SCRATCH: a protein structure and structural feature prediction server

By Jianlin Cheng, Arlo Z. Randall, Michael J. Sweredoski, Pierre Baldi. 2005

1,046 citations recorded by the source corpus.

SCRATCH is a server for predicting protein tertiary structure and structural features. The SCRATCH software suite includes predictors for secondary structure, relative solvent accessibility, disordered regions, domains, disulfide bridges, single mutation stability, residue contacts versus average, individual residue contacts and tertiary structure. The user simply provides an amino acid sequence and selects the desired predictions, then submits to the server. Results are emailed to the user. The server is available at .

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. Jianlin Cheng
2. Torsten Schwede
3. Björn Wallner
4. Yang Zhang
5. Badri Adhikari
6. Liam J. McGuffin
7. Lukasz Kurgan
8. Pascal Benkert

In Molecular Biology

1. Jianlin Cheng
2. Torsten Schwede
3. Yang Zhang
4. Björn Wallner
5. Jinbo Xu
6. Badri Adhikari
7. Liam J. McGuffin
8. Lukasz Kurgan

Citations in the corpus

• The SWISS-MODEL Repository—new features and functionality (2016, methodology): “Additionally, each entry is annotated with a sequence profile (HMM (16)), predicted secondary structure via SSpro (17) and PSIPRED (18), secondary structure via DSSP (19), predicted solvent accessibility via ACCpro (17), and per residue solvent accessibility via NACCESS (S. Hubbard and J.M. Thornton”
• Toward the estimation of the absolute quality of individual protein structure models (2010, background): “In homology modelling, the quality of a model is largely dictated by the evolutionary distance of the protein of interest (target) ∗To whom correspondence should be addressed. to the available template structures.”
• SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information (2014, background): “Sequence Pro-files, predicted secondary structure (SSpro (14), PSIPRED (15)), predicted solvent accessibility (ACCpro (14)), per-residue solvent accessibility, (NACCESS (S. Hubbard and J. Thornton)), secondary structure (DSSP (16)) are calculated and stored alongside the structure.”
• A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Neural networks have been effectively used in a variety of prediction algorithms, including speech and facial recognition as well as the aforementioned usage in protein secondary structure prediction [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [24], [33], [34], [35], [36]”
• Large-scale model quality assessment for improving protein tertiary structure prediction (2015, methodology): “We evaluated the performance of each predictor’s first model by calculating the GDT-TS score between it and its native structure.”
• Predicting protein residue-residue contacts using deep networks and boosting (2012, methodology): “The secondary structure and solvent accessibility were predicted using SSpro and ACCpro from the SCRATCH suite (Cheng, et al., 2005).”
• Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, methodology): “measured using the Q3 index, of over 75% [11,15] and certain algorithms such as PROTEUS [16] and SSpro [17], which incorporate alignments to known”
• SELECTpro: effective protein model selection using a structure-based energy function resistant to BLUNDERs (2008, methodology): “SELECTpro is one of several protein structure tools in the SCRATCH suite of predictors [25], and is available through: http:// www.”
• MUFOLD: A new solution for protein 3D structure prediction (2010, methodology): “More specifically, we predict secondary structures of a target protein using in-house tool MUPRED,26 PSIPRED,27 and SSPro,28 and we convert the predicted secondary structures of H, E, C to 9 or 21 alphabets according to their predicted secondary structure types and associated confidence values.”
• QMEAN server for protein model quality estimation (2009, background): “Two terms reflecting the agreement between predicted and calculated secondary structure (22) and solvent accessibility (23) are included.”