The effects of bovine necrotic vulvo-vaginitis on reproductive and production performance of Israeli 1st calf heifers

Bovine necrotic vulvovaginitis (BNVV) is a syndrome unique to Israel characterized by necrotic lesion in the caudal vagina mainly in first calf heifers after calving, associated with Porphyromonas levii. The objectives of this study were to analyze the impact of BNVV on reproductive performance, milk production and survival in the heard of first calf dairy heifers in affected farms, and to verify if the effects of BNVV are severity-dependent. For assessment of the severity level a scale of 4 degrees was formed, and cows were scored 4 to 6 d after calving. Data were obtained from two dairy farms during 2006-07, consisting of 603 lactations. The incidence and the severity of BNVV declined between 2006 and 2007, and severe BNVV tended to be more prevalent in the summer. The odds to conceive in the first artificial insemination of BNVV cow tended to be lower than healthy cows (OR = 0.676, P = 0.052). Cows with BNVV had longer empty period (145.8 d vs. 135.1 d of healthy cows, P = 0.031), but only severe BNVV had a negative effect on the odds of the cow to be empty at 150 d in milk (DIM) (OR = 2.05, P = 0.052). Severe BNVV also affected the mean survival time to conception (155.9 d vs. 142.3 d, P = 0.042). All BNVV severity degrees had a negative effect on milk production. The effect on milk production was not limited only to the beginning of the lactation, cows with BNVV produced 338.1 kg milk less than healthy cows (P = 0.016) in 305 d corrected lactation. The effect on milk production was not severity depended. No effect on survival time in the herd was demonstrated.     

The tumor suppressor Lgl1 regulates NMII-A cellular distribution and focal adhesion morphology to optimize cell migration

The Drosophila tumor suppressor Lethal (2) giant larvae (Lgl) regulates the apical-basal polarity in epithelia and asymmetric cell division. However, little is known about the role of Lgl in cell polarity in migrating cells. In this study we show direct physiological interactions between the mammalian homologue of Lgl (Lgl1) and the nonmuscle myosin II isoform A (NMII-A). We demonstrate that Lgl1 and NMII-A form a complex in vivo and provide data that Lgl1 inhibits NMII-A filament assembly in vitro. Furthermore, depletion of Lgl1 results in the unexpected presence of NMII-A in the cell leading edge, a region that is not usually occupied by this protein, suggesting that Lgl1 regulates the cellular localization of NMII-A. Finally, we show that depletion of Lgl1 affects the size and number of focal adhesions, as well as cell polarity, membrane dynamics, and the rate of migrating cells. Collectively these findings indicate that Lgl1 regulates the polarity of migrating cells by controlling the assembly state of NMII-A, its cellular localization, and focal adhesion assembly.     

Efficient algorithms to explore conformation spaces of flexible protein loops

Several applications in biology - e.g., incorporation of protein flexibility in ligand docking algorithms, interpretation of fuzzy X-ray crystallographic data, and homology modeling - require computing the internal parameters of a flexible fragment (usually, a loop) of a protein in order to connect its termini to the rest of the protein without causing any steric clash. One must often sample many such conformations in order to explore and adequately represent the conformational range of the studied loop. While sampling must be fast, it is made difficult by the fact that two conflicting constraints - kinematic closure and clash avoidance - must be satisfied concurrently. This paper describes two efficient and complementary sampling algorithms to explore the space of closed clash-free conformations of a flexible protein loop. The "seed sampling" algorithm samples broadly from this space, while the "deformation sampling" algorithm uses seed conformations as starting points to explore the conformation space around them at a finer grain. Computational results are presented for various loops ranging from 5 to 25 residues. More specific results also show that the combination of the sampling algorithms with a functional site prediction software (FEATURE) makes it possible to compute and recognize calcium-binding loop conformations. The sampling algorithms are implemented in a toolkit (LoopTK), which is available at https://simtk.org/home/looptk.     

The FEATURE framework for protein function annotation: modeling new functions,improving performance,and extending to novel applications

Structural genomics efforts contribute new protein structures that often lack significant sequence and fold similarity to known proteins. Traditional sequence and structure-based methods may not be sufficient to annotate the molecular functions of these structures. Techniques that combine structural and functional modeling can be valuable for functional annotation. FEATURE is a flexible framework for modeling and recognition of functional sites in macromolecular structures. Here, we present an overview of the main components of the FEATURE framework, and describe the recent developments in its use. These include automating training sets selection to increase functional coverage, coupling FEATURE to structural diversity generating methods such as molecular dynamics simulations and loop modeling methods to improve performance, and using FEATURE in large-scale modeling and structure determination efforts.     

SiteLight: binding-site prediction using phage display libraries

Phage display enables the presentation of a large number of peptides on the surface of phage particles. Such libraries can be tested for binding to target molecules of interest by means of affinity selection. Here we present SiteLight, a novel computational tool for binding site prediction using phage display libraries. SiteLight is an algorithm that maps the 1D peptide library onto a three-dimensional (3D) protein surface. It is applicable to complexes made up of a protein Template and any type of molecule termed Target. Given the three-dimensional structure of a Template and a collection of sequences derived from biopanning against the Target, the Template interaction site with the Target is predicted. We have created a large diverse data set for assessing the ability of SiteLight to correctly predict binding sites. SiteLight predictive mapping enables discrimination between the binding and nonbinding parts of the surface. This prediction can be used to effectively reduce the surface by 75% without excluding the binding site. In 63% of the cases we have tested, there is at least one binding site prediction that overlaps the interface by at least 50%. These results suggest the applicability of phage display libraries for automated binding site prediction on three-dimensional structures. For most effective binding site prediction we propose using a random phage display library twice, to scan both binding partners of a given complex. The derived peptides are mapped to the other binding partner (now used as a Template). Here, the surface of each partner is reduced by 75%, focusing their relative positions with respect to each other significantly. Such information can be utilized to improve docking algorithms and scoring functions.