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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Deep Conditional Random Field Approach to Transmembrane Topology Prediction and Application to GPCR Three-Dimensional Structure Modeling
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Deep Conditional Random Field Approach to Transmembrane Topology Prediction and Application to GPCR Three-Dimensional Structure Modeling

机译：跨膜拓扑预测的深条件随机场方法及其在GPCR三维结构建模中的应用

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Transmembrane proteins play important roles in cellular energy production, signal transmission, and metabolism. Many shallow machine learning methods have been applied to transmembrane topology prediction, but the performance was limited by the large size of membrane proteins and the complex biological evolution information behind the sequence. In this paper, we proposed a novel deep approach based on conditional random fields named as dCRF-TM for predicting the topology of transmembrane proteins. Conditional random fields take into account more complicated interrelation between residue labels in full-length sequence than HMM and SVM-based methods. Three widely-used datasets were employed in the benchmark. DCRF-TM had the accuracy 95 percent over helix location prediction and the accuracy 78 percent over helix number prediction. DCRF-TM demonstrated a more robust performance on large size proteins (>350 residues) against 11 state-of-the-art predictors. Further dCRF-TM was applied to ab initio modeling three-dimensional structures of seven-transmembrane receptors, also known as G protein-coupled receptors. The predictions on 24 solved G protein-coupled receptors and unsolved vasopressin V2 receptor illustrated that dCRF-TM helped abGPCR-I-TASSER to improve TM-score 34.3 percent rather than using the random transmembrane definition. Two out of five predicted models caught the experimental verified disulfide bonds in vasopressin V2 receptor.
机译：跨膜蛋白在细胞能量产生，信号传递和代谢中起重要作用。许多浅层机器学习方法已应用于跨膜拓扑预测，但性能受到膜蛋白的大尺寸和序列后面复杂的生物进化信息的限制。在本文中，我们提出了一种基于条件随机场的新型深层方法，称为dCRF-TM，用于预测跨膜蛋白的拓扑结构。与基于HMM和基于SVM的方法相比，条件随机字段考虑了全长序列中残基标签之间更复杂的相互关系。在基准测试中使用了三个广泛使用的数据集。 DCRF-TM的精度比螺旋位置预测高95％，精度比螺旋数预测高78％。 DCRF-TM展示了针对11种最新预测因子的大分子蛋白质（> 350个残基）更强大的性能。进一步将dCRF-TM应用于从头开始建模七跨膜受体（也称为G蛋白偶联受体）的三维结构。对24种溶解的G蛋白偶联受体和未溶解的加压素V2受体的预测表明，dCRF-TM可以帮助abGPCR-I-TASSER将TM得分提高34.3％，而不是使用随机的跨膜定义。五分之二的预测模型中有两个被实验验证的血管加压素V2受体中的二硫键。

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