Protein Structure Prediction Tools and Computational Approaches
DOI:
https://doi.org/10.63995/MWCU4408Keywords:
Ab initio modelling; AlphaFold; Bioinformatics; Homology modelling; Machine learning; Molecular dynamics simulations; Protein structure predictionAbstract
Protein structure prediction is a critical aspect of bioinformatics, aimed at determining the three-dimensional configuration of proteins from their amino acid sequences. With the advent of sophisticated computational approaches, this field has seen significant advancements. Methods like homology modeling, which relies on the similarity between the target protein and known structures, and ab initio modeling, which predicts structures from scratch, have become fundamental tools. Additionally, molecular dynamics simulations and machine learning techniques, such as AlphaFold, have revolutionized the accuracy and speed of predictions. These tools not only enhance our understanding of protein functions and interactions but also facilitate drug discovery and development. The integration of these computational approaches with experimental data is paving the way for more precise and reliable protein structure predictions, ultimately contributing to advancements in various scientific and medical fields.
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