Tegge Allison N, Southey Bruce R, Sweedler Jonathan V, Rodriguez-Zas Sandra L
Department of Animal Sciences, University of Illinois, Urbana, Illinois, 61801, USA.
Mamm Genome. 2008 Feb;19(2):106-20. doi: 10.1007/s00335-007-9090-9. Epub 2008 Jan 23.
Neuropeptides are an important class of signaling molecules that result from complex and variable posttranslational processing of precursor proteins and thus are difficult to identify based solely on genomic information. Bioinformatics prediction of precursor cleavage sites can support effective biochemical characterization of neuropeptides. Neuropeptide cleavage models were developed using comprehensive human, mouse, rat, and cattle precursor data sets and used to compare predicted neuropeptide processing across these species. Logistic regression and artificial neural network models were used to predict cleavages based on amino acid and physiochemical properties of amino acids at precursor sequence locations proximal to cleavage. Correct cleavage classification rates across species and models ranged from 85% to 100%, suggesting that amino acid and amino acid properties have major impact on the probability of cleavage and that these factors have comparable effects in human, mouse, rat, and cattle. The variable accuracy of each species-specific model to predict cleavage sites indicated that there are species- and precursor-specific processing patterns. Prediction of mouse cleavages using rat models was highly accurate, yet the reverse was not observed. Sensitivity and specificity revealed that logistic models are well suited to maximize the rate of true noncleavage predictions with moderate rates of true cleavage predictions; meanwhile, artificial neural networks maximize the rate of true cleavage predictions with moderate to low true noncleavage predictions. Logistic models also provided insights into the strength of the amino acid associations with cleavage. Prediction of neuropeptide cleavage sites using human, mouse, rat, and cattle models are available at http://www.neuroproteomics.scs.uiuc.edu/neuropred.html .
神经肽是一类重要的信号分子，它们由前体蛋白经过复杂且多变的翻译后加工产生，因此仅基于基因组信息很难识别。前体蛋白切割位点的生物信息学预测可以支持神经肽的有效生化特性鉴定。利用人类、小鼠、大鼠和牛的全面前体数据集开发了神经肽切割模型，并用于比较这些物种间预测的神经肽加工情况。基于切割位点近端前体序列位置的氨基酸和氨基酸理化性质，使用逻辑回归和人工神经网络模型来预测切割。跨物种和模型的正确切割分类率在85%到100%之间，这表明氨基酸和氨基酸性质对切割概率有重大影响，并且这些因素在人类、小鼠、大鼠和牛中具有可比的作用。每个物种特异性模型预测切割位点的准确性各不相同，这表明存在物种特异性和前体特异性的加工模式。使用大鼠模型预测小鼠的切割情况非常准确，但反之则不然。敏感性和特异性表明，逻辑模型非常适合在中等真实切割预测率的情况下最大化真实非切割预测率；同时，人工神经网络在中等至低真实非切割预测率的情况下最大化真实切割预测率。逻辑模型还提供了关于氨基酸与切割关联强度的见解。可通过http://www.neuroproteomics.scs.uiuc.edu/neuropred.html使用人类、小鼠、大鼠和牛的模型预测神经肽切割位点。
