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Aleksander Kuriata

dblp:235/8204 · DBLP profile ↗
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4ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 4 · 1 first-author · 1 since 2021

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
4 papers
Bioinformatics and computational biology · 89% Computational science and engineering · 11%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein structure prediction
1.132019
Aggrescan3D standalone package for structure-based prediction of protein aggregation properties · Bioinform. 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
Bioinformatics and computational biology › protein analysis › protein bioinformatics
protein aggregation prediction
1.022022
A3D database: structure-based predictions of protein aggregation for the human proteome · Bioinform. 2022
Aggrescan3D standalone package for structure-based prediction of protein aggregation properties · Bioinform. 2019
Computational science and engineering › computational chemistry › molecular simulation › molecular dynamics
coarse-grained simulation
0.412019
CABS-flex standalone: a simulation environment for fast modeling of protein flexibility · Bioinform. 2019
Bioinformatics and computational biology
protein design
0.412019
Aggrescan3D standalone package for structure-based prediction of protein aggregation properties · Bioinform. 2019
Bioinformatics and computational biology › molecular informatics › molecular modeling › molecular docking
protein-peptide docking
0.412019
CABS-dock standalone: a toolbox for flexible protein-peptide docking · Bioinform. 2019
Bioinformatics and computational biology
structural biology
0.112019
CABS-flex standalone: a simulation environment for fast modeling of protein flexibility · Bioinform. 2019

Methods — techniques the papers use, named apart from their topics

alphafold structure prediction · 0.6aggrescan3d · 0.6structure-based prediction · 0.4structural clustering · 0.4conformational fluctuation analysis · 0.4coarse-grained simulation · 0.4coarse-grained modeling · 0.4
YearPublicationVenuePosition
2022 A3D database: structure-based predictions of protein aggregation for the human proteome
abstract
SUMMARY: Protein aggregation is associated with many human disorders and constitutes a major bottleneck for producing therapeutic proteins. Our knowledge of the human protein structures repertoire has dramatically increased with the recent development of the AlphaFold (AF) deep-learning method. This structural information can be used to understand better protein aggregation properties and the rational design of protein solubility. This article uses the Aggrescan3D (A3D) tool to compute the structure-based aggregation predictions for the human proteome and make the predictions available in a database form. In the A3D database, we analyze the AF-predicted human protein structures (for over 20.5 thousand unique Uniprot IDs) in terms of their aggregation properties using the A3D tool. Each entry of the A3D database provides a detailed analysis of the structure-based aggregation propensity computed with A3D. The A3D database implements simple but useful graphical tools for visualizing and interpreting protein structure datasets. It also enables testing the influence of user-selected mutations on protein solubility and stability, all integrated into a user-friendly interface. AVAILABILITY AND IMPLEMENTATION: A3D database is freely available at: http://biocomp.chem.uw.edu.pl/A3D2/hproteome. The data underlying this article are available in the article and in its online supplementary material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Aleksandra E. Badaczewska-Dawid, Javier Garcia-Pardo, Aleksander Kuriata, Jordi Pujols, Salvador Ventura, Sebastian Kmiecik
Bioinform.3
2019 CABS-dock standalone: a toolbox for flexible protein-peptide docking
abstract
SUMMARY: 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.4
2019 CABS-flex standalone: a simulation environment for fast modeling of protein flexibility
abstract
SUMMARY: 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.4
2019 Aggrescan3D standalone package for structure-based prediction of protein aggregation properties
abstract
SUMMARY: Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation-it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools. AVAILABILITY AND IMPLEMENTATION: A3D standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. The A3D standalone source code, wiki with documentation and examples of use, and installation instructions for Linux, macOS and Windows are available in the A3D standalone repository at https://bitbucket.org/lcbio/aggrescan3d.
Aleksander Kuriata, Valentín Iglesias, Mateusz Kurcinski, Salvador Ventura, Sebastian Kmiecik
Bioinform.1