Evidence map›Paper›PMID 29097708›Full record

ArticleScientific reports2017

Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments.

Yuhong Wang, Junzhou Huang, Wei Li, Sheng Wang, Chuanfan Ding

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 48% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 3 institutions in 2 countries.

Yuhong WangDepartment of chemistry and Laser Chemistry Institute, Fudan University, Shanghai, 200433, P.R. China. lake.chao@gmail.com.
Junzhou HuangDepartment of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA.
Wei LiSchool of life science, Jilin University, Changchun, 130012, P.R. China.
Sheng WangDepartment of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA.
Chuanfan DingDepartment of chemistry and Laser Chemistry Institute, Fudan University, Shanghai, 200433, P.R. China.
Fudan University · CNThe University of Texas at Arlington · USChangchun University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that intrinsic sequence patterns between intra-protein binding peptide fragments exist, they can be extracted using a deep learning algorithm, and they bear an interesting semblance to the DNA double helix model. The intra-protein binding peptide fragments have specific and intrinsic sequence patterns, distinct from non-binding peptide fragments, and multi-millions of binding and non-binding peptide fragments from currently available protein X-ray structures are classified with an accuracy of up to 93%. The specific binding between short peptide fragments may provide an important driving force for protein folding and protein-protein interaction, two open and fundamental problems in molecular biology, and it may have significant potential in design, discovery, and development of peptide, protein, and antibody drugs.

Indexed as

Deep LearningAlgorithmsAmino Acid SequenceBinding SitesDatabases, ProteinPeptide FragmentsProtein BindingProtein ConformationProteinsPeptide FragmentsProteins

Identifiers

PMID29097708
PMCPMC5668431
OpenAlexW2766009108

What OpenQuestion holds

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LicenceCC BY
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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.