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The transition from primary to secondary protein structure involves the folding of linear amino acid sequences (primary structure) into regular patterns like alpha-helices and beta-sheets (secondary structure). Deep learning-based prediction algorithms leverage neural network architectures to infer these patterns from primary sequence data.

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Sovik-Ghosh/Protien-Primary-to-Secondary-using-FCNN

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PROTIEN STRUCTURE PREDICTION

Predicting Protein Secondary Structure using a Fully Convolutional Network

OBJECTIVE

The overall goal is to write a Fully Convolutional PyTorch model that can input protein sequence data (often called the Protein Primary Structure), or additionally using PSSM Profiles to predict the protein secondary structure (H = Helix, E = Extended Sheet, C = Coil symbols). The PDB Database contains the protein structures of over 200,000 proteins. Each has a unique PDB_ID code such as 1A0S (the first one in the training data) which is the structure shown above (sucrose-specific porin of salmonella) which is used to transfer sucrose across the cell membrane of salmonella bacteria which causes food poisoning. The protein has a 3D Structure which shows that most of this protein is extended beta sheet (flat arrows) and coil (random lines).

MEAN AND STANDARD DEVIATION

  • It specifies the parameters lr (learning rate) and epochs on the X and Y axes, respectively, and the accuracy metric to be plotted.

OPTIMIZATION TRACE

  • The optimization trace typically includes information about the iterations of an optimization process, such as parameter values tested and corresponding objective function values.

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The transition from primary to secondary protein structure involves the folding of linear amino acid sequences (primary structure) into regular patterns like alpha-helices and beta-sheets (secondary structure). Deep learning-based prediction algorithms leverage neural network architectures to infer these patterns from primary sequence data.

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