Radaeva, M. | Search bioRxiv

bioRxiv2024

Targeting eIF4G1-dependent translation in melanoma

Expression of components of the translation initiation complex (eIF4F) is frequently elevated in cancer, resulting in enhanced synthesis of oncogenic proteins. We thus set out to limit eIF4F pro-oncogenic activity, a notable challenge given its essential role in normal tissues. CRISPR-Cas9-based functional screen using tiling sgRNAs identified the eIF4G1 MA3 domain, a subunit of the eIF4F complex, as a target for developing small molecule inhibitors. Combination of structure-guided in silico modeling and chemical library screening led to the identification of small molecule candidates M19 and its analog M19-6 that binds to the MA3 domain of eIF4G1 and disrupts eIF4F complex. M19-6 treatment reprograms the melanoma translatome, limiting synthesis of factors that promote cell proliferation and neoplastic growth, as well as reducing translation of mRNAs encoding ferroptosis suppressors. Whole genome CRISPR screen indentified ferroptosis activators to augment M19-6 activity, which was confirmed in cultured melanoma cells. M19-6 alleviates melanoma resistance to BRAF and MEK inhibitors, while eliciting anti-tumor and anti-metastatic effects in preclinical mouse models. Among several biomarkers found in M19-6 sensitive cell lines, THBS1 and TGF{beta}I expression were elevated in patients who are non-responders to PD-1 therapy as in patients with metastasis. Our studies identify M19-6 as a therapeutic candidate, offering a novel insights into targeting the eIF4F complex to overcome melanoma resistance to therapy and metastasis. SignificanceWe identify M19-6 as a small molecule that disrupts the eIF4F, translation initiation complex, by targeting the MA3 domain of eIF4G1, resulting in elimination of melanoma cells in culture, overcoming therapy resistance while inhibiting melanoma growth and metastasis in vivo. As M19-6 causes minimal toxicity to melanocytes, it offers a therapeutic modality for melanoma.

cancer biology↗

bioRxiv2022

Ligand Binding Prediction using Protein Structure Graphs and Residual Graph Attention Networks

MotivationComputational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph -Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. ResultsThe developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)- the hallmark target of SARS-CoV-2 coronavirus. AvailabilityThe code for PSG-BAR is made available at https://github.com/diamondspark/PSG-BAR Contactacherkasov@prostatecentre.com

bioinformatics↗