Molecular underpinning of intracellular pH regulation on TMEM16F

TMEM16F, a dual-function phospholipid scramblase and ion channel, is important in blood coagulation, skeleton development, HIV infection, and cell fusion. Despite advances in understanding its structure and activation mechanism, how TMEM16F is regulated by intracellular factors remains largely elusive. Here we report that TMEM16F lipid scrambling and ion channel activities are strongly influenced by intracellular pH (pH_i). We found that low pH_i attenuates, whereas high pH_i potentiates, TMEM16F channel and scramblase activation under physiological concentrations of intracellular Ca²⁺ ([Ca²⁺]_i). We further demonstrate that TMEM16F pH_i sensitivity depends on [Ca²⁺]_i and exhibits a bell-shaped relationship with [Ca²⁺]_i: TMEM16F channel activation becomes increasingly pH_i sensitive from resting [Ca²⁺]_i to micromolar [Ca²⁺]_i, but when [Ca²⁺]_i increases beyond 15 µM, pH_i sensitivity gradually diminishes. The mutation of a Ca²⁺-binding residue that markedly reduces TMEM16F Ca²⁺ sensitivity (E667Q) maintains the bell-shaped relationship between pH_i sensitivity and Ca²⁺ but causes a dramatic shift of the peak [Ca²⁺]_i from 15 µM to 3 mM. Our biophysical characterizations thus pinpoint that the pH_i regulatory effects on TMEM16F stem from the competition between Ca²⁺ and protons for the primary Ca²⁺-binding residues in the pore. Within the physiological [Ca²⁺]_i range, the protonation state of the primary Ca²⁺-binding sites influences Ca²⁺ binding and regulates TMEM16F activation. Our findings thus uncover a regulatory mechanism of TMEM16F by pH_i and shine light on our understanding of the pathophysiological roles of TMEM16F in diseases with dysregulated pH_i, including cancer.     

Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.     

Design,synthesis and biological evaluation of novel 1H-1,2,4-triazole,benzothiazole and indazole-based derivatives as potent FGFR1 inhibitors viafragment-based virtual screening

Abstract

Fibroblast growth-factor receptor (FGFR) is a potential target for cancer therapy. We designed three novel series of FGFR1 inhibitors bearing indazole, benzothiazole, and 1H-1,2,4-triazole scaffold via fragment-based virtual screening. All the newly synthesised compounds were evaluated in vitro for their inhibitory activities against FGFR1. Compound 9d bearing an indazole scaffold was first identified as a hit compound, with excellent kinase inhibitory activity (IC₅₀ = 15.0?nM) and modest anti-proliferative activity (IC₅₀ = 785.8?nM). Through two rounds of optimisation, the indazole derivative 9?u stood out as the most potent FGFR1 inhibitors with the best enzyme inhibitory activity (IC₅₀ = 3.3?nM) and cellular activity (IC₅₀ = 468.2?nM). Moreover, 9?u also exhibited good kinase selectivity. In addition, molecular docking study was performed to investigate the binding mode between target compounds and FGFR1.     

Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM,pharmacophore and docking model

Abstract

Cyclin-dependent kinase 2 (CDK2) is the family of Ser/Thr protein kinases that has emerged as a highly selective with low toxic cancer therapy target. A multistage virtual screening method combined by SVM, protein-ligand interaction fingerprints (PLIF) pharmacophore and docking was utilised for screening the CDK2 inhibitors. The evaluation of the validation set indicated that this method can be used to screen large chemical databases because it has a high hit-rate and enrichment factor (80.1% and 332.83 respectively). Six compounds were screened out from NCI, Enamine and Pubchem database. After molecular dynamics and binding free energy calculation, two compounds had great potential as novel CDK2 inhibitors and they also showed selective inhibition against CDK2 in the kinase activity assay.     

Identification of Direct Tyrosine Kinase Substrates Based on Protein Kinase Assay-Linked Phosphoproteomics

Protein kinases are implicated in multiple diseases such as cancer, diabetes, cardiovascular diseases, and central nervous system disorders. Identification of kinase substrates is critical to dissecting signaling pathways and to understanding disease pathologies. However, methods and techniques used to identify bona fide kinase substrates have remained elusive. Here we describe a proteomic strategy suitable for identifying kinase specificity and direct substrates in high throughput. This approach includes an in vitro kinase assay-based substrate screening and an endogenous kinase dependent phosphorylation profiling. In the in vitro kinase reaction route, a pool of formerly phosphorylated proteins is directly extracted from whole cell extracts, dephosphorylated by phosphatase treatment, after which the kinase of interest is added. Quantitative proteomics identifies the rephosphorylated proteins as direct substrates in vitro. In parallel, the in vivo quantitative phosphoproteomics is performed in which cells are treated with or without the kinase inhibitor. Together, proteins phosphorylated in vitro overlapping with the kinase-dependent phosphoproteome in vivo represents the physiological direct substrates in high confidence. The protein kinase assay-linked phosphoproteomics was applied to identify 25 candidate substrates of the protein-tyrosine kinase SYK, including a number of known substrates and many novel substrates in human B cells. These shed light on possible new roles for SYK in multiple important signaling pathways. The results demonstrate that this integrated proteomic approach can provide an efficient strategy to screen direct substrates for protein tyrosine kinases.Protein phosphorylation plays a pivotal role in regulating biological events such as protein–protein interactions, signal transduction, subcellular localization, and apoptosis (1). Deregulation of kinase-substrate interactions often leads to disease states such as human malignancies, diabetes, and immune disorders (2). Although a number of kinases are being targeted to develop new drugs, our understanding of the precise relationships between protein kinases and their direct substrates is incomplete for the majority of protein kinases (3). Thus, mapping kinase–substrate relationships is essential for the understanding of biological signaling networks and the discovery and development of drugs for targeted therapies (4). Toward this goal, various in vitro kinase assays using synthetic peptide libraries (5), phage expression libraries (6), protein arrays (7–9), or cell extracts (10, 11) have been explored for the screening of kinase substrates.Besides classical biochemical and genetic methods, mass spectrometry-based high throughput approaches have become increasingly attractive because they are capable of sequencing proteins and localizing phosphorylation sites at the same time. Mass spectrometry-based proteomic methods have been extensively applied to kinase-substrate interaction mapping (12) and global phosphorylation profiling (13–15). Although thousands of phosphorylation events can be inspected simultaneously (16, 17), large-scale phosphoproteomics does not typically reveal direct relationships between protein kinases and their substrates.Recently, several mass spectrometry-based proteomic strategies have been introduced for identifying elusive kinase substrates (7, 18, 19). Taking advantage of recent advances of high speed and high-resolution mass spectrometry, these methods used purified, active kinases to phosphorylate cell extracts in vitro, followed by mass spectrometric analysis to identify phosphoproteins. These approaches commonly face the major challenge of distinguishing phosphorylation events triggered by the kinase reaction from background signals introduced by endogenous kinase activities (20). To dissect the phosphorylation cascade, Shokat and colleagues developed an approach named Analog-Sensitive Kinase Allele (ASKA)¹ (21). In their approach, a kinase is engineered to accept a bulky-ATP analog exclusively so that direct phosphorylation caused by the analog-sensitive target kinase can be differentiated from that of wild type kinases. As a result, indirect effects caused by contaminating kinases during the in vitro kinase assay are largely eliminated. ASKA has recently been coupled with quantitative proteomics, termed Quantitative Identification of Kinase Substrates (QIKS) (12), to identify substrate proteins of Mek1. Recently, one extension of the ASKA technique is for the analog ATP to carry a γ-thiophosphate group so that in vitro thiophosphorylated proteins can be isolated for mass spectrometric detection (22–24). In addition to ASKA, radioisotope labeling using [γ-³²P]ATP (10), using concentrated purified kinase (25), inactivating endogenous kinase activity by an additional heating step (11), and quantitative proteomics (26, 27) are alternative means aimed to address the same issues. All of these methods, however, have been limited to the identification of in vitro kinase substrates.To bridge the gap between in vitro phosphorylation and physiological phosphorylation events, we have recently introduced an integrated strategy termed Kinase Assay-Linked Phosphoproteomics (KALIP) (28). By combining in vitro kinase assays with in vivo phosphoproteomics, this method was demonstrated to have exceptional sensitivity for high confidence identification of direct kinase substrates. The main drawback for the KALIP approach is that the kinase reaction is performed at the peptide stage to eliminate any problems related to contamination by endogenous kinases. However, the KALIP method may not be effective for kinases that require a priming phosphorylation event (i.e. a previous phosphorylation, on substrate or kinase, has effect on following phosphorylation) (29), additional interacting surfaces (30), or a docking site on the protein (31). For example, basophilic kinases require multiple basic resides for phosphorylation and tryptic digestion will abolish these motifs, which are needed for effective kinase reactions.We address the shortcoming by introducing an alternative strategy termed Protein Kinase Assay-Linked Phosphoproteomics (proKALIP). The major difference between this method and the previous KALIP method is the utilization of protein extracts instead of digested peptides as the substrate pool. The major issue is how to reduce potential interference by endogenous kinase activities. One effective solution is to use a generic kinase inhibitor, 5′-(4-fluorosulfonylbenzoyl)adenosine (FSBA), which was widely used for covalent labeling of kinases (32, 33), kinase isolation (34), kinase activity exploration (35, 36), and more recently kinase substrate identification by Kothary and co-workers (37). However, an extra step is required to effectively remove the inhibitor before the kinase reaction, which may decrease the sensitivity. ProKALIP addresses the issue by carrying out the kinase reaction using formerly in vivo phosphorylated proteins as candidates. This step efficiently improves the sensitivity and specificity of the in vitro kinase reaction. Coupled with in vivo phosphoproteomics, proKALIP has gained a high sensitivity and provided physiologically relevant substrates with high confidence.To demonstrate the proKALIP strategy, the protein-tyrosine kinase SYK was used as our target kinase. SYK is known to play a crucial role in the adaptive immune response, particularly in B cells, by facilitating the antigen induced B-cell receptor (BCR) signaling pathways and modulating cellular responses to oxidative stress in a receptor-independent manner (38, 39). SYK also has diverse biological functions such as innate immune recognition, osteoclast maturation, cellular adhesion, platelet activation, and vascular development (38). In addition, the expression of SYK is highly correlated to tumorigenesis by promoting cell–cell adhesion and inhibiting the motility, growth, and invasiveness of certain cancer cells (40). In this study, we attempt to identify bona fide substrates of SYK in human B cells using the proKALIP approach and demonstrate the specificity and sensitivity of this strategy.