The PredictProtein server

By Burkhard Rost, Jinfeng Liu. 2003

1,028 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Torsten Schwede
2. Jinbo Xu
3. Sheng Wang
4. Yang Zhang
5. Burkhard Rost
6. Andrej Sali
7. Ben M. Webb
8. Jianlin Cheng

In Molecular Biology

1. Jinbo Xu
2. Burkhard Rost
3. Torsten Schwede
4. Sheng Wang
5. Yang Zhang
6. Andrej Sali
7. Jianlin Cheng
8. Lukasz Kurgan

Citations in the corpus

• Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields (2015, positive): “ct to the label at the 𝑖-th position and can be calculated by the forward-backward algorithm. Finally, the gradient of the parameter � at the 𝑘-th layer is: 𝛻� � 𝑘(�,�′)=∑ [𝐸𝑖 𝑘+1(�)∗𝐻 +� Å 𝑘(�′)] =1 (9) L2 regularization and L-BFGS To reduce over-fitting, the log-likelihood objective funct”
• JPred4: a protein secondary structure prediction server (2015, methodology): “There is no recent blind prediction test for the PROFphd secondary structure prediction algorithm in the PredictProtein (29) secondary structure prediction method, though the earlier PROFsec reported 76% (30).”
• PredictProtein—an open resource for online prediction of protein structural and functional features (2014, positive): “In the following, we introduce some of the recent and upcoming additions since 2004 (31) in more detail.”
• SNAP predicts effect of mutations on protein function (2008, positive): “It is also highly recommended to check existing mutant evaluations [available immediately under the ‘known variants’ tab; referenced by RefSeq id (Pruitt et al., 2007) and dbSNP id (Sherry et al., 2001)] prior to submitting sequences for processing.”
• Genome-Wide Analysis of Arabidopsis Pentatricopeptide Repeat Proteins Reveals Their Essential Role in Organelle Biogenesis (2004, methodology): “Secondary structure predictions of PPR motifs were performed with the PredictProtein server (Rost and Liu, 2003) using the alignments of motifs described previously.”
• Sequence and structural analysis of BTB domain proteins (2005, methodology): “Secondary structure prediction Secondary structure predictions on representative members of each BTB family were completed using the PredictProtein server and the PHD algorithm [122].”
• Computational prediction of secondary and supersecondary structures. (2013, neutral): “A recent review (37) reported that seven methods were cited over 100 times and two of them, PSIPRED (36, 38, 39) and PHD (40, 41) were cited over 1300 times.”
• Outcome of a workshop on archiving structural models of biological macromolecules. (2006, background): “So-called web metaservers can take an input sequence, submit it to a variety of protein structure prediction servers, collate the results, and return an ensemble of the modeling results to the user (Ginalski et al., 2003; Rost et al., 2004).”
• SUPERFAMILY—sophisticated comparative genomics, data mining, visualization and phylogeny (2008, background): “Meta II (27) is a web server for protein structure and function prediction.”
• The ProDom database of protein domain families: more emphasis on 3D (2004, background): “belonging to this family; corresponding family in ProDom-CG; family in the MSF or FASTA file formats; download PSI-BLAST generated profile; run Predict Protein on family (20); and family display enhanced with ESPript (21).”
• DISULFIND: a disulfide bonding state and cysteine connectivity prediction server (2006, neutral): “Hundreds of queries per month have been served via PredictProtein since July 2004.”
• Predict impact of single amino acid change upon protein structure (2012, methodology): “All features were derived from protein sequence alone and were extracted from PredictProtein [23], a wrapper that combines a large number of independent prediction methods.”