Accelerating Drug Discovery Using GenFold-AI for Protein Structure Prediction in Genetic Disease Research

Accelerating drug development depends on accurate prediction of protein structure, especially in relation to genetic illness studies. Although AlphaFold2 has greatly improved wild-type protein modelling, structural change prediction from genetic mutations still presents a difficulty. To introduce proposed technique named as GenFold-AI, an advanced sophisticated framework enhancing mutant protein structure predictions by combining AlphaFold2 with Molecular Dynamics (MD) simulations. MD simulations improve the stability evaluation of mutant proteins by including dynamic conformational changes and atomic-level interactions. Additionally investigate, using a yeast-based complementing sensor, the effects of insertions and deletions (indels) on protein folds. Experimental validation shows that GenFold-AI beats AlphaFold2 in forecasting structural changes caused by mutations, hence producing more consistent therapeutic target identification. By enhancing the accuracy of disease-associated mutation modelling and refining protein function analysis, this method speeds up drug development and thereby advances treatments for genetic conditions.

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