Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India.
J Mol Model. 2021 Jan 7;27(2):25. doi: 10.1007/s00894-020-04640-w.
Protein structural information is essential for the detailed mapping of a functional protein network. For a higher modelling accuracy and quicker implementation, template-based algorithms have been extensively deployed and redefined. The methods only assess the predicted structure against its native state/template and do not estimate the accuracy for each modelling step. A divergence measure is therefore postulated to estimate the modelling accuracy against its theoretical optimal benchmark. By freezing the domain boundaries, the divergence measures are predicted for the most crucial steps of a modelling algorithm. To precisely refine the score using weighting constants, big data analysis could further be deployed.
蛋白质结构信息对于详细绘制功能蛋白质网络至关重要。为了提高建模精度和加快实现速度，基于模板的算法得到了广泛的应用和重新定义。这些方法仅根据其天然状态/模板评估预测结构，而不估计每个建模步骤的准确性。因此，提出了一种发散度度量标准来估计其与理论最优基准的建模准确性。通过冻结结构域边界，可以预测建模算法中最关键步骤的发散度度量标准。为了使用加权常数精确细化分数，可以进一步部署大数据分析。
