PROTEIN DOCKING ANALYSIS

High-resolution protein-protein docking that explores flexibility during modeling.

Flexible protein-protein docking for more accurate interaction predictions.

Protein-protein docking interactions are often more complex to model than individual protein structures, making prediction accuracy a critical consideration. Within Protean 3D, the NovaDock method enables protein-protein docking predictions using the SwarmDock algorithm, which accounts for protein flexibility during docking to support more accurate interaction modeling. Users can provide ligand and receptor PDB or structure files and optionally define specific residue contacts between binding partners. Resulting models include metrics such as energy score, cluster size, and ligand contact information to support downstream analysis and visualization workflows.

Protean 3D’s guided prediction wizard simplifies protein-protein docking workflows through a centralized, user-friendly setup interface. Step-by-step configuration tools help researchers enter sequence or structure data, choose prediction methods, and customize modeling parameters for efficient docking analysis.

For large-scale interaction studies, the wizard supports selection of a fixed protein structure that can be evaluated against multiple variable binding partners in a single workflow. This approach streamlines comparative docking experiments and accelerates identification of potential protein-protein interactions.

Protein-protein docking predictions can be accessed through NovaCloud using bundled prediction tokens included with Lasergene Protein or through NovaLocal for locally hosted modeling workflows and high-throughput batch predictions.

Protein docking in 4 simple steps

Step 1

Select receptor and ligand structures and specify prediction options

Step 2

Evaluate summary of ligand position and energy for each predicted model

Step 3

Analyze data for individual models and visualize residue contacts

Step 4

Open models to further explore structure and export images for publication

Quality models and useful insights

“Using the suite of DNASTAR programs, NovaFold, NovaFold Antibody, and NovaDock, I have generated functional models of antibody-antigen complexes from the component’s amino acid sequences. The quality of the models and the useful insights they generate more than justify the effort and computing time.”

Dr. Kurt Deshayes, Antibody Solutions

FAQs

How do I access docking predictions?

Protein-protein docking predictions are available through NovaCloud using bundled prediction tokens included with Lasergene Protein or through NovaLocal for locally hosted prediction workflows and high-throughput batch prediction studies.

How can I predict protein-protein binding interactions?

By using the NovaDock method within Protean 3D. NovaDock enables protein-protein docking predictions between two binding partners, both of which must be proteins. One binding partner is the ligand (‘L’: usually the smaller partner or an antibody); the other is the receptor (‘R’: usually the larger partner or an antigen). Each binding partner can consist of one or multiple protein chains.

What method is used for protein docking?

The NovaDock method is based on the SwarmDock algorithm developed by Paul Bates at the Biomolecular Modelling Laboratory at the Francis Crick Institute in the UK. SwarmDock…

The NovaDock method 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.”

What is the process of protein-protein docking?

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…

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.

What are the challenges of protein-protein docking?

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 evaluate opportunities to integrate it into Protean 3D structure prediction workflows.

What file types are supported for importing proteins?

Protean 3D supports the following protein file formats: .aa, .fap, .fas, .fasta, .gp, .gbk, .sbd, .pro.

How do I visualize the predicted docked structures?

Results of completed docking predictions are shown in a specialized Report view in Protean 3D. After running a prediction, the Report view opens automatically. The Report uses interactive images and tables to show which templates were used in the prediction and the models that were the best match to the query. Scroll to the prediction model of interest and click the link “Open model in new document” to open it as a new Protean 3D document for further analysis.

What details are available for each protein-protein interaction prediction?

Available statistics for each predicted model include the Energy, Cluster Size, Residue Contacts and Contacts Fulfilled. For each residue, you can also see its Intermolecular Contacts.

What analysis options are available after generating protein docking models?

The docking Report view in Protean 3D starts with information about the receptor and ligand structures that were used as inputs and continues with a table showing the top ten ligand-receptor docking models. 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.

Can I export a structure model for publication?

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.

How can I model antibody-antigen interactions?

You can use the NovaDock method within Protean 3D 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 for downstream docking predictions.

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