CDR Annotation is an antibody numbering and annotation module used to number the variable region (Fv) of antibody sequences, accurately marking the specific locations of the framework region (Framework Region, FWR) and the complementarity determining region (Complementarity Determining Region, CDR). It supports the IMGT, Kabat, Chothia, Martin, AHo and Wolfguy schemes. When multiple sequences are input at once, you can analyze sequence variations and conservation by viewing the sequence visualization and amino acid frequency plots.

Multiple Sequence Alignment is used for aligning DNA and protein sequences, and visualizing the results of the sequence alignment. It aids in sequence clustering, analyzing diversity among sequences, identifying conserved regions and mutations. It includes automatic alignment tools such as ClustalW and MUSCLE, with MUSCLE incorporating clustering methods like NJ(Neighbor Joining), UPGMA(Unweighted Pair Group Method with Arithmetic Mean), and UPGMB(Unweighted Pair Group Method with Banded Mean).

Phylogenetic Tree takes aligned antibody sequences as input to construct a phylogenetic tree diagram, which aids in analyzing the evolutionary relationships between the sequences and reveals the origins and evolutionary processes of the antibodies. The phylogenetic inference methods include NJ (Neighbor Joining), UPGMA (Unweighted Pair Group Method with Arithmetic Mean), ME (Minimum Evolution), ML (Maximum Likelihood), and MP (Maximum Parsimony).

The module can determine the probability that an antibody belongs to human based on its V-region sequence.

Monoclonal antibody therapeutics typically originate from non-human sources (usually mice), which may trigger immune responses in humans. Antibody humanization aims to modify the variable region sequences of antibodies to obtain antibodies that do not elicit immune responses. We utilized nearly one billion antibody sequences from the OAS database to establish an antibody humanness evaluation AI model capable of distinguishing between human and non-human antibody variable region sequences. The scores output by the model are negatively correlated with the experimental immunogenicity (ADA) of existing FDA-approved antibody therapies. Following the approach of Marks and Hummer, we combined this model with a Beam Search algorithm to develop an antibody sequence humanization tool. This tool aims to maximize the level of humaness of antibodies while minimizing number of mutations and maintaining key characteristics such as affinity, thereby reducing their immunogenicity.