Benchmarking fold detection by DaliLite v.5
By Liisa Holm. 2019
301 citations recorded by the source corpus.
MOTIVATION Protein structure comparison plays a fundamental role in understanding the evolutionary relationships between proteins. Here, we release a new version of the DaliLite standalone software. The novelties are hierarchical search of the structure database organized into sequence based clusters, and remote access to our knowledge base of structural neighbors. The detection of fold, superfamily and family level similarities by DaliLite and state-of-the-art competitors was benchmarked against a manually curated structural classification (SCOP). RESULTS Database search strategies were evaluated using Fmax with query-specific thresholds. DaliLite and DeepAlign outperformed TM-score based methods at all levels of the benchmark, and DaliLite outperformed DeepAlign at fold level. Hierarchical and knowledge-based searches got close to the performance of systematic pairwise comparison. The knowledge-based search was four times as efficient as the hierarchical search. The knowledge-based search dynamically adjusts the depth of the search, enabling a trade-off between speed and recall. AVAILABILITY http://ekhidna2.biocenter.helsinki.fi/dali/README.v5.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Subfields: Molecular Biology
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In Molecular Biology
- Aleksi Laiho
- Marco Salgado
- Petri Törönen
- Liisa Holm
- Aurelie C. Lozano
- Zuobai Zhang
- Jian Tang
- Payel Das
Citations in the corpus
- DALI shines a light on remote homologs: One hundred discoveries (2022, background): “…by the sudden availability of a plethora of structural models, tools for fast and efficient structural comparison are more needed than ever (Aderinwale et al., 2022; Bordin et al., 2022; van Kempen et al., 2002), and in this context, the DALI algorithm is one of the most sensitive (Holm, 2019).”
- Dali server: structural unification of protein families (2022, unknown): “Dali structure comparison has been shown to outperform other methods in the sensitivity of detecting similar folds in the twilight or midnight zones of sequence similarity (15,19,28).”
- Protein Representation Learning by Geometric Structure Pretraining (2022, background): “There have been a large amount of tools proposed for aligning protein sequences (Altschul et al., 1997; Steinegger & Söding, 2017; Steinegger et al., 2019) and structures (Yang & Tung, 2006; Zhang & Skolnick, 2005; van Kempen et al., 2022; Holm, 2019).”