JPred4: a protein secondary structure prediction server

By Alexey Drozdetskiy, Christian Cole, James B. Procter, Geoffrey J. Barton. 2015

1,715 citations recorded by the source corpus.

JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “Performance values for the same data set exist also for other recent methods such as Spider3 [62], RaptorX [63, 64] and JPred4 [65].”
Evaluating Protein Transfer Learning with TAPE (2019, methodology): “SS prediction tools are very commonly used to create richer input features for higher-level models [37].”
NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, neutral): “0,(10) Spider3,(4) SPOTDisorder,(3) RaptorX,(20,21) and JPred4.(19) In order to check whether the results of the methods were significantly different, we calculated a”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing (2020, neutral): “0 [15], and Jpred4 [70], RaptorX [71], [72], Spider3 [73].”
RaptorX-Property: a web server for protein structure property prediction (2016, methodology): “…prediction as an example, when template information is not used and only sequence profile is considered, so far the best Q3 accuracy is ∼ 80% obtained by a few predictors such as PSIPRED (8) and JPRED (7), which is significantly lower than the estimated prediction accuracy limit 88–90% (9).”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning (2021, methodology): “…or pre-training was measured. mmseqs2 was run on an Intel Skylake Gold 6248 processor with 40 threads, SSD and 377GB main memory, while protein LMs were run on a single Nvidia Quadro RTX 8000 with 48GB memory using half precision and dynamic batch size depending on sequence length (blue bar).”
Sixty-five years of the long march in protein secondary structure prediction: the final stretch? (2016, background): “Jpred4 was able to achieve 82% for a small representative set (150 sequences from 150 superfamilies not used in training) by training on 1338 proteins [93].”
Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks (2016, methodology): “, 2012], PSIPRED [Jones, 1999], RaptorXSS8, JPRED [Drozdetskiy et al., 2015] and DeepCNF.”
New CRISPR-Cas systems from uncultivated microbes (2016, methodology): “High scoring HHpred hits were used to infer domain architecture based on comparison to solved crystal structures 50,51 , and secondary structure that was predicted by JPred4 52 .”
Discovery and functional characterization of diverse Class 2 CRISPR-Cas systems (2015, methodology): “Protein secondary structure was predicted using Jpred 4 (Drozdetskiy et al., 2015).”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Performance values for the same data set exist also for other recent methods such as Spider3 [30], RaptorX [31, 32] and JPred4 [33].”
Clustal Omega for making accurate alignments of many protein sequences (2018, methodology): “18 Benchmarks were performed either on an 8-core Intel i7–3779 CPU, clocked at 3.40 GHz with 8 MB cache, 8 GB RAM and 8 cores, running Ubuntu 16.04.2 or a 48-core AMD Opteron 6234, clocked at 2.4 GHz with 2 MB cache, 256 GB of RAM, and running Ubuntu 14.04.5.”
MUFOLD-SS: New Deep Inception-Inside-Inception Networks for Protein Secondary Structure Prediction (2018, background): “Protein secondary structures can also help identify protein function domains and may guide the rational design of site-specific mutation experiments.(5) Protein secondary structure prediction is often evaluated by the Q3 accuracy - the accuracy of a three-class classification, that is, helix (H), st”