CONICET | Buscador de Institutos y Recursos Humanos

p { margin-bottom: 0.25cm; direction: ltr; color: rgb(0, 0, 10); line-height: 120%; text-align: left; widows: 2; orphans: 2; }p.western { font-family: "Times New Roman",serif; font-size: 12pt; }p.cjk { font-family: "宋体"; font-size: 12pt; }p.ctl { font-family: "Times New Roman"; font-size: 12pt; } Computational techniques for modeling biomolecules have emerged during the last decades as an important tool to complement experimental information. In this context, the investigation of phenomena which do not involve formation or breaking of chemical bonds may be achieved by employing classical force fields. However, dealing with reactive processes requires quantum mechanical based techniques and in complex environments, such as biomolecules, is an extremely demanding task. An elegant way to tackle this issue consists in employing multi level quantum-classical schemes (QM -MM). We will present in this talk an overview of our group QM-MM implementation, which employs a Gaussian basis set Density Functional Theory scheme, coupled to the Amber program [1] as well as applications to some representative examples: i) Molecular basis of peroxiredoxin action. This extremelly relevant protein family detoxifies peroxides by a very efficient thiol oxidation reaction. We will show results for the reaction mechanism in a peroxiredoxin from M. Tuberculosis (AhPE), compared to results for the uncatalyzed reaction in aqueous solution. [2,3] ii) Protein nitration is a process that may modulate protein function in oxidative stress conditions. We will show results for the reaction mechanism in human Mn superoxide dismutase, a case in which nitration practically abolishes protein function. [4] References: [1] M.A. Nitsche et al, J. Chem. Theory and Comput. 10, 959 (2014). [2] A. Zeida et al, Chem. Commun. 50, 10070 (2014). [3] A. Zeida et al, Chem. Res. Toxicol. 25, 741 (2012). [4] D.M. Moreno et al, Arch. Biochem. Biophys., 507, 304 (2011).