Evidence map›Paper›PMID 41113973›Full record

ArticleAmerican journal of cancer research2025

AlphaPPIweb: an integration platform for protein-protein interaction prediction and evaluation.

Yen-Yi Liu, Chi-Ching Lee, Pei-Hsuan Li, Sung-Huan Yu, Mien-Chie Hung, Hsin-Mien Hsu, Chih-Chao Yang, Chen-Chieh Huang, Pei-Yao Kuo, Yi-Chuan Li

Abstract read
In one paragraph

Article in American journal of cancer research, 2025. 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
–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

0 citing papers in PubMed.

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

10 authors.

Yen-Yi LiuDepartment of Biology, National Changhua University of Education Changhua, Taiwan.
Chi-Ching LeeDepartment of Computer Science and Information Engineering, Chang Gung University Taoyuan, Taiwan.
Pei-Hsuan LiDepartment of Computer Science and Information Engineering, Chang Gung University Taoyuan, Taiwan.
Sung-Huan YuInstitute of Precision Medicine, College of Medicine, National Sun Yat-sen University Kaohsiung, Taiwan.
Mien-Chie HungResearch Center for Cancer Biology, China Medical University Taichung, Taiwan.
Hsin-Mien HsuCancer Biology and Precision Therapeutics Center, China Medical University Taichung, Taiwan.
Chih-Chao YangDepartment of Biological Science and Technology, China Medical University Taichung, Taiwan.
Chen-Chieh HuangDepartment of Biological Science and Technology, China Medical University Taichung, Taiwan.
Pei-Yao KuoDepartment of Biology, National Changhua University of Education Changhua, Taiwan.
Yi-Chuan LiCancer Biology and Precision Therapeutics Center, China Medical University Taichung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of AlphaFold has markedly advanced structural biology by enabling highly accurate predictions of protein structures. This breakthrough has been further extended by AlphaFold-Multimer, which enables the prediction of protein complex structures. However, currently available tools for predictions have limitations, including usage constraints and the need for extensive postprediction analysis to identify interface residues. To address these challenges, we introduce AlphaPPIweb, a comprehensive web-based platform designed to leverage AlphaPulldown, an AlphaFold-Multimer-based tool, for predicting protein complex structures. Users simply input amino acid sequences, after which AlphaPPIweb generates three-dimensional structural predictions, provides confidence scores, and presents results through intuitive visualizations. The platform facilitates the prediction of interactions between a single bait protein and multiple candidate proteins, thus aiding in the identification of potential ligand-receptor interactions. Furthermore, AlphaPPIweb incorporates automated interface residue analysis immediately following structure prediction, streamlining the workflow and providing comprehensive insights into the predicted complexes. This integrated approach significantly enhances the usability of AlphaPPIweb for biomedical studies and educational applications. By democratizing access to advanced structural prediction tools and enabling a comprehensive analysis of protein interactions, AlphaPPIweb promotes extensive exploration and understanding within the biological research community. AlphaPPIweb is accessible at http://alphappiweb.cmu.edu.tw/.

Indexed as

AlphaFoldReceptor Tyrosine KinaseRNasestructural biologyweb service

Identifiers

PMID41113973
PMCPMC12531294

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.