Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data

Automated docking of phospholipids to the phospholipase D active site: insight into the catalytic mechanism

Streptomyces sp. PMF phospholipase D (PLD) both hydrolyzes and transphosphatidylates phospholipids. The three-dimensional x-ray crystal structure of this enzyme has been recently determined to 1.4 Å resolution. To better understand the structure-function relationships of this enzyme with different potential substrates, automated docking calculations were conducted to model the structures of various PLD/phospholipid complexes. Several parameters, including the identity of the phospholipid head group and the fatty acid chain length, were varied and the effects on the docked structures were investigated. The docking results revealed a delicate balance between head group/active site interactions and hydrophobic binding of the fatty acid chains outside the active site. Specificity is therefore achieved through the use of this balance of forces. In general the docking agrees at least qualitatively well with the limited experimental information available for the Streptomyces PLD

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

G-protein coupled receptors activation mechanism: from ligand binding to the transmission of the signal inside the cell

G-protein coupled receptors (GPCRs) are the largest family of pharmaceutical drug targets in the human genome and are modulated by a large variety of en- dogenous and synthetic ligands. GPCRs activation usually depends on agonist binding (except for receptors with basal activity), which stabilizes receptor con- formations and allow the requirement and activation of intracellular transducers. GPCRs are unique receptors and very well studied, since they play an important role in a great number of diseases. They interact with different type of ligands (such as light, peptides, proteins) and different partners in the intracellular part (such as G-proteins or β-arrestins). Based on homology and function GPCRs are divided in five classes: Class A or Rhodopsin, Class B1 or Secretin, Class B2 or Adhesion, Class C or Glutamate, Class F or Frizzled. What is still missing in the state of the art of these receptor, and in particular in Class A, is a global study on different binding cavities with divergent properties, with the aim to discover common binding characteristics, preserved during years of evolution. Gaining more knowledge on common features for ligand recognition shared among all the recep- tors may become crucial to deeply understand the mechanism used to transmit the signal into the cell. In the first step of this thesis we have used all the solved Class A receptors structures to analyze and find, if exist, a common way to transmit the signal inside the cell. We identified and validated ten positions shared between all the binding cavities and always involved in the interaction with ligands. We demonstrated that residues in these positions are conserved and have co-evolved together. In a second step, we used these positions to understand how ligands could be positioned in the binding cavities of three study cases: Muscarinic receptors, Kisspeptin receptors and the GPR3 receptor. We did not have any experimental information a priori. We used homology modeling and docking techniques for the first two cases, adding molecular dynamics simulations in the third case. All the predictions and suggestions from the computational point of view, turned out to be very successful. In particular for the GPR3 receptor we were able to identify and validate by alanine-scanning mutagenesis the role of three functionally relevant residues. The latter were correlated with the constitutive and agonist-stimulated adenylate cyclase activity of GPR3 receptor. Taken together, these results suggest an important role of computational structural biology and pave the way of strong collaborations between computational and experimental researches

Predictive and experimental approaches for elucidating protein–protein interactions and quaternary structures

The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development

Modeling of Human Prokineticin Receptors: Interactions with Novel Small-Molecule Binders and Potential Off-Target Drugs

The Prokineticin receptor (PKR) 1 and 2 subtypes are novel members of family A GPCRs, which exhibit an unusually high degree of sequence similarity. Prokineticins (PKs), their cognate ligands, are small secreted proteins of ∼80 amino acids; however, non-peptidic low-molecular weight antagonists have also been identified. PKs and their receptors play important roles under various physiological conditions such as maintaining circadian rhythm and pain perception, as well as regulating angiogenesis and modulating immunity. Identifying binding sites for known antagonists and for additional potential binders will facilitate studying and regulating these novel receptors. Blocking PKRs may serve as a therapeutic tool for various diseases, including acute pain, inflammation and cancer.Ligand-based pharmacophore models were derived from known antagonists, and virtual screening performed on the DrugBank dataset identified potential human PKR (hPKR) ligands with novel scaffolds. Interestingly, these included several HIV protease inhibitors for which endothelial cell dysfunction is a documented side effect. Our results suggest that the side effects might be due to inhibition of the PKR signaling pathway. Docking of known binders to a 3D homology model of hPKR1 is in agreement with the well-established canonical TM-bundle binding site of family A GPCRs. Furthermore, the docking results highlight residues that may form specific contacts with the ligands. These contacts provide structural explanation for the importance of several chemical features that were obtained from the structure-activity analysis of known binders. With the exception of a single loop residue that might be perused in the future for obtaining subtype-specific regulation, the results suggest an identical TM-bundle binding site for hPKR1 and hPKR2. In addition, analysis of the intracellular regions highlights variable regions that may provide subtype specificity