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

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

Rational drug design implies usage of molecular modeling techniques such as pharmacophore modeling, molecular dynamics, virtual screening, and molecular docking to explain the activity of biomolecules, define molecular determinants for interaction with the drug target, and design more efficient drug candidates. Kinases play an essential role in cell function and therefore are extensively studied targets in drug design and discovery. Kinase inhibitors are clinically very important and widely used antineoplastic drugs. In this review, computational methods used in rational drug design of kinase inhibitors are discussed and compared, considering some representative case studies

Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach

Aurora kinase B plays an important role in the cell cycle to orchestrate the mitotic process. The amplification and overexpression of this kinase have been implicated in several human malignancies. Therefore, Aurora kinase B is a potential drug target for anticancer therapies. Here, we combine atom-based 3D-QSAR analysis and pharmacophore model generation to identify the principal structural features of acylureidoindolin derivatives that could potentially be responsible for the inhibition of Aurora kinase B. The selected CoMFA and CoMSIA model showed significant results with cross-validation values (q(2)) of 0.68, 0.641 and linear regression values (r(2)) of 0.971, 0.933 respectively. These values support the statistical reliability of our model. A pharmacophore model was also generated, incorporating features of reported crystal complex structures of Aurora kinase B. The pharmacophore model was used to screen commercial databases to retrieve potential lead candidates. The resulting hits were analyzed at each stage for diversity based on the pharmacophore model, followed by molecular docking and filtering based on their interaction with active site residues and 3D-QSAR predictions. Subsequently, MD simulations and binding free energy calculations were performed to test the predictions and to characterize interactions at the molecular level. The results suggested that the identified compounds retained the interactions with binding residues. Binding energy decomposition identified residues Glu155, Trp156 and Ala157 of site B and Leu83 and Leu207 of site C as major contributors to binding affinity, complementary to 3D-QSAR results. To best of our knowledge, this is the first comparison of WaterSwap field and 3D-QSAR maps. Overall, this integrated strategy provides a basis for the development of new and potential AK-B inhibitors and is applicable to other protein targets

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

Aktiivsete ühendite disain neurodegeneratiivsete haiguste raviks

Väitekirja elektrooniline versioon ei sisalda publikatsiooneNeurodegeneratiivsed haigused on tänapäeval kujunenud keskseks meditsiiniliseks ja sotsiaalseks probleemiks. Ühest küljest on selle põhjuseks haigustega kaasnevad rasked füüsilised ja vaimsed puuded ning mõjusate ravimeetodite puudumine. Teisalt on valdav enamus neurodegeneratiivseid haigusi seotud vananemisega. Oodatava eluea märkimisväärne pikenemine on põhjustanud patsientide arvu olulist kasvu. Üks peamisi takistusi neurodegeneratiivsete häirete jaoks radikaalsete ravimeetodite leidmisel on uute ravimite väljatöötamise protsessi pikkus ja kulukus. Viimase kümnendi jooksul on aga molekulaarse modelleerimise ja tehisintellekti arvutusmeetodite kaasamine võimaldanud märkimisväärselt lühendada nii uute ravimite väljatöötamiseks kuluvat aega kui ka maksumust. Käesolevas väitekirjas rakendati mitmesuguseid arvutipõhiseid ravimite otsimise meetodeid uute potentsiaalsete aktiivsete keemiliste ühendite väljatöötamiseks neurodegeneratiivsete haiguste raviks. Kaasaegsete arvutikeemia molekulaarsildamise ja molekulaardünaamika meetodite abil sõeluti virtuaalselt suuri keemiliste ühendite andmebaase, leidmaks neurodegeneratiivsete haigustega seotud valkudele toimivaid aineid. Nii tehti kindlaks rida uusi looduslikke ühendeid, mis toimivad erinevate ensüümvalkude inhibiitoritena, aga ka uudne ühend, mis toimib efektiivselt samaaegselt kahele Alzheimeri tõvega seotud valgule. Üks peamisi neurodegeneratiivsete haiguste tekke põhjusi on nn närvikasvufaktorite puudulikkus neuronites. Seetõttu on väga huvitavaks ja perspektiivseks suunaks keemiliste ühendite leidmine, mis käituksid analoogselt nende faktoritega ning kaitseks närvirakke suremise eest. Käesoleva töö käigus uuriti erinevate arvutusmeetodite abil põhjalikult ühe taolise närvikasvufaktori (gliia närvikasvufaktor GDNF) toimemehhanismi ning ennustati seda faktorit imiteeriv aktiivne ühend. Kuigi selle eksperimentaalselt mõõdetud neuroneid kaitsev toime ei vasta veel ravimitele esitatavatele nõutele, on siiski tegemist esimese sellelaadse ühendiga maailmas, mille alusel oleks võimalik välja arendada täiesti uut tüüpi ravimeid nii Parkinsoni kui ka Huntingtoni tõve raviksToday, neurodegenerative diseases are one of the most acute medical and social problems. This is due to both severe physical and mental disabilities resulting from the constant progression of the process, and the age-dependent nature of the vast majority of neurodegenerative diseases. The current accelerating increase in life expectancy inevitably leads to a significant increase in the number of such patients. There is currently no radical treatment for neurodegenerative disorders. One of the main obstacles to finding effective drugs is the length and cost of the process of developing a new drug. However, the development of modern molecular modelling and artificial intelligence methods has substantially shortened the time to dispense new medicines and reduced their cost. This dissertation provides examples of the use of various methods of computer-aided drug design such as molecular docking and molecular dynamics to develop new potential candidates against neurodegenerative diseases. The high-throughput virtual screening of large molecular libraries enabled to identify effective compounds against target proteins related to neurodegenerative diseases. In result, a series of natural compounds acting as inhibitors to enzymes related to different diseases was established. Notably, a fully novel compound acting against two proteins related to Alzheimer’s disease was predicted and experimentally verified. One of the main causes of the neurodegeneration is the mostly age-related deficiency of so called neurotrophic factors. Small molecules that can mimic the activity of these factors in cells would be thus very attractive novel drug candidates. In the present thesis, the computational modelling was used for detailed study of the mechanism of action of one of the most important neurotrophic factors (glial cell-derived neurotrophic factor GDNF). The results of this study enabled to develop first time a compound that acted similarly to this factor itself. Whereas the experimentally measured activity of this compound was moderate, it creates a basis for the development of fully new type of drugs against Parkinson’s and Huntington’s diseases.https://www.ester.ee/record=b535990

Computational studies of drug-binding kinetics

The drug-receptor binding kinetics are defined by the rate at which a given drug associates with and dissociates from its binding site on its macromolecular receptor. The lead optimization stage of drug discovery programs usually emphasizes optimizing the affinity (as described by the equilibrium dissociation constant, Kd) of a drug which depends on the strength of its binding to a specific target. Since affinity is optimized under equilibrium conditions, it does not always ensures higher potency in vivo. There has been a growing consensus that, in addition to Kd, kinetic parameters (kon and koff ) should be optimized to improve the chances of a good clinical outcome. However, current understanding of the physicochemical features that contribute to differences in binding kinetics is limited. Experimental methods that are used to determine kinetic parameters for drug binding and unbinding are often time consuming and labor-intensive. Therefore, robust, high-throughput in silico methods are needed to predict binding kinetic parameters and to explore the mechanistic determinants of drug-protein binding. As the experimental data on drug-binding kinetics is continuously growing and the number of crystallographic structures of ligand-receptor complexes is also increasing, methods to compute three dimensional (3D) Quantitative-Structure-Kinetics relationships (QSKRs) offer great potential for predicting kinetic rate constants for new compounds. COMparative BINding Energy(COMBINE) analysis is one example of such approach that was developed to derive target-specific scoring functions based on molecular mechanics calculations. It has been used extensively to predict properties such as binding affinity, target selectivity, and substrate specificity. In this thesis, I made the first application of COMBINE analysis to derive Quantitative Structure-Kinetics Relationships (QSKRs) for the dissociation rates. I obtained models for koff of inhibitors of HIV-1 protease and heat shock protein 90 (HSP90) with very good predictive power and identified the key ligand-receptor interactions that contribute to the variance in binding kinetics. With technological and methodological advances, the use of all-atom unbiased Molecular Dynamics (MD) simulations can allow sampling upto the millisecond timescale and investigation of the kinetic profile of drug binding and unbinding to a receptor. However, the residence times of drug-receptor complexes are usually longer than the timescales that are feasible to simulate using conventional molecular dynamics techniques. Enhanced sampling methods can allow faster sampling of protein and ligand dynamics, thereby resulting in application of MD techniques to study longer timescale processes. I have evaluated the application of Tau-Random Acceleration Molecular Dynamics (Tau-RAMD), an enhanced sampling method based on MD, to compute the relative residence times of a series of compounds binding to Haspin kinase. A good correlation (R2 = 0.86) was observed between the computed residence times and the experimental residence times of these compounds. I also performed interaction energy calculations, both at the quantum chemical level and at the molecular mechanics level, to explain the experimental observation that the residence times of kinase inhibitors can be prolonged by introducing halogen-aromatic pi interactions between halogen atoms of inhibitors and aromatic residues at the binding site of kinases. I determined different energetic contributions to this highly polar and directional halogen-bonding interaction by partitioning the total interaction energy calculated at the quantum-chemical level into its constituent energy components. It was observed that the major contribution to this interaction energy comes from the correlation energy which describes second-order intermolecular dispersion interactions and the correlation corrections to the Hartree-Fock energy. In addition, a protocol to determine diffusional kon rates of low molecular weight compounds from Brownian Dynamics (BD) simulations of protein-ligand association was established using SDA 7 software. The widely studied test case of benzamidine binding to trypsin was used to evaluate a set of parameters and a robust set of optimal parameters was determined that should be generally applicable for computing the diffusional association rate constants of a wide range of protein-ligand binding pairs. I validated this protocol on inhibitors of several targets with varying complexity such as Human Coagulation Factor Xa, Haspin kinase and N1 Neuraminidase, and the computed diffusional association rate constants were compared with the experiments. I contributed to the development of a toolbox of computational methods: KBbox (http://kbbox.h-its.org/toolbox/), which provides information about various computational methods to study molecular binding kinetics, and different computational tools that employ them. It was developed to guide researchers on the use of the different computational and simulation approaches available to compute the kinetic parameters of drug-protein binding

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

In Silico Strategies for Prospective Drug Repositionings

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions