LocTree2 predicts localization for all domains of life

By Tatyana Goldberg, Tobias Hamp, Burkhard Rost. 2012

59 citations recorded by the source corpus.

Motivation: Subcellular localization is one aspect of protein function. Despite advances in high-throughput imaging, localization maps remain incomplete. Several methods accurately predict localization, but many challenges remain to be tackled. Results: In this study, we introduced a framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data. Availability: Online through PredictProtein (predictprotein.org); as standalone version at http://www.rostlab.org/services/loctree2. Contact: localization@rostlab.org Supplementary Information: Supplementary data are available at Bioinformatics online.

Subfields: Biophysics, Genetics, Molecular Biology

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “This concept resembles that of using k-mers for database searches [33], clustering [81], motifs [82, 83], and prediction methods [72, 76, 84 – 88].”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing (2020, methodology): “…except for one method using neither pLMs nor MSA ( no LM no MSA : DeepLoc-BLOSUM62 [16]), and two methods using MSAs ( MSA input evolutionary information ): the current state-of-the-art ( SOA ) method (performance marked by horizontal thin lines in magenta and cyan) DeepLoc [16], and LocTree2 [77].”
Neutral and weakly nonneutral sequence variants may define individuality (2013, background): “Computational predictions, although more accurate than some high-throughput experiments (7), are never as speci ﬁ c as careful, detailed experiments.”
LocTree3 prediction of localization (2014, unknown): “LocTree2 predicts a single localization for all proteins in all domains of life through machine learning (13).”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Localization prediction was trained and evaluated using the DeepLoc data set [40] for which performance was measured for several methods, namely: LocTree2 [42], MultiLoc2 [43], SherLoc2 [44], CELLO [45], iLoc-Euk [46], WoLF PSORT [47] and YLoc [48].”
PredictProtein—an open resource for online prediction of protein structural and functional features (2014, unknown): “LocTree3 successfully combines de novo (41) and homology-based predictions (7), reaching an 18-state prediction accuracy over 80% for eukaryotes and a 6-state accuracy over 89% for bacteria.”
COMPARTMENTS: unification and visualization of protein subcellular localization evidence (2014, methodology): “Published benchmarks (21, 23) suggest that these methods are two of the best that cover many compartments, in particular for human proteins.”
Convolutional LSTM Networks for Subcellular Localization of Proteins (2015, background): “MultiLoc1/2 accuracies are reprinted from (Goldberg et al., 2012) and the SherLoc accuracy from (Briesemeister et al., 2009). dependencies.”