Mapping Functional Group Free Energy Patterns at Protein Occluded Sites: Nuclear Receptors and G-Protein Coupled Receptors

Lakkaraju, Sirish Kaushik; Yu, Wenbo; Raman, E. Prabhu; Hershfeld, Alena; Fang, Lei; Deshpande, Deepak A.; MacKerell, Alexander D.

doi:10.1021/ci500729k

Cited by 49 publications

(81 citation statements)

References 59 publications

(99 reference statements)

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“…Hydrogen-bonding FragMaps are set to a cutoff of −0.5 kcal/mol, while the nonpolar and charged FragMaps are set to a cutoff of −1.2 kcal/mol. This is Figure 4A of reference 46; for clarity, the protein surface has been lightened and the amino acid labels are changed to black font.…”

Section: Figurementioning

confidence: 99%

Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics

2016

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Identifying binding hotspots on protein surfaces is of prime interest in structure-based drug discovery, either to assess the tractability of pursuing a protein target or to drive improved potency of lead compounds. Computational approaches to detect such regions have traditionally relied on energy minimization of probe molecules onto static protein conformations in the absence of the natural water environment. Advances in high performance computing now allow us to assess hotspots using molecular dynamics (MD) simulations. MD simulations integrate protein flexibility and the complicated role of water, thereby providing a more realistic assessment of the complex kinetics and thermodynamics at play. In this review, we describe the evolution of various cosolvent-based MD techniques and highlight a myriad of potential applications for such technologies in computational drug development.

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Section: Figurementioning

confidence: 99%

Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics

2016

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“…41-44 SILCS maps the free energy affinity pattern of macromolecules onto a grid and may be used to quantitatively estimate relative binding affinities of ligands, yielding ligand grid free energies (LGFE). Details of the SILCS calculation and LGFE analysis are presented in the supporting information.…”

Section: Resultsmentioning

confidence: 99%

Ribosome-Templated Azide–Alkyne Cycloadditions: Synthesis of Potent Macrolide Antibiotics by In Situ Click Chemistry

et al. 2016

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Over half of all antibiotics target the bacterial ribosome-Nature's complex, 2.5 MDa nanomachine responsible for decoding mRNA and synthesizing proteins. Macrolide antibiotics, exemplified by erythromycin, bind the 50S subunit with nM affinity and inhibit protein synthesis by blocking the passage of nascent oligopeptides. Solithromycin (1), a third-generation semisynthetic macrolide discovered by combinatorial copper-catalyzed click chemistry, was synthesized in situ by incubating either E. coli 70S ribosomes or 50S subunits with macrolidefunctionalized azide 2 and 3-ethynylaniline (3) precursors. The ribosome-templated in situ click method was expanded from a binary reaction (i.e., one azide and one alkyne) to a sixcomponent reaction (i.e., azide 2 and five alkynes) and ultimately to a sixteen-component reaction (i.e., azide 2 and fifteen alkynes). The extent of triazole formation correlated with ribosome affinity for the anti (1,4)-regioisomers as revealed by measured K d values. Computational analysis using the Site-Identification by Ligand Competitive Saturation (SILCS) approach indicated that the relative affinity of the ligands was associated with the alteration of macrolactone+desosamine- HHS Public Access

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“…Here we present an updated protocol based on the use of oscillating μ ex Grand Canonical Monte Carlo/MD (GCMC/MD) simulations for SILCS (69). The GCMC/MD approach allows for the application of the SILCS method to target systems with deep or occluded pockets such as nuclear receptors and GPCRs (70).

Prepare the system in a similar way as described in section 3.1 for MD simulations.

…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Drug Design Methods

MacKerell

2016

Methods in Molecular Biology

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Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure based drug design (SBDD) and ligand based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

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Mapping Functional Group Free Energy Patterns at Protein Occluded Sites: Nuclear Receptors and G-Protein Coupled Receptors

Cited by 49 publications

References 59 publications

Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics

Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics

Ribosome-Templated Azide–Alkyne Cycloadditions: Synthesis of Potent Macrolide Antibiotics by In Situ Click Chemistry

Computer-Aided Drug Design Methods

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