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Constraining Protein Docking with Coevolution Data for Medical Research
May 2013
KrMB13
Protein interaction is essential to all biological systems, from the assembly of multimeric complexes to processes such as transport, catalysis and gene regulation. Unfortunately, the prediction of protein-protein interactions is a difficult problem, often with modest success rates, in part because docking algorithms must filter a very large number of possibilities and then attempt to identify a correct model among many incorrect candidates. This paper presents a scoring function to estimate contacts in coevolving proteins, shows how the predicted contacts can constrain the filtering stage and significantly reduce the number of incorrect candidates, and illustrates the application of this method to the docking of two complexes of medical relevance, one involving a chromosome condensation regulator homologous to a protein responsible for retinitis pigmentosa and the other a cyclin-dependent kinase, a likely target for cancer therapy.
In proceedings
Ludwig Krippahl, Fábio Madeira, Pedro Barahona
Artificial Intelligence in Medicine (Procs. AIME'13)
Lecture Notes in Computer Science
Springer Berlin / Heidelberg
-
7885
110-114
978-3-642-38325-0
0302-9743
-
http://dx.doi.org/10.1007/978-3-642-38326-7_17
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Ludwig Krippahl and Fábio Madeira and Pedro Barahona, Constraining Protein Docking with Coevolution Data for Medical Research, , Artificial Intelligence in Medicine (Procs. AIME'13), Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol. 7885, ISBN 978-3-642-38325-0, ISSN 0302-9743, Pag. 110-114, (http://dx.doi.org/10.1007/978-3-642-38326-7_17), May 2013.
<a href="/people/members/view.php?code=195d68ea5904b58472fd8c8aedcae233" class="author">Ludwig Krippahl</a>, <a href="/people/members/view.php?code=46d17da71ed1c44fe2fd9b6ad9321cec" class="author">Fábio Madeira</a> and <a href="/people/members/view.php?code=7e27bc13fad97e99cd21ea6914d55659" class="author">Pedro Barahona</a>, <b>Constraining Protein Docking with Coevolution Data for Medical Research</b>, <u>Artificial Intelligence in Medicine (Procs. AIME'13)</u>, Lecture Notes in Computer Science, Springer Berlin / Heidelberg, Vol. 7885, ISBN 978-3-642-38325-0, ISSN 0302-9743, Pag. 110-114, (<a href="http://dx.doi.org/10.1007/978-3-642-38326-7_17" target="_blank">url</a>), May 2013.
@inproceedings {KrMB13, author = {Ludwig Krippahl and F{\'a}bio Madeira and Pedro Barahona}, title = {Constraining Protein Docking with Coevolution Data for Medical Research}, booktitle = {Artificial Intelligence in Medicine (Procs. AIME'13)}, series = {Lecture Notes in Computer Science}, publisher = {Springer Berlin / Heidelberg}, volume = {7885}, pages = {110-114}, isbn = {978-3-642-38325-0}, issn = {0302-9743}, url = {http://dx.doi.org/10.1007/978-3-642-38326-7_17}, abstract = {Protein interaction is essential to all biological systems, from the assembly of multimeric complexes to processes such as transport, catalysis and gene regulation. Unfortunately, the prediction of protein-protein interactions is a difficult problem, often with modest success rates, in part because docking algorithms must filter a very large number of possibilities and then attempt to identify a correct model among many incorrect candidates. This paper presents a scoring function to estimate contacts in coevolving proteins, shows how the predicted contacts can constrain the filtering stage and significantly reduce the number of incorrect candidates, and illustrates the application of this method to the docking of two complexes of medical relevance, one involving a chromosome condensation regulator homologous to a protein responsible for retinitis pigmentosa and the other a cyclin-dependent kinase, a likely target for cancer therapy.}, keywords = {Protein Interaction; Multi-sequence Alignment; Co-evolution}, month = {May}, year = {2013}, }
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