About: Study of peptide fingerprints of parasite proteins and drug–DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

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About: Study of peptide fingerprints of parasite proteins and drug–DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Study of peptide fingerprints of parasite proteins and drug–DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks
Creator	Gonzá Lez-Díaz, Humberto Prado-Prado, Francisco Ubeira, Florencio Auxiliadora Dea-Ayuela, María Bolas-Ferná Ndez, Francisco Pé Rez-Montoto, Guillermo Zaro, Lá
Source	Elsevier; Medline; PMC
abstract	Abstract Since the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort. Here, we introduce a new class of invariants for MDTs based on the spatial distribution of Mean-Energy values ξk (L) on a 2D Euclidean space representation of the MDTs. The procedure forces one MD trajectory to fold into a 2D Cartesian coordinates system using a step-by-step procedure driven by simple rules. The ξk (L) values are invariants of a Markov matrix (1 Π), which describes the probabilities of transition between two states in the new 2D space; which is associated to a graph representation of MDTs similar to the lattice networks (LNs) of DNA and protein sequences. We also introduce a new algorithm to perform phylogenetic analysis of peptides based on MDTs instead of the sequence of the polypeptide. In a first experiment, we illustrate this algorithm for 35 peptides present on the Peptide Mass Fingerprint (PMF) of a new protein of Leishmania infantum studied in this work. We report, by the first time, 2D Electrophoresis isolation, MALDI TOF Mass Spectroscopy characterization, and MASCOT search results for this PMF. In a second experiment, we construct the LNs for 422 MDTs obtained in DNA–Drug Docking simulations of the interaction of 57 anticancer furocoumarins with a DNA oligonucleotide. We calculated the respective ξk (L) values for all these LNs and used them as inputs to train a new classifier with Accuracy=85.44% and 84.91% in training and validation respectively. The new model can be used as scoring function to guide DNA–Drug Docking studies in drug design of new coumarins for PUVA therapy. The new phylogenetics analysis algorithms encode information different from sequence similarity and may be used to analyze MDTs obtained in Docking or modeling experiments for any classes of biopolymers. The work opens new perspective on the analysis and applications of MD in polymer sciences.
has issue date	2009-07-17(xsd:dateTime)
bibo:doi	10.1016/j.polymer.2009.05.055
bibo:pmid	32287404
has license	els-covid
sha1sum (hex)	b1577fbdc56e73d9a509bf83ada71cae3c142ee3
schema:url	https://doi.org/10.1016/j.polymer.2009.05.055
resource representing a document's title	Study of peptide fingerprints of parasite proteins and drug–DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks
has PubMed Central identifier	PMC7111648
has PubMed identifier	32287404
schema:publication	Polymer
resource representing a document's body	covid:b1577fbdc56e73d9a509bf83ada71cae3c142ee3#body_text
is schema:about of	named entity 'values' named entity 'The' named entity 'phylogenetics' named entity 'Molecular Dynamics (MD)' named entity 'procedure' named entity 'sequence similarity' named entity 'PMF' named entity 'guide' named entity 'coumarins' named entity 'Mass' named entity 'driven' named entity 'parasite' covid:arg/b1577fbdc56e73d9a509bf83ada71cae3c142ee3 named entity 'LNs' named entity 'invariants' named entity 'oligonucleotide' named entity 'respective' named entity 'studied' named entity 'construct' named entity 'scoring function' named entity 'sequences' named entity 'Fingerprint' named entity 'sequence' named entity 'PUVA therapy' named entity 'MASCOT' named entity 'peptides' named entity 'protein' named entity 'Cartesian coordinates' named entity 'PMF' named entity 'PMF' named entity 'phylogenetic analysis' named entity 'algorithm' named entity 'oligonucleotide' named entity 'lattice' named entity 'proteins' named entity 'virus' named entity 'MALDI-TOF' named entity 'force field' named entity 'pyrone' named entity 'search engine' named entity 'PMF' named entity 'L. infantum' named entity 'peptides' named entity 'molecule' named entity 'lattice network' named entity 'MALDI' named entity 'phototoxicity' named entity 'L. infantum' named entity 'peptides' named entity 'phylogenetic trees' named entity 'MALDI-TOF MS' named entity 'physicochemical' named entity 'microliter' named entity 'DNA sequence' named entity 'tumor cells' named entity 'medical treatments' named entity 'phylogenetic trees' named entity '2D gels' named entity 'lattice' named entity 'database' named entity 'QSARs' named entity 'antiproliferative' named entity 'protein' named entity 'RNA secondary structure' named entity 'drug-target' named entity 'viral infections' named entity 'B form' named entity 'dielectric constant' named entity 'w/v' named entity 'w/v' named entity 'Leishmania' named entity 'LDA' named entity 'vaccine design' named entity 'drug-target' named entity 'protein'

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