Inclusion of Enclosed Hydration Effects in the Binding Free Energy Estimation of Dopamine D3 Receptor Complexes

Confined hydration and conformational flexibility are some of the challenges encountered for the rational design of selective antagonists of G-protein coupled receptors. We present a set of C3-substituted (-)-stepholidine derivatives as potent binders of the dopamine D3 receptor. The compounds are characterized biochemically, as well as by computer modeling using a novel molecular dynamics-based alchemical binding free energy approach which incorporates the effect of the displacement of enclosed water molecules from the binding site. The free energy of displacement of specific hydration sites is obtained using the Hydration Site Analysis method with explicit solvation. This work underscores the critical role of confined hydration and conformational reorganization in the molecular recognition mechanism of dopamine receptors and illustrates the potential of binding free energy models to represent these key phenomena.Comment: This is the first report of using enclosed hydration in estimating binding free energies of protein-ligand complexes using implicit solvatio

Computational approaches to shed light on molecular mechanisms in biological processes

Computational approaches based on Molecular Dynamics simulations, Quantum Mechanical methods and 3D Quantitative Structure-Activity Relationships were employed by computational chemistry groups at the University of Milano-Bicocca to study biological processes at the molecular level. The paper reports the methodologies adopted and the results obtained on Aryl hydrocarbon Receptor and homologous PAS proteins mechanisms, the properties of prion protein peptides, the reaction pathway of hydrogenase and peroxidase enzymes and the defibrillogenic activity of tetracyclines. © Springer-Verlag 2007

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

Computational Approaches for the Characterization of the Structure and Dynamics of G Protein-Coupled Receptors: Applications to Drug Design

G Protein-Coupled Receptors (GPCRs) constitute the most pharmacologically relevant superfamily of proteins. In this thesis, a computational pipeline for modelling the structure and dynamics of GPCRs is presented, properly combined with experimental collaborations for GPCR drug design. These include the discovery of novel scaffolds as potential antipsychotics, and the design of a new series of A3 adenosine receptor antagonists, employing successful combinations of structure- and ligand-based approaches. Additionally, the structure of Adenosine Receptors (ARs) was computationally assessed, with implications in ligand affinity and selectivity. The employed protocol for Molecular Dynamics simulations has allowed the characterization of structural determinants of the activation of ARs, and the evaluation of the stability of GPCR dimers of CXCR4 receptor. Finally, the computational pipeline here developed has been integrated into the web server GPCR-ModSim (http://gpcr.usc.es), contributing to its application in biochemical and pharmacological studies on GPCRs

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification

Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol−1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule–receptor protein systems

Development of novel anticancer agents targeting G protein coupled receptor: GPR120

The G-protein coupled receptor, GPR120, has ubiquitous expression and multifaceted roles in modulating metabolic and anti-inflammatory processes. GPR120 - also known as Free Fatty Acid Receptor 4 (FFAR4) is classified as a free fatty acid receptor of the Class A GPCR family. GPR120 has recently been implicated as a novel target for cancer management. GPR120 gene knockdown in breast cancer studies revealed a role of GPR120-induced chemoresistance in epirubicin and cisplatin-induced DNA damage in tumour cells. Higher expression and activation levels of GPR120 is also reported to promote tumour angiogenesis and cell migration in colorectal cancer. A number of agonists targeting GPR120 have been reported, such as TUG891 and Compound39, but to date development of small-molecule inhibitors of GPR120 is limited. This research applied a rational drug discovery approach to discover and design novel anticancer agents targeting the GPR120 receptor. A homology model of GPR120 (short isoform) was generated to identify potential anticancer compounds using a combined in silico docking-based virtual screening (DBVS), molecular dynamics (MD) assisted pharmacophore screenings, structure–activity relationships (SAR) and in vitro screening approach. A pharmacophore hypothesis was derived from analysis of 300 ns all-atomic MD simulations on apo, TUG891-bound and Compound39-bound GPR120 (short isoform) receptor models and was used to screen for ligands interacting with Trp277 and Asn313 of GPR120. Comparative analysis of 100 ns all-atomic MD simulations of 9 selected compounds predicted the effects of ligand binding on the stability of the “ionic lock” – a characteristic of Class A GPCRs activation and inactivation. The “ionic lock” between TM3(Arg136) and TM6(Asp) is known to prevent G-protein recruitment while GPCR agonist binding is coupled to outward movement of TM6 breaking the “ionic lock” which facilitates G-protein recruitment. The MD-assisted pharmacophore hypothesis predicted Cpd 9, (2-hydroxy-N-{4-[(6-hydroxy-2-methylpyrimidin-4-yl) amino] phenyl} benzamide) to act as a GPR120S antagonist which can be evaluated and characterised in future studies. Additionally, DBVS of a small molecule database (~350,000 synthetic chemical compounds) against the developed GPR120 (short isoform) model led to selection of the 13 hit molecules which were then tested in vitro to evaluate their cytotoxic, colony forming and cell migration activities against SW480 – human CRC cell line expressing GPR120. Two of the DBVS hit molecules showed significant (\u3e 90%) inhibitory effects on cell growth with micromolar affinities (at 100 μM) - AK-968/12713190 (dihydrospiro(benzo[h]quinazoline-5,1′-cyclopentane)-4(3H)-one) and AG-690/40104520 (fluoren-9-one). SAR analysis of these two test compounds led to the identification of more active compounds in cell-based cytotoxicity assays – AL-281/36997031 (IC50 = 5.89–6.715 μM), AL-281/36997034 (IC50 = 6.789 to 7.502 μM) and AP-845/40876799 (IC50 = 14.16-18.02 μM). In addition, AL-281/36997031 and AP-845/40876799 were found to be significantly target-specific during comparative cytotoxicity profiling in GPR120-silenced and GPR120-expressing SW480 cells. In wound healing assays, AL-281/36997031 was found to be the most active at 3 μM (IC25) and prevented cell migration. As well as in the assessment of the proliferation ability of a single cell to survive and form colonies through clonogenic assays, AL-281/36997031 was found to be the most potent of all three test compounds with the survival rate of ~ 30% at 3 μM. The inter-disciplinary approach applied in this work identified potential chemical scaffolds –spiral benzo-quinazoline and fluorenone, targeting GPR120 which can be further explored for designing anti-cancer drug development studies