Evidence map›Paper›PMID 38987469›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2024

Assessment of Protein-Protein Docking Models Using Deep Learning.

Yuanyuan Zhang, Xiao Wang, Zicong Zhang, Yunhan Huang, Daisuke Kihara

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2024. 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
–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.

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.

  1. Article
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.

Yuanyuan ZhangDepartment of Computer Science, Purdue University, West Lafayette, IN, USA.
Xiao WangDepartment of Computer Science, Purdue University, West Lafayette, IN, USA.
Zicong ZhangDepartment of Computer Science, Purdue University, West Lafayette, IN, USA.
Yunhan HuangDepartment of Computer Science, Purdue University, West Lafayette, IN, USA.
Daisuke KiharaDepartment of Computer Science, Purdue University, West Lafayette, IN, USA. dkihara@purdue.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.

Indexed as

Deep LearningMolecular Docking SimulationProteinsComputational BiologyCrystallography, X-RayProtein BindingProtein ConformationProtein Interaction MappingSoftwareProteinsDeep learningDocking evaluationProtein–protein dockingProtein–protein interactionProtein structure prediction

Identifiers

PMID38987469

What OpenQuestion holds

Textmetadata
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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.