Showing posts from March, 2023

Converting image to 3D molecule using VEGA ZZ OSRA

VEGA ZZ is a free (for non-profit academic uses) molecular modelling suite. It consists of many third-party packages, which act as an interface to the VEGA ZZ software. OSRA (Optical Structure Recognition Application) is a free and open-source optical graph recognition program. The stand-alone version of OSRA is a command-line. OSRA converts a graphical representation of chemical structures from images to SMILES (Simplified Molecular Input Line Entry Specification) format. In addition, the online OSRA tool converts the SMILES string to a 3D molecule in SDF (Standard Data File) file format. OSRA can recognize over 90 graphical format documents by parsing vectors through ImageMagick software. The standard file formats include BMP, GIF, ICO, JPEG, PNG, TIFF, WMF, PDF, PS, etc. VEGA ZZ consists of an OSRA plug-in that acts as an interface to it. Moreover, it supports imaging devices such as cameras and image scanners for acquiring documents through the TWAIN interface. The VEG

Saving AutoDock Vina Docking Result to PDB using Chimera

AutoDock is a free molecular modeling simulation suite mainly used for molecular docking. It consists of two programs, namely AutoGrid and AutoDock. The three variants of AutoDock are AutoDock 4.2.6 , AutoDock Vina 1.2.3 (a successor of AutoDock), and AutoDock GPU . AutoDock or Vina use PDBQT (Protein Data Bank (PDB), Partial Charge (Q), and Atom Type (T)) molecular structure file format for input and output. But, most molecular analysis and visualization tools accept the PDB file format. The video tutorial for converting AutoDock or Vina results in a complex PDBQT file format to PDB file format is below.

RNA Secondary Structure Prediction using Nussinov Algorithm

The Nussinov algorithm is an RNA secondary structure (folding) prediction method using a dynamic programming approach. Ruth Nussinov introduced this algorithm in the year 1978. It involves computing a two-dimensional (2D) diagonal matrix with the same sequence at both dimensions. The scores are given based on complementary ( 1 ) or non-complementary ( 0 ) matches of characters. Matrix solving consists of three stages ( i ) initialization , ( ii ) matrix-filling , and ( iii ) trace-back of arrows for structures. \(\style{ color: blue; } {\begin{array} \\ \text{RNA sequence, } S=a_1a_2a_3....a_{l-1}a_l \\ \begin{align*} \\ \!\!\!\!\! \text{where,} \\ & a=\text{characters (A, U, C, G)} \\ & l=\text{length of the sequence} \\ \end{align*} \end{array}} \) In this tutorial, I have taken a sample RNA sequence ( S ) as GGGAAAUCC for prediction. Initialization The initialization step is to preset the diagonal cells with zero ( 0 ) values to perform matrix filling. \(