Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Insights into Ligand–Protein Binding from Local Mechanical Response

Computational studies of ligand–protein binding are crucial for properly designing novel compounds of potential pharmacological interest. In this respect, researchers are increasingly interested in steered molecular dynamics for ligand–protein binding and unbinding studies. In particular, it has been suggested that analyzing the work profiles along the ligand–protein undocking paths could be fruitful. Here, we propose that small portions of work profiles, termed “local mechanical responses” of the system to a steering force, could serve as a universal measure for capturing relevant information about the system under investigation. Specifically, we first collected a high number of steering trajectories using two biological systems of increasing complexity (i.e., alanine dipeptide and (R)-roscovitine/CDK5 complex). Then, we devised a novel postprocessing tool to be applied to the local mechanical responses, to extract structural information related to the biological processes under investigation. Despite the out-of-equilibrium character of the trajectories, the analysis carried out on the work profiles provided pivotal information about the investigated biological processes. This could eventually be applied to drug design

Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors

Recently, academic and industrial scientific communities involved in kinetics-based drug development have become immensely interested in predicting the drug target residence time. Screening drug candidates in terms of their computationally predicted residence times, which is a measure of drug efficacy in vivo, and simultaneously assessing computational binding affinities are becoming inevitable. Non-equilibrium molecular simulation approaches are proven to be useful in this purpose. Here, we have implemented an optimized approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model to predict the absolute residence times of the antagonist ZMA241385 and agonist NECA that target the A2A adenosine receptor of the G-protein-coupled receptor (GPCR) protein family. We have predicted the absolute ligand residence times on the timescale of seconds. However, our predictions were many folds shorter than those determined experimentally. Additionally, we calculated the thermodynamics of ligand binding in terms of ligand binding energies and the per-residue contribution of the receptor. Subsequently, binding pocket hotspot residues that would be important for further computational mutagenesis studies were identified. In the experiment, similar sets of residues were found to be in significant contact with both ligands under study. Our results build a strong foundation for further improvement of our approach by rationalizing the kinetics of ligand unbinding with the thermodynamics of ligand binding

Multiscale Molecular Dynamics Simulations of Histidine Kinase Activity

Zweikomponentensysteme (TCS), bestehend aus einer Sensorhistidinkinase (HK) und einem Antwortregulationsprotein, sind Schlüsselbausteine in bakteriellen Signaluebertragungsmechanismen. Die Fähigkeit von Bakterien auf eine breite Vielfalt von chemischen und physikalischen Stimuli angemessen zu reagieren ist ausschlaggebend für ihr Überleben. Es ist daher nicht überraschend, dass TCS zu den meistuntersuchten bakteriellen Proteinsystemen gehört. Sensorhistidinkinasen sind typischerweise in die Zellmembran integrierte, homodimere Proteine bestehend aus mehreren Domänen. Reizwahrnehmung an der Sensordomäne von HK löst eine Reihe von großskaligen Konformationsübergangen entlang der Domänen aus. Während sich die strukturellen Eigenschaften von verschiedenen HKs unterscheiden können, erhalten sie alle einen katalytischen ATP-bindenden Kern (CA) und dimerisierende Histidinphosphotransferdomänen (DHp). Während der Signalkaskade nimmt der Kern eine asymmetrische Konformation an, sodass die Kinase an einem der Protomere aktiv ist und die der anderen inaktiv. Das ermöglicht es dem ATP in einer der CA-Domänen seine $\gamma$-Phosphatgruppe an das Histidin der DHp abzugeben. Diese Phosphorylgrouppe wird anschließend an das Antwortregulationsprotein weitergegeben, die eine angemessene Reaktion der Zelle veranlasst. In der vorliegenden Arbeit untersuche ich die Konformationsdynamik des Kinasekerns mithilfe von Molekulardynamiksimulationen (MD). Der Fokus der Arbeit liegt auf zwei verschiedenen HKs: WalK und CpxA. Wegen der Größe der Systeme und den erforderlichen biologischen Zeitskalen, ist es nicht möglich die relevanten Konformationsübergänge in klassischen MD-Simulationen zu berechnen. Um dieses Problem zu umgehen, verwende ich ein strukturbasiertes Modell mit paarweisen harmonischen Potentialen. Diese Näherung erlaubt es, den Übergang zwischen dem inaktiven und den aktiven Zustand mit wesentlich geringerem rechnerischen Aufwand zu untersuchen. Nachdem ich das System mithilfe dieses vereinfachten Modells erkundet habe, benutze ich angereicherte Stichprobenverfahren mit atomistischen Modellen um detailliertere Einsichten in die Dynamik zu gewinnen. Die Ergebnisse in dieser Arbeit legen nahe, dass das Verhalten der einzelnen Unterdomänen des Kinasekerns eng miteinander gekoppelt ist

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

Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies

Computational Modeling of Protein Kinases: Molecular Basis for Inhibition and Catalysis

Protein kinases catalyze protein phosphorylation reactions, i.e. the transfer of the γ-phosphoryl group of ATP to tyrosine, serine and threonine residues of protein substrates. This phosphorylation plays an important role in regulating various cellular processes. Deregulation of many kinases is directly linked to cancer development and the protein kinase family is one of the most important targets in current cancer therapy regimens. This relevance to disease has stimulated intensive efforts in the biomedical research community to understand their catalytic mechanisms, discern their cellular functions, and discover inhibitors. With the advantage of being able to simultaneously define structural as well as dynamic properties for complex systems, computational studies at the atomic level has been recognized as a powerful complement to experimental studies. In this work, we employed a suite of computational and molecular simulation methods to (1) explore the catalytic mechanism of a particular protein kinase, namely, epidermal growth factor receptor (EGFR); (2) study the interaction between EGFR and one of its inhibitors, namely erlotinib (Tarceva); (3) discern the effects of molecular alterations (somatic mutations) of EGFR to differential downstream signaling response; and (4) model the interactions of a novel class of kinase inhibitors with a common ruthenium based organometallic scaffold with different protein kinases. Our simulations established some important molecular rules in operation in the contexts of inhibitor-binding, substrate-recognition, catalytic landscapes, and signaling in the EGFR tyrosine kinase. Our results also shed insights on the mechanisms of inhibition and phosphorylation commonly employed by many kinases

Exploring the Role of Molecular Dynamics Simulations in Most Recent Cancer Research: Insights into Treatment Strategies

Cancer is a complex disease that is characterized by uncontrolled growth and division of cells. It involves a complex interplay between genetic and environmental factors that lead to the initiation and progression of tumors. Recent advances in molecular dynamics simulations have revolutionized our understanding of the molecular mechanisms underlying cancer initiation and progression. Molecular dynamics simulations enable researchers to study the behavior of biomolecules at an atomic level, providing insights into the dynamics and interactions of proteins, nucleic acids, and other molecules involved in cancer development. In this review paper, we provide an overview of the latest advances in molecular dynamics simulations of cancer cells. We will discuss the principles of molecular dynamics simulations and their applications in cancer research. We also explore the role of molecular dynamics simulations in understanding the interactions between cancer cells and their microenvironment, including signaling pathways, proteinprotein interactions, and other molecular processes involved in tumor initiation and progression. In addition, we highlight the current challenges and opportunities in this field and discuss the potential for developing more accurate and personalized simulations. Overall, this review paper aims to provide a comprehensive overview of the current state of molecular dynamics simulations in cancer research, with a focus on the molecular mechanisms underlying cancer initiation and progression.Comment: 49 pages, 2 figure