Vincent Zoete

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.

SwissDock, a protein-small molecule docking web service based on EADock DSS

Most life science processes involve, at the atomic scale, recognition between two molecules. The prediction of such interactions at the molecular level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small molecules on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for preparing both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional molecular modeling community.

Exome sequencing identifies recurrent somatic MAP2K1 and MAP2K2 mutations in melanoma

We performed exome sequencing to detect somatic mutations in protein-coding regions in seven melanoma cell lines and donor-matched germline cells. All melanoma samples had high numbers of somatic mutations, which showed the hallmark of UV-induced DNA repair. Such a hallmark was absent in tumor sample–specific mutations in two metastases derived from the same individual. Two melanomas with non-canonical BRAF mutations harbored gain-of-function MAP2K1 and MAP2K2 (MEK1 and MEK2, respectively) mutations, resulting in constitutive ERK phosphorylation and higher resistance to MEK inhibitors. Screening a larger cohort of individuals with melanoma revealed the presence of recurring somatic MAP2K1 and MAP2K2 mutations, which occurred at an overall frequency of 8%. Furthermore, missense and nonsense somatic mutations were frequently found in three candidate melanoma genes, FAT4, LRP1B and DSC1.

Inhibition of the shade avoidance response by formation of non‐DNA binding bHLH heterodimers

In shade‐intolerant plants such as Arabidopsis, a reduction in the red/far‐red (R/FR) ratio, indicative of competition from other plants, triggers a suite of responses known as the shade avoidance syndrome (SAS). The phytochrome photoreceptors measure the R/FR ratio and control the SAS. The phytochrome‐interacting factors 4 and 5 (PIF4 and PIF5) are stabilized in the shade and are required for a full SAS, whereas the related bHLH factor HFR1 (long hypocotyl in FR light) is transcriptionally induced by shade and inhibits this response. Here we show that HFR1 interacts with PIF4 and PIF5 and limits their capacity to induce the expression of shade marker genes and to promote elongation growth. HFR1 directly inhibits these PIFs by forming non‐DNA‐binding heterodimers with PIF4 and PIF5. Our data indicate that PIF4 and PIF5 promote SAS by directly binding to G‐boxes present in the promoter of shade marker genes, but their action is limited later in the shade when HFR1 accumulates and forms non‐DNA‐binding heterodimers. This negative feedback loop is important to limit the response of plants to shade.

The endocrine disruptor monoethyl-hexyl-phthalate is a selective peroxisome proliferator-activated receptor gamma modulator that promotes adipogenesis

The ability of pollutants to affect human health is a major concern, justified by the wide demonstration that reproductive functions are altered by endocrine disrupting chemicals. The definition of endocrine disruption is today extended to broader endocrine regulations, and includes activation of metabolic sensors, such as the peroxisome proliferator-activated receptors (PPARs). Toxicology approaches have demonstrated that phthalate plasticizers can directly influence PPAR activity. What is now missing is a detailed molecular understanding of the fundamental basis of endocrine disrupting chemical interference with PPAR signaling. We thus performed structural and functional analyses that demonstrate how monoethyl-hexyl-phthalate (MEHP) directly activates PPARγ and promotes adipogenesis, albeit to a lower extent than the full agonist rosiglitazone. Importantly, we demonstrate that MEHP induces a selective activation of different PPARγ target genes. Chromatin immunoprecipitation and fluorescence microscopy in living cells reveal that this selective activity correlates with the recruitment of a specific subset of PPARγ coregulators that includes Med1 and PGC-1α, but not p300 and SRC-1. These results highlight some key mechanisms in metabolic disruption but are also instrumental in the context of selective PPAR modulation, a promising field for new therapeutic development based on PPAR modulation.

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.

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

Rational design of indoleamine 2,3-dioxygenase inhibitors

Indoleamine 2,3-dioxygenase (IDO) is an important therapeutic target for the treatment of diseases such as cancer that involve pathological immune escape. We have used the evolutionary docking algorithm EADock to design new inhibitors of this enzyme. First, we investigated the modes of binding of all known IDO inhibitors. On the basis of the observed docked conformations, we developed a pharmacophore model, which was then used to devise new compounds to be tested for IDO inhibition. We also used a fragment-based approach to design and to optimize small organic molecule inhibitors. Both approaches yielded several new low-molecular weight inhibitor scaffolds, the most active being of nanomolar potency in an enzymatic assay. Cellular assays confirmed the potential biological relevance of four different scaffolds.

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‐2Kb 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. Proteins 2007.

Evidence for a TCR Affinity Threshold Delimiting Maximal CD8 T Cell Function

Protective adaptive immune responses rely on TCR-mediated recognition of Ag-derived peptides presented by self-MHC molecules. However, self-Ag (tumor)-specific TCRs are often of too low affinity to achieve best functionality. To precisely assess the relationship between TCR–peptide–MHC binding parameters and T cell function, we tested a panel of sequence-optimized HLA-A*0201/NY–ESO-1157–165–specific TCR variants with affinities lying within physiological boundaries to preserve antigenic specificity and avoid cross-reactivity, as well as two outliers (i.e., a very high- and a low-affinity TCR). Primary human CD8 T cells transduced with these TCRs demonstrated robust correlations between binding measurements of TCR affinity and avidity and the biological response of the T cells, such as TCR cell-surface clustering, intracellular signaling, proliferation, and target cell lysis. Strikingly, above a defined TCR–peptide–MHC affinity threshold (KD < ∼5 μM), T cell function could not be further enhanced, revealing a plateau of maximal T cell function, compatible with the notion that multiple TCRs with slightly different affinities participate equally (codominantly) in immune responses. We propose that rational design of improved self-specific TCRs may not need to be optimized beyond a given affinity threshold to achieve both optimal T cell function and avoidance of the unpredictable risk of cross-reactivity.