北京大学定量生物学中心/生命科学联合中心

学术报告  

题 目: Overviews of solving chemical master equation for large bionetworks and modelling hydrogen bonding of ATP-IDE interactions using QM/MM and MD

报告人: Don Kulasiri

Professor of Computational Modelling and Systems Biology,

Head of the Centre for Advanced Computational Solutions (C-Facs),

Lincoln University, New Zealand

时  间: 11月9日（周四）15:00-16:00

地 点: 吕志和楼B101

主持人: 汤超 教授

摘要:

We present brief summaries of two computational biology problems we have been working on. The first one deals with numerical solutions of the chemical master equation (CME) to understand the stochasticity of biochemical systems. Solving CMEs is a formidable task. This task is complicated due to the nonlinear nature of the reactions and the size of the networks which result in different realizations. Most importantly, the exponential growth of the size of the state-space, with respect to the number of different species in the system makes this a challenging assignment. When the biochemical system has a large number of variables, the CME solution becomes intractable. We introduce the intelligent state projection (𝐼𝑆𝑃) method to use in the stochastic analysis of these systems. 𝐼𝑆𝑃 is based on a state-space search and the data structure standards of artificial intelligence (𝐴𝐼). It can be used to explore and update the states of a biochemical system. To support the expansion in 𝐼𝑆𝑃, we also develop a Bayesian likelihood node projection (𝐵𝐿𝑁𝑃) function to predict the likelihood of the states. To demonstrate the acceptability and effectiveness of our method, we apply the 𝐼𝑆𝑃 method to several biological models discussed in prior literature. The results of our computational experiments reveal that the 𝐼𝑆𝑃 method is effective both in terms of the speed and accuracy of the expansion, and the accuracy of the solution.

The second problem deals with the insulin-degrading enzyme (IDE) that plays a significant role in the degradation of the amyloid beta (Aβ), a peptide found in regions of the brain of patients with early Alzheimer’s disease (AD). Adenosine triphosphate (ATP) allosterically regulates the Aβ-degrading activity of IDE. Hydrogen bonding interactions between ATP-IDE including thermostabilities/flexibilities of IDE residues, at the allosteric site of IDE, are essential for drug design. The hydrogen-bonding interactions of ATP and IDE including the thermostabilities/flexibilities of IDE residues have not yet been systematically understood. The present study elucidates the hydrogen bonding of ATP-IDE interactions and the thermostabilities/flexibilities of the IDE residues using the quantum mechanics/molecular mechanics method (QM/MM) to the proposed computational model for exploring the hydrogen-bonding interactions of ATP with IDE. Molecular dynamic (MD) simulations are performed at different heat-shock temperatures for identifying flexible and stable residues of IDE. The proposed computational model predicts QM/MM minimised structures. Subsequently, it reveals the IDE residues with high binding affinity (LYS530 and ASP385). Considering RMSF values during the MD simulations at 321.15 K and 315.15 K, it indicates that LYS530 and ASP385 are also the thermostable residues of IDE, whereas SER576 and LYS858 have high flexibilities with compromised thermostabilities.

报告人简介：

Don Kulasiri holds a (personal) professorial chair and has been Head of the Centre for Advanced Computational Solutions (C-fACS) at Lincoln university since 1999. He has been a Visiting Professor at the Wolfson Centre for Mathematical Biology, Mathematical Institute, Oxford University, the UK, since 2008. He was also a visiting professor at Mathematics Department, Princeton University, USA, and the Mechanics and Computation Division, Stanford University, USA and, and a Fellow of the Modelling and Simulation Society of Australia and New Zealand (MSSANZ). Don has supervised and co-supervised 55 PhD students and 15 Masters students over the last 30 years. He co-authored over 190 publications and 6 research monographs with leading international publishers.