MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model - Zixu Wang; Yangyang Chen; Yifan Shang; Xiulong Yang; Wenqiong Pan; Xiucai Ye; Tetsuya Sakurai; Xiangxiang Zeng (BMC Biology)
Abstract Background Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting “undruggable” proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over the past decade, the FDA has approved an average of one macrocyclic peptide drug per year, with romidepsin being the only one targeting an intracellular site. Biological experiments to measure permeability are time-consuming and labor-intensive. Rapid assessment of cyclic peptide permeability is crucial for their development. Results In this work, we proposed a novel deep learning model, dubbed as MultiCycPermea, for predicting cyclic peptide permeability. MultiCycPermea extracts features from both the image information (2D structural information) and sequence information (1D structural information) of cyclic peptides. Additionally, we proposed a substructure-constrained feature alignment module to align the two types of features. MultiCycPermea has made a leap in predictive accuracy. In the in-distribution setting of the CycPeptMPDB dataset, MultiCycPermea reduced the mean squared error (MSE) by approximately 44.83% compared to the latest model Multi_CycGT (0.29 vs 0.16). By leveraging visual analysis tools, MultiCycPermea can reveal the relationship between peptide modification structures and membrane permeability, providing insights to improve the membrane permeability of cyclic peptides. Conclusions MultiCycPermea provides an effective tool that accurately predicts the permeability of cyclic peptides, offering valuable insights for improving the membrane permeability of cyclic peptides. This work paves a new path for the application of artificial intelligence in assisting the design of membrane-permeable cyclic peptides.