Biology subjects

Pate, S. C.

Publications and source records attributed to Pate, S. C..

4 recordsLinked to original sources

bioRxiv2026

Mechanism-informed rules tunably balance novelty and feasibility of predicted enzymatic reactions

Enzymes catalyze reactions with remarkable specificity and can unlock recalcitrant feedstocks that are dilute, complex, and variable in their constituent molecules. While characterized enzymatic reactions cover a wide range of chemistries, there are an undetermined number of cryptic activities for every known one. These cryptic activities can be elicited through rational design, adaptive laboratory evolution, and increasingly, generative models of proteins. However, prior to tuning a catalyst one must efficiently predict viable novel reactions. In this work we leverage the growing amount of mechanistic enzyme information, specifically the Mechanism and Catalytic Site Atlas, to construct a set of reaction rules that can meet this demand. By explicitly utilizing mechanistic information, the rule sets developed here more accurately identify molecular structures required for catalysis compared to existing curated and heuristically constructed rules. The 899 Distilled rules are constructed directly from characterized mechanisms and cover 62.5% of reactions from Rhea. The Learned rule set is generated from a classifier trained on mechanistic data, allowing full coverage of Rhea and precise identification of mechanism-required atoms (ROC-AUC = 0.98). Additionally, our Learned rules exhibit a more favorable tradeoff between novelty and feasibility and provide users with fine-grained control over this tradeoff. The rules are compatible with all SMARTS-based reaction network expansion and retrosynthesis software.

synthetic biology↗

bioRxiv2025

RC-GNN: A predictive model of enzyme-reaction pairs

Uncharacterized functions of enzymes represent untapped opportunity to develop therapeutics, unlock the sustainable synthesis of materials, and understand the evolution of life-sustaining metabolic networks. Uncharacterized enzymes and reactions, generated by protein language models and computer-aided synthesis tools, respectively, make up a large part of this opportunity. Given the technical complexity of high-throughput enzymatic activity screens, predictive models are needed that can pre-screen enzyme-reaction pairs in silico. We present Reaction-Center Graph Neural Network, (RC-GNN) a model capable of predicting whether an enzyme, represented by an amino acid sequence, can significantly catalyze a given reaction, represented by its full set of reactants and products. We explicitly evaluated RC-GNNs generalization to queries highly dissimilar from those present in the training dataset. In the most difficult conditions tested, our models achieve 0.88 and 0.84 ROC-AUC on classification tasks featuring globally selected and synthetic negatives, respectively. On a time-based split an RC-GNN achieved 0.91 ROC-AUC. The ability to successfully make predictions on enzymes and reactions distinct from those used during training makes RC-GNN especially useful for both metabolic engineers and evolutionary biologists who need to reason about uncharacterized enzymatic reactions.

bioinformatics↗

bioRxiv2021

Neuromodulators enable overlapping synaptic memory regimes and nonlinear transition dynamics in recurrent neural networks

Neuromodulators are critical controllers of neural states, with dysfunctions linked to various neuropsychiatric disorders. Although many biological aspects of neuromodulation have been studied, the computational principles underlying how neuromodulation of distributed neural populations controls brain states remain unclear. Compared with specific contextual inputs, neuromodulation is a single scalar signal that is broadcast broadly to many neurons. We model the modulation of synaptic weight in a recurrent neural network model and show that neuromodulators can dramatically alter the function of a network, even when highly simplified. We find that under structural constraints like those in brains, this provides a fundamental mechanism that can increase the computational capability and flexibility of a neural network. Diffuse synaptic weight modulation enables storage of multiple memories using a common set of synapses that are able to generate diverse, even diametrically opposed, behaviors. Our findings help explain how neuromodulators "unlock" specific behaviors by creating task-specific hyperchannels in the space of neural activities and motivate more flexible, compact and capable machine learning architectures. SignificanceNeuromodulation through the release of molecules like serotonin and dopamine provides a control mechanism that allows brains to shift into distinct behavioral modes. We use an artificial neural network model to show how the action of neuromodulatory molecules acting as a broadcast signal on synaptic connections enables flexible and smooth behavioral shifting. We find that individual networks exhibit idiosyncratic sensitivities to neuromodulation under identical training conditions, highlighting a principle underlying behavioral variability. Network sensitivity is tied to the geometry of network activity dynamics, which provides an explanation for why different types of neuromodulation (molecular vs direct current modulation) have different behavioral effects. Our work suggests experiments to test biological hypotheses and paths forward in the development of flexible artificial intelligence systems.

neuroscience↗

bioRxiv2020

Divergent pallidal pathways underlying distinct Parkinsonian behavioral deficits

The basal ganglia are a group of subcortical nuclei that regulates motor and cognitive functions1,2. Recent identification of neuronal heterogeneity in the basal ganglia suggests that functionally distinct neural circuits defined by their efferent projections exist even within the same nuclei3-5. This distinction may account for a multitude of symptoms associated with basal ganglia disorders such as Parkinsons disease (PD)6,7. However, our incomplete understanding of the basal ganglia functional organization has hindered further investigation of individual circuits that may underlie different behavioral symptoms in disease states. Here we functionally define two distinct classes of parvalbumin-expressing neurons in the mouse external globus pallidus (GPe-PV) embedded within discrete neural pathways and establish their contributions to different Parkinsonian behavioral deficits. We find that GPe-PV neurons projecting to the substantia nigra pars reticulata (SNr) or parafascicular thalamus (PF) undergo different electrophysiological adaptations in response to dopamine depletion. Furthermore, counteracting these adaptations in each population can selectively alleviate movement deficits or behavioral inflexibility in a Parkinsonian mouse model. Our findings provide a novel framework to understand the circuit basis of separate behavioral symptoms in Parkinsonian state which could provide better strategies for the treatment of PD.

neuroscience↗