Computerized Molecular Modeling for Discovering Promising Glycosaminoglycan Oligosaccharides that Modulate Protein Function.

Nehru Viji Sankaranarayanan, Umesh Desai

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

1.0field-weighted citation impact, top 23% of its field

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

Glycosaminoglycans: What Remains To Be Deciphered?JACS Au · 2023
Review

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Nehru Viji SankaranarayananDepartment of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA, USA. nvsankaranar@vcu.edu.

Umesh DesaiDepartment of Medicinal Chemistry & Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond, VA, USA.

Virginia Commonwealth University · US

Funding

High Performance Computing Cluster for Biomedical Research at VCUS10RR027411 · NCRR · VIRGINIA COMMONWEALTH UNIVERSITY · PI CUI, MENG · 2010 to 2010

$417k

NCRR NIH HHS S10 RR027411

6 · The paper itself

Abstract

Glycosaminoglycans (GAGs) are a class of highly negatively charged polysaccharides that plays a major role in various biological processes through their interaction with hundreds of proteins. A major challenge in understanding the specific protein-GAG interaction is their structural diversity and complexity. Recently, computational approaches have been used extensively in addressing this challenge. In this chapter, we present a generally-applicable methodology termed Combinatorial Virtual Library Screening (CVLS) that can identify potential high-affinity, high-specificity sequence(s) binding to a suitable GAG-binding protein from large GAG combinatorial libraries of various lengths and structural patterns.

Indexed as

GlycosaminoglycansModels, MolecularOligosaccharidesProteinsGlycosaminoglycansOligosaccharidesProteinsAffinity and SpecificityGenetic Algorithm (GA)Glycosaminoglycans (GAGs)Heparin/Heparan SulfateMolecular DockingProtein-GAG InteractionVirtual Screening (VS)

Identifiers

PMID34626405

DOI10.1016/j.jmgm.2019.04.001

OpenAlexW3204312841

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.