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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction
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Bayesian segmental models with multiple sequence alignment profiles for protein secondary structure and contact map prediction

机译：具有多个序列比对曲线的贝叶斯分段模型，用于蛋白质二级结构和接触图预测

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摘要

In this paper, we develop a segmental semi-Markov model (SSMM) for protein secondary structure prediction which incorporates multiple sequence alignment profiles with the purpose of improving the predictive performance. The segmental model is a generalization of the hidden Markov model where a hidden state generates segments of various length and secondary structure type. A novel parameterized model is proposed for the likelihood function that explicitly represents multiple sequence alignment profiles to capture the segmental conformation. Numerical results on benchmark data sets show that incorporating the profiles results in substantial improvements and the generalization performance is promising. By incorporating the information from long range interactions in /spl beta/-sheets, this model is also capable of carrying out inference on contact maps. This is an important advantage of probabilistic generative models over the traditional discriminative approach to protein secondary structure prediction. The Web server of our algorithm and supplementary materials are available at http://public.kgi.edu/-wild/bsm.html.
机译：在本文中，我们开发了用于蛋白质二级结构预测的分段半马尔可夫模型（SSMM），该模型结合了多个序列比对配置文件，以提高预测性能。段模型是对隐马尔可夫模型的概括，其中隐状态生成各种长度和二级结构类型的段。针对似然函数，提出了一种新颖的参数化模型，该模型明确表示多个序列比对配置文件以捕获片段构象。在基准数据集上的数值结果表明，合并这些配置文件可以带来实质性的改进，并且泛化性能很有希望。通过将来自远程交互的信息合并到/ spl beta / -sheets中，此模型还能够对联系图进行推断。这是概率生成模型相对于蛋白质二级结构预测的传统判别方法的重要优势。可在http://public.kgi.edu/-wild/bsm.html上获得我们算法的Web服务器和补充材料。

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