Hattotuwagama Channa K, Flower Darren R
The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK.
Bioinformation. 2006 Nov 24;1(7):257-9. doi: 10.6026/97320630001257.
Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient P (commonly expressed in logarithm form: logP), is useful for screening out unsuitable molecules and also for the development of predictive Quantitative Structure-Activity Relationships (QSARs). In this paper we develop a new approach to the prediction of LogP values for peptides based on an empirical relationship between global molecular properties and measured physical properties. Our method was successful in terms of peptide prediction (total r(2) = 0.641). The final model consisted of 5 physicochemical descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA hydrophobic and 2D-VSA polar). The approach is peptide specific and its predictive accuracy was high. Overall, 67% of the peptides were able to be predicted within +/-0.5 log units from the experimental values. Our method thus represents a novel prediction method with proven predictive ability.
肽作为疫苗和药物具有巨大的治疗潜力。了解物理化学描述符，包括分配系数P（通常以对数形式表示：logP），对于筛选不合适的分子以及开发预测性定量构效关系（QSAR）很有用。在本文中，我们基于全局分子性质与测量的物理性质之间的经验关系，开发了一种预测肽的LogP值的新方法。我们的方法在肽预测方面取得了成功（总r(2) = 0.641）。最终模型由5个物理化学描述符组成（分子量、单键数量、二维范德华体积、二维疏水溶剂可及表面积和二维极性溶剂可及表面积）。该方法是肽特异性的，其预测准确性很高。总体而言，67%的肽能够在实验值的±0.5对数单位内被预测。因此，我们的方法代表了一种具有已证实预测能力的新型预测方法。
