Molecular Dynamics Simulations Based Torque Analysis of Biological Motors and Design of Cancer Modifiers

基於分子動力學模擬的生物電機轉矩分析和癌症修飾劑的設計

Student thesis: Doctoral Thesis

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Award date10 Jun 2020

Abstract

The investigation of behaviors or mechanisms in molecular level of biological systems by computational approaches are considered as effective and convenient technique. They can be used to support or provide useful information for future experiments. In this research, molecular dynamics simulations in combination with torque approach and binding energy calculation have been adopted. In order to study the rotation of biological motors and the binding strength of soluble protein and membrane receptors which involve in cancer therapy, respectively.

F-type ATP synthase (F-ATPase) and vacuolar ATP hydrolase (V-ATPase) are well-known essential biomolecular motors, which operate by rotation and play significant catalytic roles in ATP synthesis and ATP hydrolysis reactions. The study of their rotational behavior can provide useful information for an in-depth understanding of their operation mechanism. Torques and rotation periods, two important factors involved in their rotational behavior, can be measured experimentally with considerable difficulty. Computer simulation is instead applied to study the two proteins, using a torque approach based on interatomic forces and coordinates of un-equilibrated configurations taken from brief molecular dynamics simulations (MD). The average torque values generated by F-ATPase and V-ATPase are calculated to be 52.48 pN-nm and 32.75 pN-nm, with rotation periods of 0.0115 s and 0.0090 s, respectively. More importantly, the rotation directions for both proteins can be predicted by the torque approach. F-ATPase is found to be a random rotor in the free state, while it rotates in clockwise direction (as seen from membrane toward the protein) for ATP synthesis under external force. Conversely, V-ATPase tends to rotate only in counterclockwise direction for ATP hydrolysis, with almost constant torques generated by the unidirectional rotation through three catalytic sites. The calculated results are consistent with experiment and show that the torque approach can be successfully applied in biology to gain mechanistic insight into the rotation of many biomolecular motors, with modest computation times.

Several cancer treatments are under development for decades. Recently, there is a research group reported that they have successfully reduced size of malignant tumor in mice by performing the genetic editing on a particular type of cancer cells using CRISPR/Cas9 technology. The engineered cancer cells are able to produce soluble protein called soluble-TRAIL (S-TRAIL or TRAIL) that could trigger targeted malignant tumor cell death. However, the effectiveness of this approach is not maximized. In this study, molecular dynamics simulation (MD) is used to investigate molecular interactions between TRAIL and 4 membrane receptors (2 death receptors and 2 decoy receptors) of cancer cell. With hope to improve effectiveness of this cancer treatment by providing approach that could lower binding possibility or binding strength between TRAIL and decoy receptors. Pairwise binding energy decomposition of amino acid residue pairs between TRAIL and each receptor have been obtained from molecular mechanics energies combined with generalized born surface area solvation (MM/GBSA) calculation. Lastly, with help of a machine learning algorithm called k-means clustering, the significant and non-significant interacting amino acid residue pairs can be separated in each systems. Consequently, the difference in binding mode between TRAIL with death receptors and decoy receptors is successfully identified. Thus, the calculation results indicated that 7 amino acid residues which are Lys48, Tyr49, Trp65, Asn68, Trp231, Lys233, and Asp234 should be deleted from TRAIL in order to reduce the binding strength between TRAIL and decoy receptors. Therefore, suggestion from this study could supports development of cancer treatment in near future.

    Research areas

  • F-ATPase, V-ATPase, Molecular dynamics simulation, Torque, ATP synthesis, ATP hydrolysis, Cancer treatment, TRAIL, Death receptors, Decoy Receptors