Evidence map›Paper›PMID 37102670›Full record

ArticleNucleic acids research2023

Prediction of protein structures, functions and interactions using the IntFOLD7, MultiFOLD and ModFOLDdock servers.

Liam J McGuffin, Nicholas S Edmunds, Ahmet G Genc, Shuaa M A Alharbi, Bajuna R Salehe, Recep Adiyaman

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
11.4field-weighted citation impact, top 1% 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

47 citing papers in PubMed, 74 citations in OpenAlex.

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  14. Differential Binding of ΔFN3 Proteins ofInternational journal of molecular sciences · 2025
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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

6 authors at 1 institution in 1 country.

Liam J McGuffinSchool of Biological Sciences, University of Reading, Whiteknights, ReadingRG6 6EX, UK.ORCID 0000-0003-4501-4767
Nicholas S EdmundsSchool of Biological Sciences, University of Reading, Whiteknights, ReadingRG6 6EX, UK.
Ahmet G GencSchool of Biological Sciences, University of Reading, Whiteknights, ReadingRG6 6EX, UK.
Shuaa M A AlharbiSchool of Biological Sciences, University of Reading, Whiteknights, ReadingRG6 6EX, UK.
Bajuna R SaleheSchool of Biological Sciences, University of Reading, Whiteknights, ReadingRG6 6EX, UK.
Recep AdiyamanSchool of Biological Sciences, University of Reading, Whiteknights, ReadingRG6 6EX, UK.
University of Reading · GB

Funding

Biotechnology and Biological Sciences Research Council BB/T018496/1
6 · The paper itself

Abstract

The IntFOLD server based at the University of Reading has been a leading method over the past decade in providing free access to accurate prediction of protein structures and functions. In a post-AlphaFold2 world, accurate models of tertiary structures are widely available for even more protein targets, so there has been a refocus in the prediction community towards the accurate modelling of protein-ligand interactions as well as modelling quaternary structure assemblies. In this paper, we describe the latest improvements to IntFOLD, which maintains its competitive structure prediction performance by including the latest deep learning methods while also integrating accurate model quality estimates and 3D models of protein-ligand interactions. Furthermore, we also introduce our two new server methods: MultiFOLD for accurately modelling both tertiary and quaternary structures, with performance which has been independently verified to outperform the standard AlphaFold2 methods, and ModFOLDdock, which provides world-leading quality estimates for quaternary structure models. The IntFOLD7, MultiFOLD and ModFOLDdock servers are available at: https://www.reading.ac.uk/bioinf/.

Indexed as

ProteinsSoftwareLigandsModels, MolecularProtein ConformationProtein Structure, TertiaryLigandsProteins

Identifiers

PMID37102670
PMCPMC10320135
OpenAlexW4367174996

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.