Previously a fingerprint based on monomer composition (MCFP) of nonribosomal peptides (NRPs) has been introduced. MCFP is a novel method for obtaining a representative description of NRP structures from their monomer composition in a fingerprint form. An effective screening and prediction of biological activities has been obtained from Norine NRPs database. In this paper, we present an extension of the MCFP fingerprint. This extension is based on adding few columns into the fingerprint; representing monomer clusters, 2D structures, peptide categories, and peptide diversity. All these data have been extracted from the NRP structure. Experiments with Norine NRPs database showed that the extended MCFP, that can be called Monomer Structure FingerPrint (MSFP) produced high prediction accuracy (> 95%) together with a high recall rate (86%) obtained when MSFP was used for prediction and similarity searching. From this study it appeared that MSFP mainly built from monomer composition can substantially be improved by adding more columns representing useful information about monomer composition and 2D structure of NRPs.

以前已经引入了基于非核糖体肽 (NRP) 的单体组成 (MCFP) 的指纹。MCFP 是一种新方法，可从指纹形式的单体组成中获得 NRP 结构的代表性描述。从Norine NRPs数据库中获得了对生物活性的有效筛选和预测。在本文中，我们提出了 MCFP 指纹的扩展。此扩展基于在指纹中添加几列；代表单体簇、二维结构、肽类别和肽多样性。所有这些数据都是从 NRP 结构中提取的。Norine NRPs 数据库的实验表明，可称为单体结构指纹 (MSFP) 的扩展 MCFP 产生了高预测精度 (> 95%) 以及当 MSFP 用于预测和相似性搜索时获得的高召回率 (86%)。从这项研究看来，主要由单体组成构建的 MSFP 可以通过添加更多代表有关单体组成和 NRP 2D 结构的有用信息的列来显着改进。