Biology subjects

Ramakrishnan, K.

Publications and source records attributed to Ramakrishnan, K..

4 recordsLinked to original sources

bioRxiv2018

New tools for carbohydrate sulphation analysis: Heparan Sulphate 2-O-sulphotranserase (HS2ST) is a target for small molecule protein kinase inhibitors

Sulphation of carbohydrate residues occurs on a variety of glycans destined for secretion, and this modification is essential for efficient matrix-based signal transduction. Heparan sulphate (HS) glycosaminoglycans control physiological functions ranging from blood coagulation to cell proliferation. HS biosynthesis involves membrane-bound Golgi sulphotransferases, including heparan sulphate 2-O-sulphotransferase (HS2ST), which transfers sulphate from the co-factor PAPS (3-phosphoadenosine 5-phosphosulphate) to the 2-O position of -L-iduronate in the maturing oligosaccharide chain. The current lack of simple non-radioactive enzyme assays that can be used to quantify the levels of carbohydrate sulphation hampers kinetic analysis of this process and the discovery of HS2ST inhibitors. In this paper, we describe a new procedure for thermal shift analysis of purified HS2ST. Using this approach, we quantify HS2ST-catalyzed oligosaccharide sulphation using a novel synthetic fluorescent substrate and screen the Published Kinase Inhibitor Set (PKIS), to evaluate compounds that inhibit catalysis. We report the susceptibility of HS2ST to a variety of cell permeable compounds in vitro, including polyanionic polar molecules, the protein kinase inhibitor rottlerin and oxindole-based RAF kinase inhibitors. In a related study, published back-to-back with this article, we demonstrate that Tyrosyl Protein Sulpho Tranferases (TPSTs) are also inhibited by a variety of protein kinase inhibitors. We propose that appropriately validated small molecule compounds could become new tools for rapid inhibition of glycan (and protein) sulphation in cells, and that protein kinase inhibitors might be repurposed or redesigned for the specific inhibition of HS2ST.\n\nSUMMARY STATEMENTWe report that HS2ST, which is a PAPS-dependent glycan sulphotransferase, can be assayed using a variety of novel biochemical procedures, including a non-radioactive enzyme-based assay that detects glycan substrate sulphation in real time. HS2ST activity can be inhibited by different classes of compounds, including known protein kinase inhibitors, suggesting new approaches to evaluate the roles of HS2ST-dependent sulphation with small molecules in cells.

biochemistry↗

bioRxiv2018

New tools for evaluating protein tyrosine sulphation: Tyrosyl Protein Sulphotransferases (TPSTs) are novel targets for RAF protein kinase inhibitors

Protein tyrosine sulphation is a post-translational modification (PTM) best known for regulating extracellular protein-protein interactions. Tyrosine sulphation is catalysed by two Golgi-resident enzymes termed Tyrosyl Protein Sulpho Transferases (TPSTs) 1 and 2, which transfer sulphate from the co-factor PAPS (3-phosphoadenosine 5-phosphosulphate) to a context-dependent tyrosine in a protein substrate. A lack of quantitative tyrosine sulphation assays has hampered the development of chemical biology approaches for the identification of small molecule inhibitors of tyrosine sulphation. In this paper, we describe the development of a non-radioactive mobility-based enzymatic assay for TPST1 and TPST2, through which the tyrosine sulphation of synthetic fluorescent peptides can be rapidly quantified. We exploit ligand binding and inhibitor screens to uncover a susceptibility of TPST1 and 2 to different classes of small molecules, including the anti-angiogenic compound suramin and the kinase inhibitor rottlerin. By screening the Published Kinase Inhibitor Set (PKIS), we identified oxindole-based inhibitors of the Ser/Thr kinase RAF as low micromolar inhibitors of TPST1/2. Interestingly, unrelated RAF inhibitors, exemplified by the dual BRAF/VEGFR2 inhibitor RAF265, were also TPST inhibitors in vitro. We propose that target-validated protein kinase inhibitors could be repurposed, or redesigned, as more-specific TPST inhibitors to help evaluate the sulphotyrosyl proteome. Finally, we speculate that mechanistic inhibition of cellular tyrosine sulphation might be relevant to some of the phenotypes observed in cells exposed to anionic TPST ligands and RAF protein kinase inhibitors.\n\nSUMMARY STATEMENTWe develop new assays to quantify tyrosine sulphation by the human tyrosine sulphotransferases TPST1 and 2. TPST1 and 2 catalytic activities are inhibited by protein kinase inhibitors, suggesting new starting points to synthesise (or repurpose) small molecule compounds to evaluate biological TPST using chemical biology.

biochemistry↗

bioRxiv2017

Characterizing the temporal dynamics of object recognition by deep neural networks: role of depth

Convolutional neural networks (CNNs) have recently emerged as promising models of human vision based on their ability to predict hemodynamic brain responses to visual stimuli measured with functional magnetic resonance imaging (fMRI). However, the degree to which CNNs can predict temporal dynamics of visual object recognition reflected in neural measures with millisecond precision is less understood. Additionally, while deeper CNNs with higher numbers of layers perform better on automated object recognition, it is unclear if this also results into better correlation to brain responses. Here, we examined 1) to what extent CNN layers predict visual evoked responses in the human brain over time and 2) whether deeper CNNs better model brain responses. Specifically, we tested how well CNN architectures with 7 (CNN-7) and 15 (CNN-15) layers predicted electro-encephalography (EEG) responses to several thousands of natural images. Our results show that both CNN architectures correspond to EEG responses in a hierarchical spatio-temporal manner, with lower layers explaining responses early in time at electrodes overlying early visual cortex, and higher layers explaining responses later in time at electrodes overlying lateral-occipital cortex. While the explained variance of neural responses by individual layers did not differ between CNN-7 and CNN-15, combining the representations across layers resulted in improved performance of CNN-15 compared to CNN-7, but only after 150 ms after stimulus-onset. This suggests that CNN representations reflect both early (feed-forward) and late (feedback) stages of visual processing. Overall, our results show that depth of CNNs indeed plays a role in explaining time-resolved EEG responses.

neuroscience↗

bioRxiv2017

Visual pathways from the perspective of cost functions and multi-task deep neural networks

Vision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action. To evaluate the functional organization in multi-task deep neural networks, we propose a method that measures the contribution of a unit towards each task, applying it to two networks that have been trained on either two related or two unrelated tasks, using an identical stimulus set. Results show that the network trained on the unrelated tasks shows a decreasing degree of feature representation sharing towards higher-tier layers while the network trained on related tasks uniformly shows high degree of sharing. We conjecture that the method we propose can be used to analyze the anatomical and functional organization of the visual system and beyond. We predict that the degree to which tasks are related is a good descriptor of the degree to which they share downstream cortical-units.

neuroscience↗