NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.
NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.
复制标题
Nnalign：构建和评估受体配体相互作用的人工神经网络模型的平台。
DOI：
10.1093/nar/gkx276
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发表时间：
2017-07-03
影响因子：
14.9
通讯作者：
Andreatta M
中科院分区：
文献类型：
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作者：
Nielsen M;Andreatta M
肽广泛用于表征生物系统中受体-配体相互作用的功能或（线性）结构方面，例如SH2、SH3、PDZ 肽识别域、MHC 膜受体以及激酶和磷酸酶等酶。 NNNAlign 是一种识别生物序列中此类线性基序的方法。该算法比对作为训练集提供的氨基酸或核苷酸序列，并生成数据中检测到的序列基序的模型。网络服务器允许设置交叉验证实验来估计模型的性能以及对独立数据的评估。训练序列的许多特征可以被编码为输入，并且网络架构是高度可定制的。服务器返回的结果包括该方法识别的基序的图形表示、性能值和可下载的模型，该模型可用于扫描蛋白质序列以查找基序的出现。虽然其在表征肽-MHC 相互作用方面的性能已被广泛记录，但我们将 NNAlign 扩展为也适用于其他受体-配体系统。 2.0 版支持插入和删除的比对、受体伪序列的编码以及训练序列的自定义字母表。该服务器位于 http://www.cbs.dtu.dk/services/NNAlign-2.0。
Peptides are extensively used to characterize functional or (linear) structural aspects of receptor–ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and phosphatases. NNAlign is a method for the identification of such linear motifs in biological sequences. The algorithm aligns the amino acid or nucleotide sequences provided as training set, and generates a model of the sequence motif detected in the data. The webserver allows setting up cross-validation experiments to estimate the performance of the model, as well as evaluations on independent data. Many features of the training sequences can be encoded as input, and the network architecture is highly customizable. The results returned by the server include a graphical representation of the motif identified by the method, performance values and a downloadable model that can be applied to scan protein sequences for occurrence of the motif. While its performance for the characterization of peptide–MHC interactions is widely documented, we extended NNAlign to be applicable to other receptor–ligand systems as well. Version 2.0 supports alignments with insertions and deletions, encoding of receptor pseudo-sequences, and custom alphabets for the training sequences. The server is available at http://www.cbs.dtu.dk/services/NNAlign-2.0.