MAMMOTH

MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison. Advances in structural genomics and protein structure prediction require the design of automatic, fast, objective, and well benchmarked methods capable of comparing and assessing the similarity of low-resolution three-dimensional structures, via experimental or theoretical approaches. Here, a new method for sequence-independent structural alignment is presented that allows comparison of an experimental protein structure with an arbitrary low-resolution protein tertiary model. The heuristic algorithm is given and then used to show that it can describe random structural alignments of proteins with different folds with good accuracy by an extreme value distribution. From this observation, a structural similarity score between two proteins or two different conformations of the same protein is derived from the likelihood of obtaining a given structural alignment by chance. The performance of the derived score is then compared with well established, consensus manual-based scores and data sets. We found that the new approach correlates better than other tools with the gold standard provided by a human evaluator. Timings indicate that the algorithm is fast enough for routine use with large databases of protein models. Overall, our results indicate that the new program (MAMMOTH) will be a good tool for protein structure comparisons in structural genomics applications. MAMMOTH is available from our web site at http://physbio.mssm.edu/ ortizg/.


References in zbMATH (referenced in 11 articles )

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  1. Ejlali, Nasim; Faghihi, Mohammad Reza; Sadeghi, Mehdi: Bayesian comparison of protein structures using partial Procrustes distance (2017)
  2. Najibi, S. M.; Faghihi, M. R.; Golalizadeh, M.; Arab, S. S.: Bayesian alignment of proteins via Delaunay tetrahedralization (2015)
  3. Rodriguez, Abel; Schmidler, Scott C.: Bayesian protein structure alignment (2014)
  4. Zok, Tomasz; Popenda, Mariusz; Szachniuk, Marta: MCQ4Structures to compute similarity of molecule structures (2014)
  5. Capriotti, Emidio; Martí-Renom, Marc A.: Quantifying the relationship between sequence and three-dimensional structure conservation in RNA (2010) ioport
  6. 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)
  7. Vapnik, Vladimir; Vashist, Akshay: A new learning paradigm: learning using privileged information (2009)
  8. Andreani, Roberto; Martínez, José Mario; Martínez, Leandro; Yano, Flávio: Continuous optimization methods for structure alignments (2008)
  9. Teyra, Joan; Paszkowski-Rogacz, Maciej; Anders, Gerd; Pisabarro, M. Teresa: SCOWLP classification: Structural comparison and analysis of protein binding regions (2008) ioport
  10. Wang, Jun; Zheng, Xiaoqi: Comparison of protein secondary structures based on backbone dihedral angles (2008)
  11. Wang, Kai; Samudrala, Ram: Automated functional classification of experimental and predicted protein structures (2006) ioport