Biology subjects

Meirson, T.

Publications and source records attributed to Meirson, T..

4 recordsLinked to original sources

bioRxiv2023

FAK family proteins regulate in vivo breast cancer metastasis via distinct mechanisms.

Breast cancer is the most commonly diagnosed malignancy and the major leading cause of tumor-related deaths in women. It is estimated that the majority of breast tumor-related deaths are a consequence of metastasis, to which no cure exists at present. The FAK family proteins Proline-rich tyrosine kinase (PYK2) and focal adhesion kinase (FAK) are highly expressed in breast cancer, but the exact cellular and signaling mechanisms by which they regulate in vivo tumor cell invasiveness and consequent metastatic dissemination are mostly unknown. Using a PYK2 and FAK knockdown xenograft model we show here, for the first time, that ablation of either PYK2 or FAK decreases primary tumor size and significantly reduces Tumor MicroEnvironment of Metastasis (TMEM) doorway activation, leading to decreased intravasation and reduced spontaneous lung metastasis. Intravital imaging analysis further demonstrates that PYK2, but not FAK, regulates a motility phenotype switch between focal adhesion-mediated fast motility and invadopodia-dependent, ECM-degradation associated slow motility within the primary tumor. Furthermore, we validate our in vivo and intravital imaging results with integrated transcriptomic and proteomic data analysis from xenograft knockdown tumors and reveal new and distinct pathways by which these two homologous kinases regulate breast tumor cell invasiveness and consequent metastatic dissemination. Our findings identify PYK2 and FAK as novel mediators of mammary tumor progression and metastasis and as candidate therapeutic targets for breast cancer metastasis.

cancer biology↗

bioRxiv2023

Peptriever: A Bi-Encoder approach for large-scale protein-peptide binding search

MotivationIn peptide therapeutics, the successful interaction between a designed peptide and a specific receptor is crucial, while minimizing interactions with other receptors is equally essential. Current computational methods excel at estimating the probability of the former but estimating the latter requires excessive computational resources, making it challenging. ResultsIn this study, we propose transformers-based protein embeddings that can quickly identify and rank millions of interacting proteins. Furthermore, the proposed approach outperforms existing sequence- and structure-based methods, with a mean AUC-ROC and AUC-PR of 0.73. AvailabilityTraining data, scripts, and fine-tuned parameters are available at https://github.com/RoniGurvich/Peptriever. A live demonstration of the application can be found at https://peptriever.app/. Contacttomermrsn@gmail.com and zia.rehman@helsinki.fi

bioinformatics↗

bioRxiv2022

A novel Pyk2-derived peptide inhibits invadopodia-mediated breast cancer metastasis

The non-receptor tyrosine kinase Pyk2 is highly expressed in breast cancer, where it mediates invadopodia formation and function via interaction with the actin-nucleation promoting factor cortactin. Here, we designed a cell-permeable peptide inhibitor that contains the second proline-rich region (PRR2) sequence of Pyk2, which binds to the SH3 domain of cortactin and blocks spontaneous lung metastasis in immune-competent mice by inhibiting invadopodia maturation and function. The native structure of the Pyk2-PRR2:cortactin-SH3 complex was determined using nuclear magnetic resonance (NMR), revealing an extended class II interaction surface spanning the canonical binding groove and a second hydrophobic surface which significantly contributes to ligand affinity. Using structure-guided design, we created a mutant peptide lacking critical residues involved in binding that failed to inhibit invadopodia maturation and function and consequent metastatic dissemination in mice. Our findings shed light on the specific molecular interactions between Pyk2 and cortactin and suggest that their inhibition may be used as a novel strategy for blocking breast cancer metastasis.

cancer biology↗

bioRxiv2020

Structural basis of SARS-CoV-2 spike protein induced by ACE2

MotivationThe recent emergence of the novel SARS-coronavirus 2 (SARS-CoV-2) and its international spread pose a global health emergency. The viral spike (S) glycoprotein binds the receptor (angiotensin-converting enzyme 2) ACE2 and promotes SARS-CoV-2 entry into host cells. The trimeric S protein binds the receptor using the distal receptor-binding domain (RBD) causing conformational changes in S protein that allow priming by host cell proteases. Unravelling the dynamic structural features used by SARS-CoV-2 for entry might provide insights into viral transmission and reveal novel therapeutic targets. Using structures determined by X-ray crystallography and cryo-EM, we performed structural analysis and atomic comparisons of the different conformational states adopted by the SARS-CoV-2-RBD. ResultsHere, we determined the key structural components induced by the receptor and characterized their intramolecular interactions. We show that {kappa}-helix (also known as polyproline II) is a predominant structure in the binding interface and in facilitating the conversion to the active form of the S protein. We demonstrate a series of conversions between switch-like {kappa}-helix and {beta}-strand, and conformational variations in a set of short -helices which affect the proximal hinge region. This conformational changes lead to an alternating pattern in conserved disulfide bond configurations positioned at the hinge, indicating a possible disulfide exchange, an important allosteric switch implicated in viral entry of various viruses, including HIV and murine coronavirus. The structural information presented herein enables us to inspect and understand the important dynamic features of SARS-CoV-2-RBD and propose a novel potential therapeutic strategy to block viral entry. Overall, this study provides guidance for the design and optimization of structure-based intervention strategies that target SARS-CoV-2.

bioinformatics↗