CAREER: Enhanced Sampling Methods to Characterize Nucleic Acid Structure, Recognition Mechanisms and Function

在化学系化学理论、模型和计算方法(CTMC)计划的支持下，佛罗里达大学的Alberto Perez将开发新的计算方法，以更好地了解核酸。核酸是遗传信息的储存库，在生物学中具有多种功能，在纳米技术、纳米电子学和传感器技术等领域也有应用。尽管它们很重要，但该工具包仍然限于能够成功地对核酸采用的最有可能的构象(原子的三维配置)进行建模和预测。Alberto Perez和他的研究小组将致力于开发新的计算工具，以应对DNA(2‘-脱氧核糖核酸)、RNA(核糖核酸)及其与蛋白质的络合物的结构预测方面的挑战。这些新方法旨在将这些分子的结构与它们的功能角色联系起来，以帮助破译它们的作用机制。这些工具将免费提供给社区，以增加它们对化学生物学和生物技术的影响。此外，佩雷斯博士和他的团队将通过身临其境的虚拟现实活动培训新一代科学家，让学生扮演分子模型师的角色。这些互动模块将使学生能够通过基于探究的方法发展与STEM相关的技能。核酸高度带电和灵活的性质阻碍了对接和模拟工具的发展，以采样构象变化，包括那些涉及三维折叠和结合的构象变化。Alberto Perez和他的团队致力于解决对核酸中的大型构象转变进行采样的问题，并得出对序列-功能关系的物理化学理解。为了实现这一目标，Perez博士和他的团队将开发基于广义集成方法的方法，这些方法对大的构象变化进行采样，特别是那些涉及相变(例如，结合或折叠)的变化。他们将进一步开发基于贝叶斯推理的新策略，利用本身不足以确定核酸及其复合体结构的实验数据。这些方法有望提高采样效率、稳健性和模拟结果的重现性。最后，为了为可用核酸序列的组合爆炸提供指导，并对随序列长度变化的结构-功能关系的物理化学理解，佩雷斯博士和他的团队将开发和应用基于马尔可夫状态和动态图形模型的方法。如果成功，从长远来看，这些方法将有助于使蛋白质-核酸识别发生的机制合理化。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry, Alberto Perez of the University of Florida will develop new computational methods directed at better understanding nucleic acids. Nucleic acids are the repository of genetic information and have numerous functional roles in biology as well as having applications in fields such as nanotechnology, nanoelectronics and sensor technology. Despite their importance, the tool kit is still limited to enable the successful modeling and prediction of the most likely conformations (three dimensional disposition of atoms) nucleic acids adopt. Alberto Perez and his research group will pursue the development of new computational tools that address challenges in the structure prediction of DNA (2'-deoxyribonucleic acid), RNA (ribonucleic acid), and their complexes with proteins. The new methods are designed to connect the structures of these molecules with their functional roles to help decipher their mechanisms of action. The tools are to be made freely available to the community to increase their impact on chemical biology and biotechnology. Additionally, Dr. Perez and his group will train new generations of scientists through immersive virtual reality activities that place students in the role of molecular modelers. These interactive modules will enable students to develop STEM-related skills through an inquiry-based approach. The highly charged and flexible nature of nucleic acids has hindered progress in the development of docking and simulation tools to sample conformational changes, including those involved in three dimensional folding and binding. Alberto Perez and his team aim to address the problem of sampling large conformational transitions in nucleic acids and to derive a physico-chemical understanding of sequence-function relationships. To achieve this goals, Dr. Perez and his group will develop approaches based on generalized ensemble methods that sample large conformational changes, especially those involving a phase transition (e.g., binding or folding). They will further develop new strategies based on Bayesian inference to leverage experimental data that is insufficient on its own to determine the structures of nucleic acids and their complexes. These methodologies are expected increase sampling efficiency, robustness, and reproducibility of simulation results. Finally, to provide guidance for the combinatorial explosion of available nucleic acid sequences and derive a physico-chemical understanding of structure-function relationships that scale with sequence length, Dr. Perez and his team will develop and apply methods based on Markov State and Dynamical Graphical Models. If successful, in the longer term, these methods will help rationalize the mechanism by which protein-nucleic acid recognition takes place.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.