Rational Evolution by Computational Methods Applied in Enzymes Related to Bioethanol Production

Abstract

One of the most difficult challenges in science is to find clean and cheap energy sources. The second generation bioethanol seems to be viable a solution to substitute fossil fuel usage. The bioethanol production makes use of sugarcane biomass residues not used directly in the fermentation process. This process involves the degradation of cellulose by specific enzymatic hydrolysis. The technological challenge now is to obtain and optimize the enzymes to perform the hydrolysis process with more efficiency. The motivation of this project is to work computationally on enzymes thermostability optimization, suggesting mutations to be tested experimentally. This ongoing project seeks to evaluate and optimize enzyme cocktails for second generation bioethanol production. The focus of the optimization is to increase the catalytic activity, the thermostability and pH control, adapting the enzymes to have the optimum activity at the reactor operation conditions. A theoretical method based on optimization of charge-charge interaction in enzyme surface have been used to suggest the mutations. It is under development an approach using bioinformatics tools to suggest mutations based on ancestral sequence reconstruction and consensus-based sequence. It was observed in preliminary data that polar charged residues which are not conserved in the multiple sequence alignments are also pointed as a candidate to be mutated based on the protein charge-charge interaction optimization method. It was also observed that mutations involving polar charged residues happen in pairs. The investigation of the compensatory mutations analyzing the structural information of the pair contacts is a fundamental step to improve the rational mutation methods. The Direct-Coupling Analysis (DCA) and the Structure-Based Models (SBM) developed by Prof. Dr. José Onuchic group ( Rice University - Houston/TX-USA) will be used to improve the accuracy of the suggested mutation. The DCA method is based on a statistical inference framework used to infer direct co-evolutionary couplings among residues pairs in multiple sequence alignments and will be applied to investigate the compensatory mutation. The SBM will be used to capture the essential protein dynamic in the folding process and in the complex formation in the investigation of the enzyme stability and its interaction with other enzymes in reactor cocktail. The suggested mutations will be experimentally validated by the Brazilian Bioethanol Science and Technology Laboratory (CTBE) group.