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首页> 外文会议>International conference on pattern recognition and machine intelligence >Efficient and Effective Multiple Protein Sequence Alignment Model Using Dynamic Progressive Approach with Novel Look Back Ahead Scoring System
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Efficient and Effective Multiple Protein Sequence Alignment Model Using Dynamic Progressive Approach with Novel Look Back Ahead Scoring System

机译：高效且有效的多蛋白质序列比对模型，采用动态累进方法和新颖的提前回溯评分系统

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摘要

Multiple protein sequence alignment is the elementary hurdle towards addressing further challenges like prediction of protein structure and its functions, protein sub-cellular localization, drug discovery etc. For the last 3 decades numerous models have been proposed to address this challenge however the models are either computationally complex or not effective with respect to aligned results. In this paper, a computationally efficient and effective model is proposed to solve multiple protein sequence alignment. Our proposed model follows dynamic progressive global alignment approach in which a sequence pair is merged dynamically based on novel scoring system, named Look Back Ahead (LBA). Proposed model results were validated with aligned reference results on benchmark datasets (PREFAB4refm and SABrem), using four metrics: Sum-of-Pairs (SP), Total Gap Penalty (TGP), Column Score (CS) and Total Mutation Count Pair-wise (TMCP). Experimental results demonstrate that the proposed method outperforms benchmark reference results in any three evaluation metrics by 77.46% and 68.65% for PREFAB4refm and SABrem datasets respectively.
机译：多种蛋白质序列比对是应对进一步挑战的基本障碍，例如预测蛋白质结构及其功能，蛋白质亚细胞定位，药物发现等。在过去的三十年中，已经提出了许多模型来应对这一挑战，但是这些模型要么对于对齐结果，计算复杂或无效。在本文中，提出了一种计算有效且有效的模型来解决多个蛋白质序列比对问题。我们提出的模型遵循动态渐进全局比对方法，在该方法中，基于名为“回头看”（LBA）的新型评分系统，动态地合并了一个序列对。拟议的模型结果通过基准数据集（PREFAB4refm和SABrem）上的对齐参考结果进行了验证，并使用四个指标：对和（SP），总空位罚分（TGP），列得分（CS）和总突变计数成对（TMCP）。实验结果表明，对于PREFAB4refm和SABrem数据集，该方法在任何三个评估指标中均优于基准参考结果，分别达到77.46％和68.65％。

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