Search

The phenomenon of protein aggregation has been associated with several neurodegenerative diseases, such as Parkinson's and Alzheimer's. Computational tools have been used to predict regions prone to aggregate in proteins with relative success. We have developed a tool called MAGRE for such predictions, based on the machine learning and sliding window techniques. We have applied the Support Vector Machine algorithm to generate classification models. In order to accomplish classification training, we adopted information of primary structure - protein sequence - from the Amyloid Data Bank. We have implemented two predictor categories according to protein structural information: General and Folding Class. We have selected the best performances of the sliding windows method and considered the folding class in order to develop the predictor. We conducted testing with randomly selected protein sequences from the PDB data bank - MAGRE's performance was compared with two predictors from literature: Aggrescan and Zyggregator, being considered satisfactory.

We describe a new method and a software, called GANM (Genetic Algorithm with Normal Modes), that combines genetic algorithm and normal mode analysis in order to achieve protein–ligand docking simulations that take into account global and local conformational adjustments. The energetical and structural optimization during the ligand binding process is achieved with GA that uses a rotamer library for changing the side chain conformations within and around the binding site. The methodology uses normal modes to account for the global conformational changes of the protein. This software is implemented in distinct versions using different levels of approximations. In this article, we describe the methodology and discuss the results and the performance obtained with the first two versions of GANM. The protein–ligand complexes considered for the test calculations are: (1) Dihydrofolate Reductase (DHFR) and MTX as ligands, (2) HIV-1 Aspartyl Protease with ritonavir, nelfinavir and DMP323 as ligands, and (3) CDK-2 and DT-5 as ligands.