Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Broad Institute of MIT and Harvard, Cambridge, MA 02143, United States.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae602.
Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their analyses compromise success.
We evaluated the use of known PTM sites in prediction via sequence-based deep learning algorithms. For each PTM, known locations of that PTM were encoded as a separate amino acid before sequences were encoded via word embedding and passed into a convolutional neural network that predicts the probability of that PTM at a given site. Without labeling known PTMs, our models are on par with others. With labeling, however, we improved significantly upon extant models. Moreover, knowing PTM locations can increase the predictability of a different PTM. Our findings highlight the importance of PTMs for the installation of additional PTMs. We anticipate that including known PTM locations will enhance the performance of other proteomic machine learning algorithms.
Sitetack is available as a web tool at https://sitetack.net; the source code, representative datasets, instructions for local use, and select models are available at https://github.com/clair-gutierrez/sitetack.
翻译后修饰 (PTMs) 增加了蛋白质组的多样性，对生物的生命和治疗策略至关重要。深度学习已被用于预测 PTM 位置。然而，数据集及其分析的局限性影响了成功。
我们通过基于序列的深度学习算法评估了使用已知 PTM 位点进行预测的效果。对于每种 PTM，将该 PTM 的已知位置编码为单独的氨基酸，然后通过单词嵌入对序列进行编码，并将其输入到预测给定位置 PTM 概率的卷积神经网络中。在不标记已知 PTM 的情况下，我们的模型与其他模型相当。然而，通过标记，我们大大提高了现有的模型。此外，了解 PTM 位置可以提高另一种 PTM 的可预测性。我们的研究结果强调了 PTM 对于安装附加 PTM 的重要性。我们预计包含已知 PTM 位置将增强其他蛋白质组机器学习算法的性能。
Sitetack 可作为网络工具在 https://sitetack.net 上使用;源代码、代表性数据集、本地使用说明和选择模型可在 https://github.com/clair-gutierrez/sitetack 上获得。
