the vast majority of the human genome sequences are transcribed to noncoding RNAs (ncRNAs), which serve as enzymatic, structural, and regulatory elements in cellular pathways of most human diseases. Targeting noncoding RNAs would open up remarkable new opportunities for disease treatment. The first step in modeling the RNA-drug interaction is to understand the 3D structure of the given RNA target. With current theoretical models, accurate prediction of 3D structures for large RNAs from sequence remains computationally infeasible. One of the major challenges comes from the flexibility in the RNA molecule, especially in loop/junction regions, and the resulting rugged energy landscape. However, structure probing techniques, such as the “selective 20-hydroxyl acylation analyzed by primer extension” (SHAPE) experiment, enable the quantitative detection of the relative flexibility and hence structure information of RNA structural elements. Therefore, one may incorporate the SHAPE data into RNA 3D structure prediction. In the first project, we investigate the feasibility of using a machine-learning-based approach to predict the SHAPE reactivity from the 3D RNA structure and compare the machine-learning result to that of a physics-based model. In the second project, in order to provide a user-friendly tool for RNA biologists, we developed a fully automated web interface, “SHAPE predictoR” (SHAPER) for predicting SHAPE profile from any given 3D RNA structure. In a cellular environment, various factors, such as metal ions and small molecules, interact with an RNA molecule to modulate RNA cellular activity. RNA is a highly charged polymer with each backbone phosphate group carrying one unit of negative (electronic) charge. In order to fold into a compact functional tertiary structure, it requires metal ions to reduce Coulombic repulsive electrostatic forces by neutralizing the backbone charges. In particular, Mg2+ ion is essential for the folding and stability of RNA tertiary structures. In the third project, we introduce a machine-learning-based model, the “Magnesium convolutional neural network” (MgNet) model, to predict Mg2+ binding site for a given 3D RNA structure, and show the use of the model in investigating the important coordinating RNA atoms and identifying novel Mg2+ binding motifs. Besides Mg2+ ions, small molecules, such as drug molecules, can also bind to an RNA to modulate its activities. Motivated by the tremendous potential of RNA-targeted drug discovery, in the fourth project, we develop a novel approach to predicting RNA-small molecule binding. Specifically, we develop a statistical potential-based scoring/ranking method (SPRank) to identify the native binding mode of the small molecule from a pool of decoys and estimate the binding affinity for the given RNA-small molecule complex. The results tested on a widely used data set suggest that SPRank can achieve (moderately) better performance than the current state-of-art models

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