A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape.

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.

Bound ion effects: Using machine learning method to study the kinesin Ncd's binding with microtubule.

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.

BiasNet: A Model to Predict Ligand Bias Toward GPCR Signaling.

Signaling bias is a feature of many G protein-coupled receptor (GPCR) targeting drugs with potential clinical implications. Whether it is therapeutically advantageous for a drug to be G protein biased or ß-arrestin biased depends on the context of the signaling pathway. Here, we explored GPCR ligands that exhibit biased signaling to gain insights into scaffolds and pharmacophores that lead to bias. More specifically, we considered BiasDB, a database containing information about GPCR biased ligands, and focused our analysis on ligands which show either a G protein or ß-arrestin bias. Five different machine learning models were trained on these ligands using 15 different sets of features. Molecular fragments which were important for training the models were analyzed. Two of these fragments (number of secondary amines and number of aromatic amines) were more prevalent in ß-arrestin biased ligands. After training a random forest model on HierS scaffolds, we found five scaffolds, which demonstrated G protein or ß-arrestin bias. We also conducted t-SNE clustering, observing correspondence between unsupervised and supervised machine learning methods. To increase the applicability of our work, we developed a web implementation of our models, which can predict bias based on user-provided SMILES, drug names, or PubChem CID. Our web implementation is available at: drugdiscovery.utep.edu/biasnet.

How does the ion concentration affect the functions of kinesin BimC.

BimC family proteins are bipolar motor proteins belonging to the kinesin superfamily which promote mitosis by crosslinking and sliding apart antiparallel microtubules. Understanding the binding mechanism between the kinesin and the microtubule is crucial for researchers to make advances in the treatment of cancer and other malignancies. Experimental research has shown that the ion concentration affects the function of BimC significantly. But the insights of the ion-dependent function of BimC remain unclear. By combining molecular dynamics (MD) simulations with a series of computational approaches, we studied the electrostatic interactions at the binding interfaces of BimC and the microtubule under different KCl concentrations. We found the electrostatic interaction between BimC and microtubule is stronger at 0 mM KCl compared to 150 mM KCl, which is consistent with experimental conclusions. Furthermore, important salt bridges and residues at the binding interfaces of the complex were identified, which illustrates the details of the BimC-microtubule interactions. Molecular dynamics analyses of salt bridges identified that the important residues on the binding interface of BimC are positively charged, while those residues on the binding interface of the tubulin heterodimer are negatively charged. The finding in this work reveals some important mechanisms of kinesin-microtubule binding, which helps the future drug design for cancer therapy.

A Comprehensive Study on the Electrostatic Properties of Tubulin-Tubulin Complexes in Microtubules.

Microtubules are key players in several stages of the cell cycle and are also involved in the transportation of cellular organelles. Microtubules are polymerized by α/ß tubulin dimers with a highly dynamic feature, especially at the plus ends of the microtubules. Therefore, understanding the interactions among tubulins is crucial for characterizing microtubule dynamics. Studying microtubule dynamics can help researchers make advances in the treatment of neurodegenerative diseases and cancer. In this study, we utilize a series of computational approaches to study the electrostatic interactions at the binding interfaces of tubulin monomers. Our study revealed that among all the four types of tubulin-tubulin binding modes, the electrostatic attractive interactions in the α/ß tubulin binding are the strongest while the interactions of α/α tubulin binding in the longitudinal direction are the weakest. Our calculations explained that due to the electrostatic interactions, the tubulins always preferred to form α/ß tubulin dimers. The interactions between two protofilaments are the weakest. Thus, the protofilaments are easily separated from each other. Furthermore, the important residues involved in the salt bridges at the binding interfaces of the tubulins are identified, which illustrates the details of the interactions in the microtubule. This study elucidates some mechanistic details of microtubule dynamics and also identifies important residues at the binding interfaces as potential drug targets for the inhibition of cancer cells.

HIT-2: Implementing machine learning algorithms to treat bound ions in biomolecules.

Electrostatic features are fundamental to protein functions and protein-protein interactions. Studying highly charged biomolecules is challenging given the heterogeneous distribution of the ionic cloud around such biomolecules. Here we report a new computational method, Hybridizing Ions Treatment-2 (HIT-2), which is used to model biomolecule-bound ions using the implicit solvation model. By modeling ions, HIT-2 allows the user to calculate important electrostatic features of the biomolecules. HIT-2 applies an efficient algorithm to calculate the position of bound ions from molecular dynamics simulations. Modeling parameters were optimized by machine learning methods from thousands of datasets. The optimized parameters produced results with errors lower than 0.2 Å. The testing results on bound Ca2+ and Zn2+ in NAMD simulations also proved that HIT-2 can effectively identify bound ion types, numbers, and positions. Also, multiple tests performed on HIT-2 suggest the method can handle biomolecules that undergo remarkable conformational changes. HIT-2 can significantly improve electrostatic calculations for many problems in computational biophysics.

Phosphorylation of Tyrosine 841 Plays a Significant Role in JAK3 Activation.

Janus Kinase 3 (JAK3) plays a key role in the development, proliferation, and differentiation of various immune cells. It regulates gene expression by phosphorylation of Signal Transducers and Activators of Transcriptions (STATs) via the JAK/STAT pathway. Recently, we found a new JAK3 phosphorylation site, tyrosine 841 (Y841). The results showed that pY841 helps the kinase domain flip around the pseudo kinase domain, which may cause JAK3 conformational changes. It also reduces the size of the cleft between the N-lobe and the C-lobe of the JAK3 kinase domain. However, pY841 was found to enlarge the cleft when ATP/ADP was bound to the kinase. The increase in the cleft size suggested that pY841 enhanced the elasticity of the kinase domain. For unphosphorylated JAK3 (JAK3-Y841), the binding forces between the kinase domain and ATP or ADP were similar. After phosphorylation of Y841, JAK3-pY841 exhibited more salt bridges and hydrogen bonds between ATP and the kinase than between ADP and the kinase. Consequently, the electrostatic binding force between ATP and the kinase was higher than that between ADP and the kinase. The result was that compared to ADP, ATP was more attractive to JAK3 when Y841 was phosphorylated. Therefore, JAK3-pY841 tended to bind ATP rather than ADP. This work provides new insights into the role of phosphorylation in kinase activation and ATP hydrolysis and sheds light on the importance of understanding the molecular mechanisms that regulate the kinase function.

Using a comprehensive approach to investigate the interaction between Kinesin-5/Eg5 and the microtubule.

Kinesins are microtubule-based motor proteins that play important roles ranging from intracellular transport to cell division. Human Kinesin-5 (Eg5) is essential for mitotic spindle assembly during cell division. By combining molecular dynamics (MD) simulations with other multi-scale computational approaches, we systematically studied the interaction between Eg5 and the microtubule. We find the electrostatic feature on the motor domains of Eg5 provides attractive interactions to the microtubule. Additionally, the folding and binding energy analysis reveals that the Eg5 motor domain performs its functions best when in a weak acidic environment. Molecular dynamics analyses of hydrogen bonds and salt bridges demonstrate that, on the binding interfaces of Eg5 and the tubulin heterodimer, salt bridges play the most significant role in holding the complex. The salt bridge residues on the binding interface of Eg5 are mostly positive, while salt bridge residues on the binding interface of tubulin heterodimer are mostly negative. Such salt bridge residue distribution is consistent with electrostatic potential calculations. In contrast, the interface between α and ß-tubulins is dominated by hydrogen bonds rather than salt bridges. Compared to the Eg5/α-tubulin interface, the Eg5/ß-tubulin interface has a greater number of salt bridges and higher occupancy for salt bridges. This asymmetric salt bridge distribution may play a significant role in Eg5's directionality. The residues involved in hydrogen bonds and salt bridges are identified in this work and may be helpful for anticancer drug design.