Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling
By Armin Meier, Johannes Söding. 2015
129 citations recorded by the source corpus.
Homology modeling predicts the 3D structure of a query protein based on the sequence alignment with one or more template proteins of known structure. Its great importance for biological research is owed to its speed, simplicity, reliability and wide applicability, covering more than half of the residues in protein sequence space. Although multiple templates have been shown to generally increase model quality over single templates, the information from multiple templates has so far been combined using empirically motivated, heuristic approaches. We present here a rigorous statistical framework for multi-template homology modeling. First, we find that the query proteins’ atomic distance restraints can be accurately described by two-component Gaussian mixtures. This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. Second, we derive theoretically optimal weights to correct for the redundancy among related templates. Third, a heuristic template selection strategy is proposed. We improve the average GDT-ha model quality score by 11% over single template modeling and by 6.5% over a conventional multi-template approach on a set of 1000 query proteins. Robustness with respect to wrong constraints is likewise improved. We have integrated our multi-template modeling approach with the popular MODELLER homology modeling software in our free HHpred server http://toolkit.tuebingen.mpg.de/hhpred and also offer open source software for running MODELLER with the new restraints at https://bitbucket.org/soedinglab/hh-suite.
Subfields: Molecular Biology
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In Molecular Biology
- Robin Pearce
- Yang Zhang
- Liam J. McGuffin
- Arne Elofsson
- David Ménendez Hurtado
- Chengxin Zhang
- Eric W. Bell
- Yang Li
Citations in the corpus
- IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “Additionally, the HHpred (37) method and the template free method I-TASSER light (38) (for sequence <500 residues; run in ‘light mode’ with wall-time restricted to 5h) contributed models for ranking.”
- Large-scale structure prediction by improved contact predictions and model quality assessment (2017, methodology): “HHblits was run against the uniprot20 database from HHsuite (date: 2016-02-26).”
- A Genome-Wide CRISPR Screen in Toxoplasma Identifies Essential Apicomplexan Genes (2016, unknown): “A hidden Markov model-based search was performed for the alignment using HHpred (Meier and Söding, 2015).”
- Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, methodology): “, 2019b), three profile HMM-based threading programs HHpred (Meier and Söding, 2015), HHsearch (Söding, 2005), and PRC (Madera, 2008), and seven profile-based threading programs FFAS3D (Xu et al.”