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@ARTICLE{FernandezBallester:93926,
author = {Fernandez-Ballester, G. and Beltrao, P. and Gonzalez, J. M.
and Song, Y.-H. and Wilmanns, M. and Valencia, A. and
Serrano, L. and DESY},
title = {{S}tructure-based prediction of the {S}accharomyces
cerevisiae {SH}3-ligand interactions},
journal = {Journal of molecular biology},
volume = {388},
issn = {0022-2836},
address = {Amsterdam [u.a.]},
publisher = {Elsevier},
reportid = {PHPPUBDB-12664},
pages = {902-916},
year = {2009},
note = {© Elsevier Ltd.; Post referee fulltext in progress 2;
Embargo 12 months from publication},
abstract = {A great challenge in the proteomics and structural genomics
era is to discover protein structure and function, including
the identification of biological partners. Experimental
investigation is costly and time-consuming, making
computational methods very attractive for predicting protein
function. In this work, we used the existing structural
information in the SH3 family to first extract all SH3
structural features important for binding and then used this
information to select the right templates to homology model
most of the Saccharomyces cerevisiae SH3 domains. Second, we
classified, based on ligand orientation with respect to the
SH3 domain, all SH3 peptide ligands into 29 conformations,
of which 18 correspond to variants of canonical type I and
type II conformations and 11 correspond to non-canonical
conformations. Available SH3 templates were expanded by
chimera construction to cover some sequence variability and
loop conformations. Using the 29 ligand conformations and
the homology models, we modelled all possible complexes.
Using these complexes and in silico mutagenesis scanning, we
constructed position-specific ligand binding matrices. Using
these matrices, we determined which sequences will be
favorable for every SH3 domain and then validated them with
available experimental data. Our work also allowed us to
identify key residues that determine loop conformation in
SH3 domains, which could be used to model human SH3 domains
and do target prediction. The success of this methodology
opens the way for sequence-based, genome-wide prediction of
protein-protein interactions given enough structural
coverage.},
keywords = {Algorithms / Amino Acid Sequence / Computer Simulation /
Humans / Ligands / Models, Molecular / Molecular Sequence
Data / Peptides: chemistry / Peptides: metabolism / Protein
Binding / Protein Conformation / Reproducibility of Results
/ Saccharomyces cerevisiae: chemistry / Saccharomyces
cerevisiae: metabolism / Saccharomyces cerevisiae Proteins:
chemistry / Saccharomyces cerevisiae Proteins: genetics /
Saccharomyces cerevisiae Proteins: metabolism / Sequence
Alignment / src Homology Domains / Ligands (NLM Chemicals) /
Peptides (NLM Chemicals) / Saccharomyces cerevisiae Proteins
(NLM Chemicals)},
cin = {EMBL(-2012)},
ddc = {570},
cid = {$I:(DE-H253)EMBL_-2012_-20130307$},
pnm = {FS Beamline without reference (POF1-550)},
pid = {G:(DE-H253)POF1-No-Ref-20130405},
experiment = {EXP:(DE-H253)Unknown-BL-20150101},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:19324052},
UT = {WOS:000266302500018},
doi = {10.1016/j.jmb.2009.03.038},
url = {https://bib-pubdb1.desy.de/record/93926},
}