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The crystal structure of an anionic form of salmon trypsin has been determined at 1.82 A resolution. We report the first structure of a trypsin from a phoikilothermic organism in a detailed comparison to mammalian trypsins in order to look for structural rationalizations for the cold-adaption features of salmon trypsin. This form of salmon trypsin (ST II) comprises 222 residues, and is homologous to bovine trypsin (BT) in about 65% of the primary structure. The tertiary structures are similar, with an overall displacement in main chain atomic positions between salmon trypsin and various crystal structures of bovine trypsin of about 0.8 A. Intramolecular hydrogen bonds and hydrophobic interactions are compared and discussed in order to estimate possible differences in molecular flexibility which might explain the higher catalytic efficiency and lower thermostability of salmon trypsin compared to bovine trypsin. No overall differences in intramolecular interactions are detected between the two structures, but there are differences in certain regions of the structures which may explain some of the observed differences in physical properties. The distribution of charged residues is different in the two trypsins, and the impact this might have on substrate affinity has been discussed.

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Serine proteinases and their protein inhibitors belong to one of the most comprehensively studied models of protein-protein interactions. It is well established that the narrow trypsin specificity is caused by the presence of a negatively charged aspartate at the specificity pocket. X-ray crystallography as well as association measurements revealed, surprisingly, that BPTI with glutamatic acid as the primary binding (P1) residue was able to bind to trypsin. Previous free energy calculations showed that there was a substantially unfavorable binding free energy associated with accommodation of ionized P1 Glu at the S1-site of trypsin. In this study, the binding of P1 Glu to trypsin has been systematically investigated in terms of the protonation states of P1 Glu and Asp189, the orientation of Gln192, as well as the possible presence of counterions using the linear interaction energy (LIE) approach and the free energy perturbation (FEP) method. Twenty-four conceivable binding arrangements were evaluated and quantitative agreement with experiments is obtained when the P1 Glu binds in its protonated from. The results suggest that P1 Glu is one of the variants of BPTI that inhibit trypsin strongest at low pH, contrary to the specificity profile of trypsin, suggesting a new regulation mechanism of trypsin-like enzymes.

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Aims The aim of this study was to use a sensitive method to screen and quantify 57 Vibrionaceae strains for the production of acyl-homoserine lactones (AHLs) and map the resulting AHL profiles onto a host phylogeny.Methods and Results We used a high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) protocol to measure AHLs in spent media after bacterial growth. First, the presence/absence of AHLs (qualitative analysis) was measured to choose internal standard for subsequent quantitative AHL measurements. We screened 57 strains from three genera (Aliivibrio,Photobacterium and Vibrio) of the same family (i.e. Vibrionaceae). Our results show that about half of the isolates produced multiple AHLs, typically at 25–5000 nmol l−1.Conclusions This work shows that production of AHL quorum sensing signals is found widespread among Vibrionaceae bacteria and that closely related strains typically produce similar AHL profiles.Significance and Impact of the Study The AHL detection protocol presented in this study can be applied to a broad range of bacterial samples and may contribute to a wider mapping of AHL production in bacteria, for example, in clinically relevant strains.

The binding of P1 variants of bovine pancreatic trypsin inhibitor (BPTI) to trypsin has been investigated by means of molecular dynamics simulations. The specific interaction formed between the amino acid at the primary binding (P1) position of the binding loop of BPTI and the specificity pocket of trypsin was estimated by use of the linear interaction energy (LIE) method. Calculations for 13 of the naturally occurring amino acids at the P1 position were carried out, and the results obtained were found to correlate well with the experimental binding free energies. The LIE calculations rank the majority of the 13 variants correctly according to the experimental association energies and the mean error between calculated and experimental binding free energies is only 0.38 kcal/mole, excluding the Glu and Asp variants, which are associated with some uncertainties regarding protonation and the possible presence of counter-ions. The three-dimensional structures of the complex with three of the P1 variants (Asn, Tyr, and Ser) included in this study have not at present been solved by any experimental techniques and, therefore, were modeled on the basis of experimental data from P1 variants of similar size. Average structures were calculated from the MD simulations, from which specific interactions explaining the broad variation in association energies were identified. The present study also shows that explicit treatment of the complex water-mediated hydrogen bonding network at the protein-protein interface is of crucial importance for obtaining reliable binding free energies. The successful reproduction of relative binding energies shows that this type of methodology can be very useful as an aid in rational design and redesign of biologically active macromolecules. Keywords: Molecular dynamics; binding free energy; protein binding; trypsinThe ability to predict the strength of noncovalent binding between molecules has been a longstanding goal in computational chemistry. Various methods have been developed during the past 10-15 yr to address the question of ligandreceptor binding, and several comprehensive reviews are available (Kollman 1993;Lamb and Jorgensen 1997;Böhm and Stahl 1999). Free energy perturbation (FEP) theory (Beveridge and DiCapua 1989;Jorgensen 1989;Kollman 1993) combined with conformational sampling by molecular dynamics or Monte Carlo simulations provides a rigorous way of calculating free energies upon modifications of a ligand or a receptor. Most FEP calculations take advantage of a thermodynamic perturbation cycle (Fig. 1), and modifications of the ligand or receptor are achieved through a nonphysical transformation process. As a result of sampling and convergence problems related to large perturbations, FEP calculations are in most cases limited to the evaluation of relative binding free energies for compounds of similar chemical structure. Even calculations of relative binding free energies can pose a major problem if extensive modifications are required to bring the system from one state ...