Biology subjects

Loomba, S.

Publications and source records attributed to Loomba, S..

6 recordsLinked to original sources

bioRxiv2026

Reframing enzyme function prediction as conditional generation

Enzymes frequently exhibit promiscuous activity beyond their native roles, providing starting-points for new functions. Finding these promiscuous enzymes, especially for non-native chemical transformations, is challenging but highly valuable, as they promise novel, sustainable solutions for chemistry and biotechnology. However, current machine learning methods are poorly suited to discovering unseen chemistry as they often frame function prediction as closed set classification or a retrieval task. Here, we present Fluxion, a generative deep learning framework that learns enzymatic catalysis by modeling dynamic electron flow trajectories across the enzyme's catalytic residues. By combining both synthetic chemistry and biochemical datasets with protein language model representations, Fluxion generates multi-step electron-flow trajectories analogous to the arrow-pushing representations used to describe enzyme reaction mechanisms. Generation is conditioned on enzyme context, including the enzyme sequence, catalytic residues, substrates, and cofactors. We show that this conditioning allows Fluxion to learn enzyme-dependent regioselectivity across cytochrome P450 enzymes with different sequences shifting the predicted reaction sites for the same substrate. We then demonstrate that Fluxion's embeddings are useful for downstream tasks, such as specificity prediction on two experimental datasets, with and without finetuning. Finally, we show that Fluxion has the potential to transfer synthetic chemical logic to biology; it can generate the observed non-native product from real-world non-native directed evolution screens. Our results establish a proof of concept that generative modeling through mechanistic representations of enzymes can shift enzyme function prediction beyond static database retrieval and closed set classification to function generation. This conceptual framework provides a stepping stone towards an in silico generative method to discover non-native biocatalysts.

biochemistry↗

bioRxiv2025

Presynaptic Release Probability Determines the Need for Sleep

Sleep is universal among animals with synapses, yet the synaptic functions determining the need for sleep remain elusive. By directly measuring synaptic transmission at anatomically defined synapses in Drosophila, we found that synaptic strength remained stable or declined after sleep deprivation in a circuit-specific manner. In contrast, presynaptic release probability (Pr) consistently decreased with sleep loss across circuits and species, stemming from reduced Ca2+ influx or weakened vesicle-channel coupling at presynaptic terminals, and recovered after sleep. Bidirectional manipulations of Pr altered sleep pressure, establishing a causal relationship between presynaptic function and sleep need. Non-synaptic sleep-regulatory signaling pathways consistently modulate Pr but not synaptic strength. Thus, our findings identify Pr, rather than synaptic strength, as the conserved synaptic substrate underlying sleep need.

neuroscience↗

bioRxiv2024

Connectomic reconstruction of a cortical column

The cerebral cortex of mammals has long been proposed to comprise unit-modules, so-called cortical columns. The detailed synaptic-level circuitry of such a neuronal network of about 104 neurons is still unknown. Here, using 3-dimensional electron microscopy, AI-based image processing and automated proofreading, we report the connectomic reconstruction of a defined cortical column in mouse barrel cortex. The cortical column appears as a structural feature in the connectome, without need for geometrical or morphological landmarks. We then used the connectome for definition of neuronal cell types in the column, to determine intracolumnar circuit modules, analyze the logic of inhibitory circuits, investigate the circuits for combination of bottom-up and top-down signals in the column and the specificity of bottom-up and top-down cortical input, search for higher-order circuit structure within homogeneous neuronal populations, and estimate the degree and symmetry of Hebbian learning in the various connection types. With this, we provide a first column-level connectomic description of the cerebral cortex, the likely substrate for a synaptic-level mechanistic understanding of sensory-conceptual integration and learning.

neuroscience↗

bioRxiv2022

Connectomic Analysis of Thalamus-driven Disinhibition in Cortical Layer 4

In mammals, sensory signals are transmitted via the thalamus primarily to layer 4 of the primary sensory cortices. While information about average neuronal connectivity in this layer is available, the detailed and higher-order circuit structure is not known. Here, we used 3-dimensional electron microscopy for a connectomic analysis of the thalamus-driven inhibitory network in a layer 4 barrel. We find that thalamic input drives a subset of interneurons with high specificity. These interneurons in turn target spiny stellate and star pyramidal excitatory neurons with subtype specificity. In addition, they create a directed disinhibitory network directly driven by the thalamic input. Together, this circuit can create differential windows of opportunity for activation of the types of excitatory neurons in dependence of strength and timing of thalamic input. With this, we have identified a so-far unknown degree of specialization of the microcircuitry in the main thalamocortical recipient layer of the primary sensory cortex.

neuroscience↗

bioRxiv2022

Target-agnostic discovery of Rett Syndrome therapeutics by coupling computational network analysis and CRISPR-enabled in vivo disease modeling

Many neurodevelopmental genetic disorders, such as Rett syndrome, are caused by a single gene mutation but trigger changes in expression and regulation of numerous other genes. This severely impair functions of multiple organs and organ systems beyond the central nervous system (CNS), adding to the challenge of developing broadly effective treatments based on a single drug target. This challenge is further complicated by the lack of sufficiently broad and biologically relevant drug screens, and the inherent complexity in identifying clinically relevant targets responsible for diverse phenotypes. Here, we combined human gene regulatory network-based computational drug prediction with in vivo screening in a population-level diversity, CRISPR-edited, Xenopus laevis tadpole model of Rett syndrome to carry out target-agnostic drug discovery, which rapidly led to the identification of the FDA-approved drug vorinostat as a potential repurposing candidate. Vorinostat broadly improved both CNS and non-CNS (e.g., gastrointestinal, respiratory, inflammatory) abnormalities in a pre-clinical mouse model of Rett syndrome. This is the first Rett syndrome treatment to demonstrate pre-clinical efficacy across multiple organ systems when dosed after the onset of symptoms, and network analysis revealed a putative therapeutic mechanism for its cross-organ normalizing effects based on its impact on acetylation metabolism and post-translational modifications of microtubules. Although traditionally considered an inhibitor of histone deacetylases (HDAC), vorinostat unexpectedly restored protein acetylation across both hypo- and hyperacetylated tissues, suggesting non-HDAC-mediated therapeutic mechanisms supported by proteomic analysis.

neuroscience↗

bioRxiv2018

Dense connectomic reconstruction in layer 4 of the somatosensory cortex

The dense circuit structure of the mammalian cerebral cortex is still unknown. With developments in 3-dimensional (3D) electron microscopy, the imaging of sizeable volumes of neuropil has become possible, but dense reconstruction of connectomes from such image data is the limiting step. Here, we report the dense reconstruction of a volume of about 500,000 m3 from layer 4 of mouse barrel cortex, about 300 times larger than previous dense reconstructions from the mammalian cerebral cortex. Using a novel reconstruction technique, FocusEM, we were able to reconstruct a total of 0.9 meters of dendrites and about 1.8 meters of axons investing only about 4,000 human work hours, about 10-25 times more efficient than previous dense circuit reconstructions. We find that connectomic data alone allows the definition of inhibitory axon types that show established principles of synaptic specificity for subcellular postsynaptic compartments. We find that also a fraction of excitatory axons exhibit such subcellular target specificity. Only about 35% of inhibitory and 55% of excitatory synaptic subcellular innervation can be predicted from the geometrical availability of membrane surface, revoking coarser models of random wiring for synaptic connections in cortical layer 4. We furthermore find evidence for enhanced variability of synaptic input composition between neurons at the level of primary dendrites in cortical layer 4. Finally, we obtain evidence for Hebbian synaptic weight adaptation in at least 24% of connections; at least 35% of connections show no sign of such previous plasticity. Together, these results establish an approach to connectomic phenotyping of local dense neuronal circuitry in the mammalian cortex.

neuroscience↗