Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Radoslav Krivak"'
Publikováno v:
Bioinformatics (Oxford, England). 38(24)
Understanding the mechanism of action of a protein or designing better ligands for it often requires access to a bound (holo) and an unbound (apo) state of the protein. Resources for the quick and easy retrieval of such conformations are severely lim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f06dce091c48af5592b995385a732ba
https://pubmed.ncbi.nlm.nih.gov/36282546
https://pubmed.ncbi.nlm.nih.gov/36282546
Autor:
David Bednar, Sucharita Dey, Emmanuel D. Levy, Natarajan Kannan, Bissan Al-Lazikani, Damiano Piovesan, Luis A Rodriguez, Sameer Velankar, Mihaly Varadi, Jan Stourac, Jaime Prilusky, Manjeet Kumar, Radoslav Krivak, Michael J.E. Sternberg, Juan Fernandez Recio, Daniel Zaidman, David R. Armstrong, Nathan J Rollins, Gulzar Singh, Jiri Damborsky, Dandan Xue, Stephen Anyango, Vivek Modi, Antonio Rosato, Christine A. Orengo, Valeria Putignano, Radka Svobodová, Alessia David, Debora S. Marks, Roland L. Dunbrack, Jose Ramon Macias, David Jakubec, Mark N. Wass, Luis Serrano, Silvio C. E. Tosatto, John M. Berrisford, Ahsan Tanweer, Sreenath Nair, Geoffrey J. Barton, Wim F. Vranken, Lukáš Pravda, Karel Berka, Stuart A McGowan, Janet M. Thornton, Nir London, Madhusudhan M Srivatsan, Lennart Martens, Atilio O Rausch, Toby J. Gibson, Pawel Rubach, Joanna I. Sulkowska, Petr Škoda, Gerardo Pepe, Nathalie Reuter, Natalia Tichshenko, Mandar Deshpande, Franca Fraternali, David Hoksza, Tom L. Blundell, R. Gonzalo Parra, Preeti Choudhary, José María Carazo, Claudia Andreini, Jake E McGreig, Leandro G Radusky, Thomas A. Hopf, Pathmanaban Ramasamy, Carlos Oscar S. Sorzano, Manuela Helmer-Citterich, Kelly P Brock, Nurul Nadzirin
Publikováno v:
Nucleic acids research
Nucleic Acids Research
Nucleic Acids Research (NAR)
D534-D542
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
D542
D534
Nucleic Acids Research
Nucleic Acids Research (NAR)
D534-D542
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
D542
D534
The Protein Data Bank in Europe – Knowledge Base (PDBe-KB, https://pdbe-kb.org) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::050cb89e62952b78f542717c68240da1
https://kar.kent.ac.uk/95049/1/gkab988.pdf
https://kar.kent.ac.uk/95049/1/gkab988.pdf
Autor:
Harry Jubb, Aleksandras Gutmanas, Radka Svobodová, Stephen Anyango, Sreenath Nair, Manjeet Kumar, Jonathan D. Tyzack, Leandro G Radusky, Toby J. Gibson, Liang-Chin Huang, Luis Serrano, Eloy Villasclaras Fernandez, Sameer Velankar, Petr Škoda, Michael J.E. Sternberg, Mark N. Wass, Fábio Madeira, Christine A. Orengo, Rishabh Jain, Stuart A. MacGowan, Patrizio Di Micco, Sayoni Das, Emmanuel D. Levy, Natarajan Kannan, John M. Berrisford, Tom L. Blundell, Janet M. Thornton, Radoslav Krivak, Christos C. Kannas, Lukáš Pravda, Bissan Al-Lazikani, Jose M. Dana, Abhik Mukhopadhyay, David R. Armstrong, Saqib Mir, Mihaly Varadi, Franca Fraternali, Karel Berka, Mallur S. Madhusudhan, Jake E McGreig, Mandar Deshpande, Neera Borkakoti, Luca Parca, António J. M. Ribeiro, Ian Sillitoe, Henry J Martell, Manuela Helmer-Citterich, Sucharita Dey, David Hoksza, Gulzar Singh, Jaroslav Koča, Typhaine Paysan-Lafosse, Geoffrey J. Barton, Alfonso Valencia, Wim F. Vranken
Publikováno v:
Nucleic Acids Research
D353
D344
D353
D344
The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa27c0dd4bcd59db90b1d8c5a0ca1042
http://europepmc.org/articles/PMC6943075
http://europepmc.org/articles/PMC6943075
Publikováno v:
Nucleic Acids Research
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability cent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::4a864bab644f55a713da83f6f4528f5e
https://pubmed.ncbi.nlm.nih.gov/31114880
https://pubmed.ncbi.nlm.nih.gov/31114880
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art ligand binding site prediction method. P2Rank is a template-free machine learning method which is based on the prediction of ligandability of local chemical neighborh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a4e26aae632787746a5468261b6503f
Publikováno v:
BCB
Protein-peptide binding interactions play an important role in cellular regulation and are functionally important in many diseases. If no prior knowledge of the location of a binding site is available, prediction may be needed as a starting point for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89c78a992e0f436c1e86d5faaced11dd
https://doi.org/10.1145/3233547.3233708
https://doi.org/10.1145/3233547.3233708
Publikováno v:
BIBM
Ligand binding site prediction from protein structure plays an important role in various complex rational drug design efforts. Its applications include drug side effects prediction, docking prioritization in inverse virtual screening and elucidation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cf148cc7f49733929d011343d9eebd0
https://doi.org/10.1109/bibm.2017.8218024
https://doi.org/10.1109/bibm.2017.8218024
Autor:
David Hoksza, Radoslav Krivak
Publikováno v:
Journal of Cheminformatics
Background Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detecti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7128a53cb8dd48fb3721c3ee6c44afba
https://doi.org/10.1186/s13321-015-0059-5
https://doi.org/10.1186/s13321-015-0059-5
Autor:
David Hoksza, Radoslav Krivak
Publikováno v:
Algorithms for Computational Biology ISBN: 9783319212326
AlCoB
AlCoB
The knowledge of protein-ligand binding sites is vital prerequisite for any structure-based virtual screening campaign. If no prior knowledge about binding sites is available, the ligand-binding site prediction methods are the only way to obtain the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a3985920f7e92163b6eae0fb8fac2cb
https://doi.org/10.1007/978-3-319-21233-3_4
https://doi.org/10.1007/978-3-319-21233-3_4
Autor:
Radoslav Krivák, David Hoksza
Publikováno v:
Journal of Cheminformatics, Vol 10, Iss 1, Pp 1-12 (2018)
Abstract Background Ligand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug des
Externí odkaz:
https://doaj.org/article/34715cd7cb01436e9a681b511d5677a9