1 - 19 of 19 results

ABodyBuilder

An antibody modeling pipeline that uses SAbDab knowledge-base of antibody structures to guide decision-making in modeling antibodies. ABodyBuilder can rapidly build accurate models of antibodies from sequence. Once a model has been generated, it can be downloaded by the user, or interactively analysed through a web server application. ABodyBuilder is a fully automated method for antibody model generation, making it ideal for challenges such as modeling large, next-generation sequencing (NGS) data sets. It can also model complete Fvs or nanobodies.

LYRA / LYmphocyte Receptor Automated modeling

Implements a complete and automated method for building of B- and T-cell receptor structural models starting from their amino acid sequence alone. LYRA is based on the canonical structure method and can produce, easily and within minutes, extremely reliable models of lymphocyte receptors. It is freely available and easy to use for non-specialists. Upon submission, LYRA automatically generates alignments using ad hoc profiles, predicts the structural class of each hypervariable loop, selects the best templates in an automatic fashion, and provides within minutes a complete 3D model that can be downloaded or inspected online. Experienced users can manually select or exclude template structures according to case specific information.

ANARCI / Antigen receptor Numbering And Receptor ClassificatIon

A tool for annotating antigen receptor variable domain amino-acid sequences with five commonly used numbering schemes. It can annotate sequences with the five most popular numbering schemes: Kabat, Chothia, Enhanced Chothia, IMGT and AHo. ANARCI can be run as command-line tool or imported as a Python module for incorporation in custom scripts. We also provide a public web-browser interface that can annotate small numbers of sequences.

RosettaAntibody

Predicts the structure of an antibody variable region given the amino-acid sequences of the respective light and heavy chains. In an initial stage, RosettaAntibody identifies and displays the most sequence homologous template structures for the light and heavy framework regions and each of the complementarity determining region (CDR) loops. Subsequently, the most homologous templates are assembled into a side-chain optimized crude model, and the server returns a picture and coordinate file. For users requesting a high-resolution model, the server executes the full RosettaAntibody protocol which additionally models the hyper-variable CDR H3 loop.

PIGS

Obsolete
A web server for the automatic modeling of immunoglobulin variable domains based on the canonical structure method. PIGS has a user-friendly and flexible interface, that allows the user to choose templates (for the frameworks and the loops) and modeling strategies in an automatic or manual fashion. Its final output is a complete three-dimensional model of the target antibody that can be downloaded or displayed on-line. The server is freely accessible to academic users, with no restriction on the number of submitted sequences.

SmrtMolAntibody

Takes an input antibody sequence and returns an energy optimized accurate structure in minutes. SmrtMolAntibody is part of a suite of modeling tools provided by Macromoltek. It creates a structure of an antibody from an individual sequence. The tool was improved with sequences for eleven Fvs and asked to produce structures of each of the antibody sequences. In 10 of 11 cases, the results using SmrtMolAntibody show good agreement between the submitted models and X-ray crystal structures.

OptCDR

Is used in the de novo design of antibody binding pockets against specified antigen epitopes. OptCDR proposes a four-step procedure. First, canonical structures are selected for the six complementarity determining regions (CDRs). Next, the amino acid sequences of the selected structures are initialized. This is followed by several thousand iterations of the iterative protein redesign and optimization (IPRO) procedure to refine the backbones and amino acids of the CDRs. Finally, accumulating the most promising mutations generates a library of antibodies.