Fast docking using the CHARMM force field with EADock DSS.

The prediction of binding modes (BMs) occurring between a small molecule and a target protein of biological interest has become of great importance for drug development. The overwhelming diversity of needs leaves room for docking approaches addressing specific problems. Nowadays, the universe of docking software ranges from fast and user friendly programs to algorithmically flexible and accurate approaches. EADock2 is an example of the latter. Its multiobjective scoring function was designed around the CHARMM22 force field and the FACTS solvation model. However, the major drawback of such a software design lies in its computational cost. EADock dihedral space sampling (DSS) is built on the most efficient features of EADock2, namely its hybrid sampling engine and multiobjective scoring function. Its performance is equivalent to that of EADock2 for drug-like ligands, while the CPU time required has been reduced by several orders of magnitude. This huge improvement was achieved through a combination of several innovative features including an automatic bias of the sampling toward putative binding sites, and a very efficient tree-based DSS algorithm. When the top-scoring prediction is considered, 57% of BMs of a test set of 251 complexes were reproduced within 2 Å RMSD to the crystal structure. Up to 70% were reproduced when considering the five top scoring predictions. The success rate is lower in cross-docking assays but remains comparable with that of the latest version of AutoDock that accounts for the protein flexibility. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011.

Docking to heme proteins.

In silico screening has become a valuable tool in drug design, but some drug targets represent real challenges for docking algorithms. This is especially true for metalloproteins, whose interactions with ligands are difficult to parametrize. Our docking algorithm, EADock, is based on the CHARMM force field, which assures a physically sound scoring function and a good transferability to a wide range of systems, but also exhibits difficulties in case of some metalloproteins. Here, we consider the therapeutically important case of heme proteins featuring an iron core at the active site. Using a standard docking protocol, where the iron-ligand interaction is underestimated, we obtained a success rate of 28% for a test set of 50 heme-containing complexes with iron-ligand contact. By introducing Morse-like metal binding potentials (MMBP), which are fitted to reproduce density functional theory calculations, we are able to increase the success rate to 62%. The remaining failures are mainly due to specific ligand-water interactions in the X-ray structures. Testing of the MMBP on a second data set of non iron binders (14 cases) demonstrates that they do not introduce a spurious bias towards metal binding, which suggests that they may reliably be used also for cross-docking studies.

SwissParam: a fast force field generation tool for small organic molecules.

The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch.

Expanding molecular modeling and design tools to non-natural sidechains.

Protein-protein interactions encode the wiring diagram of cellular signaling pathways and their deregulations underlie a variety of diseases, such as cancer. Inhibiting protein-protein interactions with peptide derivatives is a promising way to develop new biological and therapeutic tools. Here, we develop a general framework to computationally handle hundreds of non-natural amino acid sidechains and predict the effect of inserting them into peptides or proteins. We first generate all structural files (pdb and mol2), as well as parameters and topologies for standard molecular mechanics software (CHARMM and Gromacs). Accurate predictions of rotamer probabilities are provided using a novel combined knowledge and physics based strategy. Non-natural sidechains are useful to increase peptide ligand binding affinity. Our results obtained on non-natural mutants of a BCL9 peptide targeting beta-catenin show very good correlation between predicted and experimental binding free-energies, indicating that such predictions can be used to design new inhibitors. Data generated in this work, as well as PyMOL and UCSF Chimera plug-ins for user-friendly visualization of non-natural sidechains, are all available at http://www.swisssidechain.ch. Our results enable researchers to rapidly and efficiently work with hundreds of non-natural sidechains.

Fourier transform convolution integrals applied to generalized Born molecular volume.

Generalized Born methods are currently among the solvation models most commonly used for biological applications. We reformulate the generalized Born molecular volume method initially described by (Lee et al, 2003, J Phys Chem, 116, 10606; Lee et al, 2003, J Comp Chem, 24, 1348) using fast Fourier transform convolution integrals. Changes in the initial method are discussed and analyzed. Finally, the method is extensively checked with snapshots from common molecular modeling applications: binding free energy computations and docking. Biologically relevant test systems are chosen, including 855-36091 atoms. It is clearly demonstrated that, precision-wise, the proposed method performs as good as the original, and could better benefit from hardware accelerated boards.

Blind docking of 260 protein-ligand complexes with EADock 2.0.

Molecular docking softwares are one of the important tools of modern drug development pipelines. The promising achievements of the last 10 years emphasize the need for further improvement, as reflected by several recent publications (Leach et al., J Med Chem 2006, 49, 5851; Warren et al., J Med Chem 2006, 49, 5912). Our initial approach, EADock, showed a good performance in reproducing the experimental binding modes for a set of 37 different ligand-protein complexes (Grosdidier et al., Proteins 2007, 67, 1010). This article presents recent improvements regarding the scoring and sampling aspects over the initial implementation, as well as a new seeding procedure based on the detection of cavities, opening the door to blind docking with EADock. These enhancements were validated on 260 complexes taken from the high quality Ligand Protein Database [LPDB, (Roche et al., J Med Chem 2001, 44, 3592)]. Two issues were identified: first, the quality of the initial structures cannot be assumed and a manual inspection and/or a search in the literature are likely to be required to achieve the best performance. Second the description of interactions involving metal ions still has to be improved. Nonetheless, a remarkable success rate of 65% was achieved for a large scale blind docking assay, when considering only the top ranked binding mode and a success threshold of 2 A RMSD to the crystal structure. When looking at the five-top ranked binding modes, the success rate increases up to 76%. In a standard local docking assay, success rates of 75 and 83% were obtained, considering only the top ranked binding mode, or the five top binding modes, respectively.

Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure-based computer-aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand-protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state-of-the-art docking programs. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Comparison between computational alanine scanning and per-residue binding free energy decomposition for protein-protein association using MM-GBSA: application to the TCR-p-MHC complex.

Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by Class I major histocompatibility complexes (MHC) is the key event in the immune response against virus-infected cells or tumor cells. A study of the 2C TCR/SIYR/H-2K(b) system using a computational alanine scanning and a much faster binding free energy decomposition based on the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method is presented. The results show that the TCR-p-MHC binding free energy decomposition using this approach and including entropic terms provides a detailed and reliable description of the interactions between the molecules at an atomistic level. Comparison of the decomposition results with experimentally determined activity differences for alanine mutants yields a correlation of 0.67 when the entropy is neglected and 0.72 when the entropy is taken into account. Similarly, comparison of experimental activities with variations in binding free energies determined by computational alanine scanning yields correlations of 0.72 and 0.74 when the entropy is neglected or taken into account, respectively. Some key interactions for the TCR-p-MHC binding are analyzed and some possible side chains replacements are proposed in the context of TCR protein engineering. In addition, a comparison of the two theoretical approaches for estimating the role of each side chain in the complexation is given, and a new ad hoc approach to decompose the vibrational entropy term into atomic contributions, the linear decomposition of the vibrational entropy (LDVE), is introduced. The latter allows the rapid calculation of the entropic contribution of interesting side chains to the binding. This new method is based on the idea that the most important contributions to the vibrational entropy of a molecule originate from residues that contribute most to the vibrational amplitude of the normal modes. The LDVE approach is shown to provide results very similar to those of the exact but highly computationally demanding method.

Mutational and computational analysis of the alpha(1b)-adrenergic receptor. Involvement of basic and hydrophobic residues in receptor activation and G protein coupling.

To investigate their role in receptor coupling to G(q), we mutated all basic amino acids and some conserved hydrophobic residues of the cytosolic surface of the alpha(1b)-adrenergic receptor (AR). The wild type and mutated receptors were expressed in COS-7 cells and characterized for their ligand binding properties and ability to increase inositol phosphate accumulation. The experimental results have been interpreted in the context of both an ab initio model of the alpha(1b)-AR and of a new homology model built on the recently solved crystal structure of rhodopsin. Among the twenty-three basic amino acids mutated only mutations of three, Arg(254) and Lys(258) in the third intracellular loop and Lys(291) at the cytosolic extension of helix 6, markedly impaired the receptor-mediated inositol phosphate production. Additionally, mutations of two conserved hydrophobic residues, Val(147) and Leu(151) in the second intracellular loop had significant effects on receptor function. The functional analysis of the receptor mutants in conjunction with the predictions of molecular modeling supports the hypothesis that Arg(254), Lys(258), as well as Leu(151) are directly involved in receptor-G protein interaction and/or receptor-mediated activation of the G protein. In contrast, the residues belonging to the cytosolic extensions of helices 3 and 6 play a predominant role in the activation process of the alpha(1b)-AR. These findings contribute to the delineation of the molecular determinants of the alpha(1b)-AR/G(q) interface.

Virtual screening and computational optimization for the discovery of covalent prolyl oligopeptidase inhibitors with activity in human cells.

Our docking program, Fitted, implemented in our computational platform, Forecaster, has been modified to carry out automated virtual screening of covalent inhibitors. With this modified version of the program, virtual screening and further docking-based optimization of a selected hit led to the identification of potential covalent reversible inhibitors of prolyl oligopeptidase activity. After visual inspection, a virtual hit molecule together with four analogues were selected for synthesis and made in one-five chemical steps. Biological evaluations on recombinant POP and FAPα enzymes, cell extracts, and living cells demonstrated high potency and selectivity for POP over FAPα and DPPIV. Three compounds even exhibited high nanomolar inhibitory activities in intact living human cells and acceptable metabolic stability. This small set of molecules also demonstrated that covalent binding and/or geometrical constraints to the ligand/protein complex may lead to an increase in bioactivity.

A combined computational and functional approach identifies new residues involved in pH-dependent gating of ASIC1a.

Acid-sensing ion channels (ASICs) are key receptors for extracellular protons. These neuronal nonvoltage-gated Na(+) channels are involved in learning, the expression of fear, neurodegeneration after ischemia, and pain sensation. We have applied a systematic approach to identify potential pH sensors in ASIC1a and to elucidate the mechanisms by which pH variations govern ASIC gating. We first calculated the pK(a) value of all extracellular His, Glu, and Asp residues using a Poisson-Boltzmann continuum approach, based on the ASIC three-dimensional structure, to identify candidate pH-sensing residues. The role of these residues was then assessed by site-directed mutagenesis and chemical modification, combined with functional analysis. The localization of putative pH-sensing residues suggests that pH changes control ASIC gating by protonation/deprotonation of many residues per subunit in different channel domains. Analysis of the function of residues in the palm domain close to the central vertical axis of the channel allowed for prediction of conformational changes of this region during gating. Our study provides a basis for the intrinsic ASIC pH dependence and describes an approach that can also be applied to the investigation of the mechanisms of the pH dependence of other proteins.

Structure-function relationships of the alpha1b-adrenergic receptor.

The alpha1b-adrenergic receptor (AR) is a member of the large superfamily of seven transmembrane domain (TMD) G protein-coupled receptors (GPCR). Combining site-directed mutagenesis of the alpha1b-AR with computational simulations of receptor dynamics, we have explored the conformational changes underlying the process of receptor activation, i.e. the transition between the inactive and active states. Our findings suggest that the structural constraint stabilizing the alpha1b-AR in the inactive form is a network of H-bonding interactions amongst conserved residues forming a polar pocket and R143 of the DRY sequence at the end of TMDIII. We have recently reported that point mutations of D142, of the DRY sequence and of A293 in the distal portion of the third intracellular loop resulted in ligand-independent (constitutive) activation of the alpha1b-AR. These constitutively activating mutations could induce perturbations resulting in the shift of R143 out of the polar pocket. The main role of R143 may be to mediate receptor activation by triggering the exposure of several basic amino acids of the intracellular loops towards the G protein. Our investigation has been extended also to the biochemical events involved in the desensitization process of alpha1b-AR. Our results indicate that immediately following agonist-induced activation, the alpha1b-AR can undergo rapid agonist-induced phosphorylation and desensitization. Different members of the G protein coupled receptor kinase family can play a role in agonist-induced regulation of the alpha1b-AR. In addition, constitutively active alpha1b-AR mutants display different phosphorylation and internalization features. The future goal is to further elucidate the molecular mechanism underlying the complex equilibrium between activation and inactivation of the alpha1b-AR and its regulation by pharmacological substances. These findings can help to elucidate the mechanism of action of various agents displaying properties of agonists or inverse agonists at the adrenergic system.

Optimized venous return with a self-expanding cannula: from computational fluid dynamics to clinical application.

The Smart canula concept allows for collapsed cannula insertion, and self-expansion within a vein of the body. (A) Computational fluid dynamics, and (B) bovine experiments (76+/-3.8 kg) were performed for comparative analyses, prior to (C) the first clinical application. For an 18F access, a given flow of 4 l/min (A) resulted in a pressure drop of 49 mmHg for smart cannula versus 140 mmHg for control. The corresponding Reynolds numbers are 680 versus 1170, respectively. (B) For an access of 28F, the maximal flow for smart cannula was 5.8+/-0.5 l/min versus 4.0+/-0.1 l/min for standard (P<0.0001), for 24F 5.5+/-0.6 l/min versus 3.2+/-0.4 l/min (P<0.0001), and for 20F 4.1+/-0.3 l/min versus 1.6+/-0.3 l/min (P<0.0001). The flow obtained with the smart cannula was 270+/-45% (20F), 172+/-26% (24F), and 134+/-13% (28F) of standard (one-way ANOVA, P=0.014). (C) First clinical application (1.42 m2) with a smart cannula showed 3.55 l/min (100% predicted) without additional fluids. All three assessment steps confirm the superior performance of the smart cannula design.

Cell-based computational modeling of vascular morphogenesis using Tissue Simulation Toolkit.

Computational modeling has become a widely used tool for unraveling the mechanisms of higher level cooperative cell behavior during vascular morphogenesis. However, experimenting with published simulation models or adding new assumptions to those models can be daunting for novice and even for experienced computational scientists. Here, we present a step-by-step, practical tutorial for building cell-based simulations of vascular morphogenesis using the Tissue Simulation Toolkit (TST). The TST is a freely available, open-source C++ library for developing simulations with the two-dimensional cellular Potts model, a stochastic, agent-based framework to simulate collective cell behavior. We will show the basic use of the TST to simulate and experiment with published simulations of vascular network formation. Then, we will present step-by-step instructions and explanations for building a recent simulation model of tumor angiogenesis. Demonstrated mechanisms include cell-cell adhesion, chemotaxis, cell elongation, haptotaxis, and haptokinesis.