As torrents of new data now emerge from microbial genomics, bioinformatic prediction of immunogenic epitopes remains challenging but vital. In silico methods often produce paradoxically inconsistent results: good prediction rates on certain test sets but not others. The inherent complexity of immune presentation and recognition processes complicates epitope prediction. Two encouraging developments - data driven artificial intelligence sequence-based methods for epitope prediction and molecular modeling methods based on three-dimensional protein structures - offer hope for the future.

中文翻译：

---

迈向计算机预测免疫原性表位。

随着微生物基因组学的涌现，新数据的激增，免疫原性表位的生物信息学预测仍然具有挑战性，但至关重要。计算机方法通常会产生矛盾的不一致结果：某些测试集的预测率很高，而其他测试集则没有。免疫呈递和识别过程的内在复杂性使表位的预测变得复杂。两项令人鼓舞的发展-基于数据驱动的基于人工智能序列的表位预测方法和基于三维蛋白质结构的分子建模方法-为未来提供了希望。