Protein misfolding in the late-onset neurodegenerative diseases: Common themes and the unique case of amyotrophic lateral sclerosis
Enormous strides have been made in the last hundred years to extend human life expectancy and to combat the major infectious diseases. Today, the major challenges for medical science are age-related diseases, including cancer, heart disease, lung disease, renal disease, and late-onset neurodegenerative disease. Of these, only the neurodegenerative diseases represent a class of disease so poorly understood that no general strategies for prevention or treatment exist. These diseases, which include Alzheimer's disease, Parkinson's disease, Huntington's disease, the transmissible spongiform encephalopathies, and amyotrophic lateral sclerosis (ALS), are generally fatal and incurable. The first section of this review summarizes the diversity and common features of the late-onset neurodegenerative diseases, with a particular focus on protein misfolding and aggregation – a recurring theme in the molecular pathology. The second section focuses on the particular case of ALS, a late-onset neurodegenerative disease characterized by the death of central nervous system motor neurons, leading to paralysis and patient death. Of the 10% of ALS cases that show familial inheritance (familial ALS), the largest subset is caused by mutations in the SOD1gene, encoding the Cu, Zn superoxide dismutase (SOD1). The unusual kinetic stability of SOD1 has provided a unique opportunity for detailed structural characterization of conformational states potentially involved in SOD1-associated ALS. This review discusses past studies exploring the stability, folding, and misfolding behaviour of SOD1, as well as the therapeutic possibilities of using detailed knowledge of misfolding pathways to target the molecular mechanisms underlying ALS and other neurodegenerative diseases. Proteins 2013. © 2013 Wiley Periodicals, Inc.
Catalytic Asymmetric Diaziridination
This work introduces the support vector rank regression (SVRR) algorithm for the optimization of molecular docking scores. Seven original docking scores reported by two docking software were integrated by the SVRR algorithm. The resulting SVRR scores showed an average of 12.1% improvement (59.5% to 66.7%) in binding conformation prediction tests to rank the correctly computed conformation in the first place, along with 16.7% RMSD improvement (2.5414 Å vs. 2.1162 Å) for the top ranked conformations. In compound library screening tests, an average of 46.3% improvement (18.2% to 26.6%) was also observed to rank the correct ligand in the first place. Furthermore, it was shown that SVRR scores trained with different example datasets, using different training strategies, all exhibited exceedingly consistent accuracies, suggesting that the SVRR algorithm is highly robust and generalizable. In contrast, using the same training datasets, traditional support vector classification and regression algorithms failed to comparably improve the accuracy of library screening and conformation prediction. These results suggested that, with additional features to indicate the comparative fitness between computed binding conformations, the SVRR algorithm holds the potential to create a new category of more accurate integrative docking scores. Proteins 2013. © 2013 Wiley Periodicals, Inc.
Inositol phosphates compete with nucleic acids for binding to bovine leukemia virus matrix protein: Implications for deltaretroviral assembly
The matrix (MA) domain of retroviral Gag proteins plays a crucial role in virion assembly. In human immunodeficiency virus type 1 (HIV-1), a lentivirus, the presence of phosphatidylinositol-(4,5)-bisphosphate (PIP2) triggers a conformational change allowing the MA domain to bind the plasma membrane. In this study, the MA protein from bovine leukemia virus was used to investigate the mechanism of viral Gag binding to the membrane during replication of a deltaretrovirus. Fluorescence spectroscopy was used to measure the binding affinity of MA for two RNA constructs derived from the BLV genome as well as for single-stranded DNA. The importance of electrostatic interactions and the ability of inositol hexakisphosphate (IP6) to compete with nucleic acids for binding to MA were also investigated. Our data show that IP6 effectively competes with RNA and DNA for BLV MA binding, while [NaCl] of greater than 100 mM is required to produce any observable effect on DNA-MA binding. These results suggest that BLV assembly may be highly dependent upon the specific interaction of the MA domain with components of the plasma membrane, as observed previously with HIV-1. The mode of MA binding to nucleic acids and the implications for BLV assembly are discussed. Proteins 2013. © 2013 Wiley Periodicals, Inc.
Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences
Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this paper, we investigated the problem of identifying hot spots by using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method ignificantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx. Proteins 2013. © 2013 Wiley Periodicals, Inc.