Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.

Protein structure prediction continues to pose multiple challenges despite outstanding progress that is largely attributable to the use of novel machine learning techniques. One of the widely used representations of local 3D structure-protein blocks (PBs)-can be treated in a similar way to secondary structure classes. Here, we present a new approach for predicting local conformation in terms of PB classes solely from amino acid sequences. We apply the RMSD metric to ensure unambiguous future 3D protein structure recovery. The selection of statistically assessed features is a key component of the proposed method. We suggest that ML input features should be created from the statistically significant predictors that are derived from the amino acids' physicochemical properties and the resolved structures' statistics. The statistical significance of the suggested features was assessed using a stepwise regression analysis that permitted the evaluation of the contribution and statistical significance of each predictor. We used the set of 380 statistically significant predictors as a learning model for the regression neural network that was trained using the PISCES30 dataset. When using the same dataset and metrics for benchmarking, our method outperformed all other methods reported in the literature for the CB513 nonredundant dataset (for the PBs, Q16 = 81.01%, and for the DSSP, Q3 = 85.99% and Q8 = 79.35%).

Modelling interaction between HIV-1 Nef and calnexin.

BACKGROUND: HIV-associated atherosclerosis is a major comorbidity due, in part, to systemic effects of the virus on cholesterol metabolism. HIV protein Nef plays an important role in this pathology by impairing maturation of the main cellular cholesterol transporter ATP-Binding Cassette (ABCA) 1. ABCA1 maturation critically depends on calnexin, an integral endoplasmic reticulum membrane chaperone, and Nef binds to the cytoplasmic domain of calnexin and impairs interaction of calnexin with ABCA1. Overarching goal of the present study was to model Nef-calnexin interaction interface, and identify small molecule compounds potentially inhibiting this interaction. METHODS: Molecular dynamics was utilized to build structure model of calnexin cytoplasmic domain, followed by global docking combined with application of QASDOM software developed by us for efficient analysis of receptor-ligand complexes. Structure-based virtual screening was performed for all sites identified by docking. A soluble analogue of a compound from the screening results list was tested for ability to down-regulate ABCA1. RESULTS: We identified major interaction sites in calnexin and reciprocal sites in Nef. Virtual screening yielded a number of small-molecule compounds potentially blocking a calnexin site. Interestingly, one of the compounds, NSC13987, was previously identified by us as an inhibitor targeting a Nef site. An analogue of NSC13987, AMS-55, potently reversed the negative effect of Nef on ABCA1 abundance. CONCLUSIONS: We have modelled Nef-calnexin interaction, predicted small molecule compounds that can potentially inhibit this interaction, and experimentally tested one of these compounds, confirming its effectiveness. These findings provide a platform for searching for new therapeutic agents to treat HIV-associated comorbidities.

Structural attributes of nucleotide sequences in promoter regions of supercoiling-sensitive genes: how to relate microarray expression data with genomic sequences.

The level of supercoiling in the chromosome can affect gene expression. To clarify the basis of supercoiling sensitivity, we analyzed the structural features of nucleotide sequences in the vicinity of promoters for the genes with expression enhanced and decreased in response to loss of chromosomal supercoiling in Escherichia coli. Fourier analysis of promoter sequences for supercoiling-sensitive genes reveals the tendency in selection of sequences with helical periodicities close to 10nt for relaxation-induced genes and to 11nt for relaxation-repressed genes. The helical periodicities in the subsets of promoters recognized by RNA polymerase with different sigma factors were also studied. A special procedure was developed for the study of correlations between the intensities of periodicities in promoter sequences and the expression levels of corresponding genes. Significant correlations of expression with the AT content and with AT periodicities about 10, 11, and 50nt indicate their role in regulation of supercoiling-sensitive genes.