kinDOCK: a tool for comparative docking of protein kinase ligands

@TOME 3.0: Interfacing Protein Structure Modeling and Ligand Docking

Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr.

Profilage in silico des inhibiteurs de protéine kinases

Les protéine kinases ont été rapidement identifiées comme favorisant l’apparition de cancers, à travers leur implication dans la régulation du développement et du cycle cellulaire. Il y a une vingtaine d’années, la mise sur le marché des premiers traitements par inhibiteur de protéine kinase, ouvrait la voie vers de nouvelles solutions médicamenteuses plus ciblées contre le cancer. Depuis, nombreuses sont les données structurales et fonctionnelles acquises sur ces cibles thérapeutiques. Les techniques informatiques ont elles aussi évolué, notamment les méthodes d’apprentissage automatique. En tirant parti de la grande quantité d’informations disponibles aujourd’hui, ces méthodes devraient permettre prochainement la prédiction fine de l’interaction d’un inhibiteur donné avec chaque protéine kinase humaine et donc, à terme, la construction d’outils de profilage de leurs inhibiteurs spécifiques. Cette approche intégrative devrait aider la découverte de solutions thérapeutiques anti-cancéreuses plus efficaces et plus sûres.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

The Rational Design, Synthesis, and Antimicrobial Properties of Thiophene Derivatives That Inhibit Bacterial Histidine Kinases

The emergence of multidrug-resistant bacteria emphasizes the urgent need for novel antibacterial compounds targeting unique cellular processes. Two-component signal transduction systems (TCSs) are commonly used by bacteria to couple environmental stimuli to adaptive responses, are absent in mammals, and are embedded in various pathogenic pathways. To attenuate these signaling pathways, we aimed to target the TCS signal transducer histidine kinase (HK) by focusing on their highly conserved adenosine triphosphate-binding domain. We used a structure-based drug design strategy that begins from an inhibitor-bound crystal structure and includes a significant number of structurally simplifiying "intuitive" modifications to arrive at the simple achiral, biaryl target structures. Thus, ligands were designed, leading to a series of thiophene derivatives. These compounds were synthesized and evaluated in vitro against bacterial HKs. We identified eight compounds with significant inhibitory activities against these proteins, two of which exhibited broad-spectrum antimicrobial activity. The compounds were also evaluated as adjuvants for the treatment of resistant bacteria. One compound was found to restore the sensivity of these bacteria to the respective antibiotics.

Virtual screening studies reveal linarin as a potential natural inhibitor targeting CDK4 in retinoblastoma

OBJECTIVE

To find out whether linarin can be used as a potential natural inhibitor to target CDK4 in retinoblastoma using virtual screening studies.

MATERIALS AND METHODS

In this study, molecular modeling and protein structure optimization was performed for crystal structure of CDK4 (PDB id: 3G33), and was subjected to Molecular Dynamics (MD) simulation for 10 nanoseconds, as a preparatory process for docking. Furthermore, the stable conformation obtained in the MD simulation was utilized for virtual screening against the library of natural compounds in Indian Plant Anticancer Compounds Database (InPACdb) using AutoDock Vina. Finally, best docked ligands were revalidated individually through semi-flexible docking by AutoDock 4.0.

RESULTS

The CDK4 structure was stereochemically optimized to fix clashes and bad angles, which placed 96.4% residues in the core region of Ramachandran plot. The final structure of CDK4 that emerged after MD simulation was proven to be highly stable as per different validation tools. Virtual screening and docking was carried out for CDK4 against optimized ligands from InPACdb through AutoDock Vina. This inferred Linarin (Inpacdb AC.NO. acd0073) as a potential therapeutic agent with binding energy of -8.9 kJ/mol. Furthermore, it was also found to be valid as per AutoDock 4.0 semi-flexible docking procedure, with the binding energy of -8.18 kJ/mol and Ki value of 1.01 μM.

CONCLUSION

The docking results indicate linarin, a flavonoid plant compound, as a potential inhibitor of CDK4 compared to some of the currently practiced anticancer drugs for retinoblastoma. This finding can be extended to experimental validation to assess the in vivo efficacy of the identified compound.

A new target for an old drug: identifying mitoxantrone as a nanomolar inhibitor of PIM1 kinase via kinome-wide selectivity modeling

The rational design of selective kinase inhibitors remains a great challenge. Here we describe a physics-based approach to computationally modeling the kinase inhibitor selectivity profile. We retrospectively assessed this protocol by computing the binding profiles of 17 well-known kinase inhibitors against 143 kinases. Next, we predicted the binding profile of the chemotherapy drug mitoxantrone, and chose the predicted top five kinase targets for in vitro kinase assays. Remarkably, mitoxantrone was shown to possess low nanomolar inhibitory activity against PIM1 kinase and to inhibit the PIM1-mediated phosphorylation in cancer cells. We further determined the crystal complex structure of PIM1 bound with mitoxantrone, which reveals the structural and mechanistic basis for a novel mode of PIM1 inhibition. Although mitoxantrone's mechanism of action had been originally thought to act through DNA intercalation and type II topoisomerase inhibition, we hypothesize that PIM1 kinase inhibition might also contribute to mitoxantrone's therapeutic efficacy and specificity.

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

Homology-modelling protein-ligand interactions: allowing for ligand-induced conformational change

Current homology-modelling methods do not consider small molecules in their automated processes. Therefore, the development of a reliable tool for protein-ligand homology modelling is an important next step in generating plausible models for molecular interactions. Two automated protein-ligand homology-modelling strategies, requiring no expert knowledge from the user, are investigated here. Both employ the "induced fit" concept with flexibility in side chains and ligand. The most successful strategy superimposes the new ligand over the original ligand before homology modelling, allowing the new ligand to be taken into consideration during protein modelling (rather than after), facilitating conformational change in the local backbone if necessary. We show that this approach results in successful modelling of the ligand and key binding-site residues of angiotensin-converting enzyme 2 (ACE2) from its homologue ACE, which is not possible via conventional homology modelling or by homology modelling followed by docking. Several other difficult target complexes are also successfully modelled, reproducing native protein-ligand contacts with significantly different biological substrates and different binding-site conformations. These include the modelling of Cdk5 (cyclin-dependent kinase 5) from Cdk2, thymidine phosphorylase from a bacterial homologue, and dihydrofolate reductase from a recombinant variant with a markedly different inhibitor. In terms of average modelling quality across 82 targets, the ligand RMSD with respect to the experimental structure is 1.4 A (and 2.0 A for the protein binding site) for "easy" cases and 2.9 A for the ligand (and 2.7 A for the protein binding site) in "hard" cases. This demonstrates the importance of selecting an optimal template. Ligand-modelling accuracy is strongly dependent on target-template ligand structural similarity, rather than target-template sequence identity. However, protein-modelling accuracy is dependent on both. Our automated protein-ligand homology-modelling strategy generates a higher degree of accuracy than homology modelling followed by docking, generating an average ligand RMSD that is 1-2 A better than docking with homology models.

FINDSITE: a threading-based approach to ligand homology modeling

Ligand virtual screening is a widely used tool to assist in new pharmaceutical discovery. In practice, virtual screening approaches have a number of limitations, and the development of new methodologies is required. Previously, we showed that remotely related proteins identified by threading often share a common binding site occupied by chemically similar ligands. Here, we demonstrate that across an evolutionarily related, but distant family of proteins, the ligands that bind to the common binding site contain a set of strongly conserved anchor functional groups as well as a variable region that accounts for their binding specificity. Furthermore, the sequence and structure conservation of residues contacting the anchor functional groups is significantly higher than those contacting ligand variable regions. Exploiting these insights, we developed FINDSITE(LHM) that employs structural information extracted from weakly related proteins to perform rapid ligand docking by homology modeling. In large scale benchmarking, using the predicted anchor-binding mode and the crystal structure of the receptor, FINDSITE(LHM) outperforms classical docking approaches with an average ligand RMSD from native of approximately 2.5 A. For weakly homologous receptor protein models, using FINDSITE(LHM), the fraction of recovered binding residues and specific contacts is 0.66 (0.55) and 0.49 (0.38) for highly confident (all) targets, respectively. Finally, in virtual screening for HIV-1 protease inhibitors, using similarity to the ligand anchor region yields significantly improved enrichment factors. Thus, the rather accurate, computationally inexpensive FINDSITE(LHM) algorithm should be a useful approach to assist in the discovery of novel biopharmaceuticals.

@TOME-2: a new pipeline for comparative modeling of protein-ligand complexes

@TOME 2.0 is new web pipeline dedicated to protein structure modeling and small ligand docking based on comparative analyses. @TOME 2.0 allows fold recognition, template selection, structural alignment editing, structure comparisons, 3D-model building and evaluation. These tasks are routinely used in sequence analyses for structure prediction. In our pipeline the necessary software is efficiently interconnected in an original manner to accelerate all the processes. Furthermore, we have also connected comparative docking of small ligands that is performed using protein-protein superposition. The input is a simple protein sequence in one-letter code with no comment. The resulting 3D model, protein-ligand complexes and structural alignments can be visualized through dedicated Web interfaces or can be downloaded for further studies. These original features will aid in the functional annotation of proteins and the selection of templates for molecular modeling and virtual screening. Several examples are described to highlight some of the new functionalities provided by this pipeline. The server and its documentation are freely available at http://abcis.cbs.cnrs.fr/AT2/

ROP2 from Toxoplasma gondii: a virulence factor with a protein-kinase fold and no enzymatic activity

The ROP2 protein and its paralogs are important virulence factors secreted into the host cell by the parasite Toxoplasma gondii. Here we describe the crystal structure of a large and soluble domain of mature ROP2, representative of the ROP2-like protein family. This is a structure of a protein-kinase fold that is devoid of catalytic residues and does not bind ATP. Various structural extensions constitute a signature of this protein family and act to maintain the protein kinase in an open conformation. Our ROP2 structure rules out a previous structural model of attachment of ROP2-like proteins to the parasitophorous vacuole membrane. We propose an alternative mode of membrane attachment implicating basic and amphiphatic helices present in the flexible N terminus of ROP2.

Protein structure analysis online

Computational biology/chemistry tools are used in most areas of life/health science research. These methods are continually being developed and their use can present difficulties for both experienced and novice investigators. To facilitate the use of these applications, many packages have been implemented online during these last 5 years. This unit focuses on online computational methods with a special emphasis on structural refinement/atomic simulations, protein electrostatic calculations, searches for functional sites, searches for druggable pockets, protein docking and small molecule docking, and prediction of potential impact of amino acid variations on the structure and function of the protein molecules.

MODPROPEP: a program for knowledge-based modeling of protein-peptide complexes

MODPROPEP is a web server for knowledge-based modeling of protein–peptide complexes, specifically peptides in complex with major histocompatibility complex (MHC) proteins and kinases. The available crystal structures of protein–peptide complexes in PDB are used as templates for modeling peptides of desired sequence in the substrate-binding pocket of MHCs or protein kinases. The substrate peptides are modeled using the same backbone conformation as in the template and the side-chain conformations are obtained by the program SCWRL. MODPROPEP provides a number of user-friendly interfaces for visualizing the structure of the modeled protein–peptide complexes and analyzing the contacts made by the modeled peptide ligand in the substrate-binding pocket of the MHC or protein kinase. Analysis of these specific inter-molecular contacts is crucial for understanding structural basis of the substrate specificity of these two protein families. This software also provides appropriate interfaces for identifying, putative MHC-binding peptides in the sequence of an antigen or phosphorylation sites on the substrate protein of a kinase, by scoring these inter-molecular contacts using residue-based statistical pair potentials. MODPROPEP would complement various available sequence-based programs (SYFPEITHI, SCANSITE, etc.) for predicting substrates of MHCs and protein kinases. The program is available at http://www.nii.res.in/modpropep.html