ClEPaps: Fast Pair Alignment of protein Structures Based on conformational Letters
By Sheng Wang, Weinan Zheng. 2007
48 citations recorded by the source corpus.
Fast, efficient, and reliable algorithms for pairwise alignment of protein structures are in ever-increasing demand for analyzing the rapidly growing data on protein structures. CLePAPS is a tool developed for this purpose. It distinguishes itself from other existing algorithms by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of the three angles formed by Calpha pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. CLePAPS regards an aligned fragment pair (AFP) as an ungapped string pair with a high sum of pairwise CLESUM scores. Using CLESUM scores as the similarity measure, CLePAPS searches for AFPs by simple string comparison. The transformation which best superimposes a highly similar AFP can be used to superimpose the structure pairs under comparison. A highly scored AFP which is consistent with several other AFPs determines an initial alignment. CLePAPS then joins consistent AFPs guided by their similarity scores to extend the alignment by several "zoom-in" iteration steps. A follow-up refinement produces the final alignment. CLePAPS does not implement dynamic programming. The utility of CLePAPS is tested on various protein structure pairs.
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
- Lukasz Kurgan
- Sheng Wang
- Jianzhu Ma
- W. Sheng
- Vladimir N Uversky
- Weinan Zheng
- Shunyan Weng
- Qingming Tang
In Molecular Biology
- Sheng Wang
- Jinbo Xu
- Jianzhu Ma
- Lukasz Kurgan
- Martin Steinegger
- Milot Mirdita
- Vladimir N Uversky
- Johannes Söding
Citations in the corpus
- DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, methodology): “…general strategies to prevent over-fitting and to further improve the performance of the deep network architecture: (a) a dropout framework [32–36] which takes into consideration the hidden structures of the neurons; (b) a dimension reduction technique [37] which could be used to reduce…”
- Fast Multiple Alignment of Protein Structures Using Conformational Letter Blocks (2009, methodology): “BLOMAPS is developed from our CLEPAPS, a fast tool for pairwise protein structure alignment [20].”
- Fast and accurate protein structure search with Foldseek (2022, background): “Most, such as CLE, 3D-BLAST and Protein Blocks, discretize the conformations of short stretches of usually 3–5 C α atoms 17–19 .”