2024-03-29T02:06:52Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/242382020-01-14T08:15:43Zcom_10261_79com_10261_1col_10261_332
2010-05-12T14:49:28Z
urn:hdl:10261/24238
Cross-Over between Discrete and Continuous Protein Structure Space: Insights into Automatic Classification and Networks of Protein Structures
Pascual-García, Alberto
Abia, David
Ortiz, Ángel R.
Bastolla, Ugo
Ministerio de Educación y Ciencia (España)
Comunidad de Madrid
Fundación Ramón Areces
MAMMOTH
Protein Structure
Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein
domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We
base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C
implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should
approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed
by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of
the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering
algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over
point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and
large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is
justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be
represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the
automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering
algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen
algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices
among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert
classifications. As a domain set, we have selected a consensus set of 2,890 domains decomposed very similarly in SCOP and
CATH. As an alignment algorithm, we used a global version of MAMMOTH developed in our group, which is both rapid and
accurate. As a similarity measure, we used the size-normalized contact overlap, and as a clustering algorithm, we used
average linkage. The resulting automatic classification at the cross-over point was more consistent than expert ones with
respect to the structure similarity measure, with 86% of the clusters corresponding to subsets of either SCOP or CATH
superfamilies and fewer than 5% containing domains in distinct folds according to both SCOP and CATH. Almost 15% of
SCOP superfamilies and 10% of CATH superfamilies were split, consistent with the notion of fold change in protein
evolution. These results were qualitatively robust for all choices that we tested, although we did not try to use alignment
algorithms developed by other groups. Folds defined in SCOP and CATH would be completely joined in the regime of large
transitivity violations where clustering is more arbitrary. Consistently, the agreement between SCOP and CATH at fold level
was lower than their agreement with the automatic classification obtained using as a clustering algorithm, respectively,
average linkage (for SCOP) or single linkage (for CATH). The networks representing significant evolutionary and structural
relationships between clusters beyond the cross-over point may allow us to perform evolutionary, structural, or functional
analyses beyond the limits of classification schemes. These networks and the underlying clusters are available at http://ub.cbm.uam.es/research/ProtNet.php
2010-05-12T14:49:28Z
2010-05-12T14:49:28Z
2009-03-27
artículo
PLoS Comput Biol 5(3): e1000331 (2009)
1553-7358
http://hdl.handle.net/10261/24238
http://dx.doi.org/10.13039/100008054
http://dx.doi.org/10.13039/100012818
eng
Publisher’s version
10.1371/journal.pcbi.1000331
openAccess
Public Library of Science