Computational methods for the prediction of the structure and interactions of coiled-coil peptides

The past several years have seen significant advances in the development of computational methods for the prediction of the structure and interactions of coiled-coil peptides. These methods are generally based on pairwise correlations of amino acids, helical propensity, thermal melts and the energetics of sidechain interactions, as well as statistical patterns based on Hidden Markov Model (HMM) and Support Vector Machine (SVM) techniques. These methods are complemented by a number of public databases that contain sequences, motifs, domains and other details of coiled-coil structures identified by various algorithms. Some of these computational methods have been developed to make predictions of coiled-coil structure on the basis of sequence information; however, structural predictions of the oligomerisation state of these peptides still remains largely an open question due to the dynamic behaviour of these molecules. This review focuses on existing in silico methods for the prediction of coiled-coil peptides of functional importance using sequence and/or three-dimensional structural data.

Prediction of heparin binding sites in bone morphogenetic proteins (BMPs)

Heparin is a glycosaminoglycan known to bind bone morphogenetic proteins (BMPs) and the growth and differentiation factors (GDFs) and has strong and variable effects on BMP osteogenic activity. In this paper we report our predictions of the likely heparin binding sites for BMP-2 and 14. The N-terminal sequences upstream of TGF-β-type cysteine-knot domains in BMP-2, 7 and 14 contain the basic residues arginine and lysine, which are key components of the heparin/HS-binding sites, with these residues being highly non-conserved. Importantly, evolutionary conserved surfaces on the beta sheets are required for interactions with receptors and antagonists. Furthermore, BMP-2 has electropositive surfaces on two sides compared to BMP-7 and BMP-14. Molecular docking simulations suggest the presence of high and low affinity binding sites in dimeric BMP-2. Histidines were found to play a role in the interactions of BMP-2 with heparin; however, a pKa analysis suggests that histidines are likely not protonated. This is indicative that interactions of BMP-2 with heparin do not require acidic pH. Taken together, non-conserved amino acid residues in the N-terminus and residues protruding from the beta sheet (not overlapping with the receptor binding sites and the dimeric interface) and not C-terminal are found to be important for heparin–BMP interactions.

Prediction of individual genetic risk to prostate cancer using a polygenic score

BACKGROUND Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. Prostate 75:1467–1474, 2015. © 2015 Wiley Periodicals, Inc.