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Gianluca Degliesposti

Researcher at Laboratory of Molecular Biology

Publications -  34
Citations -  2210

Gianluca Degliesposti is an academic researcher from Laboratory of Molecular Biology. The author has contributed to research in topics: Virtual screening & Protein subunit. The author has an hindex of 18, co-authored 33 publications receiving 1679 citations.

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Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA

TL;DR: It is found that correlations obtained with the use of a single protein‐ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy.
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Atomic structure of the entire mammalian mitochondrial complex I

TL;DR: The structure provides insight into the mechanism, assembly, maturation and dysfunction of mitochondrial complex I, and allows detailed molecular analysis of disease-causing mutations, as well as observing two different conformations of the complex.
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Structures of Respiratory Supercomplex I+III2 Reveal Functional and Conformational Crosstalk

TL;DR: It is demonstrated that CoQ trapping in the isolated SC I+III2 limits complex (C)I turnover, arguing against channeling, and the state of CI affects the conformational flexibility within CIII2, demonstrating crosstalk between the enzymes.
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Architecture of eukaryotic mRNA 3′-end processing machinery

TL;DR: Cryo-electron microscopy, mass spectrometry, and biochemical reconstitutions elucidate the modular nature of the mRNA 3′-end processing machinery and show that the nuclease, polymerase, and phosphatase activities of yeast CPF are organized into three modules.
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Validation of an automated procedure for the prediction of relative free energies of binding on a set of aldose reductase inhibitors

TL;DR: An automated workflow that integrates all the necessary steps required to generate structures and calculate free energies of binding is developed and suggests that the workflow can be a valuable tool for ligand identification and optimization, being able to automatically and efficiently refine docking poses, and rank the compounds based on more accurate scoring functions.