Narayanan, M. — Page 2 | bioRxiv Science

bioRxiv2022

In-silico identification of Tyr232 in AMPKα2 as a dephosphorylation site for the protein tyrosine phosphatase PTP-PEST

The AMP-activated protein kinase (AMPK) is known to be activated by the protein tyrosine phosphatase non-receptor type 12 (PTP-PEST) under hypoxic conditions. This activation is mediated by tyrosine dephosphorylation of the AMPK subunit. However, the identity of the phosphotyrosine residues remains unknown. In this study we first predicted the structure of the complex of the AMPK2 subunit and PTP-PEST catalytic domain using bioinformatics tools and further confirm the stability of the complex using molecular dynamics simulations. Evaluation of the protein-protein interfaces indicates that residue Tyr232 is the most likely site of dephosphorylation on AMPK2. In addition, we explored the effect of phosphorylation of PTP-PEST residue Tyr64 on the stability of the complex. The phosphorylation of Tyr64, an interface residue, enhances the stability of the complex via the rearrangement of a network of electrostatic interactions in conjunction with conformational changes in the catalytic WPD loop. Our findings present a plausible structural basis of AMPK regulation mediated by PTP-PEST and shows how phosphorylation of PTP-PEST could be involved in its activation.

biochemistry↗

bioRxiv2021

Predicting cross-tissue hormone-gene relations usingbalanced word embeddings

MotivationLarge volumes of biomedical literature present an opportunity to build whole-body human models comprising both within-tissue and across-tissue interactions among genes. Current studies have mostly focused on identifying within-tissue or tissue-agnostic associations, with a heavy emphasis on associations among disease, genes and drugs. Literature mining studies that extract relations pertaining to inter-tissue communication, such as between genes and hormones, are solely missing. ResultsWe present here a first study to identify from literature the genes involved in inter-tissue signaling via a hormone in the human body. Our models BioEmbedS and BioEmbedS-TS respectively predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormones production or response. Our models are classifiers trained on word embeddings that we had carefully balanced across different strata of the training data such as across production vs. response genes of a hormone (or) well-studied vs. poorly-represented hormones in the literature. Model training and evaluation are enabled by a unified dataset called HGv1 of ground-truth associations between genes and known endocrine hormones that we had compiled. Our models not only recapitulate known gene mediators of tissue-tissue signaling (e.g., at average 70.4% accuracy for BioEmbedS), but also predicts novel genes involved in inter-tissue communication in humans. Furthermore, the species-agnostic nature of our ground-truth HGv1 data and our predictive modeling approach, demonstrated concretely using human data and generalized to mouse, hold much promise for future work on elucidating inter-tissue signaling in other multi-cellular organisms. AvailabilityProposed HGv1 dataset along with our models predictions, and the associated code to reproduce this work are available respectively at https://cross-tissue-signaling.herokuapp.com/, and https://github.com/BIRDSgroup/BioEmbedS. Contactnmanik@cse.iitm.ac.in

bioinformatics↗