Gianmarc Grazioli
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Explore the profile of Gianmarc Grazioli including associated specialties, affiliations and a list of published articles.
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11
Citations
126
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Recent Articles
1.
Grazioli G, Tao A, Bhatia I, Regan P
J Phys Chem B
. 2024 Feb;
128(8):1854-1865.
PMID: 38359362
The time scales of long-time atomistic molecular dynamics simulations are typically reported in microseconds, while the time scales for experiments studying the kinetics of amyloid fibril formation are typically reported...
2.
Duong V, Diessner E, Grazioli G, Martin R, Butts C
Biomolecules
. 2021 Dec;
11(12).
PMID: 34944432
Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph...
3.
Yu Y, Grazioli G, Unhelkar M, Martin R, Butts C
Sci Rep
. 2020 Sep;
10(1):15668.
PMID: 32973286
Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer's disease and prion diseases. Despite an ever growing collection of amyloid...
4.
Grazioli G, Martin R, Butts C
Front Mol Biosci
. 2019 Jun;
6:42.
PMID: 31245383
Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are...
5.
Grazioli G, Yu Y, Unhelkar M, Martin R, Butts C
J Phys Chem B
. 2019 May;
123(26):5452-5462.
PMID: 31095387
Amyloid fibrils are locally ordered protein aggregates that self-assemble under a variety of physiological and in vitro conditions. Their formation is of fundamental interest as a physical chemistry problem and...
6.
Grazioli G, Roy S, Butts C
J Chem Inf Model
. 2019 May;
59(6):2753-2764.
PMID: 31063694
A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic...
7.
Grazioli G, Andricioaei I
J Chem Phys
. 2018 Sep;
149(8):084103.
PMID: 30193480
The milestoning algorithm of Elber and co-workers creates a framework for computing the time scale of processes that are too long and too complex to be studied using simply brute...
8.
Grazioli G, Andricioaei I
J Chem Phys
. 2018 Sep;
149(8):084104.
PMID: 30193477
In the milestoning framework, and more generally in related transition interface sampling schemes, one significantly enhances the calculation of relaxation rates for complex equilibrium kinetics from molecular dynamics simulations between...
9.
Grazioli G, Butts C, Andricioaei I
J Chem Phys
. 2017 Oct;
147(15):152727.
PMID: 29055331
Several recent implementations of algorithms for sampling reaction pathways employ a strategy for placing interfaces or milestones across the reaction coordinate manifold. Interfaces can be introduced such that the full...
10.
Zhou H, Kimsey I, Nikolova E, Sathyamoorthy B, Grazioli G, McSally J, et al.
Nat Struct Mol Biol
. 2016 Aug;
23(9):803-10.
PMID: 27478929
The B-DNA double helix can dynamically accommodate G-C and A-T base pairs in either Watson-Crick or Hoogsteen configurations. Here, we show that G-C(+) (in which + indicates protonation) and A-U...