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      Computational design of ligand binding membrane receptors with high selectivity

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          Accurate modeling and design of protein-ligand interactions have broad applications in cell, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein homology modeling-ligand docking-protein design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced fit modes of ligand binding. In structure modeling tests, we blindly predicted near-atomic accuracy ligand conformations bound to G protein-coupled receptors (GPCRs) that were rarely identified by traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally-uncharacterized GPCRs. We then applied the technique to design functional human dopamine receptors with novel ligand binding selectivity. Most blindly predicted ligand binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand binding profile of membrane receptors that remain difficult to crystallize.

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          Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

          Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.
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            Protein structure modeling with MODELLER.

            Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
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              DREADDs for Neuroscientists.

               Bryan Roth (2016)
              To understand brain function, it is essential that we discover how cellular signaling specifies normal and pathological brain function. In this regard, chemogenetic technologies represent valuable platforms for manipulating neuronal and non-neuronal signal transduction in a cell-type-specific fashion in freely moving animals. Designer Receptors Exclusively Activated by Designer Drugs (DREADD)-based chemogenetic tools are now commonly used by neuroscientists to identify the circuitry and cellular signals that specify behavior, perceptions, emotions, innate drives, and motor functions in species ranging from flies to nonhuman primates. Here I provide a primer on DREADDs highlighting key technical and conceptual considerations and identify challenges for chemogenetics going forward.

                Author and article information

                Nat Chem Biol
                Nat. Chem. Biol.
                Nature chemical biology
                8 February 2017
                01 May 2017
                July 2017
                01 November 2017
                : 13
                : 7
                : 715-723
                [1 ]Department of Pharmacology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
                [2 ]Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
                [3 ]Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
                Author notes
                []Correspondences should be addressed to: P.B. ( patrickb@ )

                Present address: Center for Petroleum and Geosystems Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX 78712, USA


                Present address: Department of Cell Biology and Human Anatomy, University of California at Davis, 4303 Tupper Hall, Davis, CA 95616, USA.


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