TM-align: a protein structure alignment algorithm based on TM-score. We have developed TM-align, a new algorithm to identify the best structural alignment between protein pairs that combines the TM-score rotation matrix and Dynamic Programming (DP). The algorithm is ∼4 times faster than CE and 20 times faster than DALI and SAL. On average, the resulting structure alignments have higher accuracy and coverage than those provided by these most often-used methods. TM-align is applied to an all-against-all structure comparison of 10 515 representative protein chains from the Protein Data Bank (PDB) with a sequence identity cutoff <95%: 1996 distinct folds are found when a TM-score threshold of 0.5 is used. We also use TM-align to match the models predicted by TASSER for solved non-homologous proteins in PDB. For both folded and misfolded models, TM-align can almost always find close structural analogs, with an average root mean square deviation, RMSD, of 3 Å and 87% alignment coverage. Nevertheless, there exists a significant correlation between the correctness of the predicted structure and the structural similarity of the model to the other proteins in the PDB. This correlation could be used to assist in model selection in blind protein structure predictions. The TM-align program is freely downloadable at .

References in zbMATH (referenced in 15 articles )

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  1. Gullotto, Danilo: Fine tuned exploration of evolutionary relationships within the protein universe (2021)
  2. Ejlali, Nasim; Faghihi, Mohammad Reza; Sadeghi, Mehdi: Bayesian comparison of protein structures using partial Procrustes distance (2017)
  3. Gonçalves, Douglas S.; Mucherino, Antonio; Lavor, Carlile; Liberti, Leo: Recent advances on the interval distance geometry problem (2017)
  4. Andonov, Rumen; Djidjev, Hristo; Klau, Gunnar W.; Boudic-Jamin, Mathilde Le; Wohlers, Inken: Automatic classification of protein structure using the maximum contact map overlap metric (2015)
  5. Gullotto, Danilo; Nolassi, Mario Salvatore; Bernini, Andrea; Spiga, Ottavia; Niccolai, Neri: Probing the protein space for extending the detection of weak homology folds (2013)
  6. Kim, Woo-Cheol; Park, Sanghyun; Won, Jung-Im: CORE: common region extension based multiple protein structure alignment for producing multiple solution (2013) ioport
  7. Wang, Han; Liu, Bo; Sun, Pingping; Ma, Zhiqiang: A topology structure based outer membrane proteins segment alignment method (2013) ioport
  8. Hollup, Siv Midtun; Sadowski, Michael I.; Jonassen, Inge; Taylor, William R.: Exploring the limits of fold discrimination by structural alignment: a large scale benchmark using decoys of known fold (2011)
  9. Rahbar, Mohammad Reza; Rasooli, Iraj; Mousavi Gargari, Seyed Latif; Amani, Jafar; Fattahian, Yaser: In silico analysis of antibody triggering biofilm associated protein in \textitAcinetobacterbaumannii (2010)
  10. Bauer, Raphael André; Rother, Kristian; Moor, Peter; Reinert, Knut; Steinke, Thomas; Bujnicki, Janusz M.; Preissner, Robert: Fast structural alignment of biomolecules using a hash table, n-grams and string descriptors (2009)
  11. Bu, Dongbo; Li, Ming; Li, Shuai Cheng; Qian, Jianbo; Xu, Jinbo: Finding compact structural motifs (2009)
  12. Liu, Yu-Shen; Fang, Yi; Ramani, Karthik: Using least median of squares for structural superposition of flexible proteins (2009) ioport
  13. Zhang, Yang: I-TASSER server for protein 3D structure prediction (2008) ioport
  14. Martínez, Leandro; Andreani, Roberto; Martínez, José Mario: Convergent algorithms for protein structural alignment (2007) ioport
  15. Mcguffin, Liam J.: Benchmarking consensus model quality assessment for protein fold recognition (2007) ioport