Milik M, Sauer D, Brunmark A P, Yuan L, Vitiello A, Jackson M R, Peterson P A, Skolnick J, Glass C A
R.W. Johnson Pharmaceutical Research Institute, San Diego, CA 92121, USA.
Nat Biotechnol. 1998 Aug;16(8):753-6. doi: 10.1038/nbt0898-753.
Computational methods were used to predict the sequences of peptides that bind to the MHC class I molecule, K(b). The rules for predicting binding sequences, which are limited, are based on preferences for certain amino acids in certain positions of the peptide. It is apparent though, that binding can be influenced by the amino acids in all of the positions of the peptide. An artificial neural network (ANN) has the ability to simultaneously analyze the influence of all of the amino acids of the peptide and thus may improve binding predictions. ANNs were compared to statistically analyzed peptides for their abilities to predict the sequences of K(b) binding peptides. ANN systems were trained on a library of binding and nonbinding peptide sequences from a phage display library. Statistical and ANN methods identified strong binding peptides with preferred amino acids. ANNs detected more subtle binding preferences, enabling them to predict medium binding peptides. The ability to predict class I MHC molecule binding peptides is useful for immunolological therapies involving cytotoxic-T cells.
采用计算方法预测与MHC I类分子K(b)结合的肽段序列。预测结合序列的规则有限，是基于肽段特定位置对某些氨基酸的偏好。然而，很明显，肽段所有位置的氨基酸都可能影响结合。人工神经网络（ANN）能够同时分析肽段所有氨基酸的影响，因此可能改善结合预测。将人工神经网络与经统计分析的肽段在预测K(b)结合肽段序列的能力方面进行了比较。人工神经网络系统在来自噬菌体展示文库的结合和非结合肽段序列文库上进行训练。统计方法和人工神经网络方法都识别出具有偏好氨基酸的强结合肽段。人工神经网络检测到更细微的结合偏好，使其能够预测中等结合肽段。预测I类MHC分子结合肽段的能力对于涉及细胞毒性T细胞的免疫治疗很有用。
