A Machine-Learning model for predicting cleavage products of neuropeptide precursors

Please fill below the amino-acid sequences to predict in FASTA format. You may either upload a FASTA file, or paste it as a free text.

CleavePred is powered by ASAP, a generic API for easily learning local protein annotations with minimal fine-tuning using powerful feature engineering combined with standard Machine-Learning models. Inside ASAP's GitHub project, you will also find the source code of CleavePred, which comes with a handy API as well. To learn more about either of ASAP's or CleavePred's APIs (which offer more options than this website), read the Wiki page in GitHub (in particular this tutorial). To learn more about the underlying algorithm, please read our paper "ASAP: A Machine-Learning Framework for Local Protein Properties". If you found our work to be useful for your research, please cite it.

For any issue/request, feel free to contact us: Nadav Brandes ( and Dan Ofer (