Tapia Lydia, Tang Xinyu, Thomas Shawna, Amato Nancy M
Parasol Lab, Department of Computer Science, Texas A&M University, College Station, TX 77843, USA.
Bioinformatics. 2007 Jul 1;23(13):i539-48. doi: 10.1093/bioinformatics/btm199.
Protein motions play an essential role in many biochemical processes. Lab studies often quantify these motions in terms of their kinetics such as the speed at which a protein folds or the population of certain interesting states like the native state. Kinetic metrics give quantifiable measurements of the folding process that can be compared across a group of proteins such as a wild-type protein and its mutants.
We present two new techniques, map-based master equation solution and map-based Monte Carlo simulation, to study protein kinetics through folding rates and population kinetics from approximate folding landscapes, models called maps. From these two new techniques, interesting metrics that describe the folding process, such as reaction coordinates, can also be studied. In this article we focus on two metrics, formation of helices and structure formation around tryptophan residues. These two metrics are often studied in the lab through circular dichroism (CD) spectra analysis and tryptophan fluorescence experiments, respectively. The approximated landscape models we use here are the maps of protein conformations and their associated transitions that we have presented and validated previously. In contrast to other methods such as the traditional master equation and Monte Carlo simulation, our techniques are both fast and can easily be computed for full-length detailed protein models. We validate our map-based kinetics techniques by comparing folding rates to known experimental results. We also look in depth at the population kinetics, helix formation and structure near tryptophan residues for a variety of proteins.
We invite the community to help us enrich our publicly available database of motions and kinetics analysis by submitting to our server: http://parasol.tamu.edu/foldingserver/.
蛋白质运动在许多生化过程中起着至关重要的作用。实验室研究通常根据其动力学来量化这些运动，例如蛋白质折叠的速度或某些有趣状态（如天然状态）的群体数量。动力学指标给出了折叠过程的可量化测量结果，可以在一组蛋白质（如野生型蛋白质及其突变体）之间进行比较。
我们提出了两种新技术，基于图谱的主方程求解和基于图谱的蒙特卡罗模拟，以通过近似折叠景观（称为图谱的模型）中的折叠速率和群体动力学来研究蛋白质动力学。从这两种新技术中，还可以研究描述折叠过程的有趣指标，如反应坐标。在本文中，我们重点关注两个指标，螺旋的形成和色氨酸残基周围的结构形成。这两个指标通常分别在实验室中通过圆二色性（CD）光谱分析和色氨酸荧光实验进行研究。我们这里使用的近似景观模型是我们之前提出并验证过的蛋白质构象图谱及其相关转变。与传统主方程和蒙特卡罗模拟等其他方法不同，我们的技术既快速，又能轻松地针对全长详细蛋白质模型进行计算。我们通过将折叠速率与已知实验结果进行比较来验证我们基于图谱的动力学技术。我们还深入研究了多种蛋白质的群体动力学、螺旋形成以及色氨酸残基附近的结构。
我们邀请各界通过向我们的服务器提交数据来帮助我们丰富公开可用的运动和动力学分析数据库：http://parasol.tamu.edu/foldingserver/ 。
