Proteins<i>Plus</i>: a publicly available resource for protein structure mining

The openly accessible ProteinsPlus web server at https://proteins.plus is a unique resource enabling protein structure mining and modeling, focussing on protein-ligand interactions. Since its launch in 2017, the number of tools is steadily increasing. Currently, the server comprises six methods for protein structure analysis, four tools for mining the Protein Data Bank (PDB), and five prediction approaches regarding protein-ligand complex modeling. Users can use experimental structures from the PDB or computationally predicted structures from the AlphaFold Protein Structure Database as starting points. Alternatively, they can upload individual structure files. Recent updates include novel methods for detecting binding sites and predicting solvent channels in crystallographic structures, as well as updates of tools for protein-ligand interaction depiction in 2D and binding site mining. Given these updates, we present a real-life application scenario that underpins the novelties and applicability of the web server's tools for modern structure-based design projects. It also highlights the next steps for the web server, which will be redesigned using a different technology stack to improve the inter-usability of the tools, ease maintainability, and make it future-proof.

Authors

Institutions

Publication Details

Journal
Nucleic Acids Research
Published
2025-05-06
DOI
https://doi.org/10.1093/nar/gkaf377
Citations
13
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
10.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ProteinsPlus: a publicly available resource for protein structure mining

Konrad Diedrich, J. Pletzer-Zelgert, Christiane Ehrt, Joel Graef et al.
13 citations
Nucleic Acids Research
Protein Structure and Dynamics
10.48
article

ProteinsPlus: a publicly available resource for protein structure mining

Konrad Diedrich, J. Pletzer-Zelgert, Christiane Ehrt, Joel Graef, Matthias Rarey, T.G. Schulze
article en
13 citations

Abstract

The openly accessible ProteinsPlus web server at https://proteins.plus is a unique resource enabling protein structure mining and modeling, focussing on protein-ligand interactions. Since its launch in 2017, the number of tools is steadily increasing. Currently, the server comprises six methods for protein structure analysis, four tools for mining the Protein Data Bank (PDB), and five prediction approaches regarding protein-ligand complex modeling. Users can use experimental structures from the PDB or computationally predicted structures from the AlphaFold Protein Structure Database as starting points. Alternatively, they can upload individual structure files. Recent updates include novel methods for detecting binding sites and predicting solvent channels in crystallographic structures, as well as updates of tools for protein-ligand interaction depiction in 2D and binding site mining. Given these updates, we present a real-life application scenario that underpins the novelties and applicability of the web server's tools for modern structure-based design projects. It also highlights the next steps for the web server, which will be redesigned using a different technology stack to improve the inter-usability of the tools, ease maintainability, and make it future-proof.

Nucleic Acids ResearchVol. 53(W1)
Albert Einstein College of Medicine (US), Universität Hamburg (DE)
Ministry of Higher Education and Scientific Research, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 1%
Protein Structure and Dynamics
10.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.