Structure-based Drug Design

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Structure-Based Drug Design (SBDD) is an approach that involves using the 3D structure of a target protein or receptor to guide the design and optimization of new drug molecules. It is based on the detailed understanding of the molecular interactions between a molecule and its target at the atomic level. SBDD aims to design compounds that fit well into the active site of the target protein, leading to enhanced binding affinity, selectivity, and efficacy.

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Homology modeling is a computational method used to predict the 3D structure of a protein based on its similarity to a protein with a known structure.

It is an important tool in structural biology and drug discovery when experimental methods such as X-ray crystallography or NMR spectroscopy are not feasible or available.

The accuracy of homology models depends on the sequence similarity between the target protein and the template proteins.

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AlphaFold, developed by DeepMind, is a deep-learning approach to homology modelling based on a trained neural network. It combines multiple sources of information, including known protein structures and sequence data, to generate accurate structural predictions. AlphaFold has the potential to predict structures for a wide range of proteins, including those with limited or no homologs in the Protein Data Bank (PDB).  On the other hand, homology modelling relies on the principle of sequence similarity between a target protein and a template protein of known structure. It transfers the coordinates of atoms from the template protein to build a model of the target protein.

Both methods have their strengths and limitations, and their usage depends on the specific requirements and constraints of the protein being studied.

…that Atazanavir, Erlotinib and Trastuzumab are some examples of the many drugs that have been discovered with the help of homology modelling

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