MolSurfer - a Macromolecular Interface Navigator - is a Java-based program which can be used to study protein-protein and protein-DNA/RNA interfaces. The 2D projections of the computed interface aid visualization of complicated interfacial geometries in 3D. Molecular properties, including hydrophobicity and electrostatic potential, can be projected onto the interface. MolSurfer can thereby aid exploration of molecular complementarity, identification of binding "hot spots" and prediction of the effects of mutations. MolSurfer can also facilitate the location of cavities at macromolecular interfaces. (Reference: R.R. Gabdoulline et al. (1999) Trends Biochem. Sci., 24: 285-287)
PIPSA (Protein Interaction Property Similarity Analysis) - To perform PIPSA on this webserver, you need to upload a set of related protein structures in PDB format. After calculation of the protein electrostatic potentials, the server will calculate similarity indices for all pairs of proteins based on the electrostatic similarity. These indices will be computed for complete protein 'skins' or for a user defined region. The similarity indices are then converted to electrostatic 'distances'. The electrostatic potential distance matrix is displayed in color coded form (heat map) and as a tree (epogram). (Reference: Richter S et al. (2008) Nucleic Acids Res; 36(Web Server issue): W276-80).
PDBePISA (Protein Interfaces, Surfaces and Assemblies) - is an interactive tool for the exploration of macromolecular (protein, DNA/RNA and ligand) interfaces, prediction of probable quaternary structures (assemblies), database searches of structurally similar interfaces and assemblies, as well as searches on various assembly and PDB entry parameters. (Reference: E. Krissinel & K. Henrick (2007). J. Mol. Biol. 372: 774-797).
TRAPP (TRAnsient Pockets in Proteins) - provides an automated workflow that allows users to explore the dynamics of a protein binding site and to detect pockets or sub-pockets that may transiently open due to protein internal motion. These transient or cryptic sub-pockets may be of interest in the design and optimization of small molecular inhibitors for a protein target of interest. (Reference: Stank A et al. (2017) Nucl. Acids Res. 45(W1): W325-W330).
STRING (Search Tool for Recurring Instances of Neighbouring Genes) database aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co- expression data. (Reference: D. Szklarczyk et al. 2015. Nucl. Acids Res. 43 (D1): D447-D452).
AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures. The identified aggregation-prone residues can be virtually mutated to design variants with increased solubility, or to test the impact of pathogenic mutations. (Reference: R. Zambrano et al. 2015. Nucl. Acids Res. 43 (W1): W306-W313.
NPDock (Nucleic acid–Protein Docking) - a novel web server for predicting complexes of protein–nucleic acid structures which implements a computational workflow that includes docking, scoring of poses, clustering of the best-scored models and refinement of the most promising solutions. The NPDock server provides a user-friendly interface and 3D visualization of the results. (Reference: I. Tuszynska et al. 2015. Nucl. Acids Res. 43 (W1): W425-W430).
STITCH (Search Tool for Interactions of Chemicals) - is a database of protein–chemical interactions that integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions (Reference: Kuhn, M. et al. 2014. Nucl. Acids Res. 42 (D1): D401-D407).
meta-PPISP is built on three individual web servers: cons-PPISP, PINUP, and Promate. This is a meta web server for protein-protein interaction site prediction.(Reference: Qin, S.B. & Zhou, H.-X. 2007. Bioinformatics 23: 3386-3387)
QuatIdent: identifying the quaternary structural attribute of a protein chain based on its sequence (Reference: Shen H-B & Chou K-C. 2009. J Proteome Res. 8: 1577-1584).
QuaBingo - is a prediction system for protein quaternary structure attributes using block composition (Reference: C-H Tung et al. 2016. Biomed Res Int. 2016: 9480276).
Quat-2L - was developed for predicting the quaternary structural attribute of a protein according to its sequence information alone. The 1st layer is to identify the query protein as monomer, homo-oligomer, or hetero-oligomer. If the result thus obtained turns out to be homo-oligomer or hetero-oligomer, then the prediction will be automatically continued to further identify it belonging to one of the following six subtypes: (1) dimer, (2) trimer, (3) tetramer, (4) pentamer, (5) hexamer, and (6) octamer. The overall success rate of Quat-2L for the 1st layer identification was 71.14%; while the overall success rates of the 2nd layer for homo-oligomers and hetero-oligomers were 76.91 and 82.52%, respectively. (Reference: Xiao X et al. (2011) Molec Diversity 15(1): 149-155).
PiQSi facilitates the investigation and curation of quaternary structures. Given a PDB identifier or a protein sequence, it displays information about the quaternary structure of homologous proteins on a single web-page. This information allows a quick comparison of the quaternary structure(s) of the members of a protein family. So far, the web-server has allowed the manual curation of ~15000 structures of PDB Biological Units, of which about 15% were found to be likely errors (Reference: Levy ED. 2007;15(11):1364-7).
14-3-3-Pred - is a webserver to predict 14-3-3-binding phosphosites in human proteins (Reference: Madeira F et al. 2015. Bioinformatics 31: 2276-2283).