Effect of set up protocols on the accuracy of alchemical free energy calculation over a set of ACK1 inhibitors

Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking–MM/PBSA–AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinitiesPeer ReviewedPostprint (published version

Exploring protein flexibility during docking to investigate ligand-target recognition

Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue. The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline. Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes

Automating Free Energy Perturbation Calculations For Drug Design

Η διαδικασία σχεδιασμού φαρμάκων έχει βελτιστοποιηθεί με τη βοήθεια των ηλεκτρονικών υπολογιστών, έχοντας γίνει πιο αποδοτική από πλευράς κόστους και χρόνου. Σήμερα, χρησιμοποιώντας την τρισδιάστατη δομή του θεραπευτικού στόχου ο ορθολογιστικός σχεδιασμός φαρμάκων μπορεί να ποσοτικοποιήσει τις μοριακές αλληλεπιδράσεις που εμπλέκονται στη δέσμευση προσδέτη-πρωτεΐνης. Η ακριβής αυτή ποσοτικοποίηση βοηθά στην βελτιστοποίηση αλληλεπιδράσεων εκτός στόχου, οι οποίες παίζουν σημαντικό ρόλο στην ανίχνευση των βέλτιστων προσδετών. Ένα από τα πιο σημαντικά καθήκοντα στον σχεδιασμό φαρμάκων είναι να προβλέψουμε μεταξύ μιας σειράς υποψήφιων ποιά από αυτά θα δεσμευτούν καλύτερα στον θεραπευτικό στόχο. Σε αυτή την κατεύθυνση έχουν αναπτυχθεί μεθοδολογίες σχετικής δέσμευσης της ελεύθερης ενέργειας, οι οποίες βασίζονται σε μοριακές προσομοιώσεις, στη φυσική και στην αυστηρή στατιστική μηχανική για τον υπολογισμό των διαφορών στην ελεύθερη ενέργεια σύνδεσης μεταξύ ενός γονικού υποψήφιου φαρμάκου και αναλόγων του. Για παράδειγμα, οι υπολογισμοί της Ελεύθερης Ενεργειακής Διαταραχής (FEP) σε συνδυασμό με τις προσομοιώσεις Μοριακής Δυναμικής (MD) υπολογίζουν την ελεύθερη διαφορά ενέργειας μεταξύ ενός αρχικού και ενός τελικού μορίου. Αυτές οι μεθοδολογίες έχουν σημαντικές δυνατότητες, ωστόσο έχουν περιοριστεί από τεχνικές προκλήσεις όπως η χειροκίνητη δημιουργία μεγάλου αριθμού αρχείων εισόδου για την εγκατάσταση / εκτέλεση / ανάλυση ελεύθερων προσομοιώσεων ενέργειας. Η αυτοματοποίηση των υπολογισμών της διαταραχής της ελεύθερης ενέργειας απλοποιεί τη χρήση των υπολογισμών FEP και παρέχει υπολογισμούς υψηλής απόδοσης για ακριβείς προβλέψεις πριν από την σύνθεση μιας ένωσης και συνεπώς εξοικονομεί τεράστιο χρόνο και κόστος. Σε αυτή τη διατριβή περιγράφεται ένας αλγόριθμος, ονομαζόμενος FEPrepare, ο οποίος αυτοματοποιεί τη διαδικασία στησίματος για σχετικές δεσμευτικές προσομοιώσεις ελεύθερης ενέργειας μέσω ενός ιστόποπου. Αυτοματοποιείται τη διαδικασία του στησίματος για υπολογισμούς FEP στο πλαίσιο του NAMD, ενός από τους σημαντικότερους μηχανισμούς MD. Ο χρήστης ανεβάζει τα αρχεία δομής πρωτεΐνης και των προσδεμάτων, ο αλγόριθμος ο οποίος είναι γραμμένος σε Python χρησιμοποιεί τα αρχεία αυτά για να μετονομάσει τα άτομα, να αναδιανείμει τα φορτία των ατόμων και να δημιουργήσει τα απαραίτητα αρχεία για το VMD, ένα πρόγραμμα μοριακής προβολής που μπορεί να χρησιμοποιηθεί για τη δημιουργία προσομοίωσης του NAMD και να βοηθήσει στην ανάλυση των δεδομένων που παράγει το NAMD, για να παράξει τα αρχεία που χρειάζονται, να κάνει την υδρόλυση και τον ιονισμό. Αφού ο αλγόριθμος επιβεβαιώσει οτι όλα τα αρχεία είναι συμβατά με το NAMD τα παρέχει στον χρήστη. Οι υπολογισμοί σχετικής ελεύθερης ενέργειας στον σχεδιασμό φαρμάκων έχουν αποδειχθεί πολύ χρήσιμοι καθώς κάνουν την διαδικασία της βελτιστοποίησης πολύ πιο γρήγορη και φθηνή. Σε αυτή τη διπλωματική παρουσιάζεται η αυτοματοποιήση υπολογισμών ελεύθερης ενέργειας πρόσδεσης, για τη διαδικασία της βελτιστοποίησης.The advent of technological advances of computer-aided drug design has streamlined the drug design process, rendering it more cost- and time-efficient. Nowadays, rational structure-based drug design may quantify underlying molecular interactions involved in ligand-protein binding by utilizing the 3D structure of the therapeutic target in the process. Accurate quantification of these interactions can aid the optimization of binding affinity,selectivity, and other off -target interactions, which are a critical part of hit-to-lead and lead optimization efforts in drug discovery. One of the most important tasks in the lead optimization phase of the drug design process is to predict, among a series of lead candidates, which ones will bind more strongly to the therapeutic target. In this direction, relative binding free energy methodologies have been developed, which rely on physics-based molecular simulations and rigorous statistical mechanics to calculate the differences in the free energy of binding between a parent candidate drug and analogues. For example, Free Energy Perturbation (FEP) calculations coupled with Molecular Dynamics (MD) simulations calculate the free energy difference between an initial (reference) and an analog (target) molecule to an average of a function of their energy difference evaluated by sampling for the initial state. Such methodologies have shown significant potential in the lead optimization process, however, they have been limited by technical challenges such as manual creation of large numbers of input files to setup/run/analyze free energy simulations. Automating free energy perturbation calculations would streamline the use of FEP calculations and would be a step forward to delivering high throughput calculations for accurate predictions of relative binding affinities before a compound is synthesized, and consequently save enormous experimental and human resources. In this thesis, an algorithm called FEPrepare, which automates the set up procedure for relative binding free energy simulations has been designed and implemented as the first web-based server. The web server automates the set-up procedure for FEP calculations within the context of NAMD, one of the major MD engines. The user has to upload the structure files to the web-server. The algorithm is written in Python, utilizes the structure files uploaded by the user in order to perform atom renaming, and partial charge redistribution and create the necessary input files for VMD, a molecular viewer program, that can be used to help set up NAMD simulations and to help analyse and visualize NAMD output, to generate all needed files for the calculations. After the algorithm confirms compatibility of the required files with NAMD, it provides the user with everything needed to run a simulation. Relative binding free energy calculations in drug design have proven very effective in facilitating the lead optimization process both time and cost efficient. The automation of Free Energy Perturbation calculations to provide access to large-scale simulations for lead optimization has been presented in this thesis

Applications of artificial intelligence to alchemical free energy calculations in contemporary drug design

The work presented in this thesis resides at the interface of alchemical free energy methods (AFE) and machine-learning (ML) in the context of computer-aided drug discovery (CADD). The majority of the work consists of explorations into regions of synergy between the individual parts. The overarching hypothesis behind this work is that although areas of high potential exist for standalone ML and AFE in CADD, an additional source of value can be found in areas where ML and AFE are combined in such a way that the new methodology profits from key strengths in either part. Physics-based AFE calculations have - over several decades - grown into precise and accurate sub-kcal·mol−1 (in terms of mean absolute error versus experimental measures) methods of predicting ligand-protein binding affinities which is the main driver of its popularity in project support in drug design workflows. Data-driven ML methods have seen a similar rapid development spurred by the exponential growth in computational hardware capabilities, but are generally still lacking in accuracy versus experimental measures of binding affinities to support drug design work. Contrastingly, however, the first relies mainly on physical rules in the form of statistical mechanics and the latter profits from interpolating signals within large training domains of data. After a historical and theoretical introduction into drug discovery, AFE calculations and ML methods, the thesis will highlight several studies that reflect the above hypothesis along multiple key points in the AFE workflow. Firstly, a methodology that combines AFE with ML has been developed to compute accurate absolute hydration free energies. The hybrid AFE/ML methodology was trained on a subset of the FreeSolv database, and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, AFE/ML yields more precise estimates of free energies of hydration, and requires a fraction of the training set size to outperform standalone AFE calculations. The ML-derived correction terms are further shown to be transferable to a range of related AFE simulation protocols. The approach may be used to inexpensively improve the accuracy of AFE calculations, and to flag molecules which will benefit the most from bespoke force field parameterisation efforts. Secondly, early investigations into data-driven AFE network generators has been performed. Because AFE calculations make use of alchemical transformations between ligands in congeneric series, practitioners are required to estimate an optimal combination of transformations for each series. AFE networks constitute the collection of edges chosen such that all ligands (nodes) are included in the network and where each edge is a AFE calculation. As there are a vast number of possible configurations for such networks this step in AFE setup suffers from several shortcomings such as scalability and transferability between AFE softwares. Although AFE network generation has been automated in the past, the algorithm depends mostly on expert-driven estimation of AFE transformation reliabilities. This work presents a first iteration of a data-driven alternative to the state-of-the-art using a graph siamese neural network architecture. A novel dataset, RBFE Space, is presented as a representative and transferable training domain for AFE ML research. The workflow presented in this thesis matches state-of-the-art AFE network generation performance with several key benefits. The workflow provides full transferability of the network generator because RBFE-Space is open-sourced and ready to be applied to other AFE softwares. Additionally, the deep learning model represents the first robust ML predictor of transformation reliabilities in AFE calculations. Finally, one major shortcoming of AFE calculations is its decreased reliability for transformations that are larger than ∼5 heavy atoms. The work reported in this thesis describes investigations into whether running charge, Van der Waals and bond parameter transformations individually (with variable λ allocation per step) offers an advantage to transforming all parameters in a single step, as is the current standard in most AFE workflows. Initial results in this work qualitatively suggest that the bound leg benefits from a MultiStep protocol over a onestep (”SoftCore”) protocol, whereas the free leg does not show benefit. Further work was performed by Cresset that showed no observable benefit of the MultiStep approach over the Softcore approach. Several key findings are reported in this work that illustrate the benefits of dissecting an FEP approach and comparing the two approaches side-by-side

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

Application of computer-aided drug design for identification of P. falciparum inhibitors

Malaria is a millennia-old disease with the first recorded cases dating back to 2700 BC found in Chinese medical records, and later in other civilizations. It has claimed human lives to such an extent that there are a notable associated socio-economic consequences. Currently, according to the World Health Organization (WHO), Africa holds the highest disease burden with 94% of deaths and 82% of cases with P. falciparum having ~100% prevalence. Chemotherapy, such as artemisinin combination therapy, has been and continues to be the work horse in the fight against the disease, together with seasonal malaria chemoprevention and the use of insecticides. Natural products such as quinine and artemisinin are particularly important in terms of their antimalarial activity. The emphasis in current chemotherapy research is the need for time and cost-effective workflows focussed on new mechanisms of action (MoAs) covering the target candidate profiles (TCPs). Despite a decline in cases over the past decades with, countries increasingly becoming certified malaria free, a stalling trend has been observed in the past five years resulting in missing the 2020 Global Technical Strategy (GTS) milestones. With no effective vaccine, a reduction in funding, slower drug approval than resistance emergence from resistant and invasive vectors, and threats in diagnosis with the pfhrp2/3 gene deletion, malaria remains a major health concern. Motivated by these reasons, the primary aim of this work was a contribution to the antimalarial pipeline through in silico approaches focusing on P. falciparum. We first intended an exploration of malarial targets through a proteome scale screening on 36 targets using multiple metrics to account for the multi-objective nature of drug discovery. The continuous growth of structural data offers the ideal scenario for mining new MoAs covering antimalarials TCPs. This was combined with a repurposing strategy using a set of orally available FDA approved drugs. Further, use was made of time- and cost-effective strategies combining QVina-W efficiency metrics that integrate molecular properties, GRIM rescoring for molecular interactions and a hydrogen mass repartitioning (HMR) molecular dynamics (MD) scheme for accelerated development of antimalarials in the context of resistance. This pipeline further integrates a complex ranking for better drug-target selectivity, and normalization strategies to overcome docking scoring function bias. The different metrics, ranking, normalization strategies and their combinations were first assessed using their mean ranking error (MRE). A version combining all metrics was used to select 36 unique protein-ligand complexes, assessed in MD, with the final retention of 25. From the 16 in vitro tested hits of the 25, fingolimod, abiraterone, prazosin, and terazosin showed antiplasmodial activity with IC50 2.21, 3.37, 16.67 and 34.72 μM respectively and of these, only fingolimod was found to be not safe with respect to human cell viability. These compounds were predicted active on different molecular targets, abiraterone was predicted to interact with a putative liver-stage essential target, hence promising as a transmission-blocking agent. The pipeline had a promising 25% hit rate considering the proteome-scale and use of cost-effective approaches. Secondly, we focused on Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase (PfDXR) using a more extensive screening pipeline to overcome some of the current in silico screening limitations. Starting from the ZINC lead-like library of ~3M, hierarchical ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches with molecular docking and re-scoring using eleven scoring functions (SFs) were used. Later ranking with an exponential consensus strategy was included. Selected hits were further assessed through Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA), advanced MD sampling in a ligand pulling simulations and (Weighted Histogram Analysis Method) WHAM analysis for umbrella sampling (US) to derive binding free energies. Four leads had better predicted affinities in US than LC5, a 280 nM potent PfDXR inhibitor with ZINC000050633276 showing a promising binding of -20.43 kcal/mol. As shown with fosmidomycin, DXR inhibition offers fast acting compounds fulfilling antimalarials TCP1. Yet, fosmidomycin has a high polarity causing its short half-life and hampering its clinical use. These leads scaffolds are different from fosmidomycin and hence may offer better pharmacokinetic and pharmacodynamic properties and may also be promising for lead optimization. A combined analysis of residues’ contributions to the free energy of binding in MM-PBSA and to steered molecular dynamics (SMD) Fmax indicated GLU233, CYS268, SER270, TRP296, and HIS341 as exploitable for compound optimization. Finally, we updated the SANCDB library with new NPs and their commercially available analogs as a solution to NP availability. The library is extended to 1005 compounds from its initial 600 compounds and the database is integrated to Mcule and Molport APIs for analogs automatic update. The new set may contribute to virtual screening and to antimalarials as the most effective ones have NP origin.Thesis (PhD) -- Faculty of Science, Biochemistry and Microbiology, 202