MDock is an automated molecular docking software which can simultaneously docking ligands against multiple protein structures/conformations by using the ensemble docking algorithm (Huang and Zou, 2007a,b). It supports docking/optimizing ligand(s) against either a single protein structure or an ensemble of multiple protein structures and score calculations for given protein-ligand complexes. The energy function used in MDock is the knowledge-based scoring function, ITScore (Huang and Zou, 2006a,b).
MDock uses the sphere-ligand matching algorithm of UCSF DOCK to generate possible ligand conformations. Therefore, a UCSF DOCK license is required to generate the sphere points that represent a negative image of the protein to be matched with ligand atomic centers, for the use of MDock. UCSF DOCK is free for academic users. Files prepared for UCSF DOCK 4.0 can be applied to MDock without modification. In general, docking preparation would be easier for MDock than for UCSF DOCK, because MDock does not require adding hydrogens and charges for proteins and ligands.
In parallel to the method development for ligand-protein docking, studies on protein-protein docking are also under way (Huang and Zou, 2008).
MUFOLD is a comprehensive platform designed for efficient and consistently accurate protein tertiary structure prediction. The long-term objective of MUFOLD is to help experimental biologists understand structures and functions of the proteins of their interest thereby facilitating hypotheses for experimental design. Currently, in MUFOLD platform we already provided pdbLight, a web-based database which integrates protein sequence and structure data from multiple sources for protein structure prediction and analysis, MUFOLD 3D structure prediction, a web-server which provides the structure predictions from the sequences that users submitted, and MUFOLD_CL, a fast tool for protein structural model clustering, visualization and quality assessment.
Musite is a Java-based standalone application for predicting both general and kinase-specific protein phosphorylation sites. Musite 1.0 pre-trained prediction models for 6 eukaryotic organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, and Arabidopsis thaliana. It is advisable to train your own prediction models from your specific training data, using the customized model training tool in Musite.
Plant Protein Phosphorylation Database (P3DB) web resource hosts the phosphorylation data specifically for plants. In the P3DB 3.0v users can create a secure website for paper publication, share their data among groups, search for more kinase specific data, browse data using network tools, get detailed annotation for proteins and sites, setup VIP accounts and enter comments.
Global protein structure comparison is the first real-time search engine for retrieving similar protein tertiary structures in Protein Data Bank (PDB). This system provides two types of query method: query by PDB protein chain ID and by 3D coordinates of newly-discovered proteins in PDB format. With computational techniques, similar protein structures with ranked order will be returned to users in seconds. Our database proteins are weekly updated to be consistent with PDB database.
Local protein substructure comparison (i.e., Index-based Protein Substructure Alingment, IPSA) is designed for efficient and accurate protein substructure alignment and retrieval. It allows user to upload protein structure files and then performs one-against-all substructure comparison with database proteins on the server. IPSA can show the results of (1) the best match of substructure from top 10 SCOP folds and (2)substructures of top 100 matches.
Soybean Knowledge Base (SoyKB), is a comprehensive all-inclusive web resource for bridging translational genomics and molecular breeding in soybean. SoyKB is designed to handle the storage and integration of the genes/proteins, iRNA/sRNA, metabolites, SNP, traits and PI information. SoyKB has many tools for analysis, integration and visualization of genomics, EST, microarray, transcriptomics, proteomics, metabolomics, pathway and phenotypic data.
Discoveries in biology often require extensive knowledge of the genetics of an organism, a keen eye for phenotypes, a deep understanding of related species, and efficient strategies for collecting, combining, analyzing, and comparing data. Currently, public database tools that retrieve phenotypic and genomic information allow only relatively simplistic queries, and viable software tools to capture, parse, and return information from digital images are lacking. We hope to enable biologists to simultaneously query phenotype data by image example, sequence, ontology, genetic and physical map information, and text annotations by developing the first web-based visual phenotypic information management system to allow such complex queries.
The database framework will consist of five modules: