Improving Inter-Helix Contact Prediction With Local 2D Topological Information
Improving Inter-Helix Contact Prediction With Local 2D Topological Information
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DOI：
10.1109/tcbb.2023.3274361
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发表时间：
2023-09-01
影响因子：
4.5
通讯作者：
Liao，Li
中科院分区：
文献类型：
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作者：
Li，Jiefu;Sawhney，Aman;Liao，Li
螺旋间接触预测是为了识别螺旋整合膜蛋白中不同螺旋之间的残基接触。尽管通过各种计算方法取得了进展，但接触预测仍然是一项具有挑战性的任务，并且据我们所知，没有以无对齐方式直接进入接触图的方法。我们从一个独立的数据集建立2D接触模型，以捕获残基对附近的拓扑模式，这取决于它是否是接触，并将模型应用于最先进的方法的预测，以提取反映2D螺旋间接触模式的特征。二级分类器在这些特征上进行训练。意识到可实现的改进本质上取决于原始预测的质量，我们设计了一种机制来处理这个问题，通过引入，1）原始预测分数的部分离散化，以更有效地利用有用信息2）模糊分数来评估原始预测的质量，以帮助选择更可实现改进的残差对。交叉验证结果表明，即使不使用细化选择方案，我们的方法的预测也明显优于其他方法，包括最先进的方法（DeepHelicon）。通过应用细化选择方案，我们的方法优于国家的最先进的方法显着在这些选定的序列。
Inter-helix contact prediction is to identify residue contact across different helices in-helical integral membrane proteins. Despite the progress made by various computational methods, contact prediction remains as a challenging task, and there is no method to our knowledge that directly tap into the contact map in an alignment free manner. We build 2D contact models from an independent dataset to capture the topological patterns in the neighborhood of a residue pair depending it is a contact or not, and apply the models to the state-of-art method's predictions to extract the features reflecting 2D inter-helix contact patterns. A secondary classifier is trained on such features. Realizing that the achievable improvement is intrinsically hinged on the quality of original predictions, we devise a mechanism to deal with the issue by introducing, 1) partial discretization of original prediction scores to more effectively leverage useful information 2) fuzzy score to assess the quality of the original prediction to help with selecting the residue pairs where improvement is more achievable. The cross-validation results show that the prediction from our method outperforms other methods including the state-of-the-art method (DeepHelicon) by a notable degree even without using the refinement selection scheme. By applying the refinement selection scheme, our method outperforms the state-of-the-art method significantly in these selected sequences.