Matt: Local Flexibility Aids Protein Multiple Structure Alignment

By Matthew Menke, Bonnie Berger, Lenore J. Cowen. 2008

235 citations recorded by the source corpus.

Even when there is agreement on what measure a protein multiple structure alignment should be optimizing, finding the optimal alignment is computationally prohibitive. One approach used by many previous methods is aligned fragment pair chaining, where short structural fragments from all the proteins are aligned against each other optimally, and the final alignment chains these together in geometrically consistent ways. Ye and Godzik have recently suggested that adding geometric flexibility may help better model protein structures in a variety of contexts. We introduce the program Matt (Multiple Alignment with Translations and Twists), an aligned fragment pair chaining algorithm that, in intermediate steps, allows local flexibility between fragments: small translations and rotations are temporarily allowed to bring sets of aligned fragments closer, even if they are physically impossible under rigid body transformations. After a dynamic programming assembly guided by these “bent” alignments, geometric consistency is restored in the final step before the alignment is output. Matt is tested against other recent multiple protein structure alignment programs on the popular Homstrad and SABmark benchmark datasets. Matt's global performance is competitive with the other programs on Homstrad, but outperforms the other programs on SABmark, a benchmark of multiple structure alignments of proteins with more distant homology. On both datasets, Matt demonstrates an ability to better align the ends of α-helices and β-strands, an important characteristic of any structure alignment program intended to help construct a structural template library for threading approaches to the inverse protein-folding problem. The related question of whether Matt alignments can be used to distinguish distantly homologous structure pairs from pairs of proteins that are not homologous is also considered. For this purpose, a p-value score based on the length of the common core and average root mean squared deviation (RMSD) of Matt alignments is shown to largely separate decoys from homologous protein structures in the SABmark benchmark dataset. We postulate that Matt's strong performance comes from its ability to model proteins in different conformational states and, perhaps even more important, its ability to model backbone distortions in more distantly related proteins.

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

1. Jinbo Xu
2. Sheng Wang
3. Jianzhu Ma
4. Jianyi Yang
5. Zhiyong Wang
6. Jian Peng
7. Yang Zhang
8. W. Sheng

In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Jianzhu Ma
4. Yang Zhang
5. Zhiyong Wang
6. Jian Peng
7. Jianyi Yang
8. Bonnie Berger

Citations in the corpus

• MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, methodology): “To reduce bias, we use three very different structure alignment tools to generate reference alignments, including TM-align [34], Matt [35], and DeepAlign [31].”
• RaptorX: exploiting structure information for protein alignment by statistical inference (2011, methodology): “34 Then, we run our probabilistic-consistency transformation algorithm to iteratively adjust all the PAMs, to maximize the consistency among all PAMs, and to improve the alignment between two proteins using the others.”
• mTM-align: a server for fast protein structure database search and multiple protein structure alignment (2018, methodology): “A few MSTA algorithms are available, such as mTM-align (8), Matt (9) and POSA (10).”
• SMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zone (2012, methodology): “Many researchers use hidden Markov models (HMMs) to annotate proteins according to homology, with popular systems such as Pfam (Finn et al., 2008) and Superfamily (Wilson et al., 2007) based on HMM methods integrated into UniProt.”
• GR-Align: fast and flexible alignment of protein 3D structures using graphlet degree similarity (2014, methodology): “In g raphlet degree distribution agreement , using all 2–5 node graphlets and their corresponding 73 automorphism orbits, the degree distribution is extended to 73 graphlet degree distributions (the first of the 73 graphlet degree distributions is the degree distribution).”
• Fast Multiple Alignment of Protein Structures Using Conformational Letter Blocks (2009, methodology): “MATT (Multiple Alignment with Translations and Twists), including the difference between transformations for superposing fragment duads as a part of penalty for dynamic programming, is able to deal with local flexibility [12].”