Demo Data from EDBT 2026 / reviewers in the wild / expert
Maciej Pawel Ciemny
dblp:235/8474
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 77% Computational science and engineering · 23% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
protein structure prediction |
0.8 | 2 | 2019 | CABS-flex standalone: a simulation environment for fast modeling of protein flexibility · Bioinform. 2019 CABS-dock standalone: a toolbox for flexible protein-peptide docking · Bioinform. 2019 |
Computational science and engineering › computational chemistry › molecular simulation › molecular dynamics
coarse-grained simulation |
0.4 | 1 | 2019 | CABS-flex standalone: a simulation environment for fast modeling of protein flexibility · Bioinform. 2019 |
Bioinformatics and computational biology › molecular informatics › molecular modeling › molecular docking
protein-peptide docking |
0.4 | 1 | 2019 | CABS-dock standalone: a toolbox for flexible protein-peptide docking · Bioinform. 2019 |
Bioinformatics and computational biology
structural biology |
0.1 | 1 | 2019 | CABS-flex standalone: a simulation environment for fast modeling of protein flexibility · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
structural clustering · 0.4coarse-grained simulation · 0.4coarse-grained modeling · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Protein-peptide docking using CABS-dock and contact informationabstractCABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/). SHORT ABSTRACT: CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking. Maciej Blaszczyk, Maciej Pawel Ciemny, Andrzej Kolinski, Mateusz Kurcinski, Sebastian Kmiecik |
Briefings Bioinform. | 2 |
| 2019 | CABS-dock standalone: a toolbox for flexible protein-peptide dockingabstractSUMMARY: CABS-dock standalone is a multiplatform Python package for protein-peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein-peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation-it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results. AVAILABILITY AND IMPLEMENTATION: CABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock. Mateusz Kurcinski, Maciej Pawel Ciemny, Tymoteusz Oleniecki, Aleksander Kuriata, Aleksandra E. Badaczewska-Dawid, Andrzej Kolinski, Sebastian Kmiecik |
Bioinform. | 2 |
| 2019 | CABS-flex standalone: a simulation environment for fast modeling of protein flexibilityabstractSUMMARY: CABS-flex standalone is a Python package for fast simulations of protein structure flexibility. The package combines simulations of protein dynamics using CABS coarse-grained protein model with the reconstruction of selected models to all-atom representation and analysis of modeling results. CABS-flex standalone is designed to allow for command-line access to the CABS computations and complete control over simulation process. CABS-flex standalone is equipped with features such as: modeling of multimeric and large-size protein systems, contact map visualizations, analysis of similarities to the reference structure and configurable modeling protocol. For instance, the user may modify the simulation parameters, distance restraints, structural clustering scheme or all-atom reconstruction parameters. With these features CABS-flex standalone can be easily incorporated into other methodologies of structural biology. AVAILABILITY AND IMPLEMENTATION: CABS-flex standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. CABS-flex source code, wiki with examples of use and installation instructions for Linux, macOS and Windows are available from the CABS-flex standalone repository at https://bitbucket.org/lcbio/cabsflex. Mateusz Kurcinski, Tymoteusz Oleniecki, Maciej Pawel Ciemny, Aleksander Kuriata, Andrzej Kolinski, Sebastian Kmiecik |
Bioinform. | 3 |