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Obtaining compounds with specificity for certain isoforms of ion channels is a significant problem of curent physiology and pharmacology. In a series of papers, we have shown that the α-hairpinin fold can serve as a template for the rational design of peptide ligands of potassium channels. Here, we used molecular modeling to optimize the structure of the previously obtained Tk-hefu-10 peptide, a selective KV1.3 channel blocker, with a half-maximal inhibitory concentration (IC50) of ≈150 nM. Molecular dynamics simulation of the Tk-hefu-10–KV1.3 complex provided information on the interaction of individual amino acid residues of the peptide and channel, and the analysis of these interactions made it possible to propose amino acid substitutions in the structure of Tk-hefu-10 to increase its affinity. Novel Tk-hefu-12 peptide is a truncated analog of Tk-hefu-10 by one residue with five substitutions; it is characterized by an IC50 value of ≈70 nM against KV1.3. In addition, there are no methionine residues in the structure of Tk-hefu-12, which makes it possible to obtain Tk-hefu peptides using cyanogen bromide. [ABSTRACT FROM AUTHOR]

To investigate the structure-dependent peptide mobility behavior in ion mobility spectrometry (IMS), quantitative structure-spectrum relationship (QSSR) is systematically modeled and predicted for the collision cross section Ω values of totally 162 single-protonated tripeptide fragments extracted from the Bacillus subtilis lipase A. Two different types of structure characterization methods, namely, local and global descriptor as well as three machine learning methods, namely, partial least squares (PLS), support vector machine (SVM) and Gaussian process (GP), are employed to parameterize and correlate the structures and Ω values of these peptide samples. In this procedure, the local descriptor is derived from the principal component analysis (PCA) of 516 physicochemical properties for 20 standard amino acids, which can be used to sequentially characterize the three amino acid residues composing a tripeptide. The global descriptor is calculated using CODESSA method, which can generate > 200 statistically significant variables to characterize the whole molecular structure of a tripeptide. The obtained QSSR models are evaluated rigorously via tenfold cross-validation and Monte Carlo cross-validation (MCCV). A comprehensive comparison is performed on the resulting statistics arising from the systematic combination of different descriptor types and machine learning methods. It is revealed that the local descriptor-based QSSR models have a better fitting ability and predictive power, but worse interpretability, than those based on the global descriptor. In addition, since the QSSR modeling using local descriptor does not consider the three-dimensional conformation of tripeptide samples, the method would be largely efficient as compared to the global descriptor. [ABSTRACT FROM AUTHOR]