PROTEIN DOCKING ANALYSIS

NovaDock is based on the SwarmDock algorithm developed by Paul Bates at the Biomolecular Modelling Laboratory at the Francis Crick Institute in the UK. SwarmDock (also called “Bates”) consistently places in the top 5% of algorithms in recent Critical Assessment of Predicted Interactions (CAPRI) blind docking assessments. Swarmdock and NovaDock do not use a library of templates, but instead make docking predictions based on a type of energy calculation known as “particle swarm optimization.”

In the cellular environment, molecular recognition occurs when one protein binds with another in a specific fashion based on traits such as steric complementarity (“shape recognition”), charge complementarity, and other nonbonded forces (e.g., van der Waals forces, hydrogen binding, electrostatic, solvation contributions).

In two-body docking, the proteins involved are often a ligand (e.g., an antigen) and a receptor (e.g., an antibody). Multimeric associations involving additional bodies are not well modeled by two body docking approaches.

A docking algorithm must consider the structure of each individual protein molecule and use this information to predict the structure of the bound complex, while also considering protein flexibility, multiple point interactions between the ligand and receptor, molecular energy and biophysical interactions.

The modeling of macromolecular complexes is an exciting and rapidly advancing niche for the protein software industry. However, the modeling of protein docking is a complex problem, and many challenges remain. For instance, current docking algorithms do not take into account all possible types of molecules and cannot fully model the conformational flexibility that occurs during binding. In addition, real life protein-protein interactions in a cell take place in a busy and diverse environment, and this is impossible to model realistically using a software algorithm. These challenges may be tackled in the future using deep learning and artificial intelligence-based modeling algorithms.

DNASTAR regularly looks at the results of objective trials comparing different prediction algorithms (e.g., CASP, CAPRI). If a new algorithm is extremely accurate and advanced, we may approach the creator to set up a licensing agreement to use that algorithm in our Nova Applications.

The NovaDock Report view starts with information about the receptor and ligand structures that were used as inputs in the NovaDock prediction and continues with a table showing the top ten ligand-receptor docking models predicted by NovaDock. Below this are sections for each predicted model, including an image showing the predicted docking model with the receptor and ligand chain shown in contrasting colors; and interactive tables with information about the model.

Yes. Protean 3D lets you export the secondary structure itself or an image of the structure. After running a NovaDock prediction, the Report view opens automatically. Scroll to the prediction model of interest and click the link “Open model in new document.” You can then export the predicted structure as a .pdb or .cif file using Export Data > Export Structure. You can also export an image of the predicted structure in .png, .jpg or .gif formats using File > Export Image > Structure.

You can use NovaDock to predict the structure of antibody-antigen complexes by inputting the antibody and antigen structures. If you don’t have an experimental antibody structure, first model the structure using NovaFold Antibody, which is specifically designed to generate models of antibodies and antibody fragments (Fv, Fab, VH, sdAb) starting from sequence data. The NovaFold Antibody algorithm utilizes a combination of homology modeling and ab initio loop prediction, resulting in highly accurate predictions which can then be used in NovaDock.