PROTAC-Design-Evaluator (PRODE) -- An Advanced Method for in-silico PROTAC design
Geoffrey AS, B.; Agrawal, D.; Kulkarni, N.; Vetrivel, R.; Gurram, K.
Show abstract
PROTAC (proteolysis-targeting chimeras) is a rapidly evolving technology to target undruggable targets. The mechanism by which this happens is when a bifunctional molecule binds to a target protein and also brings in proximity an E3 ubiquitin ligase to trigger ubiquitination and degradation of the target protein. Yet in-silico driven approaches to design these hetero-bifunctional molecules that have the desired functional properties to induce proximity between the target protein and E3 ligase remains to be established. In this paper we present a novel in-silico method for PROTAC design and to demonstrate the validity of our approach. We show that for a BRD4-VHL PROTAC ternary complex known in the literature, we are able to reproduce the PROTAC binding mode, the structure of ternary complex formed therein and the free energy ({Delta}G) thermodynamics favoring ternary complexation through theoretical computational methodologies. Further, we demonstrate the use of Thermal Titration Molecule Dynamics (TTMD) to differentiate the stability of PROTAC mediated ternary complexes. We employ the proposed methodology to design a PROTAC for a new system of FGFR1-MDM2 to degrade the FGFR1 (Fibroblast growth factor receptor 1) which is overexpressed in cancer. Our work presented here and named as PROTAC-Designer-Evaluator (PRODE) contributes to the growing literature of in-silico approaches to PROTAC design and evaluation by incorporating the latest in-silico methods and demonstrates advancement over previously published PROTAC in-silico literature.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Investigating the role of N-terminal domain in phosphodiesterase 4B-inhibition by molecular dynamics simulation 97%
- A program to automate the discovery of drugs for West Nile and Dengue virus -- programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated In Silico modelling 97%
- An insight into SARS-CoV-2 Membrane protein interaction with Spike, Envelope, and Nucleocapsid proteins 97%
Similar papers in this journal
- The R346K Mutation in the Mu Variant of SARS-CoV-2 Alter the Interactions with Monoclonal Antibodies from Class 2: A Free Energy of Perturbation Study 96%
- DSSP in Gromacs: tool for defining secondary structures of proteins in trajectories 96%
- Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum 96%
Similar papers in this journal
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 97%
- Identification of Natural Antiviral Drug Candidates Against Tilapia Lake Virus: Computational Drug Design Approaches 96%
- Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8 96%
Similar papers in this journal
- In Silico Identification of Potential Inhibitors of Mycobacterium tuberculosis DNA Gyrase from Phytoconstituents of Indian Medicinal Plants 97%
- Combining Multi-Dimensional Molecular Fingerprints to Predict hERG Cardiotoxicity of Compounds 95%
- A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence 94%
Similar papers in this journal
- Effect of Delta and Omicron mutations on the RBD-SD1 do-main of the Spike protein in SARS-CoV-2 and the Omicron mutations on RBD-ACE2 interface complex 97%
- Possible link between higher transmissibility of B.1.617 and B.1.1.7 variants of SARS-CoV-2 and increased structural stability of its spike protein and hACE2 affinity 97%
- Protein-protein docking with large-scale backbone flexibility using coarse-grained Monte-Carlo simulations 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.