Evidence map›Paper›PMID 34578731›Full record

ArticleNanomaterials (Basel, Switzerland)2021

Modeling of Self-Assembled Peptide Nanotubes and Determination of Their Chirality Sign Based on Dipole Moment Calculations.

Vladimir Bystrov, Alla Sidorova, Aleksey Lutsenko, Denis Shpigun, Ekaterina Malyshko, Alla Nuraeva, Pavel Zelenovskiy, Svitlana Kopyl, Andrei Kholkin

Abstract read
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Vladimir BystrovInstitute of Mathematical Problems of Biology, The Branch of Keldysh Institute of Applied Mathematics, RAS, 142290 Pushchino, Russia.ORCID 0000-0003-1487-9469
Alla SidorovaFaculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia.
Aleksey LutsenkoFaculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia.
Denis ShpigunFaculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia.
Ekaterina MalyshkoFaculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia.ORCID 0000-0002-7701-8765
Alla NuraevaSchool of Natural Sciences and Mathematics, Ural Federal University, 620000 Ekaterinburg, Russia.ORCID 0000-0002-1329-2474
Pavel ZelenovskiySchool of Natural Sciences and Mathematics, Ural Federal University, 620000 Ekaterinburg, Russia.ORCID 0000-0003-3895-4785
Svitlana KopylDepartment of Physics & CICECO-Aveiro Institute of Materials, University of Aveiro, 3810-193 Aveiro, Portugal.ORCID 0000-0003-3347-8628
Andrei KholkinSchool of Natural Sciences and Mathematics, Ural Federal University, 620000 Ekaterinburg, Russia.ORCID 0000-0003-3432-7610

Funding

Russian Foundation for Basic Research 19-01-00519 А
6 · The paper itself

Abstract

The chirality quantification is of great importance in structural biology, where the differences in proteins twisting can provide essentially different physiological effects. However, this aspect of the chirality is still poorly studied for helix-like supramolecular structures. In this work, a method for chirality quantification based on the calculation of scalar triple products of dipole moments is suggested. As a model structure, self-assembled nanotubes of diphenylalanine (FF) made of L- and D-enantiomers were considered. The dipole moments of FF molecules were calculated using semi-empirical quantum-chemical method PM3 and the Amber force field method. The obtained results do not depend on the used simulation and calculation method, and show that the D-FF nanotubes are twisted tighter than L-FF. Moreover, the type of chirality of the helix-like nanotube is opposite to that of the initial individual molecule that is in line with the chirality alternation rule general for different levels of hierarchical organization of molecular systems. The proposed method can be applied to study other helix-like supramolecular structures.

Indexed as

chiralitydipeptidesdiphenylalaninedipole momentshelical structuresmolecular modelingpeptide nanotubespolarizationself-assembly

Identifiers

PMID34578731
PMCPMC8471708

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.