A Method for the Simultaneous Estimation of Selection Intensities in Overlapping Genes

Inferring the intensity of positive selection in protein-coding genes is important since it is used to shed light on the process of adaptation. Recently, it has been reported that overlapping genes, which are ubiquitous in all domains of life, seem to exhibit inordinate degrees of positive selection. Here, we present a new method for the simultaneous estimation of selection intensities in overlapping genes. We show that the appearance of positive selection is caused by assuming that selection operates independently on each gene in an overlapping pair, thereby ignoring the unique evolutionary constraints on overlapping coding regions. Our method uses an exact evolutionary model, thereby voiding the need for approximation or intensive computation. We test the method by simulating the evolution of overlapping genes of different types as well as under diverse evolutionary scenarios. Our results indicate that the independent estimation approach leads to the false appearance of positive selection even though the gene is in reality subject to negative selection. Finally, we use our method to estimate selection in two influenza A genes for which positive selection was previously inferred. We find no evidence for positive selection in both cases.     

A novel lytic peptide composed of DL-amino acids selectively kills cancer cells in culture and in mice

The high toxicity of most chemotherapeutic drugs and their inactivation by multidrug resistance phenotypes motivated extensive search for drugs with new modes of action. We designed a short cationic diastereomeric peptide composed of d- and l-leucines, lysines, and arginines that has selective toxicity toward cancer cells and significantly inhibits lung metastasis formation in mice (86%) with no detectable side effects. Its ability to depolarize the transmembrane potential of cancer cells at the same rate (within minutes) and concentration (3 micro m), at which it shows biological activity, suggests a killing mechanism that involves plasma membrane perturbation. Confocal microscopy experiments verified that the cells died as a result of acute injury, swelling, and bursting, suggesting necrosis. Biosensor binding experiments and attenuated total reflectance-Fourier transform infrared spectroscopy using model membranes have substantiated its high selectivity toward cancer cells. Although this is an initial study that looked at tumor formation rather than the ability of the peptides to reduce established tumors, the simple sequence of the peptide, its high solubility, substantial resistance to degradation, and inactivation by serum components might make it a good candidate for future anticancer treatment.     

Can we predict biological activity of antimicrobial peptides from their interactions with model phospholipid membranes?

Cationic antibacterial peptides are produced in all living organisms and possess either selective activity toward a certain type of cell or microorganism, or a broad spectrum of activity toward several types of cells including prokaryotic and mammalian cells. In order to exert their activity, peptides first interact with and traverse an outer barrier, e.g., mainly LPS and peptidoglycan in bacteria or a glycocalix layer and matrix proteins in mammalian cells. Only then, can the peptides bind and insert into the cytoplasmic membrane. The mode of action of many antibacterial peptides is believed to be the disruption of the lipidic plasma membrane. Therefore, model phospholipid membranes have been used to study the mode of action of antimicrobial peptides. These studies have demonstrated that peptides that act preferentially on bacteria are also able to interact with and permeate efficiently anionic phospholipids, whereas peptides that lyse mammalian cells bind and permeate efficiently both acidic and zwitterionic phospholipids membranes, mimicking the plasma membranes of these cells. It is now becoming increasingly clear that selective activity of these peptides against different cells depends also on other parameters that characterize both the peptide and the target cell. With respect to the peptide's properties, these include the volume of the molecule, its structure, and its oligomeric state in solution and in membranes. Regarding the target membrane, these include the structure, length, and complexity of the hydrophilic polysaccharide found in its outer layer. These parameters affect the ability of the peptides to diffuse through the cell's outer barrier and to reach its cytoplasmic plasma membrane.     

Sequence-based design of kinase inhibitors applicable for therapeutics and target identification

A platform for specifically modulating kinase-dependent signaling using peptides derived from the catalytic domain of the kinase is presented. This technology, termed KinAce, utilizes the canonical structure of protein kinases. The targeted regions (subdomain V and subdomains IX and X) are analyzed and their sequence, three-dimensional structure, and involvement in protein-protein interaction are highlighted. Short myristoylated peptides were derived from the target regions of the tyrosine kinases c-Kit and Lyn and the serine/threonine kinases 3-phosphoinositide-dependent kinase-1 (PDK1) and Akt/protein kinase B (PKB). For each kinase an active designer peptide is shown to selectively inhibit the signaling of the kinase from which it is derived, and to inhibit cancer cell proliferation in the micromolar range. This technology emerges as an applicable tool for deriving sequence-based selective inhibitors for a broad range of protein kinases as hits that may be further developed into drugs. Moreover, it enables identification of novel kinase targets for selected therapeutic indications as demonstrated in the KinScreen application.     

Esophageal muscle physiology and morphogenesis require assembly of a collagen XIX-rich basement membrane zone

Collagen XIX is an extremely rare extracellular matrix component that localizes to basement membrane zones and is transiently expressed by differentiating muscle cells. Characterization of mice harboring null and structural mutations of the collagen XIX (Col19a1) gene has revealed the critical contribution of this matrix protein to muscle physiology and differentiation. The phenotype includes smooth muscle motor dysfunction and hypertensive sphincter resulting from impaired swallowing-induced, nitric oxide-dependent relaxation of the sphincteric muscle. Muscle dysfunction was correlated with a disorganized matrix and a normal complement of enteric neurons and interstitial cells of Cajal. Mice without collagen XIX exhibit an additional defect, namely impaired smooth-to-skeletal muscle cell conversion in the abdominal segment of the esophagus. This developmental abnormality was accounted for by failed activation of myogenic regulatory factors that normally drive esophageal muscle transdifferentiation. Therefore, these findings identify collagen XIX as the first structural determinant of sphincteric muscle function, and as the first extrinsic factor of skeletal myogenesis in the murine esophagus.     

Optimal Behavioral Hierarchy

Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goal-directed activities. Arranging actions hierarchically has well established benefits, allowing behaviors to be represented efficiently by the brain, and allowing solutions to new tasks to be discovered easily. However, these payoffs depend on the particular way in which actions are organized into a hierarchy, the specific way in which tasks are carved up into subtasks. We provide a mathematical account for what makes some hierarchies better than others, an account that allows an optimal hierarchy to be identified for any set of tasks. We then present results from four behavioral experiments, suggesting that human learners spontaneously discover optimal action hierarchies.     

Competitive exclusion,beta diversity,and deterministic vs. stochastic drivers of community assembly

Species diversity has two components – number of species and spatial turnover in species composition (beta‐diversity). Using a field experiment focusing on a system of Mediterranean grasslands, we show that interspecific competition may influence the two components in the same direction or in opposite directions, depending on whether competitive exclusions are deterministic or stochastic. Deterministic exclusions reduce both patch‐scale richness and beta‐diversity, thereby homogenising the community. Stochastic extinctions reduce richness at the patch scale, but increase the differences in species composition among patches. These results indicate that studies of competitive effects on beta diversity may help to distinguish between deterministic and stochastic components of competitive exclusion. Such distinction is crucial for understanding the causal relationship between competition and species diversity, one of the oldest and most fundamental questions in ecology.     

Automating human intuition for protein design

In the design of new enzymes and binding proteins, human intuition is often used to modify computationally designed amino acid sequences prior to experimental characterization. The manual sequence changes involve both reversions of amino acid mutations back to the identity present in the parent scaffold and the introduction of residues making additional interactions with the binding partner or backing up first shell interactions. Automation of this manual sequence refinement process would allow more systematic evaluation and considerably reduce the amount of human designer effort involved. Here we introduce a benchmark for evaluating the ability of automated methods to recapitulate the sequence changes made to computer‐generated models by human designers, and use it to assess alternative computational methods. We find the best performance for a greedy one‐position‐at‐a‐time optimization protocol that utilizes metrics (such as shape complementarity) and local refinement methods too computationally expensive for global Monte Carlo (MC) sequence optimization. This protocol should be broadly useful for improving the stability and function of designed binding proteins. Proteins 2014; 82:858–866. © 2013 Wiley Periodicals, Inc.     

HIV-1 gp41 and TCRα Trans-Membrane Domains Share a Motif Exploited by the HIV Virus to Modulate T-Cell Proliferation

Viruses have evolved several strategies to modify cellular processes and evade the immune response in order to successfully infect, replicate, and persist in the host. By utilizing in-silico testing of a transmembrane sequence library derived from virus protein sequences, we have pin-pointed a nine amino-acid motif shared by a group of different viruses; this motif resembles the transmembrane domain of the α-subunit of the T-cell receptor (TCRα). The most striking similarity was found within the immunodeficiency virus (SIV and HIV) glycoprotein 41 TMD (gp41 TMD). Previous studies have shown that stable interactions between TCRα and CD3 are localized to this nine amino acid motif within TCRα, and a peptide derived from it (TCRα TMD, GLRILLLKV) interfered and intervened in the TCR function when added exogenously. We now report that the gp41 TMD peptide co-localizes with CD3 within the TCR complex and inhibits T cell proliferation in vitro. However, the inhibitory mechanism of gp41 TMD differs from that of the TCRα TMD and also from the other two known immunosuppressive regions within gp41.