Sequence-similar, structure-dissimilar protein pairs in the PDB

By Mickey Kosloff, Rachel Kolodny. 2007

126 citations recorded by the source corpus.

It is often assumed that in the Protein Data Bank (PDB), two proteins with similar sequences will also have similar structures. Accordingly, it has proved useful to develop subsets of the PDB from which “redundant” structures have been removed, based on a sequence‐based criterion for similarity. Similarly, when predicting protein structure using homology modeling, if a template structure for modeling a target sequence is selected by sequence alone, this implicitly assumes that all sequence‐similar templates are equivalent. Here, we show that this assumption is often not correct and that standard approaches to create subsets of the PDB can lead to the loss of structurally and functionally important information. We have carried out sequence‐based structural superpositions and geometry‐based structural alignments of a large number of protein pairs to determine the extent to which sequence similarity ensures structural similarity. We find many examples where two proteins that are similar in sequence have structures that differ significantly from one another. The source of the structural differences usually has a functional basis. The number of such proteins pairs that are identified and the magnitude of the dissimilarity depend on the approach that is used to calculate the differences; in particular sequence‐based structure superpositioning will identify a larger number of structurally dissimilar pairs than geometry‐based structural alignments. When two sequences can be aligned in a statistically meaningful way, sequence‐based structural superpositioning provides a meaningful measure of structural differences. This approach and geometry‐based structure alignments reveal somewhat different information and one or the other might be preferable in a given application. Our results suggest that in some cases, notably homology modeling, the common use of nonredundant datasets, culled from the PDB based on sequence, may mask important structural and functional information. We have established a data base of sequence‐similar, structurally dissimilar protein pairs that will help address this problem (http://luna.bioc.columbia.edu/rachel/seqsimstrdiff.htm). Proteins 2008. © 2007 Wiley‐Liss, Inc.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

In Molecular Biology

Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, background): “Therefore, the new embeddings could complement sequence-based structural classification as it was shown that the sequence similarity does not necessarily lead to structural similarity [55].”
A novel method to compare protein structures using local descriptors (2011, background): “Furthermore, as recently demonstrated by Kosloff and Kolodny [51], assignment of function may also be helped by focusing on structural dissimilarity among proteins that are related by homology.”
FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately (2010, background): “However, by using such sequence-nonredundant representative sets we risk excluding interesting structural variability (12).”