Evidence map›Paper›PMID 42299509›Full record

ArticleJournal of chemical information and modeling2026

Strategic Template Filtering Accelerates Fragment-Based Peptide Docking.

Nirit Trabelsi-Mescheloff, Julia K Varga, Alisa Khramushin, Sergey Lyskov, Ora Schueler-Furman

Abstract read
In one paragraph

Article in Journal of chemical information and modeling, 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.

Nirit Trabelsi-MescheloffMicrobiology and Molecular Genetics & Quantitative Molecular Medicine, The Hebrew University of Jerusalem, Jerusalem91120, Israel.
Julia K VargaMicrobiology and Molecular Genetics & Quantitative Molecular Medicine, The Hebrew University of Jerusalem, Jerusalem91120, Israel.
Alisa KhramushinMicrobiology and Molecular Genetics & Quantitative Molecular Medicine, The Hebrew University of Jerusalem, Jerusalem91120, Israel.
Sergey LyskovDepartment of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland21205, United States.
Ora Schueler-FurmanMicrobiology and Molecular Genetics & Quantitative Molecular Medicine, The Hebrew University of Jerusalem, Jerusalem91120, Israel.ORCID 0000-0002-1624-0362

Funding

H2020 Marie Sklodowska-Curie Actions UBIMOTIF- 860517Israel Academy of Sciences and Humanities 301/2021Israel Academy of Sciences and Humanities 3091/23
6 · The paper itself

Abstract

Peptide-protein interactions are often transient and structurally elusive, necessitating computational approaches to identify both binding sites and peptide conformations. PatchMAN, one of the leading but computationally expensive biophysic-based global peptide-docking protocols, addresses this challenge by treating peptide docking as a protein-folding problem, using structural motifs from solved monomer structures as templates that are subsequently refined using Rosetta FlexPepDock. Here we present PatchMAN2, which introduces (1) strategic fragment filtering and (2) local docking modes that focus sampling on relevant surfaces or known binding regions, thereby reducing the high computational cost of the original implementation due to extensive refinement of many nonproductive low-quality fragments. Benchmarking shows that PatchMAN2 removes ∼30-70% of unnecessary fragments while preserving accuracy, substantially reducing runtime and improving the practical efficiency of peptide-protein docking.

Indexed as

Molecular Docking SimulationPeptidesProteinsBinding SitesProtein BindingProtein ConformationPeptidesProteins

Identifiers

PMID42299509
PMCPMC13471421

What OpenQuestion holds

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Registered trials

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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.