The preclinical assessment of drug-induced ventricular arrhythmia, a major concern for regulators, is typically based on experimental or computational models focused on the potassium channel hERG (human ether-a-go-go-related gene, K(v)11.1). Even if the role of this ion channel in the ventricular repolarization is of critical importance, the complexity of the events involved make the cardiac safety assessment based only on hERG has a high risk of producing either false positive or negative results. We introduce a multiscale simulation system aiming to produce a better cardiotoxicity assessment. At the molecular scale, the proposed system uses a combination of docking simulations on two potassium channels, hERG and KCNQ1, plus three-dimensional quantitative structure-activity relationship modeling for predicting how the tested compound will block the potassium currents IK(r) and IK(s). The obtained results have been introduced in electrophysiological models of the cardiomyocytes and the ventricular tissue, allowing the direct prediction of the drug effects on electrocardiogram simulations. The usefulness of the whole method is illustrated by predicting the cardiotoxic effect of several compounds, including some examples in which classic hERG-based models produce false positive or negative results, yielding correct predictions for all of them. These results can be considered a proof of concept, suggesting that multiscale prediction systems can be suitable for being used for preliminary screening in lead discovery, before the compound is physically available, or in early preclinical development when they can be fed with experimentally obtained data.

Evaluation of binding free energy in receptor-ligand complexes is one of the most important challenges in theoretical drug design. Free energy is directly correlated to the thermodynamic affinity constant, and, as a first step in druglikeness, a lead compound must have this constant in the range of micro- to nanomolar activity. Many efforts have been made to calculate it by rigorous computational approaches, such as free energy perturbation or linear response approximation. However, these methods are still computationally expensive. We focus our work on XIAP, an antiapoptotic protein whose inhibition can lead to new drugs against cancer disease. We report here a comparative evaluation of two completely different methodologies to estimate binding free energy, MMPBSA (a force field based function) and XSCORE (an empirical scoring function), in seven XIAP-peptide complexes using a representative set of structures generated by previous molecular dynamics simulations. Both methods are able to predict the experimental binding free energy with acceptable errors, but if one needs to identify slight differences upon binding, MMPBSA performs better, although XSCORE is not a bad choice taking into account the low computational cost of this method.

Tuberculosis remains a major infectious disease to humans. It accounts for approximately 8-9 million new cases worldwide and an estimated 1.6 million deaths annually. Effective treatments for tuberculosis consist of a combination of several drugs administered over long periods of time. Since Mycobacterium tuberculosis often acquires multiple drug resistant mechanisms, development of new drugs with innovative actions is urgently required. The 2C-methyl-D-erythritol 4-phosphate (MEP) pathway, in charge of the essential biosynthesis of isoprenoids, represents a promising and selective target for developing new drugs against tuberculosis. To date, only fosmidomycin, a molecule that targets the second enzyme of the MEP pathway, has reached clinical trials but recent advances elucidating the structure and kinetics of the MEP enzymes are likely to change this scenario. This review describes the structure, mechanism of action and inhibitors of the seven enzymes of the MEP pathway, with special attention to the reported studies in M. tuberculosis.

Predicting the three-dimensional structure of ligand-receptor complexes involving G protein-coupled receptors (GPCRs) is still a challenging task in rational drug design. To evaluate the reliability of the GPCR structural prediction, only a couple of community-wide assessments have been carried out. Our participation in the last edition, DOCK2010, involved the blind prediction of the dopaminergic D(3) receptor in complex with the D(2)/D(3) selective antagonist eticlopride for which the crystal structure has been recently released. Here, we describe a methodology that succeeded to produce a correctly predicted eticlopride-D(3) receptor complex out of three submitted models. Ranking the obtained models in the correct order is the main challenge due to subtle structural differences in the complex that are not sufficiently captured by conventional scoring functions. Importantly, our work reveals that a correct ranking is obtained by including a more sophisticated description of conformational ligand energy on binding. All in all, this case study highlights the current progress in modeling GPCR complexes and underlines that in silico modeling can be a valuable complement in GPCR drug discovery.

Protein surface roughness is a structural property associated with ligand-protein and protein-protein binding interfaces. In this work we apply for the first time the concept of surface roughness, expressed as the fractal dimension, to address structure and function of G protein-coupled receptors (GPCRs) which are an important group of drug targets. We calculate the exposure ratio and the fractal dimension for helix-forming residues of the β2 adrenergic receptor (β2AR), a model system in GPCR studies, in different conformational states: in complex with agonist, antagonist and partial inverse agonists. We show that both exposure ratio and roughness exhibit periodicity which results from the helical structure of GPCRs. The pattern of roughness and exposure ratio of a protein patch depends on its environment: the residues most exposed to membrane are in general most rough whereas parts of receptors mediating interhelical contacts in a monomer or protein complex are much smoother. We also find that intracellular ends (TM3, TM5, TM6 and TM7) which are relevant for G protein binding and thus receptor signaling, are exposed but smooth. Mapping the values of residual fractal dimension onto receptor 3D structures makes it possible to conclude that the binding sites of orthosteric ligands as well as of cholesterol are characterized with significantly higher roughness than the average for the whole protein. In summary, our study suggests that identification of specific patterns of roughness could be a novel approach to spot possible binding sites which could serve as original drug targets for GPCRs modulation.FigureThe significance of surface roughness for protein structure and function.

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Survivin, the smallest inhibitor of apoptosis protein (IAP), is a valid target for cancer research. It mediates both the apoptosis pathway and the cell cycle and has been proposed to form a complex with the cyclin-dependent kinase protein CDK4. The resulting complex transports CDK4 from the cytosol to the nucleus, where CDK4 participates in cell division. Survivin has been recognized as a node protein that interacts with several partners; disruption of the formed complexes can lead to new anticancer compounds. We propose a rational model of the survivin/CDK4 complex that fulfills the experimental evidence and that can be used for structure-based design of inhibitors modifying its interface recognition. In particular, the suggested complex involves the alpha helical domain of survivin and resembles the mode of binding of survivin in the survivin/borealin X-ray structure. The proposed model has been obtained by combining protein-protein docking, fractal-based shape complementarity, electrostatics studies and extensive molecular dynamics simulations.

Apoptosis, also called programmed cell death, is a conserved mechanism inherent to all cells that sentences them to death when they receive the appropriate external stimuli. Inhibitor of apoptosis proteins (IAPs) are a family of regulatory proteins that suppress such cell death. XIAP is the most commonly studied member of the IAP family. It binds to and inhibits Caspases, an important family of apoptotic proteases. In addition, XIAP over-expression has been detected in numerous types of cancer. Smac/DIABLO, a mitochondrial protein that binds to IAPs and promotes Caspase activation, has the opposite action to XIAP and can be considered a key protein in the regulation of IAPs. Survivin, the smallest IAP protein, has received a lot of attention due to its specific expression in many cancer cell lines. It has been shown to interact with Smac/DIABLO, even though the structure of this complex has not yet been reported.We analysed the protein-protein interactions appearing in the Smac/DIABLO-XIAP and Smac/DIABLO-Survivin complexes fully, using molecular dynamics simulations. This information is a first step towards the design of Smac/DIABLO peptidomimetics that could be used as innovative therapeutic agents for the treatment of malignancy. Our results complement the experimental interactions described for the first complex and provide a detailed description for the second. We show that Smac/DIABLO interacts in a similar way with both targets through its amino terminal residues. In addition, we identify a pharmacophore formed by eight stable protein-protein interactions for the XIAP complex and seven stable protein-protein interactions for the Survivin complex, which describe the whole contact surface. This information is used to suggest the binding mode of embelin, the first non-peptidic inhibitor of XIAP, and two of its derivatives. Molecular docking and molecular dynamics simulations were also carried out to describe ligand and receptor flexibility. Finally, an MMGBSA protocol was used to obtain a more quantitative description of the binding in all the complexes studied.