As a positive control for maximal aggregation, ADP only was added to PRP and monitored for 6 min. The values obtained for Batroxase Navitoclax were compared with those obtained for ADP only. A 150 μg sample of the purified protein was diluted in 50 mM ambic buffer, pH 8.0, and reduced with dithiothreitol (DTT) at a molar ratio of 50:1 (w/w) for 1 hour at 56 °C. The material was then alkylated with 10 μL iodoacetamide (1 mg/mL) for 30 min in the dark. A 50 μg sample of this reduced

and alkylated Batroxase (RA-Batroxase) was submitted to trypsin proteolytic digestion at a molar ratio of 2:100 (w/w, enzyme:protein) for 4 h at 37 °C. For chymotrypsin hydrolysis, 50 μg of RA-Batroxase was suspended in 100 mM Tris–HCl containing 10 mM CaCl2, pH 7.8,

at a molar ratio of 1:60 (w/w, enzyme:protein) and incubated for 3 h at 37 °C. Streptococcus aureus V8 protease (in 10 mM ambic pH 8.0) was then added at a molar ratio of 3:100 (w/w, enzyme:protein), and the reaction was incubated at 37 °C for 18 h. The hydrolyzed material was subjected to electrospray ionization mass spectrometry (ESI) using a quadrupole-time-of-flight mass spectrometer (Q-Tof Ultima, Waters/Micromass) coupled to an ultra-performance liquid chromatography (UPLC) system (NanoAcquity, Waters). The peptides generated by digestion EX 527 price were desalted on-line using a Waters Symmetry C18 trap column (5 mm × 180 mm × 20 mm). Elution was performed in a BEH 130 C18 (1.7 mm × 75 mm × 100 mm) column using a 0–60% (v/v) acetonitrile gradient for 1 h. The spectra were acquired using data-directed analysis by selecting the doubly and triply charged peptides for MS/MS experiments. All of the MS/MS spectra were processed using the Mascot Distiller software and the MASCOT search engine (Matrix Science, Fenbendazole Boston).

The N-terminal amino acid sequence of Batroxase was determined using the native protein obtained from reverse-phase chromatography using a C-18 column (as described previously). The sequencing procedure was performed using a PPSQ-33A automated protein microsequencer (Shimadzu, Japan). Both the N-terminal protein sequence obtained by automatic sequencer and the internal peptide digested material obtained from the mass spectrometry were used to search for related protein sequences in the SWISS-PROT/TREMBL database with the BLAST FASTA program (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The homology between Batroxase and other proteinases was evaluated using the NCBI protein data bank. Alignments were refined using the program CLUSTAL 2.0.11. The atomic coordinates of class I snake venom metalloproteinase from Bothrops moojeni (BmooMPα-I, PDB ID: 3GBO, Akao et al., 2010) were used as a 3D template for restraint-based modeling and implemented in the MODELLER program ( Fiser and Sali, 2003a).

The states of the variable are described as intervals, which are quite large but can be easily modified if necessary. There is one specific amount for the oil spills, of 30 000 ton, which is the largest oil spill considered by the authorities in Finland, and reflects the preparedness level for Finland, see SYKE (2011). It is an independent variable, which exists in three states: spring (Mar.–May), summer (Jun.–Aug.) and autumn (Sept.–Nov.). Winter

is excluded for several reasons, first as oil-spill combating during ice season is different than during the other seasons. Second, some of the oil-combating vessels are not capable of operating in ice conditions. Third, there is no reliable MEK inhibitor prediction model for the movement of oil in ice conditions in the GOF, (Helle et al., 2011). The prior distribution for the variable Season is presented in Table 2 and informs about the probability that an accident resulting in an oil spill would occur on the Gulf of Finland specifically during this season of the year. The distribution was gained from the compiled accident statistics of HELCOM between the years 1989 and 2005 – ( HELCOM, 2013). It is one of the most important factors affecting the cost of the clean-up operation. It affects the cost in a multitude of ways, starting from the C59 wnt chemical structure way that the spilled

oil spreads in water, which affects the time it takes for the spill to reach the shoreline. In addition, heavier oil has the tendency to sink; this in turn affects the possible recovery Adenosine percentage of the oil-combating vessels.

The oil type also affects the efficiencies of the combating vessels, due to the fact that some oils are less likely to adhere to the brushes used by the combating vessels. In the presented model, this variable exists in three states: light, medium and heavy. The probabilities for each state are given in Table 3. They are based on an estimation made by experts from the Finnish Environment Institute considering the oil tankers traffic in the Gulf of Finland, see for example Juntunen et al. (2005). For the Gulf of Finland, it is estimated that an oil slick would arrive ashore quite quickly. In the case of an accident taking place in the middle of the sea, it could take between one to nine days for the oil to reach the shoreline, see for example Andrejev et al., 2011, Viikmäe and Soomere, 2013 and Soomere et al., 2011. Therefore the variable is set to consist altogether of ten intervals, ranging from zero to ten days. We assume, the prior distribution for this variable follows the Gaussian distribution, with μ = 5 days and σ = 2 days. However, if the spill takes place in Finnish waters of the Gulf of Finland, it is estimated that it would take a maximum of three days before the oil reaches the shore, ( Hietala and Lampela, 2007).