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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >PredictFP2: A New Computational Model to Predict Fusion Peptide Domain in All Retroviruses
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PredictFP2: A New Computational Model to Predict Fusion Peptide Domain in All Retroviruses

机译：预测FP2：一种新的计算模型，用于预测所有逆转录病毒中的融合肽结构域

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摘要

Fusion peptide (FP) is a pivotal domain for the entry of retrovirus into host cells to continue self-replication. The crucial role indicates that FP is a promising drug target for therapeutic intervention. A FP model proposed in our previous work is relatively not efficient to predict FP in retroviruses. Thus in this work, we come up with a new computational model to predict FP domains in all the retroviruses. It basically predicts FP domains through recognizing their start and end sites separately with SVM method combing the hydrophobicity knowledge of the subdomain around furin cleavage site. The classification accuracy rates are 91.91, 91.20 and 89.13 percent respectively corresponding to jack-knife, 10-fold cross-validation and 5-fold cross-validation test. Second, this model discovered 69,753 and 493 putative FPs after scanning amino acid sequences and HERV DNA sequences both without FP annotations. Subsequently, a statistical analysis was performed on the 69,753 putative FP sequences, which confirms that FP is a hydrophobic domain. Lastly, we depicted the distribution of the 493 putative FP sequences on each human chromosome and each HERV family, which shows that FP of HERV probably has chromosome and family preference.
机译：融合肽（FP）是逆转录病毒进入宿主细胞以继续自复制的枢转结构域。至关重要的作用表明FP是治疗干预的有希望的药物目标。在我们以前的工作中提出的FP模型相对不效益于预测逆转录病毒的FP。因此，在这项工作中，我们提出了一种新的计算模型来预测所有逆转录病毒中的FP域。它基本上通过用SVM方法识别它们的开始和结束网站来预测FP域，梳理Furin切割位点周围的子域的疏水性知识。分类精度率为91.91,91.20和89.13％，分别对应于千斤顶，10倍交叉验证和5倍交叉验证测试。其次，在没有FP注释的情况下扫描氨基酸序列和Herv DNA序列后，该模型发现了69,753和493个推定的FPS。随后，对69,753个推定的FP序列进行统计分析，证实FP是疏水结构域。最后，我们描绘了每种人类染色体和每个腰科家族的493个推定的FP序列的分布，这表明肾炎的FP可能具有染色体和家庭偏好。

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