Hot-Spots Detection - Application to a Variety of Different Protein-Based Systems

Abstract

Protein-based complexes are the basis of many key systems in nature and have been the subject of intense research in the last decades [1]. Computational Alanine Scanning Mutagenesis approaches have been extensively used in the determination of the most important residues for complex formation, the Hot-spots [1-3]. However, this approach is usually applied to the study of protein-protein interfaces. In our work we focused on study of protein-DNA interfaces, which are also crucial in nature, but have not been the subject of as much research. We carry out MD simulations of several protein-DNA complexes and tested the influence of the variation of different parameters on the determination of the binding free energy terms of 78 mutations: solvent representation, internal dielectric constant, Linear and Nonlinear Poisson-Boltzmann equation, simulation time, number of structures analyzed and energetic terms involved [4]. In a different perspective, we have also explored the machine-learning approaches as these are faster while maintaining accuracy. We tested a group of twelve SASA-based features for their ability to correlate and differentiate hot-spots. The features tested were shown to be capable of discerning between hot- and null-spots, while presenting low correlations. A new method using support machine learning algorithms was developed: SBHD (Sasa-Based Hot-spot Detection) [5]. With these advances in the field druggable sites can be easier found in a variety of protein-based systems.[1] Moreira IS, Fernandes PA, Ramos MJ, Proteins, 2007, 68: 803-812.[2] Moreira IS, Fernandes PA, Ramos MJ, J. Comput. Chem., 2007, 28:644-654.[3] Pimenta AC, Martins JM, Fernandes R, Moreira IS, 2013, J. Chem. Info. Model- Online.[4] Ramos RM, Moreira IS, 2013, J. Chem. Theor. Comput., 9, 4243-4256.[5] Martins JM, Ramos RM, Pimenta AC, Moreira IS, 2013, Proteins- Online.