NAD
Description
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
Files
Steps to reproduce
Shape Identification of NADH Structures and Classification: ----------------------------------------------------------------- In this work, we have studied and analysed 345 PDB entries containing NADH as the co-crystallized complex with enzymes. To derive better insights, all the NADH structures extracted from 345 complexes from RCSB PDB were superimposed using the “Quick Align” feature in the Maestro workspace (Schrödinger Suite, Release 2022–1, Academic license). Upon closer observation of the various natural shapes of NADH, it was seen that out of 345, 259 of the 3D NADH conformations broadly fell into 6 shapes. The list of PDB Ids of these six groups have been provided in the Root/data/groups/ folder. Data and Programs: ---------------------- Data: ------ (1)The PDB Ids of the 6 groups have been placed under /Root/data/groups folder (6 files: group1.txt through group6.txt); These serve as input files for generating structure based statistics (2) A consolidated file containing structure-based statistics (an output from NAICofactor.py) which serves as an input for k-means-based classification (/Root/data/statistics/consolidated.xlsx) Programs: ----------- (1) Generation of structure-based statistics: (a) Configuration file: /Root/code/generator/NAIConfig.py (b) Structure-based statistics generator: /Root/code/generator/NAICofactor.py (2) Validation of the visual inspection based classification of NADH conformers through statistical approach (k-means): (a) Statistical validation of structure-based classification method: /Root/code/clustering/NAIClsSlt.py Running the Programs: -------------------------- NAICofactor.py: (1) Install Python and all dependencies and packages that are imported in both: NAIConfig.py and NAICofactor.py (2) Change the high-level path: path_higher (in NAIConfig.py) to point to the right directory (3) Place the list of RCSB PDB ids corresponding to a group in a directory and override the variable: pdblistfilepath (in NAIConfig.py) with the right location for this file. (4) Change group_ (NAIConfig.py) variable to appropriately point to the right group of complexes (5) Run the following command in Linux environment: nohup python -u NAICofactor.py> outputfile.out 2>&1 & NAIClsSlt.py: (1) Install Python, all dependencies and packages that are imported in: NAIClsSlt.py (2) Search for the phrase: ‘Please change this appropriately’ and change the variables or paths; the annotations for the variables and paths have been provided in the comments in the code (3) Update the variable path_input_consolidated_file to point to the location of the input file: consolidated.xlsx (4) Run the following command in Linux environment: nohup python -u NAIClsSlt.py > outputfilecls.out 2>&1 &
Institutions
- Indian Institute of Technology HyderabadTelangana, Hyderabad