Increased Y-chromosome resolution of haplogroup O suggests genetic ties between the Ami aborigines of Taiwan and the Polynesian Islands of Samoa and Tonga

The Austronesian expansion has left its fingerprint throughout two thirds of the circumference of the globe reaching the island of Madagascar in East Africa to the west and Easter Island, off the coast of Chile, to the east. To date, several theories exist to explain the current genetic distribution of Austronesian populations, with the “slow boat” model being the most widely accepted, though other conjectures (i.e., the “express train” and “entangled bank” hypotheses) have also been widely discussed. In the current study, 158 Y chromosomes from the Polynesian archipelagos of Samoa and Tonga were typed using high resolution binary markers and compared to populations across Mainland East Asia, Taiwan, Island Southeast Asia, Melanesia and Polynesia in order to establish their patrilineal genetic relationships. Y-STR haplotypes on the C2 (M38), C2a (M208), O1a (M119), O3 (M122) and O3a2 (P201) backgrounds were utilized in an attempt to identify the differing sources of the current Y-chromosomal haplogroups present throughout Polynesia (of Melanesian and/or Asian descent). We find that, while haplogroups C2a, S and K3-P79 suggest a Melanesian component in 23%-42% of the Samoan and Tongan Y chromosomes, the majority of the paternal Polynesian gene pool exhibits ties to East Asia. In particular, the prominence of sub-haplogroup O3a2c* (P164), which has previously been observed at only minimal levels in Mainland East Asians (2.0-4.5%), in both Polynesians (ranging from 19% in Manua to 54% in Tonga) and Ami aborigines from Taiwan (37%) provides, for the first time, evidence for a genetic connection between the Polynesian populations and the Ami.     

Anti-EGFR biparatopic-SEED antibody has enhanced combination-activity in a single molecule

Certain combinations of non-competitive anti-EGFR antibodies have been reported to produce new effects on cells compared to either antibody used separately. New and enhanced combination-activity includes increased inhibition of signaling, increased receptor internalization and degradation, reduced proliferation of tumor cell lines and induction of complement-dependent cytotoxicity (CDC) effector function. To test requirements and mechanisms to elicit enhanced combination-activity with different EGFR binding domains, we created an anti-EGFR biparatopic antibody. A biparatopic antibody interacts through two different antigen-binding sites to a single antigen. A heterodimeric antibody with one binding domain derived from the C225 antibody and one binding domain derived from the humanized 425 (hu425) antibody was built on the strand-exchange engineered domain (SEED) scaffold. This anti-EGFR biparatopic-SEED antibody was compared to parental antibodies used alone and in combination, and to the corresponding monovalent anti-EGFR-SEED antibodies used alone or in combination. We found that the anti-EGFR biparatopic-SEED had enhanced activity, similar to the combination of the two parental antibodies. Combinations of monovalent anti-EGFR-SEED antibodies did not produce enhanced effectiveness in cellular assays. Our results show that the anti-EGFR biparatopic antibody created using the SEED scaffold has enhanced combination-activity in a single molecule. Furthermore, these data suggest that the potential to cross-link the two different epitopes is an important requirement in the mechanism of enhanced combination-activity.     

Discovery of benzodihydroisofurans as novel, potent, bioavailable and brain-penetrant prolylcarboxypeptidase inhibitors

A series of benzodihydroisofurans were discovered as novel, potent, bioavailable and brain-penetrant prolylcarboxypeptidase (PrCP) inhibitors. The structure-activity relationship (SAR) is focused on improving PrCP activity and metabolic stability, and reducing plasma protein binding. In the established diet-induced obese (eDIO) mouse model, compound ent-3a displayed target engagement both in plasma and in brain. However, this compound failed to induce significant body weight loss in eDIO mice in a five-day study.     

BCL::Fold - De Novo Prediction of Complex and Large Protein Topologies by Assembly of Secondary Structure Elements

Computational de novo protein structure prediction is limited to small proteins of simple topology. The present work explores an approach to extend beyond the current limitations through assembling protein topologies from idealized α-helices and β-strands. The algorithm performs a Monte Carlo Metropolis simulated annealing folding simulation. It optimizes a knowledge-based potential that analyzes radius of gyration, β-strand pairing, secondary structure element (SSE) packing, amino acid pair distance, amino acid environment, contact order, secondary structure prediction agreement and loop closure. Discontinuation of the protein chain favors sampling of non-local contacts and thereby creation of complex protein topologies. The folding simulation is accelerated through exclusion of flexible loop regions further reducing the size of the conformational search space. The algorithm is benchmarked on 66 proteins with lengths between 83 and 293 amino acids. For 61 out of these proteins, the best SSE-only models obtained have an RMSD100 below 8.0 Å and recover more than 20% of the native contacts. The algorithm assembles protein topologies with up to 215 residues and a relative contact order of 0.46. The method is tailored to be used in conjunction with low-resolution or sparse experimental data sets which often provide restraints for regions of defined secondary structure.     

Tissue Specific Electrochemical Fingerprinting

Background

Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore of extreme interest. Numerous methods are now available as commercial or open source tools for data processing and modelling ready to support the analysis of various datasets. The analysis of scientific data remains a big challenge, because each new task sets its specific requirements and constraints that call for the design of a targeted data pre-processing approach.

Methodology/Principal Findings

This study proposes a mathematical approach for evaluating and classifying datasets obtained by electrochemical analysis of metallothionein in rat 9 tissues (brain, heart, kidney, eye, spleen, gonad, blood, liver and femoral muscle). Tissue extracts were heated and then analysed using the differential pulse voltammetry Brdicka reaction. The voltammograms were subsequently processed. Classification models were designed making separate use of two groups of attributes, namely attributes describing local extremes, and derived attributes resulting from the level = 5 wavelet transform.

Conclusions/Significance

On the basis of our results, we were able to construct a decision tree that makes it possible to distinguish among electrochemical analysis data resulting from measurements of all the considered tissues. In other words, we found a way to classify an unknown rat tissue based on electrochemical analysis of the metallothionein in this tissue.     

Membrane bending by protein-protein crowding

Curved membranes are an essential feature of dynamic cellular structures, including endocytic pits, filopodia protrusions and most organelles. It has been proposed that specialized proteins induce curvature by binding to membranes through two primary mechanisms: membrane scaffolding by curved proteins or complexes; and insertion of wedge-like amphipathic helices into the membrane. Recent computational studies have raised questions about the efficiency of the helix-insertion mechanism, predicting that proteins must cover nearly 100% of the membrane surface to generate high curvature, an improbable physiological situation. Thus, at present, we lack a sufficient physical explanation of how protein attachment bends membranes efficiently. On the basis of studies of epsin1 and AP180, proteins involved in clathrin-mediated endocytosis, we propose a third general mechanism for bending fluid cellular membranes: protein-protein crowding. By correlating membrane tubulation with measurements of protein densities on membrane surfaces, we demonstrate that lateral pressure generated by collisions between bound proteins drives bending. Whether proteins attach by inserting a helix or by binding lipid heads with an engineered tag, protein coverage above ~20% is sufficient to bend membranes. Consistent with this crowding mechanism, we find that even proteins unrelated to membrane curvature, such as green fluorescent protein (GFP), can bend membranes when sufficiently concentrated. These findings demonstrate a highly efficient mechanism by which the crowded protein environment on the surface of cellular membranes can contribute to membrane shape change.