Exploration of protein sequence embeddings for protein-ligand binding site detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10455486" target="_blank" >RIV/00216208:11320/22:10455486 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/BIBM55620.2022.9995025" target="_blank" >https://doi.org/10.1109/BIBM55620.2022.9995025</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BIBM55620.2022.9995025" target="_blank" >10.1109/BIBM55620.2022.9995025</a>
Alternative languages
Result language
angličtina
Original language name
Exploration of protein sequence embeddings for protein-ligand binding site detection
Original language description
Detection of protein-ligand binding sites is essential not only for protein function investigation but also in fields such as drug discovery or bioengineering. In this paper, we show that the recently-developed pre-trained language models can be used for protein-ligand binding site prediction. Specifically, we present a neural network architecture where inputs correspond to amino acids embeddings obtained from a protein language model. We show that increasing complexity of the language model improves the predictive performance of the method, eventually leading to results comparable to or surpassing state-of-the-art approaches. Unlike the existing methods, the presented approach does not require time-consuming computation of evolutionary information, resulting in faster running times.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA22-21696S" target="_blank" >GA22-21696S: Deep Visual Representations of Unstructured Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
ISBN
978-1-66546-819-0
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
3356-3361
Publisher name
IEEE
Place of publication
USA
Event location
Las Vegas, NV, USA
Event date
Dec 6, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—