Protein-Protein Interaction Site Prediction via EquiPPIS and Its Application in Studying Viral Replication.

Rahmatullah Roche, Sumit Tarafder, Parkesh Suseendran, Xiaofeng Wang, Debswapna Bhattacharya

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. 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

5 authors.

Rahmatullah RocheTSYS School of Computer Science, Columbus State University, Columbus, GA, USA.

Sumit TarafderDepartment of Computer Science, Virginia Tech, Blacksburg, VA, USA.

Parkesh SuseendranSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA.

Xiaofeng WangSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA.

Debswapna BhattacharyaDepartment of Computer Science, Virginia Tech, Blacksburg, VA, USA. dbhattacharya@vt.edu.

Funding

GPU-accelerated high-performance computing to supercharge foundational deep learning method development for scalable and accurate prediction of protein structuresR35GM138146 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI Debswapna Bhattacharya · 2020 to 2026

$2.5M

NIGMS NIH HHS R35 GM138146

6 · The paper itself

Abstract

Understanding the binding sites within proteins is vital for determining their functional properties including the host-virus interactions. Therefore, to meet the pressing demand for rapid identification of these binding sites, fast computational prediction techniques have become essential. The recently developed method EquiPPIS enables accurate and scalable prediction of generalized, partner-independent protein-protein interaction sites for a wide range of proteins and thus can deepen our understanding of viral replication. The predicted interaction sites within these proteins can guide us how to disrupt these viral replications, offering rapid antiviral strategies that are applicable to multiple (+)RNA viruses. Here, we present the detailed procedures of protein-protein interaction site prediction by EquiPPIS and its application in studying viral replication.

Indexed as

Computational BiologyProtein Interaction MappingSoftwareViral ProteinsVirus ReplicationBinding SitesHost-Pathogen InteractionsHumansProtein BindingViral ProteinsDeep learningProtein–protein interactionsProtein–protein interaction site predictionViral replication

Identifiers

PMID41174316

DOI10.1007/978-1-0716-4828-5_14

What Socratic 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 Socratic graph.