Peptide backbone chemistry and membrane channel function: effects of a single amide-to-ester replacement on gramicidin channel structure and function

To examine the structural and functional importance of backbone amide groups in ion channels for subunit folding, hydrogen bonding, ion solvation, and ion permeation, we replaced the peptide bond between Val(1) and Gly(2) in gramicidin A by an ester bond. The substitution is at the junction between the two channel subunits, where it removes an intramolecular hydrogen bond between the NH of Gly(2) and the C==O of Val(7) and perturbs an intermolecular hydrogen bond between the C==O of Val(1) in one subunit and the NH of Ala(5) in the other subunit. The substitution thus perturbs not only subunit folding but also dimer assembly, in addition to any effects on ion permeation. This backbone modification has large effects on channel function: It alters channel stability, as monitored by the channel forming ability and channel lifetime, and ion permeability, as monitored by changes in single-channel conductance and cation permeability ratios. In fact, the homodimeric channels, with two ester-containing subunits, have lifetimes so short that it becomes impossible to characterize them in any detail. The peptide --> ester substitution, however, does not affect the basic subunit fold because heterodimeric channels can form between a subunit with an ester bond and a native subunit. These heterodimeric channels, with only a single ester bond, are more easily characterized; the lone ester reduces the single-channel conductance about 4-fold and the lifetime about 200-fold as compared to the native homodimeric channels. The altered channel function results from a perturbation/disruption of the hydrogen bond network that stabilizes the backbone, as well as the membrane-spanning dimer, and that forms the lining of the ion-conducting pore. Molecular dynamics simulations show the expected destabilization of the modified heterodimeric or homodimeric channels, but the changes in backbone structure and dynamics are remarkably small. The ester bond is somewhat unstable, which precluded further structural characterization. The lability also led to a hydrolysis product that terminates with an alcohol and lacks formyl-Val. Symmetric channels formed by the hydrolyzed product again have short lifetimes, but the channels are distinctly different from those formed by the ester gramicidin A. Furthermore, well-behaved asymmetric channels form between the hydrolysis product and reference subunits that have either an L- or a D-residue at the formyl-NH-terminus.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.