P
Paolo Calligari
Researcher at University of Rome Tor Vergata
Publications - 32
Citations - 688
Paolo Calligari is an academic researcher from University of Rome Tor Vergata. The author has contributed to research in topics: Allosteric regulation & Proto-oncogene tyrosine-protein kinase Src. The author has an hindex of 12, co-authored 31 publications receiving 491 citations. Previous affiliations of Paolo Calligari include International School for Advanced Studies & École Normale Supérieure.
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From NMR Relaxation to Fractional Brownian Dynamics in Proteins: Results from a Virtual Experiment
TL;DR: Numerical experiments perform numerical experiments to explore the possibility of using a fractional Brownian dynamics model directly in the analysis of experimental NMR relaxation data, and show that in most cases, close agreement is found.
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Design, Synthesis, Antibacterial Potential, and Structural Characterization of N-Acylated Derivatives of the Human Autophagy 16 Polypeptide.
Kyriakos G. Varnava,Sk. Abdul Mohid,Paolo Calligari,Lorenzo Stella,Jóhannes Reynison,Jóhannes Reynison,Anirban Bhunia,Vijayalekshmi Sarojini,Vijayalekshmi Sarojini +8 more
TL;DR: A synthetic antimicrobial peptide library based on the human autophagy 16 polypeptide has been developed and a 21-residue fragment of Atg16 conjugated to 4-methylhexanoic acid (K30) emerged as the most potent antibacterial, with negligible hemolysis.
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Insights into internal dynamics of 6-phosphogluconolactonase from Trypanosoma brucei studied by nuclear magnetic resonance and molecular dynamics.
Paolo Calligari,Gilmar F. Salgado,Philippe Pelupessy,Philippe Pelupessy,Philippe Pelupessy,Philippe Lopes,Jamal Ouazzani,Geoffrey Bodenhausen,Daniel Abergel,Daniel Abergel,Daniel Abergel +10 more
TL;DR: The presence of perturbations located in remote regions of this well‐structured globular protein in which no large‐amplitude motions are involved suggests that delocalized changes in dynamics that occur upon binding could be a general feature of protein–target interactions.
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Decomposition of Proteins into Dynamic Units from Atomic Cross-Correlation Functions.
TL;DR: A clustering method of atoms in proteins based on the analysis of the correlation times of interatomic distance correlation functions computed from MD simulations to provide a coarse-grained description of the protein in terms of fewer elements that can be treated as dynamically independent subunits.
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ScrewFit: combining localization and description of protein secondary structure
TL;DR: A new application of the ScrewFit algorithm is presented which adds the detection of protein secondary-structure elements to their detailed geometrical description in terms of a curve with intrinsic torsion for the enzyme adenylate kinase.