14,815 research outputs found
Mapping the druggable allosteric space of G-protein coupled receptors: a fragment-based molecular dynamics approach.
To address the problem of specificity in G-protein coupled receptor (GPCR) drug discovery, there has been tremendous recent interest in allosteric drugs that bind at sites topographically distinct from the orthosteric site. Unfortunately, structure-based drug design of allosteric GPCR ligands has been frustrated by the paucity of structural data for allosteric binding sites, making a strong case for predictive computational methods. In this work, we map the surfaces of the beta1 (beta1AR) and beta2 (beta2AR) adrenergic receptor structures to detect a series of five potentially druggable allosteric sites. We employ the FTMAP algorithm to identify 'hot spots' with affinity for a variety of organic probe molecules corresponding to drug fragments. Our work is distinguished by an ensemble-based approach, whereby we map diverse receptor conformations taken from molecular dynamics (MD) simulations totaling approximately 0.5 micros. Our results reveal distinct pockets formed at both solvent-exposed and lipid-exposed cavities, which we interpret in light of experimental data and which may constitute novel targets for GPCR drug discovery. This mapping data can now serve to drive a combination of fragment-based and virtual screening approaches for the discovery of small molecules that bind at these sites and which may offer highly selective therapies
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
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
Mind the Gap - Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence
G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs
G Protein-Coupled Receptors: Conformational “Gatekeepers” of Transmembrane Signal Transduction and Diversification
Proteins in the cellular signaling machinery accomplish an amazing spectrum of functions necessary for the growth and survival of life by a network of signaling events separated in both space and time. Membrane proteins enable signal transduction across the cell membrane, which results in these signaling events inside the cell leading to a physiological response. G protein-coupled receptors (GPCRs) form the largest family of membrane proteins that process a very diverse set of extracellular signals and are capable of transducing multiple intracellular signaling pathways, mediated by G proteins and/or Arrestins, each with potentially different functional consequences. This “pleiotropic” nature of GPCRs is enabled by a high conformational flexibility of GPCRs, which allows for a unique ensemble of possible conformations depending on the state of the GPCR, whether it is in the apo form, or interacting with a ligand/antibody, or interacting with another protein. Each ligand can induce a different set of conformations in a GPCR, which can interact with G protein and Arrestin pathways in different ways, resulting in different physiological outcomes. This chapter provides an overview of how GPCRs use their conformational flexibility to perform a complex array of functions and how this can be used advantageously to bias signaling within the cell. A detailed understanding of the signaling pathways that are turned on by GPCRs, combined with the development of biased agonists and allosteric modulators to select specific outcomes, provides a promising avenue for developing therapeutics with minimal side-effects
Open Boundary Simulations of Proteins and Their Hydration Shells by Hamiltonian Adaptive Resolution Scheme
The recently proposed Hamiltonian Adaptive Resolution Scheme (H-AdResS)
allows to perform molecular simulations in an open boundary framework. It
allows to change on the fly the resolution of specific subset of molecules
(usually the solvent), which are free to diffuse between the atomistic region
and the coarse-grained reservoir. So far, the method has been successfully
applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of
proteins, such as the Molecular Mechanics/Coarse-Grained (MM/CG) scheme, is a
promising approach for rigorous calculations of ligand binding free energies in
low-resolution protein models. Towards this goal, here we apply for the first
time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its
ability to reproduce structural and dynamic properties of both the proteins and
the solvent, as obtained from atomistic simulations.Comment: This document is the Accepted Manuscript version of a Published Work
that appeared in final form in Journal of Chemical Theory and Computation,
copyright \c{opyright} American Chemical Society after peer review and
technical editing by the publishe
A New Method for Ligand-supported Homology Modelling of Protein Binding Sites: Development and Application to the neurokinin-1 receptor
In this thesis, a novel strategy (MOBILE
(Modelling Binding Sites Including
Ligand Information
Explicitly)) was developed that models protein
binding-sites
simultaneously considering information about the binding mode
of bioactive ligands during the homology modelling process. As
a result,
protein binding-site models of higher accuracy and
relevance can be
generated. Starting with the (crystal)
structure of one or more template
proteins, in the first step
several preliminary homology models of the target
protein are
generated using the homology modelling program MODELLER.
Ligands
are then placed into these preliminary models using
different strategies
depending on the amount of experimental
information about the binding mode of
the ligands. (1.) If a
ligand is known to bind to the target protein and the
crystal
structure of the protein-ligand complex with the related
template
protein is available, it can be assumed that the
ligand binding modes are
similar in the target and template
protein. Accordingly, ligands are then
transferred among
these structures keeping their orientation as a restraint
for
the subsequent modelling process. (2.) If no complex crystal
structure
with the template is available, the ligand(s) can
be placed into the template
protein structure by docking, and
the resulting orientation can then be used
to restrain the
following protein modelling process. Alternatively, (3.) in
cases where knowledge about the binding mode cannot be inferred
by the
template protein, ligand docking is performed into an
ensemble of homology
models. The ligands are placed into a
crude binding-site representation via
docking into averaged
property fields derived from knowledge-based
potentials. Once
the ligands are placed, a new set of homology models is
generated. However, in this step, ligand information is
considered as
additional restraint in terms of the
knowledge-based DrugScore protein-ligand
atom pair
potentials. Consulting a large ensemble of produced models
exhibiting di erent side-chain rotamers for the binding-site
residues, a
composite picture is assembled considering the
individually best scored
rotamers with respect to the ligand.
After a local force-field optimisation,
the obtained
binding-site models can be used for structure-based drug
design
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