Insight into the binding of DFG-out allosteric inhibitors to B-Raf Kinase using molecular dynamics and free energy calculations

B-Raf mutations are identified in 40-50% of patients with melanoma and among them, the substitution of valine for glutamic acid at position 600 (V600EB-Raf) is the most frequent. Treatment of these patients with B-Raf inhibitors has been associated with a clear clinical benefit. Unfortunately, multiple resistance mechanisms have been identified and new potent and selective inhibitors are currently needed. In this work, five different type II inhibitors, which bind V600EB-Raf in its DFG-out conformation, have been studied using molecular dynamics, free energy calculations and energy decomposition analysis. The ranking of calculated MM-PB/GBSA binding affinities is in good agreement with the experimentally measured ones. The per-residue decomposition of ΔGbinding, within the MM-GBSA approach, has been used to identify the key residues governing the allosteric binding of the studied compounds to the V600EB-Raf protein kinase. Results indicate that although van der Waals interactions are key determinants for binding, hydrogen bonds also play an important role. This work also provides a better structural understanding of the binding of DFG-out inhibitors to V600EB-Raf, which can be used in a further step for rational design of a new class of B-Raf potent inhibitors

Drug discovery and computational strategies in the multitarget drugs era

The pharmaceutical industry is increasingly joining chemoinformatics in the search for the development of new drugs to be used in the treatment of diseases. These computational studies have the advantage of being less expensive and optimize the study time, and thus the interest in this area is increasing. Among the techniques used is the development of multitarget directed ligands (MTDLs), which has become an ascending technique, mainly due to the improvement in the quality of treatment involving several drugs. Multitarget therapy is more effective than traditional drug therapy that emphasizes maximum selectivity for a single target. In this review a multitarget drug survey was carried out as a promising strategy in several important diseases: neglected diseases, neurodegenerative diseases, AIDS, and cancer. In addition, we discuss Computer-Aided Drug Design (CADD) techniques as a tool in the projection of multitarget compounds against these diseases

Design, synthesis and biological evaluation of novel thiadiazoline-thiazolone hybrids as kinase inhibitors.

Masters Degree. University of KwaZulu-Natal, Durban. 2017Cancer is a leading cause of death globally, and it was responsible for 8.8 million deaths in 2015. It is predicted that there will be 22 million new cancer cases by 2030 worldwide. Approximately, 70% of deaths from cancer occur in low- and middle-income countries. Furthermore, breast cancer is the second most common cancer among South African women and is reported to affect 1 in every 26 women. The social and economic burdens associated with cancers are severe at national and international levels hence, there is an urgent need for the development of more effective cancer therapeutics. To accomplish this aspect, in this study, thiadiazole-thiazolone (TDT) hybrids were developed as dual inhibitors of cyclindependent kinase (CDK) and kinesin spindle protein (KSP), respectively. Twenty-two novel TDT hybrid compounds (8a-v) were synthesized using multistep organic synthesis and were characterized using thin layer chromatography (TLC), infrared spectroscopy (FT-IR), nuclear magnetic resonance spectroscopy (1H and 13C NMR), and high-resolution mass spectrometry (HR-MS). All the compounds (8a-v) were screened for their potential in vitro inhibition of validated anticancer drug targets (CDK, Abl and KSP) and cancer cell lines (MCF-7 and K562). Results obtained from these evaluations suggested that the synthesized compounds were potent inhibitors of CDK and KSP thus confirming the dual mode of action. Amongst, 8h was identified as the most potent compound with an IC50 value of 3.1 µM against CDK2 enzyme and exhibited good cytotoxicity (GI50 = 6.25 µM) against the tested cancer cell lines (MCF-7 and K-562). A brief structure-activity relationship (SAR) analysis indicated that 2- chloro and 4-nitro substituents on the phenyl ring of the thiazolone motif contributed significantly to the inhibition of both of the anticancer drug targets (CDK and KSP). An in silico molecular docking study using the crystal structures of the target enzymes (CDK-2 and KSP) further supported the SAR and extrapolated the importance of crucial molecular interactions in influencing the enzyme inhibitory activitie

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

Multi-target selection and high throughput quantitative structure-activity relationship model development

Ph.DDOCTOR OF PHILOSOPH

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening Computer-aided drug design is an essential part of the modern medicinal chemistry, and has led to the acceleration of many projects. The herein described thesis presents examples for its application in the field of lead optimization and lead identification for three metalloproteins. DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent isoprenoid biosynthesis. Structure-activity relationships for 43 DXR inhibitors are established, derived from protein-based docking, ligand-based 3D QSAR and a combination of both approaches as realized by AFMoC. As part of an effort to optimize the properties of the established inhibitor Fosmidomycin, analogues have been synthesized and tested to gain further insights into the primary determinants of structural affinity. Unfortunately, these structures still leave the active Fosmidomycin conformation and detailed reaction mechanism undetermined. This fact, together with the small inhibitor data set provides a major challenge for presently available docking programs and 3D QSAR tools. Using the recently developed protein tailored scoring protocol AFMoC precise prediction of binding affinities for related ligands as well as the capability to estimate the affinities of structurally distinct inhibitors has been achieved. Farnesyltransferase is a zinc-metallo enzyme that catalyzes the posttranslational modification of numerous proteins involved in intracellular signal transduction. The development of farnesyltransferase inhibitors is directed towards the so-called non-thiol inhibitors because of adverse drug effects connected to free thiols. A first step on the way to non-thiol farnesyltransferase inhibitors was the development of an CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its basis bisubstrate analogues were developed as one class of non-thiol farnesyltransferase inhibitors. In further studies two aryl binding and two distinct specificity sites were postulated. Flexible docking of model compounds was applied to investigate the sub-pockets and design highly active non-thiol farnesyltransferase inhibitor. In addition to affinity, special attention was paid towards in vivo activity and species specificity. The second part of this thesis describes a possible strategy for computer-aided lead discovery. Assembling a complex ligand from simple fragments has recently been introduced as an alternative to traditional HTS. While frequently applied experimentally, only a few examples are known for computational fragment-based approaches. Mostly, computational tools are applied to compile the libraries and to finally assess the assembled ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a computational fragment-based screening protocol has been established. Starting with a data set of commercially available chemical compounds, a fragment library has been compiled considering (1) fragment likeness and (2) similarity to known drugs. The library is screened for target specificity, resulting in 112 fragments to target the zinc binding area and 75 fragments targeting the hydrophobic specificity pocket of the enzyme. After analyzing the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking experiments were performed for reference fragment to derive a general applicable crystallization protocol for TLN and subsequently for new protein-fragment complex structures. 3-Methylsaspirin could be determined to bind to TLN. Additional studies addressed a retrospective performance analysis of the applied scoring functions and modification on the screening hit. Curios about the differences of aspirin and 3-methylaspirin, 3-chloroaspirin has been synthesized and affinities could be determined to be 2.42 mM; 1.73 mM und 522 μM respectively. The results of the thesis show, that computer aided drug design approaches could successfully support projects in lead optimization and lead identification. fragments in general, the fragments derived from the screening are docke

Molecular Docking Study of Nutmeg (Myristica Fragrans) Constituents as Anti-Skin Cancer Agents

Molecular docking analysis was carried out to understand better the interaction between DHODH and inhibitor from nutmeg in this series. The nutmeg constituent binding orientations in the active site of DHODH was seen in a molecular docking analysis and helped design a potentially new inhibitor. This work aimed to study the molecular docking of nutmeg constituents with the DHODH inhibitor using a computer-aided drug design. Molecular docking using AutoDock 4.2 was done to explore the models of binding complexes. The 3D structure was derived using Discovery Studio to investigate the essential chemical interaction of complex structures. Dihydroguaiaretic acid was the most potent ligand having a docking score of -9.3 kcal/mol. This value was better than the standard drug 5-FU. The dihydroguaiaretic acid structure interacted with Tyr365 and Thr63 through a hydrogen bond similar to the native ligand. These results suggest that nutmeg seed could serve as the lead compound for potent DHODH inhibitors against skin

High-Throughput Screening for Drug Discovery

The book focuses on various aspects and properties of high-throughput screening (HTS), which is of great importance in the development of novel drugs to treat communicable and non-communicable diseases. Chapters in this volume discuss HTS methodologies, resources, and technologies and highlight the significance of HTS in personalized and precision medicine

Protein kinases: Structure modeling, inhibition, and protein-protein interactions

Human protein kinases belong to a large and diverse enzyme family that contains more than 500 members. Deregulation of protein kinases is associated with many disorders, and this is why protein kinases are attractive targets for drug discovery. Due to the high conservation of the ATP binding pocket among this family, designing specific and/or selective inhibitors against certain member(s) is challenging. Several studies have been conducted on protein kinases to validate them as suitable drug targets. Although there are numerous target-validated protein kinases, the efforts to develop small molecule inhibitors have so far led to only a limited number of therapeutic agents and drug candidates. In our studies, we tried to understand the basic structural features of protein kinases using available computational tools. There are wide structural variations between different states of the same protein kinase that affect the enzyme specificity and inhibition. Many protein kinases do not yet have an available X-ray crystal structure and have not yet been validated to be drug targets. For these reasons, we developed a new homology modeling approach to facilitate modeling non-crystallized protein kinases and protein kinase states. Our homology modeling approach was able to model proteins having long amino acid sequences and multiple protein domains with reliable model quality and a manageable amount of computational time. Then, we checked the applicability of different docking algorithms (the routinely used computational methodology in virtual screening) in protein kinase studies. After performing the basic study of kinase structure modeling, we focused our research on cyclin dependent kinase 2 (CDK2) and glycogen synthase kinase-3β (GSK-3β). We prepared a non-redundant database from 303 available CDK2 PDB structures. We removed all structural anomalies and proceeded to use the CDK2 database in studying CDK2 structure in its different states, upon ATP, ligand and cyclin binding. We clustered the database based on our findings, and the CDK2 clusters were used to generate protein ligand interaction fingerprints (PLIF). We generated a PLIF-based pharmacophore model which is highly selective for CDK2 ligands. A virtual screening workflow was developed making use of the PLIF-based pharmacophore model, ligand fitting into the CDK2 active site and selective CDK2 shape scoring. We studied the structural basis for selective inhibition of CDK2 and GSK-3β. We compared the amino acid sequence, the 3D features, the binding pockets, contact maps, structural geometry, and Sphoxel maps. From this study we found 1) the ligand structural features that are required for the selective inhibition of CDK2 and GSK-3β, and 2) the amino acid residues which are essential for ligand binding and selective inhibition. We used the findings of this study to design a virtual screening workflow to search for selective inhibitors for CDK2 and GSK-3β. Because protein–protein interactions are essential in the function of protein kinases, and in particular of CDK2, we used protein–protein docking knowledge and binding energy calculations to examine CDK2 and cyclin binding. We applied this study to the voltage dependent calcium channel 1 (VDAC1) binding to Bax. We were able to provide important data relevant to future experimental researchers such as on the possibility of Bax to cross biological membranes and the most relevant amino acid residues in VDAC1 that interact with Bax