Bielecki, M. | Search bioRxiv

bioRxiv2025

Proteomic Ligandability Maps of Phosphorus(V) Stereoprobes Iden-tify Covalent TLCD1 Inhibitors

Activity-based protein profiling (ABPP) of stereoisomerically defined sets of electrophilic compounds ( stereoprobes) offers a versatile way to discover covalent ligands for proteins in native biological systems. Here we report the synthesis and chemical proteomic characterization of stereoprobes bearing a P(V)-oxathiaphospholane (OTP) reactive group. ABPP experiments identified numerous proteins in human cancer cells that showed stereoselective reactivity with OTP stereoprobes, and we confirmed several of these liganding events with recombinant proteins. OTP stereoprobes engaging the poorly characterized transmembrane protein TLCD1 impaired the incorporation of monounsaturated fatty acids into phosphatidylethanolamine lipids in cells, a lipidomic phenotype that mirrored genetic disruption of this protein. Using AlphaFold2, we found that TLCD1 structurally resembles the ceramide synthase and fatty acid elongase families of coenzyme A-dependent lipid processing enzymes. This structural similarity included conservation of catalytic histidine residues, the mutation of which blocked the OTP stereoprobe reactivity and lipid remodeling activity of recombinant TLCD1. Taken together, these data indicate that TLCD1 acts as a lipid acyltransferase in cells, and that OTP stereoprobes function as inhibitors of this enzymatic activity. Our findings thus illuminate how the chemical proteomic analysis of electrophilic compounds can facilitate the functional annotation and chemical inhibition of a key lipid metabolic enzyme in human cells.

biochemistry↗

bioRxiv2020

Recurrent Neural Network-based Acute Concussion Classifier using Raw Resting State EEG Data

Concussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and recovering individuals are more prone to suffer additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, concussion management faces two significant challenges: there are no objective, clinically accepted, brain-based approaches for determining (i) whether an athlete has suffered a concussion, and (ii) when the athlete has recovered. Diagnosis is based on clinical testing and self-reporting of symptoms and their severity. Self-reporting is highly subjective and symptoms only indirectly reflect the underlying brain injury. Here, we introduce a deep learning Long Short Term Memory (LSTM)-based recurrent neural network that is able to distinguish between healthy and acute post-concussed adolescent athletes using only a short (i.e. 90 seconds long) sample of resting state EEG data as input. The athletes were neither required to perform a specific task nor subjected to a stimulus during data collection, and the acquired EEG data was neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and tested on data from 27 male, adolescent athletes with sports related concussion, bench marked against 35 healthy, adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of >90% and its ensemble-median Area Under the Curve (AUC) corresponds to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state EEG data. It represents a key step towards the development of an easy-to-use, brain-based, automatic classification of concussion at an individual level.

neuroscience↗