Knowledge-guided docking of flexible ligands to protein domains

Ph.DDOCTOR OF PHILOSOPH

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

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials \& methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results \& discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility

Computational de-orphaning of Mycobacterium tuberculosis targets

Tuberculosis (TB) continues to be a major health hazard worldwide due to the resurgence of drug discovery strains of Mycobacterium tuberculosis (Mtb) and co-infection. For decades drug discovery has concentrated on identifying ligands for ~10 Mtb targets, hence most of the identified essential proteins are not utilised in TB chemotherapy. Here computational techniques were used to identify ligands for the orphan Mtb proteins. These range from ligand-based and structure-based virtual screening modelling the proteome of the bacterium. Identification of ligands for most of the Mtb Proteins will provide novel TB drugs and targets and hence address drug resistance, toxicity and the duration of TB treatment

Computational Deorphaning of <em>Mycobacterium tuberculosis</em> Targets

Tuberculosis (TB) continues to be a major health hazard worldwide due to the resurgence of drug discovery strains of Mycobacterium tuberculosis (Mtb) and co-infection. For decades drug discovery has concentrated on identifying ligands for ~10 Mtb targets, hence most of the identified essential proteins are not utilised in TB chemotherapy. Here computational techniques were used to identify ligands for the orphan Mtb proteins. These range from ligand-based and structure-based virtual screening modelling the proteome of the bacterium. Identification of ligands for most of the Mtb proteins will provide novel TB drugs and targets and hence address drug resistance, toxicity and the duration of TB treatment

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

Study of macromolecular interactions using computational solvent mapping

The term "binding hot spots" refers to regions of a protein surface with large contributions to the binding free energy. Computational solvent mapping serves as an analog to the major experimental techniques developed for the identification of such hot spots using X-ray and nuclear magnetic resonance (NMR) methods. Applications of the fast Fourier-transform-based mapping algorithm FTMap show that similar binding hot spots also occur in DNA molecules and interact with small molecules that bind to DNA with high affinity. Solvent mapping results on B-DNA, with or without Hoogsteen (HG) base pairing, have revealed the significance of "HG breathing" on the reactivity of DNA with formaldehyde. Extending the method to RNA molecules, I applied the FTMap algorithm to flexible structures of HIV-1 transactivation response element (TAR) RNA and Tau exon 10 RNA. Results show that despite the extremely flexible nature of these small RNA molecules, nucleic acid bases that interact with ligands consistently have high hit rates, and thus binding sites can be successfully identified. Based on this experience as well as the prior work on DNA, I extended the FTMap algorithm to mapping nucleic acids and implemented it in an automated online server available to the research community. FTSite, a related server for finding binding sites of proteins, was also extended to develop PeptiMap, an accurate and robust protocol that can determine peptide binding sites on proteins. Analyses of structural ensembles of ligand-free proteins using solvent mapping have shown that such ensembles contain pre-existing binding hot spots, and that such hot spots can be identified without any a priori knowledge of the ligand-bound structure. Furthermore, the structures in the ensemble having the highest binding-site hit rate are closest to the ligand-bound structure, and a higher hit rate implies improved structural similarity between the unbound protein and its bound state, resulting in high correlation coefficient between the two measures. These advances should greatly enhance researchers' ability to identify functionally important interactions among biomolecules in silico

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