NAD

Background: -------------- Nicotinamide adenine dinucleotide (NADH) is a ubiquitous redox cofactor that participates in a wide range of enzymatic and regulatory processes. These include metabolism, signalling, and diseases such as cancer and neurodegeneration. Our work presents a comprehensive computational and structural analysis of NADH recognition across protein families using 345 NADH-bound crystal structures from the Protein Data Bank. Our study establishes a unified biophysical framework that links NADH shape, interaction signatures, and protein context, providing rational insights for cofactor engineering and the design of NADH-targeted inhibitors. Content Description: ------------------------ This repository contains: (1) Code repository and (2) Input datasets The code repository contains: (a) Configuration file: /Root/code/generator/NAIConfig.py (b) Code to generate structure-based statistics for NADH complexes: /Root/code/generator/NAICofactor.py: This code generates statistics derived from structures of NAI (a three-letter identifier for Nicotinamide Adenine Dinucleotide in the Protein Data Bank) and its interactions with proteins corresponding to six groups of protein-NAI interaction complexes having six distinct shapes (c) Code for classification of NADH interaction complexes through statistical approach (k-means): /Root/code/clustering/NAIClsSlt.py Input datasets: (1) /Root/data/groups/: Lists of RCSB PDB Ids for six different groups of NADH interaction complexes, corresponding to six distinct shapes of the 3-dimensional structures of the NADH conformers (2) /Root/data/statistics/consolidated.xlsx: A consolidated file containing structure-based statistics for NAI conformers (generated through NAICofactor.py) Other Data Used in the Computation: ----------------------------------------- • RCSB PDB Coordinate files: Retrieved from RCSB PDB (https://www.rcsb.org/) • Protein-Ligand Interaction Profile (PLIP) reports (retrieved programmatically using PLIP utility (https://github.com/pharmai/plip)) Tools and Databases/Resources Used: ------------------------------------------ (1) PLIP: Salentin, S., Schreiber, S., Haupt, V. J., Adasme, M. F., & Schroeder, M. (2015). PLIP: Fully automated protein–ligand interaction profiler. Nucleic Acids Research, 43(Web Server issue), W443–W447. https://doi.org/10.1093/nar/gkv315 (2) Open Babel: O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3, 33. https://doi.org/10.1186/1758-2946-3-33 (3) Biopython: Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., & de Hoon, M. J. L. (2009). Biopython: Freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics, 25(11), 1422–1423. https://doi.org/10.1093/bioinformatics/btp163 (4) Standard Python packages