Abstract
Autodock Vina is a very popular, and highly cited, open source docking program. Here we present a scoring function which we call Vinardo (Vina RaDii Optimized). Vinardo is based on Vina, and was trained through a novel approach, on state of the art datasets. We show that the traditional approach to train empirical scoring functions, using linear regression to optimize the correlation of predicted and experimental binding affinities, does not result in a function with optimal docking capabilities. On the other hand, a combination of scoring, minimization, and re-docking on carefully curated training datasets allowed us to develop a simplified scoring function with optimum docking performance. This article provides an overview of the development of the Vinardo scoring function, highlights its differences with Vina, and compares the performance of the two scoring functions in scoring, docking and virtual screening applications. Vinardo outperforms Vina in all tests performed, for all datasets analyzed. The Vinardo scoring function is available as an option within Smina, a fork of Vina, which is freely available under the GNU Public License v2.0 from http://smina.sf.net. Precompiled binaries, source code, documentation and a tutorial for using Smina to run the Vinardo scoring function are available at the same address.
Highlights
Protein-ligand docking is a computational method which attempts to predict the most probable position, orientation and conformation with which a ligand can bind to a protein
The binding free energy of a ligand to a protein can be predicted in different ways, and docking programs can be classified into one of the following three categories. 1- Force-field based 2- Empirical scoring functions 3- Knowledge-based potentials [1]
Re-docking runs are needed because low RMSD values after minimization ensure a local minimum close to the crystalline structure, but only docking can assess if this local minimum corresponds to a global minimum
Summary
Protein-ligand docking is a computational method which attempts to predict the most probable position, orientation and conformation with which a ligand (often a small organic molecule) can bind to a protein. The binding free energy of a ligand to a protein can be predicted in different ways, and docking programs can be classified into one of the following three categories. Different programs, using all three strategies, have been successfully used in many different drug discovery projects [2]. Vina is a different program and uses a different scoring function and global optimization algorithm. It is two orders of magnitude faster [3,6], and has shown similar or improved accuracy [3,6].
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