蛋白质折叠的网络方法研究
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摘要
蛋白质折叠在生命科学领域是一项重要的前沿课题。蛋白质只有当其链折叠到一个特定的三维结构才能发挥其生物学功能。如果蛋白质折叠错误,就会造成病态。大家熟悉的疯牛病,就是因为pnon蛋白折叠成致病空间结构所导致的。因此研究蛋白质折叠的路径、局域稳定态以及过渡态有着重要的意义。
本论文在蛋白质折叠的网络研究上主要做了以下三个工作：
第一,折叠网络是一种研究多肽链和蛋白质折叠高维自由能曲面的有效方法。该方法避免了将自由能曲面投影到二维序参数上的局限性。我们对基于马尔科夫聚类(Markov Cluster (MCL) algorithm)算法的网络方法进行了改进,并用这种改进的方法研究了β发卡trpzip2的折叠自由能曲面。通过分析,我们发现改进的马尔科夫聚类方法相对于二维自由能曲面投影方法,能够更加清晰准确的描述蛋白质局域稳定态和折叠路径。
第二,确定过渡态(transition state)结构对研究蛋白质折叠路径和折叠动力学非常重要。蛋白质折叠过程中过渡态结构不能直接从实验中测得,然而用分子动力学模拟方法可以有效的研究。目前用自由能投影方法和Pfold等方法确定过渡态都有它们自己的局限性。因此,我们提出一种基于网络的新方法来更加有效的确定过渡态结构。该方法将高维自由能曲面投射到蛋白质折叠网络的路径长度上,并且使用优先广度搜索方法计算路径长度。我们称这种方法为FEPL(free energy along path length)方法。我们用这种方法研究了β发卡trpzip2折叠过程中的过渡态。通过比较该方法与其它方法获得的过渡态,我们发现FEPL方法能更加准确而且全面的确定折叠过程中的过渡态。
第三,用网络的方法研究蛋白质的下坡折叠过程。用本文提出的基于网络的FEPL方法研究了β发卡trpzip2的折叠路径,其结果表明在相同的折叠条件下p发卡trpzip2有三种折叠机制：第一种是多态折叠过程；第二种是二态折叠过程；而第三种则是以前没有发现的下坡折叠过程。该结果更进一步证明了网络方法能更加有效全面的研究蛋白质折叠过程。
The protein folding is an important problem in the life science. A protein molecule can fulfill their biological function, only when they fold into their specific three-dimensional structure. Protein misfolding can lead to disease, e.g., misfolding and aggregate prion protein leads to the mad cow disease. Therefore, understanding the protein folding mechanism would greatly help us to study the protein self-assembling and protein design. Therefore it is very significant to study the protein folding pathways and transition states efficiently and accurately.
The main works in this thesis is as follows:
(1)Protein folding network analysis is an effective approach to investigate the high-dimensional free energy surface of peptide and protein folding and the method can avoid the limitations of simple projection of free energy surface based on a few order parameters. We present improvements in the effectiveness and accuracy to the folding network analysis method based on Markov Cluster (MCL) algorithm. We applied this approach to investigate the folding free energy landscape of the beta-hairpin peptide trpzip2 and found that the folding network method is able to determine the basins and folding paths of trpzip2 more clearly and accurately than the two-dimensional free energy projection method.
(2) Identifying folding transition state structures is crucial to study protein folding pathways and folding dynamics. Protein folding transition state structures cannot be obtained directly from experiments but can be investigated by using molecular dynamics simulations. Although, there are various methods that utilize data from molecular dynamics simulations to identify the folding transition state structures their results often different notably. In this paper we present a novel approach to find the folding transition state structures based on the free energy along the generalized path length of the folding network. By using this approach we analyzed the folding transition state structures of the beta-hairpin peptide trpzip2 and the results show that our method can find different kinds of the folding transition state structures identified by other methods. This shows that our method can provide more complete information for the folding transition states of proteins.
(3) The FEPL method based on the folding network has also been applied to study the downhill folding process of the beta-hairpin trpzip2. The results show that there are three types of folding mechanisms of the trpzip2 in the same folding conditions:the multistate folding, two-state folding and downhill folding. The downhill folding process of trpzip2 has not been discovered in the previous studies. The result further demonstrates that the network approach can study the protein folding completely.
本论文在蛋白质折叠的网络研究上主要做了以下三个工作：
第一,折叠网络是一种研究多肽链和蛋白质折叠高维自由能曲面的有效方法。该方法避免了将自由能曲面投影到二维序参数上的局限性。我们对基于马尔科夫聚类(Markov Cluster (MCL) algorithm)算法的网络方法进行了改进,并用这种改进的方法研究了β发卡trpzip2的折叠自由能曲面。通过分析,我们发现改进的马尔科夫聚类方法相对于二维自由能曲面投影方法,能够更加清晰准确的描述蛋白质局域稳定态和折叠路径。
第二,确定过渡态(transition state)结构对研究蛋白质折叠路径和折叠动力学非常重要。蛋白质折叠过程中过渡态结构不能直接从实验中测得,然而用分子动力学模拟方法可以有效的研究。目前用自由能投影方法和Pfold等方法确定过渡态都有它们自己的局限性。因此,我们提出一种基于网络的新方法来更加有效的确定过渡态结构。该方法将高维自由能曲面投射到蛋白质折叠网络的路径长度上,并且使用优先广度搜索方法计算路径长度。我们称这种方法为FEPL(free energy along path length)方法。我们用这种方法研究了β发卡trpzip2折叠过程中的过渡态。通过比较该方法与其它方法获得的过渡态,我们发现FEPL方法能更加准确而且全面的确定折叠过程中的过渡态。
第三,用网络的方法研究蛋白质的下坡折叠过程。用本文提出的基于网络的FEPL方法研究了β发卡trpzip2的折叠路径,其结果表明在相同的折叠条件下p发卡trpzip2有三种折叠机制：第一种是多态折叠过程；第二种是二态折叠过程；而第三种则是以前没有发现的下坡折叠过程。该结果更进一步证明了网络方法能更加有效全面的研究蛋白质折叠过程。
The protein folding is an important problem in the life science. A protein molecule can fulfill their biological function, only when they fold into their specific three-dimensional structure. Protein misfolding can lead to disease, e.g., misfolding and aggregate prion protein leads to the mad cow disease. Therefore, understanding the protein folding mechanism would greatly help us to study the protein self-assembling and protein design. Therefore it is very significant to study the protein folding pathways and transition states efficiently and accurately.
The main works in this thesis is as follows:
(1)Protein folding network analysis is an effective approach to investigate the high-dimensional free energy surface of peptide and protein folding and the method can avoid the limitations of simple projection of free energy surface based on a few order parameters. We present improvements in the effectiveness and accuracy to the folding network analysis method based on Markov Cluster (MCL) algorithm. We applied this approach to investigate the folding free energy landscape of the beta-hairpin peptide trpzip2 and found that the folding network method is able to determine the basins and folding paths of trpzip2 more clearly and accurately than the two-dimensional free energy projection method.
(2) Identifying folding transition state structures is crucial to study protein folding pathways and folding dynamics. Protein folding transition state structures cannot be obtained directly from experiments but can be investigated by using molecular dynamics simulations. Although, there are various methods that utilize data from molecular dynamics simulations to identify the folding transition state structures their results often different notably. In this paper we present a novel approach to find the folding transition state structures based on the free energy along the generalized path length of the folding network. By using this approach we analyzed the folding transition state structures of the beta-hairpin peptide trpzip2 and the results show that our method can find different kinds of the folding transition state structures identified by other methods. This shows that our method can provide more complete information for the folding transition states of proteins.
(3) The FEPL method based on the folding network has also been applied to study the downhill folding process of the beta-hairpin trpzip2. The results show that there are three types of folding mechanisms of the trpzip2 in the same folding conditions:the multistate folding, two-state folding and downhill folding. The downhill folding process of trpzip2 has not been discovered in the previous studies. The result further demonstrates that the network approach can study the protein folding completely.
引文
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