1
|
Sun S, Rodriguez G, Zhao G, Sanchez JE, Guo W, Du D, Rodriguez Moncivais OJ, Hu D, Liu J, Kirken RA, Li L. A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape. Brief Bioinform 2024; 25:bbae079. [PMID: 38446738 PMCID: PMC10939344 DOI: 10.1093/bib/bbae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/17/2024] [Accepted: 02/12/2024] [Indexed: 03/08/2024] Open
Abstract
The family of Janus Kinases (JAKs) associated with the JAK-signal transducers and activators of transcription signaling pathway plays a vital role in the regulation of various cellular processes. The conformational change of JAKs is the fundamental steps for activation, affecting multiple intracellular signaling pathways. However, the transitional process from inactive to active kinase is still a mystery. This study is aimed at investigating the electrostatic properties and transitional states of JAK1 to a fully activation to a catalytically active enzyme. To achieve this goal, structures of the inhibited/activated full-length JAK1 were modelled and the energies of JAK1 with Tyrosine Kinase (TK) domain at different positions were calculated, and Dijkstra's method was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of kinase inactivating P733L and S703I mutations, an evaluation of the reasons why these mutations lead to negative or positive regulation of JAK1 are provided. Our energy analysis suggests that activation of JAK1 is thermodynamically spontaneous, with the inhibition resulting from an energy barrier at the initial steps of activation, specifically the release of the TK domain from the inhibited Four-point-one, Ezrin, Radixin, Moesin-PK cavity. Overall, this work provides insights into the potential pathway for TK translocation and the activation mechanism of JAK1.
Collapse
Affiliation(s)
- Shengjie Sun
- Department of Biomedical Informatic, School of Life Sciences, Central South University, Changsha 410083, China
- Computational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
| | - Georgialina Rodriguez
- Department of Biological Sciences, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
- Border Biomedical Research Center, The University of Texas at El Paso, 500 W University Ave, TX, 79968, USA
| | - Gaoshu Zhao
- Google LLC, 1600 Amphitheatre Parkway Mountain View, CA 94043, USA
| | - Jason E Sanchez
- Computational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
| | - Wenhan Guo
- Computational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
| | - Dan Du
- Computational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
| | - Omar J Rodriguez Moncivais
- Department of Biological Sciences, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
- Border Biomedical Research Center, The University of Texas at El Paso, 500 W University Ave, TX, 79968, USA
| | - Dehua Hu
- Department of Biomedical Informatic, School of Life Sciences, Central South University, Changsha 410083, China
| | - Jing Liu
- Department of Hematology, The Second Xiangya Hospital of Central South University; Molecular Biology Research Center, Center for Medical Genetics, School of Life Sciences, Central South University, Changsha 410083, China
| | - Robert Arthur Kirken
- Department of Biological Sciences, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
- Border Biomedical Research Center, The University of Texas at El Paso, 500 W University Ave, TX, 79968, USA
| | - Lin Li
- Computational Science Program, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
- Google LLC, 1600 Amphitheatre Parkway Mountain View, CA 94043, USA
- Department of Physics, The University of Texas at El Paso, 500 W University Ave, TX 79968, USA
| |
Collapse
|
2
|
Guo W, Du D, Zhang H, Sanchez JE, Sun S, Xu W, Peng Y, Li L. Bound ion effects: Using machine learning method to study the kinesin Ncd's binding with microtubule. Biophys J 2023:S0006-3495(23)04176-0. [PMID: 38160255 DOI: 10.1016/j.bpj.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/26/2023] [Accepted: 12/27/2023] [Indexed: 01/03/2024] Open
Abstract
Drosophila Ncd proteins are motor proteins that play important roles in spindle organization. Ncd and the tubulin dimer are highly charged. Thus, it is crucial to investigate Ncd-tubulin dimer interactions in the presence of ions, especially ions that are bound or restricted at the Ncd-tubulin dimer binding interfaces. To consider the ion effects, widely used implicit solvent models treat ions implicitly in the continuous solvent environment without focusing on the individual ions' effects. But highly charged biomolecules such as the Ncd and tubulin dimer may capture some ions at highly charged regions as bound ions. Such bound ions are restricted to their binding sites; thus, they can be treated as part of the biomolecules. By applying multiscale computational methods, including the machine-learning-based Hybridizing Ions Treatment-2 program, molecular dynamics simulations, DelPhi, and DelPhiForce, we studied the interaction between the Ncd motor domain and the tubulin dimer using a hybrid solvent model, which considers the bound ions explicitly and the other ions implicitly in the solvent environment. To identify the importance of treating bound ions explicitly, we also performed calculations using the implicit solvent model without considering the individual bound ions. We found that the calculations of the electrostatic features differ significantly between those of the hybrid solvent model and the pure implicit solvent model. The analyses show that treating bound ions at highly charged regions explicitly is crucial for electrostatic calculations. This work proposes a machine-learning-based approach to handle the bound ions using the hybrid solvent model. Such an approach is not only capable of handling kinesin-tubulin complexes but is also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.
Collapse
Affiliation(s)
- Wenhan Guo
- College of Physical Science and Technology, Central China Normal University, Hubei, China; Computational Science Program, University of Texas at El Paso, El Paso, Texas
| | - Dan Du
- Computational Science Program, University of Texas at El Paso, El Paso, Texas
| | - Houfang Zhang
- College of Physical Science and Technology, Central China Normal University, Hubei, China
| | - Jason E Sanchez
- Computational Science Program, University of Texas at El Paso, El Paso, Texas
| | - Shengjie Sun
- Computational Science Program, University of Texas at El Paso, El Paso, Texas; School of Life Sciences, Central South University, Hunan, China
| | - Wang Xu
- College of Physical Science and Technology, Central China Normal University, Hubei, China
| | - Yunhui Peng
- College of Physical Science and Technology, Central China Normal University, Hubei, China.
| | - Lin Li
- Computational Science Program, University of Texas at El Paso, El Paso, Texas; Department of Physics, University of Texas at El Paso, El Paso, Texas.
| |
Collapse
|