RaptorX-Property: a web server for protein structure property prediction

By Sheng Wang, Shiwang Liu, Jinbo Xu. 2016

530 citations recorded by the source corpus.

RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence–structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction.

Subfields: Computational Theory and Mathematics, 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].”
Distance-based protein folding powered by deep learning (2018, background): “Sequential features include sequence profile and secondary structure and solvent accessibility predicted by RaptorX-Property 30 .”
NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, background): “We have decided to compare our tool with Spider3, RaptorX, and Jpred4, as these are among the most commonly used and accurate tools available.”
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].”
Folding membrane proteins by deep transfer learning (2017, methodology): “The sequential features include protein sequence profile and secondary structure predicted by RaptorX-Property (Wang et al., 2016b, Wang et al., 2016a).”
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].”
Deep learning methods in protein structure prediction (2020, methodology): “RaptorX-Contact has been trained on CCMpred, mutual information, pair-wise potential extraction and RaptorX-Property’s output, i.e. secondary structure and solvent accessibility predictions [23].”
Deep Learning and Its Applications in Biomedicine (2018, methodology): “Recently, RaptorX-Property, a web server employing DeepCNF, was also presented to predict protein structure properties, including secondary structure, solvent accessibility, and disorder regions [185].”
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).”
AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling (2016, methodology): “A web server is also implemented and available at http:// raptorx.uchicago.edu/StructurePropertyPred/predict/ [ 43].”
Predicting membrane protein contacts from non-membrane proteins by deep transfer learning (2017, methodology): “We predict SS and SA from sequence profile using our in-house tool RaptorX-Property [36].”
ComplexContact: a web server for inter-protein contact prediction using deep learning (2018, background): “The sequential features include protein sequence profile, predicted 3-state secondary structure (31) and 3-state sol-W434 vent accessibility (32,33).”
In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology (2022, background): “edu/ Struc tureP roper tyPred/ predi ct/) server (Wang et al. 2016).”
Analysis of deep learning methods for blind protein contact prediction in CASP12 (2017, methodology): “Sequential features include sequence profile and secondary structure and solvent accessibility predicted by RaptorX-Property.(18) Pairwise features include mutual information, pairwise contact potential and direct coevolution strength calculated by CCMpred.”