抗癌肽（ACP）是未来有前途的治疗方法，但其实验发现仍然耗时且昂贵。为了加速发现过程，我们提出了一种计算筛选工作流程，用于根据预测的类别概率、抗肿瘤活性和毒性来识别、过滤和优先考虑肽序列。该工作流程用于从白色念珠菌的基因组序列中识别具有有效抗结直肠癌活性的新型 ACP。结果，在 HCT116 结肠癌细胞系中鉴定并验证了四种候选药物。其中，PCa1 和 PCa2 最为有效，IC50 值分别为 3.75 和 56.06 μM，对癌细胞的选择性是正常细胞的 4 倍。在结肠异种移植裸鼠模型中，两种肽的施用均导致肿瘤生长的显着抑制，且没有引起明显的副作用。总之，这项工作不仅为 ACP 发现提供了经过验证的计算工作流程，而且还引入了两种肽 PCa1 和 PCa2，作为有望进一步开发为结肠癌靶向治疗的有希望的候选肽。该方法作为 Web 服务可在 https://app.cbbio.online/acpep/home 上获取，源代码可在 https://github.com/cartercheong/AcPEP_classification.git 上获取。

Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 μM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https://app.cbbio.online/acpep/home and the source code at https://github.com/cartercheong/AcPEP_classification.git.