RamaPot

We devised RamaPot that leverages statistical potential to investigate proteins.

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Constructing a Ramachandran potential involves analyzing protein structures to map energetically favorable regions of backbone dihedral angles φ and ψ, typically through statistical analysis. Future advancements may integrate machine learning techniques for more precise predictions. This potential's significance in protein folding lies in its ability to predict stable conformations crucial for understanding protein structure-function relationships. Its future in protein folding holds promise in refining computational models, enabling accurate predictions of folding pathways and protein structures. As technology advances, Ramachandran potentials will likely play an increasingly central role in deciphering the complexities of protein folding and design.