Biology subjects

Ramakrishnan, K.

Publications and source records attributed to Ramakrishnan, K..

10 recordsLinked to original sources

bioRxiv2025

Dual-feature selectivity enables bidirectional coding in visual cortical neurons

Sensory neurons are traditionally viewed as feature detectors that respond with an increase in firing rate to preferred stimuli while remaining unresponsive to others. Here, we identify a dual-feature encoding strategy in macaque visual cortex, wherein many neurons in areas V1 and V4 are selectively tuned to two distinct visual features--one that enhances and one that suppresses activity--around an elevated baseline firing rate. By combining neuronal recordings with functional digital twin models--deep learning-based predictive models of biological neurons--we were able to systematically identify each neurons preferred and non-preferred features. These feature pairs served as anchors for a continuous, low-dimensional axis in natural image similarity space, along which neuronal activity varied approximately linearly. Within a single visual area, visual features that strongly or weakly activated individual neurons also had a high probability of modulating the activity of other neurons, suggesting a shared feature selectivity across the population that structures stimulus encoding. We show that this encoding strategy is conserved across species, present in both primary and lateral visual areas of mouse cortex. Dual-feature selectivity is consistent with recent anatomical evidence for feature-specific inhibitory connectivity, complementing the feature-detector principle through circuit mechanisms in which selective excitation and inhibition may together enhance the representational capacity of the neuronal population.

neuroscience↗

bioRxiv2023

Optimising CNT-FET Biosensor Design: Predictive Modelling of Biomolecular Electrostatic Gating and its Application to Beta-Lactamase Detection

Carbon nanotube field effect transistor (CNT-FET) setups hold great promise for constructing next generation miniaturised biosensors whereby a biomolecular event gates conductance. The main issue is predicting how proteins, with their innate mosaic and distinctive electrostatic surfaces, interact with and thus modulate conductance of the CNT-FET. To overcome this barrier, we used advanced sampling molecular dynamics combined with non-canonical amino acid chemistry, to model the protein electrostatic potential imparted on SWCNTs. Here, we focused our efforts using {beta}-lactamase binding protein (BLIP2) as the receptor due to its potential as a biosensor for the most common antibiotic degrading enzymes, the {beta}-lactamases (BLs). Modelling was confirmed experimentally by attaching BLIP2 at single designed residues positions directly to SWCNTs using genetically encoded phenyl azide photochemistry. Our devices were able to successfully detect the two different BLs, TEM-1 and KPC-2, with each BL generating distinct conductance profiles due to differences in their unique surface electrostatic profiles presented close to the SWCNT surface. The changes in conductance closely matched the predicted electrostatic profile sampled by the SWCNTs on BL binding. Thus, our modelling approach combined with new and straight-forward residue-specific receptor attachment techniques, provides a general approach for more effective and optimal CNT-FET biosensor construction.

bioengineering↗

bioRxiv2022

N-linked glycosylation increases horse radish peroxidase rigidity leading to enhanced activity and stability.

Glycosylation is the most prevalent protein post-translational modification, with a quarter of glycosylated proteins having enzymatic properties. Yet the full impact of glycosylation on the protein structure-function relationship, especially in enzymes, is still limited. Here we show glycosylation rigidifies the important commercial enzyme horseradish peroxidase (HRP), which in turn increases its activity and stability. Circular dichroism spectroscopy revealed that glycosylation increased holo-HRPs thermal stability and promoted significant helical structure in the absence of haem (apo-HRP). Glycosylation also resulted in a 10-fold increase in enzymatic turnover towards o-phenylenediamine dihydrochloride when compared to its non-glycosylated form. Utilising a naturally occurring site-specific probe of active site flexibility (Trp117) in combination with red-edge excitation shift fluorescence spectroscopy, we found that glycosylation significantly rigidified the enzyme. In silico simulations confirmed that glycosylation largely decreased protein backbone flexibility, especially in regions close to the active site and the substrate access channel. Thus, our data shows that glycosylation does not just have a passive effect on HRP stability but can exert long range effects that mediate the native enzymes activity and stability through changes in inherent dynamics.

biochemistry↗

bioRxiv2022

Evolutionary and cellular analysis of the dark pseudokinase PSKH2

Pseudokinases, so named because they lack one or more conserved canonical amino acids that define their catalytically-active relatives, have evolved a variety of biological functions in both prokaryotic and eukaryotic organisms. Human PSKH2 is closely related to the canonical kinase PSKH1, which maps to the CAMK family of protein kinases. Primates encode PSKH2 in the form of a pseudokinase, which is predicted to be catalytically inactive due to loss of the invariant catalytic Asp residue. Although the biological role(s) of vertebrate PSKH2s remains unclear, we previously identified species-level adaptions in PSKH2 that have led to the appearance of kinase or pseudokinase variants in vertebrate genomes alongside a canonical PSKH1 paralog. In this paper we confirm that, as predicted, PSKH2 lacks detectable protein phosphotransferase activity, and exploit structural informatics, biochemistry and cellular proteomics to begin to characterise vertebrate PSKH2 orthologues. AlphaFold 2-based structural analysis predicts functional roles for both the PSKH2 N- and C-regions that flank the pseudokinase domain core, and cellular truncation analysis confirms that the N-terminal domain, which contains a conserved myristoylation site, is required for both stable human PSKH2 expression and localisation to a membrane-rich subcellular fraction containing mitochondrial proteins. Using mass spectrometry-based proteomics, we confirm that human PSKH2 is part of a cellular mitochondrial protein network, and that its expression is regulated through client-status within the HSP90/Cdc37 molecular chaperone system. HSP90 interactions are mediated through binding to the PSKH2 C-terminal tail, leading us to predict that this region might act as both a cis and trans regulatory element, driving outputs linked to the PSKH2 pseudokinase domain that are important for functional signalling.

biochemistry↗

bioRxiv2021

CTP regulates membrane-binding activity of the nucleoid occlusion protein Noc

ATP and GTP-dependent molecular switches are extensively used to control functions of proteins in a wide range of biological processes. However, CTP switches are rarely reported. Here, we report that a nucleoid occlusion protein Noc is a CTPase enzyme whose membrane-binding activity is directly regulated by a CTP switch. In Bacillus subtilis, Noc nucleates on 16-bp NBS sites before associating with neighboring non-specific DNA to form large membrane-associated nucleoprotein complexes to physically occlude assembly of the cell division machinery. By in vitro reconstitution, we show that (i) CTP is required for Noc to form the NBS-dependent nucleoprotein complex, and (ii) CTP binding, but not hydrolysis, switches Noc to a membrane-active state. Overall, we suggest that CTP couples membrane-binding activity of Noc to nucleoprotein complex formation to ensure productive recruitment of DNA to the bacterial cell membrane for nucleoid occlusion activity.

microbiology↗

bioRxiv2020

A Computational Model of Levodopa-Induced Toxicity in Substantia Nigra Pars Compacta in Parkinson's Disease

BackgroundParkinsons disease (PD) is caused by the progressive loss of dopaminergic cells in substantia nigra pars compacta (SNc). The root cause of this cell loss in PD is still not decisively elucidated. A recent line of thinking traces the cause of PD neurodegeneration to metabolic deficiency. Due to exceptionally high energy demand, SNc neurons exhibit a higher basal metabolic rate and higher oxygen consumption rate, which results in oxidative stress. Recently, we have suggested that the excitotoxic loss of SNc cells might be due to energy deficiency occurring at different levels of neural hierarchy. Levodopa (LDOPA), a precursor of dopamine, which is used as a symptom-relieving treatment for PD, leads to outcomes that are both positive and negative. Several researchers suggested that LDOPA might be harmful to SNc cells due to oxidative stress. The role of LDOPA in the course of PD pathogenesis is still debatable. New MethodWe hypothesize that energy deficiency can lead to LDOPA-induced toxicity (LIT) in two ways: by promoting dopamine-induced oxidative stress and by exacerbating excitotoxicity in SNc. We present a multiscale computational model of SNc-striatum system, which will help us in understanding the mechanism behind neurodegeneration postulated above and provides insights for developing disease-modifying therapeutics. ResultsIt was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During LDOPA therapy, it was observed that higher LDOPA dosage results in increased loss of somas and terminals in SNc. It was also observed that co-administration of LDOPA and glutathione (antioxidant) evades LDOPA-induced toxicity in SNc neurons. Comparison with Existing MethodsOur proposed multiscale model of SNc-striatum system is first of its kind, where SNc neuron was modelled at biophysical level, and striatal neurons were modelled at spiking level. ConclusionsWe show that our proposed model was able to capture LDOPA-induced toxicity in SNc, caused by energy deficiency.

neuroscience↗

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↗