Protein structure alignment beyond spatial proximity

By Sheng Wang, Jianzhu Ma, Jian Peng, Jinbo Xu. 2013

172 citations recorded by the source corpus.

Protein structure alignment is a fundamental problem in computational structure biology. Many programs have been developed for automatic protein structure alignment, but most of them align two protein structures purely based upon geometric similarity without considering evolutionary and functional relationship. As such, these programs may generate structure alignments which are not very biologically meaningful from the evolutionary perspective. This paper presents a novel method DeepAlign for automatic pairwise protein structure alignment. DeepAlign aligns two protein structures using not only spatial proximity of equivalent residues (after rigid-body superposition), but also evolutionary relationship and hydrogen-bonding similarity. Experimental results show that DeepAlign can generate structure alignments much more consistent with manually-curated alignments than other automatic tools especially when proteins under consideration are remote homologs. These results imply that in addition to geometric similarity, evolutionary information and hydrogen-bonding similarity are essential to aligning two protein structures.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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

1. Jinbo Xu
2. Sheng Wang
3. Jianzhu Ma
4. Siqi Sun
5. Lukasz Kurgan
6. Liisa Holm
7. Zhiyong Wang
8. Z. Li

In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Jianzhu Ma
4. Siqi Sun
5. Lukasz Kurgan
6. Vladimir N Uversky
7. Zhiyong Wang
8. Z. Li

In Computational Biology & Bioinformatics

1. Jinbo Xu
2. Sheng Wang
3. Siqi Sun
4. Jianzhu Ma
5. Liisa Holm
6. Renyu Zhang
7. Z. Li
8. Zhiyong Wang

Citations in the corpus

• Distance-based protein folding powered by deep learning (2018, methodology): “We further compare RaptorX-Contact with 2 in-house threading methods, CNFpred (36, 37) and RaptorX-TBM.”
• Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model (2016, methodology): “Searching through PDB70 created right before September 17, 2016 by our in-house structural homolog search tool DeepSearch cannot identify structurally similar proteins for this test protein.”
• MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, methodology): “We train the parameters in Eu by maximizing the occurring probability of a set of reference alignments, which are generated by a structure alignment tool DeepAlign [31].”
• DALI and the persistence of protein shape (2019, methodology): “DeepAlign 28 and UniAlign 55 include sequence similarity as a component of their scoring function and show improvement over DALI, which only uses the C-alpha coordinates.”
• Predicting protein contact map using evolutionary and physical constraints by integer programming (2013, background): “14 Wang, G. and Dunbrack, R.L.J. (2003) PISCES: a protein sequence culling server, Bioinformatics, 19, 1589-1591. Wang, S., et al. (2013) Protein structure alignment beyond spatial proximity, Scientific Reports, 1448. Wu, S., Szilagyi, A. and Zhang, Y. (2011) Improving protein structure prediction u”
• DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, background): “Such a framework can be directly applied to many sequence labeling issues such as secondary structure [21], solvent accessibility [22], and structural alphabet [32–36].”