FRODOCK is able to generates very efficiently many potential predictions of how two proteins could interact. This approximation effectively address the complexity and sampling requirements of the initial 6D docking exhaustive search by combining the projection of the interaction terms into 3D grid-based potentials with the efficiency of spherical harmonics approximations. The binding energy upon complex formation is approximated as a correlation function composed of van der Waals, electrostatics and desolvation potential terms. This initial stage exhaustive docking obtain excellent accuracy results with standard benchmarks, thus you can use it directly as a first protein-protein rigid-body docking approach.
DockingApp is a user-friendly graphical application for carrying out molecular docking and virtual screening tasks, meant to enable non-experienced users to easily perform such activities and browse the docking results via a three-dimensional visualization.
DockingApp RF is a user-friendly graphical application for carrying out molecular docking and replicated docking, meant to enable non-experienced users to easily perform such activities and browse the docking results via a three-dimensional visualization. It implements a state-of-the-art scoring function based on a Random Forest algorithm, whose effectiveness has been proven on specific use cases of molecular docking, making DockingApp RF complementary to the classic DockingApp.
GOMoDo (GPCR Online Modeling and Docking) is a web server to seamlessly model GPCR (G-protein coupled receptors ) structures and dock ligands to the models in a single consistent pipeline.
HADDOCK (High Ambiguity Driven protein-protein DOCKing) is an information-driven flexible docking approach for the modeling of biomolecular complexes. HADDOCK distinguishes itself from ab-initio docking methods in the fact that it encodes information from identified or predicted protein interfaces in ambiguous interaction restraints (AIRs) to drive the docking process. HADDOCK can deal with a large class of modeling problems including protein-protein, protein-nucleic acids and protein-ligand complexes.
GPCRautomodel allows the user to upload a GPCR sequence, choose a ligand in a library and obtain the 3D structure of the free receptor and ligand-receptor complex
Dove is a deep learning based protein docking model evluation method.It will use the atom information such as postions, types, energy scores in the interface area to judge if the docking model is reasonable.
DOCK addresses the problem of “docking” molecules to each other. In general, “docking” is the identification of the low-energy binding modes of a small molecule, or ligand, within the active site of a macromolecule, or receptor, whose structure is known. A compound that interacts strongly with, or binds, a receptor associated with a disease may inhibit its function and thus act as a drug. Solving the docking problem computationally requires an accurate representation of the molecular energetics as well as an efficient algorithm to search the potential binding modes.
3DGarden (Global and Restrained Docking Exploration Nexus) is an integrated software suite for performing protein-protein and protein-polynucleotide docking. For any pair of biomolecules structures specified by the user, 3DGarden’s primary function is to generate an ensemble of putative complexed structures and rank them. The highest-ranking candidates constitute predictions for the structure of the complex. 3DGarden cannot be used to decide whether or not a particular pair of biomolecules interacts. Complexes of protein and nucleic acid chains can also be specified as individual interactors for docking purposes.