Biology subjects

Sharafi, M.

Publications and source records attributed to Sharafi, M..

4 recordsLinked to original sources

bioRxiv2025

Functional Spectrum of USP7 Pathogenic Variants in Hao-Fountain Syndrome: Insights into the Enzyme's Activity, Stability, and Allosteric Modulation

Hao-Fountain syndrome is a rare neurodevelopmental disorder caused by mutations in the de-ubiquitinating enzyme USP7 (Ubiquitin Specific Protease 7). Due to the novelty of the disease and its poorly understood molecular mechanisms, treatments for the syndrome are currently lacking. This study examines the effects of 11 patient-derived variants located within the catalytic domain of USP7, focusing on their impact on the enzymes activity, thermodynamic stability, and substrate recognition. Our findings reveal a spectrum of functional consequences, ranging from complete inactivation to hyperactivation of USP7. Notably, we identify a specific subset of pathogenic variants whose catalytic activity can be significantly boosted using a novel allosteric activator. These results provide the first insight into USP7 malfunction in Hao-Fountain syndrome-linked variants and pave the way for improved prognostic approaches and targeted treatments in the future.

biochemistry↗

bioRxiv2025

Small-molecule allosteric activator of ubiquitin-specific protease 7 (USP7)

Ubiquitin-specific protease 7 (USP7) is a deubiquitylase essential for cell homeostasis, DNA repair, and regulation of both tumor suppressors and oncogenes. Inactivating USP7 mutations have been associated with Hao-Fountain Syndrome (HAFOUS), a rare neurodevelopmental disorder. Although a range of USP7 inhibitors have been developed over the last decade, in the context of HAFOUS as well as oncogene regulation, USP7 activators may represent a more relevant approach. To address this challenge, we report the discovery and characterization of a small-molecule activator of USP7 called MS-8. We showed that MS-8 activates USP7 by engaging the allosteric C-terminal binding pocket of USP7, thus mimicking the allosteric autoactivation by the USP7 C-terminal tail. We observed that MS-8 engages and activates mutant USP7 in a cellular context, impacting downstream proteins. Taken together, our study provides validation of the USP7 activator that paves the way towards novel activation-driven USP7 pharmacology.

biochemistry↗

bioRxiv2022

Machine learning and hypothesis driven optimization of bull semen cryopreservation media

Cryopreservation provides a critical tool for dairy herd genetics management. Due to widely varying inter- and within-bull post thaw fertility, recent research on cryoprotectant extender medium has not dramatically improved suboptimal post-thaw recovery in industry. This progress is stymied by the interactions between samples and the many components of extender media and is often compounded by industry irrelevant sample sizes. To address these challenges, here we demonstrate blank-slate optimization of bull sperm cryopreservation media by supervised machine learning. We considered two supervised learning models: artificial neural networks and Gaussian process regression (GPR). Eleven media components and initial concentrations were identified from publications in bull semen cryopreservation, and an initial 200 extender-post-thaw motility pairs were used to train and 32 extender-post-thaw motility pairs to test the machine learning algorithms. The median post-thaw motility after coupling differential evolution with GPR the increased from 52.6 {+/-} 6.9% to 68.3 {+/-} 6.0% at generations 7 and 17 respectively, with several media performing dramatically better than control media counterparts. This is the first study in which machine learning was used to determine the best combination of constituents to optimize bull sperm cryopreservation media, and provides a template for optimization in other cell types.

bioengineering↗

bioRxiv2020

A Generative Approach toward Precision Antimicrobial Peptide Design

As the emergence of bacterial resistance is outpacing the development of new antibiotics, we must find cost-effective and innovative approaches to discover new antibacterial therapeutics. Antimicrobial peptides (AMPs) represent one promising solution to fill this void, since they generally undergo faster development, display rapid onsets of killing, and most importantly, show lower risks of induced resistance. Despite prior success in AMP design with physics- and/or knowledge-based approaches, an efficient approach to precisely design peptides with high activity and selectivity is still lacking. Toward this goal, we have invented a novel approach which utilizes a generative model to predict AMP-like sequences, followed by molecular modeling to rank the candidates. Thus, we can identify peptides with desirable sequences, structures, and potential specific interactions with bacterial membranes. For the proof of concept, we curated a dataset that comprises 500,000 non-AMP peptide sequences and nearly 8,000 labeled AMP sequences to train the generative model. For 12 generated peptides that are cationic and likely helical, we assessed the membrane binding propensity via extensive all-atom molecular simulations. The top six peptides were promoted for synthesis, chemical characterizations, and antibacterial assays, showing various inhibition to bacterial growth. Three peptides were validated with broad-spectrum antibacterial activity. In aggregate, the combination of AMP generator and sophisticated molecular modeling affords enhanced speed and accuracy in AMP design. Our approach and results demonstrate the viability of a generative approach to develop novel AMPs and to help contain the rise of antibiotic resistant microbes.

biophysics↗