CanLect-Pred: A Cancer Therapeutics Tool for Prediction of Target Cancerlectins Using Experiential Annotated Proteomic Sequences

Cancerlectins are significantly important group of lectins that have an inhibitory effect on cancer cells with respect to their growth. They have a vital role in various tumor cell interactions like adhesion, growth, metastasis, differentiation and mainly in cellular infection. The investigations associated with cancerlectins are applicable to relevant studies in laboratories, diagnostics and therapy in clinical applications, and drug discoveries in targeting cancers. Prediction of cancerlectins is considered a helpful task due to the fact that they are specifically useful in dissecting cancers. Although, several Bioinformatics tools have been developed to predict cancerlectins, however, the need for improvement in the quality of its prediction model requires enhancements in the annotation and determination process of cancerlectins. In this study, a new model is proposed that builds on statistical moments based features to distinguish cancerlectins from non-cancerlectins. The currently proposed model achieved an accuracy of 88.36% using jackknife test which is better than current state-of-the-art models. These outcomes suggest that the use of statistical moments could bear more effective and efficient results. For the accessibility of the scientific community, a user-friendly web server has been developed which will associate the researchers in medical science. Web server is freely accessible at https://www.biopred.org/canlect.