Identification of Ligands with Tailored Selectivity: Strategies & Application

In the field of computer-aided drug design, docking is a computational tool, often used to evaluate the sterical and chemical complementarity between two molecules. This technique can be used to estimate the binding or non-binding of a small molecule to a protein binding site. The classical application of docking is to find those molecules within a large set of molecules that bind a certain target protein and modulate its biological activity. This setup can be considered as established for a single target protein. In contrast to this, the docking to multiple target structures offers new possible applications. It can be used, for example, to assess the binding profile of a ligand against a number of proteins. In this work, the applicability of docking is assessed in such a scenario where multiple target structures are used. The corresponding proteins mostly belong to the family of G protein-coupled receptors. This protein family is very large and numerous GPCRs have been identified as potential drug targets, explaining the their relevance in pharmaceutical research. The protein structures used herein have different relationships and thus represent different application scenarios. The first case study uses two structures belonging to different proteins. These proteins are CXCR3 and CXCR4, a pair of chemokine GPCRs. In this chapter, new ligands are identified that bind to these proteins and modulate their biological activity. More importantly, for each of these newly identified ligands it could be predicted using docking, whether this ligand binds only to one of the two target proteins or to both. This study proves the applicability of docking to identify ligands with tailored selectivity. In addition, these ligands show excellent binding affinities to their respective target or targets. In the following two studies, the docking to different structures of the same target protein is investigated. The first application aims at identifying ligands selective for either one of two isoforms of the zebrafish CXC receptor 4. Subsequently, multiple conformations of the chemokine receptor CCR5 are used to show that different starting structures can identify different ligands. Next to the plain identification of chemically new ligands, experimental hurdles to prove the biological activity of these molecules in a functional assay is discussed. These difficulties are based on the fact that docking evaluates the structural complementarity between molecules and protein structures rather than predicting the effect of these molecules on the proteins. In addition, GPCRs form a challenging set of target proteins, since their ligands can induce a variety of different effects. Finally, the general applicability of multi-target docking to a very large number of structures is investigated. For this evaluation, kinases are used as protein family since many more structures have been experimentally determined for these proteins compared to GPCRs as membrane proteins. First, using published experimental data, a dataset is created consisting of several hundred kinase structures and a set of small-molecule kinase inhibitors. This dataset is characterised by the availability of experimental binding data for each single kinase-inhibitor combination. These experimental data were subsequently compared to the docking results of each ligand into each single kinase structure. The results indicate that a reliable selectivity prediction for a ligand is highly demanding in such a large-scale setup and beyond current possibilities. However, it can be shown that the prediction accuracy of docking can be improved by normalising the docking scores over multiple ligands and proteins. Based on these findings, the idea of "protein decoys" is developed, which might in the future allow more accurate predictions of selectivity profiles using docking

Acceleration and Verification of Virtual High-throughput Multiconformer Docking

The work in this dissertation explores the use of massive computational power available through modern supercomputers as a virtual laboratory to aid drug discovery. As of November 2013, Tianhe-2, the fastest supercomputer in the world, has a theoretical performance peak of 54,902 TFlop/s or nearly 55 thousand trillion calculations per second. The Titan supercomputer located at Oak Ridge National Laboratory has 560,640 computing cores that can work in parallel to solve scientific problems. In order to harness this computational power to assist in drug discovery, tools are developed to aid in the preparation and analysis of high-throughput virtual docking screens, a tool to predict how and how well small molecules bind to disease associated proteins and potentially serve as a novel drug candidate. Methods and software for performing large screens are developed that run on high-performance computer systems. The future potential and benefits of using these tools to study polypharmacology and revolutionizing the pharmaceutical industry are also discussed

Development and Extension of Cheminformatics Techniques for Integration of Diverse Data to Enhance Drug Discovery

The scientific community has fallen headlong into the age of data. With the available crop of information available to scientists growing at an exponential pace, tools to harvest this data and process it into knowledge are needed. This blanket statement is nowhere more true than in drug discovery today. The increasing quantities of bioactivity and protein crystallographic data provide key information capable of improving the state of virtual screening. The CoLiBRI methodology attempts to learn from the large knowledge base of protein-ligand interactions to discover a comprehensive model capable of filtering large libraries very quickly using only a protein structure. This modeling procedure has been greatly expanded to encompass a wide range of descriptor techniques and to use advanced statistical methods of multidimensional mapping. The growth of virtual screening methods (including CoLiBRI) has provided a plethora of options to cheminformaticians with little guidance on their strengths and weaknesses. This oversight in methodology benchmarking should be addressed to reduce the time and effort wasted applying subpar screening protocols. To attend to this issue, we developed a benchmark dataset that will enable a flood of methodology experimentation and validation. The recent generation of gene expression data and cancer cell growth inhibition data enable identification of signatures of cellular resistance. These signatures can be used as validated prognostic markers to guide patient management thereby fueling the personalization of cancer treatment. From the available data, we have derived hypothetical biomarkers of multidrug resistance and a flood of links between gene expression and chemical specific resistance that require experimental validation. The increasing capabilities of cheminformatics techniques require dissemination to the public to produce the greatest impact. We have therefore developed a web portal providing cheminformatics software and models to fuel public drug discovery efforts

Discovery by Virtual Screening of Ethionamide Boosters for Tuberculosis Treatment

Tuberculosis remains the world’s deadliest communicable bacterial disease with an unacceptably high death rate. In 2013 an estimated 1.5 million people died as a direct result of TB, and nine million new cases were reported. Multi-drug resistant (MDR) and extensively drug-resistant (XDR) tuberculosis cases are on the rise and without novel approaches to combat their spread, tuberculosis will continue to claim the lives of millions worldwide. One such novel approach is to rejuvenate the use of the second-line antibiotic ethionamide. Ethionamide is a structural analogue of the first-line pro-drug isoniazid, which is used widely and to which there is growing resistance. Ethionamide was introduced in the 1960s and primarily used in cases of drug-resistant TB due to its severe adverse effects. This makes ethionamide an exploitable target for small-molecule booster drugs. Expression of the enzyme responsible for ethionamide activation, EthA, is regulated by a transcriptional repressor EthR which can be inhibited to improve ethionamide activation and so reduce ethionamide treatment doses and bring an old drug new life in the clinic. EthR inhibitors are currently in development; here, chemoinformatic pipelining and virtual screening in GOLD were used to identify hits with novel scaffolds for hit-to-lead efforts from an initial library of over six million drug-like molecules. Thermal shift assays were used to identify EthR-binding molecules and SPR was utilised to confirm and potentially quantify binding affinities. Herein are reported the co-crystal structures of several hit molecules, used to confirm and characterise the EthR-ligand complexes. Through the application of computational, biophysical and crystallographic methods, this thesis presents several novel scaffolds for development against EthR. These novel hits will be developed to expand our arsenal against the growing, global problem of drug-resistant TB

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

Fragment Based Protein Active Site Analysis Using Markov Random Field Combinations of Stereochemical Feature-Based Classifications

Recent improvements in structural genomics efforts have greatly increased the number of hypothetical proteins in the Protein Data Bank. Several computational methodologies have been developed to determine the function of these proteins but none of these methods have been able to account successfully for the diversity in the sequence and structural conformations observed in proteins that have the same function. An additional complication is the flexibility in both the protein active site and the ligand. In this dissertation, novel approaches to deal with both the ligand flexibility and the diversity in stereochemistry have been proposed. The active site analysis problem is formalized as a classification problem in which, for a given test protein, the goal is to predict the class of ligand most likely to bind the active site based on its stereochemical nature and thereby define its function. Traditional methods that have adapted a similar methodology have struggled to account for the flexibility observed in large ligands. Therefore, I propose a novel fragment-based approach to dealing with larger ligands. The advantage of the fragment-based methodology is that considering the protein-ligand interactions in a piecewise manner does not affect the active site patterns, and it also provides for a way to account for the problems associated with flexible ligands. I also propose two feature-based methodologies to account for the diversity observed in sequences and structural conformations among proteins with the same function. The feature-based methodologies provide detailed descriptions of the active site stereochemistry and are capable of identifying stereochemical patterns within the active site despite the diversity. Finally, I propose a Markov Random Field approach to combine the individual ligand fragment classifications (based on the stereochemical descriptors) into a single multi-fragment ligand class. This probabilistic framework combines the information provided by stereochemical features with the information regarding geometric constraints between ligand fragments to make a final ligand class prediction. The feature-based fragment identification methodology had an accuracy of 84% across a diverse set of ligand fragments and the mrf analysis was able to succesfully combine the various ligand fragments (identified by feature-based analysis) into one final ligand based on statistical models of ligand fragment distances. This novel approach to protein active site analysis was additionally tested on 3 proteins with very low sequence and structural similarity to other proteins in the PDB (a challenge for traditional methods) and in each of these cases, this approach successfully identified the cognate ligand. This approach addresses the two main issues that affect the accuracy of current automated methodologies in protein function assignment

Cribado computacional de productos naturales inhibidores de la enzima malato sintasa de Candida albicans

La candidiasis es la micosis invasiva más frecuente en el mundo y puede causar infecciones potencialmente mortales en individuos inmunodeprimidos, su patógeno, Candida albicans, ha incrementado su resistencia a los fármacos antifúngicos en los últimos años. Buscar inhibir enzimas claves de las rutas metabólicas de este patógeno es importante para proponer nuevos fármacos. El objetivo es predecir inhibidores potenciales de la enzima malato sintasa de Candida albicans en productos naturales por cribado computacional. En cuanto a la metodología, para identificar nuevos compuestos candidatos anti-MS, realizamos un cribado virtual multi puntuación basado en el acoplamiento molecular de una biblioteca de 2223 compuestos naturales de la base de datos NuBBE, en el sitio de unión del ligando de un modelo de MS de Candida albicans en AlphaFold. Después de la detección virtual mediante cuatro programas de acoplamiento, el análisis de agrupamiento de los resultados de puntuación de los mejores compuestos clasificados se investigó sus propiedades farmacocinéticas en el programa SwissAdme y sus interacciones moleculares en el programa PoseView. En los resultados se obtuvo la estructura tridimensional optimizada de la enzima malato sintasa, la cual presenta buena calidad y una estructura altamente confiable. Mediante en cribado virtual se logró identificar 9 candidatos anti-MS, NuBBE_0376, NuBBE_0906, NuBBE_0955, NuBBE_0953, NuBBE_1076, NuBBE_1823, NuBBE_1474, NuBBE_2082, NuBBE_2089, los cuales cumplen con las reglas de Lipinski sin ninguna violación, además al analizar la interacción enzima-ligando se observa una buena calidad de diseño debido a que se caracteriza por una disposición sin colisiones de todos los componentes. Se concluye que los 9 compuestos naturales analizados, son candidatos farmacológicos para el desarrollo de nuevos antifúngicos contra Candida albicans; ya que, siguen la regla de cinco de Lipinski sin ninguna violación, lo que hace suponer que presentan buena solubilidad, absorción y permeabilidad. Por lo tanto, estos compuestos son candidatos farmacológicos para el desarrollo de nuevos antifúngicos contra Candida albicans.Perú. Universidad Nacional Mayor de San Marcos – RR N° 03556-R-19 con código de proyecto A19041071

IN SILICO APPROACHES IN DRUG DESIGN AND DEVELOPMENT: APPLICATIONS TO RATIONAL LIGAND DESIGN AND METABOLISM PREDICTION

In the last decades, the applications of computational methods in medicinal chemistry have experienced significant changes which have incredibly expanded their approaches, and more importantly their objectives. The overall aim of the present research project is to explore the different fields of the modelling studies by using well-known computational methods as well as different and innovative techniques. Indeed, computational methods traditionally consisted in ligand-based and the structure-based approaches substantially aimed at optimizing the ligand structure in terms of affinity, potency and selectivity. The studies concerning the muscarinic receptors in the present thesis applied these approaches for the rational design of novel improved bioactive molecules, interacting both in the orthosteric (e.g., 1,4-dioxane agonist) and in the allosteric sites. The research includes also the application of a novel method for target optimization, which consists in the generation of the so-called conformational chimeras to explore the flexibility of the modelled GPCR structures. In parallel, computational methods are finding successful applications in the research phase which precedes the ligand design and which is focused on a detailed validation and characterization of the biological target. A proper example of this kind of studies is given by the study regarding the purinergic receptors, which is aimed at the identification and characterization of potential allosteric binding pockets for the already reported inhibitors, exploiting also innovative approaches for binding site predictions (e.g., PELE, SPILLO-PBSS). Over time, computational applications felt a rich extension of their objectives and one of the clearest examples is represented by the ever increasing attempts to optimize the ADME/Tox profile of the novel compounds, so reducing the marked attrition in drug discovery caused by unsuitable pharmacokinetic profiles. Coherently, the first and main project of the present thesis regards the field of metabolism prediction and is founded on the meta-analysis and the corresponding database called MetaSar, manually collected from the recent specialized literature. This ongoing extended project includes different studies which are overall aimed at developing a comprehensive method for metabolism prediction. In detail, this Thesis reports an interesting application of the database which exploits an innovative predictive technique, the Proteochemometric modelling (PCM). This approach is indeed at the forefront of the latest modelling techniques, as it perfectly fits the growing request of new solutions to deal with the incredibly huge amount of data recently produced by the \u201comics\u201d disciplines. In this context, MetaSar represents an alternative and still appropriate source of data for PCM studies, which also enables the extension of its fields of application to a new avenue, such as the prediction of metabolism biotransformation. In the present thesis, we present the first example of these applications, which involves the building of a classification model for the prediction of the glucuronidation reaction. The field of glucuronidation reactions is exhaustively explored also through an homology modelling study aimed at defining the complete three-dimensional structure of the enzyme UGT2B7, the main isoform of glucuronidation enzymes in humans, in complex with the cofactor UDPGA and a typical substrate, such as Naproxen. The paths of the substrate entering to the binding site and the egress of the product have been investigated by performing Steered Molecular Dynamics (SMD) simulations, which were also useful to gain deeper insights regarding the full mechanism of action and the movements of the cofactor