Conformational and structural analysis of the equilibrium between single- and double-strand ?-helix of aD,L-alternating oligonorleucine

Design of Chiral β-Double Helices from γ-Peptide Foldamers

Chirality is ubiquitous in nature, and homochirality is manifested in many biomolecules. Although β-double helices are rare in peptides and proteins, they consist of alternating L- and D-amino acids. No peptide double helices with homochiral amino acids have been observed. Here, we report chiral β-double helices constructed from γ-peptides consisting of alternating achiral (E)-α,β-unsaturated 4,4-dimethyl γ-amino acids and chiral (E)-α,β-unsaturated γ-amino acids in both single crystals and in solution. The two independent strands of the same peptide intertwine to form a β-double helix structure, and it is stabilized by inter-strand hydrogen bonds. The peptides with chiral (E)-α,β-unsaturated γ-amino acids derived from α-L-amino acids adopt a (P)-β-double helix, whereas peptides consisting of (E)-α,β-unsaturated γ-amino acids derived from α-D-amino acids adopt an (M)-β-double helix conformation. The circular dichroism (CD) signature of the (P) and (M)-β-double helices and the stability of these peptides at higher temperatures were examined. Furthermore, ion transport studies suggested that these peptides transport ions across membranes. Even though the structural analogy suggests that these new β-double helices are structurally different from those of the α-peptide β-double helices, they retain ion transport activity. The results reported here may open new avenues in the design of functional foldamers.

Artificial β-Double Helices from Achiral γ-Peptides

Double helices are not common in polypeptides and proteins except in the peptide antibiotic gramicidin A and analogous l,d-peptides. In contrast to natural polypeptides, remarkable β-double-helical structures from achiral γ-peptides built from α,β-unsaturated γ-amino acids have been observed. The crystal structures suggest that they adopted parallel β-double helical structures and these structures are stabilized by the interstrand backbone amide H-bonds. Furthermore, both NMR spectroscopy and fluorescence studies support the existence of double-helical conformations in solution. Although a variety of folded architectures featuring distinct H-bonds have been discovered from the β- and γ-peptide foldamers, this is the first report to show that achiral γ-peptides can spontaneously intertwine into β-double helical structures.

Adsorption and self-assembly of a ferrocene d- and l-nonapeptide disulfide onto gold and mica substrates

Linear nanostructures resulting form self-association of a nonapeptide (left) yield progressively to ring-shaped nanostructures (right).

Chemical construction and structural permutation of potent cytotoxin polytheonamide B: discovery of artificial peptides with distinct functions

Polytheonamide B (1), isolated from the marine sponge Theonella swinhoei, is a posttranslationally modified ribosomal peptide (MW 5030 Da) that displays extraordinary cytotoxicity. Among its 48 amino acid residues, this peptide includes a variety D- and L-amino acids that do not occur in proteins, and the chiralities of these amino acids alternate in sequence. These structural features induce the formation of a stable β6.3-helix, giving rise to a tubular structure of over 4 nm in length. In the biological setting, this fold is believed to transport cations across the lipid bilayer through a pore, thereby acting as an ion channel. In this Account, we discuss the construction and structural permutations of this potent cytotoxin. First we describe the 161-step chemical construction of this unusual peptide 1. By developing a synthetic route to 1, we established the chemical basis for subsequent SAR studies to pinpoint the proteinogenic and nonproteinogenic building blocks within the molecule that confer its toxicity and channel function. Using fully synthetic 1, we generated seven analogues with point mutations, and studies of their activity revealed the importance of the N-terminal moiety. Next, we simplified the structure of 1 by substituting six amino acid residues of 1 to design a more synthetically accessible analogue 9. This dansylated polytheonamide mimic 9 was synthesized in 127 total steps, and we evaluated its function to show that it can emulate the toxic and ion channel activities of 1 despite its multiple structural modifications. Finally, we applied a highly automated synthetic route to 48-mer 9 to generate 13 substructures of 27-39-mers. The 37-mer 12 exhibited nanomolar level toxicity through a potentially distinct mode of action from 1 and 9. The SAR studies of polytheonamide B and the 21 artificial analogues have deepened our understanding of the precise structural requirements for the biological functions of 1. They have also led to the discovery of artificial molecules with various toxicities and channel functions. These achievements demonstrate the benefits of total synthesis and the importance of efficient construction of complex molecules. The knowledge accumulated through these studies will be useful for the rational design of new tailor-made channel peptides and cytotoxic molecules with desired functions.

Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks

Since the advent of Molecular Dynamics (MD) in biopolymers science with the study by Karplus et al. on protein dynamics, MD has become the by foremost well established, computational technique to investigate structure and function of biomolecules and their respective complexes and interactions. The analysis of the MD trajectories (MDTs) remains, however, the greatest challenge and requires a great deal of insight, experience, and effort. Here, we introduce a new class of invariants for MDTs based on the spatial distribution of Mean-Energy values ξ_k (L) on a 2D Euclidean space representation of the MDTs. The procedure forces one MD trajectory to fold into a 2D Cartesian coordinates system using a step-by-step procedure driven by simple rules. The ξ_k (L) values are invariants of a Markov matrix (¹ Π), which describes the probabilities of transition between two states in the new 2D space; which is associated to a graph representation of MDTs similar to the lattice networks (LNs) of DNA and protein sequences. We also introduce a new algorithm to perform phylogenetic analysis of peptides based on MDTs instead of the sequence of the polypeptide. In a first experiment, we illustrate this algorithm for 35 peptides present on the Peptide Mass Fingerprint (PMF) of a new protein of Leishmania infantum studied in this work. We report, by the first time, 2D Electrophoresis isolation, MALDI TOF Mass Spectroscopy characterization, and MASCOT search results for this PMF. In a second experiment, we construct the LNs for 422 MDTs obtained in DNA-Drug Docking simulations of the interaction of 57 anticancer furocoumarins with a DNA oligonucleotide. We calculated the respective ξ_k (L) values for all these LNs and used them as inputs to train a new classifier with Accuracy = 85.44% and 84.91% in training and validation respectively. The new model can be used as scoring function to guide DNA-Drug Docking studies in drug design of new coumarins for PUVA therapy. The new phylogenetics analysis algorithms encode information different from sequence similarity and may be used to analyze MDTs obtained in Docking or modeling experiments for any classes of biopolymers. The work opens new perspective on the analysis and applications of MD in polymer sciences.

QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein

The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments

Three-dimensional (3D) protein structures now frequently lack functional annotations because of the increase in the rate at which chemical structures are solved with respect to experimental knowledge of biological activity. As a result, predicting structure-function relationships for proteins is an active research field in computational chemistry and has implications in medicinal chemistry, biochemistry and proteomics. In previous studies stochastic spectral moments were used to predict protein stability or function (González-Díaz, H. et al. Bioorg Med Chem 2005, 13, 323; Biopolymers 2005, 77, 296). Nevertheless, these moments take into consideration only electrostatic interactions and ignore other important factors such as van der Waals interactions. The present study introduces a new class of 3D structure molecular descriptors for folded proteins named the stochastic van der Waals spectral moments ((o)beta(k)). Among many possible applications, recognition of kinases was selected due to the fact that previous computational chemistry studies in this area have not been reported, despite the widespread distribution of kinases. The best linear model found was Kact = -9.44 degrees beta(0)(c) +10.94 degrees beta(5)(c) -2.40 degrees beta(0)(i) + 2.45 degrees beta(5)(m) + 0.73, where core (c), inner (i) and middle (m) refer to specific spatial protein regions. The model with a high Matthew's regression coefficient (0.79) correctly classified 206 out of 230 proteins (89.6%) including both training and predicting series. An area under the ROC curve of 0.94 differentiates our model from a random classifier. A subsequent principal components analysis of 152 heterogeneous proteins demonstrated that beta(k) codifies information different to other descriptors used in protein computational chemistry studies. Finally, the model recognizes 110 out of 125 kinases (88.0%) in a virtual screening experiment and this can be considered as an additional validation study (these proteins were not used in training or predicting series).

Biopolymer stochastic moments. I. Modeling human rhinovirus cellular recognition with protein surface electrostatic moments

Stochastic moments may be applied as molecular descriptors in quantitative structure-activity relationship (QSAR) studies for small molecules (H. González-Dìaz et al., Journal of Molecular Modeling, 2002, Vol. 8, pp. 237-245; 2003, Vol. 9, pp. 395-407). However, applications in the field of biopolymers are less known. Recently, the MARCH-INSIDE approach has been generalized to encode structural features of proteins and other biopolymers (H. González-Dáaz et al., Bioinformatics, 2003, Vol. 19, pp. 2079-2087; Bioorganic & Medicinal Chemistry Letters, 2004, Vol. 14, pp. 4691-4695; Polymers, 2004, Vol. 45, pp. 3845-3853; Bioorganic & Medicinal Chemistry, 2005, Vol. 13, pp. 323-331). The present article attempts to extend this research by introducing for the first time stochastic moments for a surface road map of viral proteins. These moments are afterward used to seek a model that predicts the cellular receptor for human rhinoviruses. The model correctly classified 100% of 10 viruses binding to low-density lipoprotein receptor (LDLR) and 88.9% of 9 viruses binding to the intracellular adhesion molecule (ICAM) receptors in training. The same results have been obtained in four cross-validation experiments using a resubstitution technique. The present model favorably compares, in terms of complexity, with other previously reported based on entropy considerations, and offers a quantitative basis for the visual rule previously reported by Vlasak et al.

Cyclic Peptide Helices: A Hybrid β-Hairpin/β-Helical Supersecondary Structure

This communication describes the design and characterization of a new peptide motif that is a hybrid of beta-hairpin and beta-helical structures. This motif was supported by CD and IR spectrocopies in nonpolar organic solvents and was confirmed by NMR spectroscopy and structure calculations using NMR-derived restraints. The beta-hairpin/beta-helical motif represents a potentially attractive new structural template for a variety of possible applications in biomolecular design, such as ligands for macromolecular targets and building blocks for new protein architectures.

Helix Formation in α,γ- and β,γ-Hybrid Peptides: Theoretical Insights into Mimicry of α- and β-Peptides

Alpha,gamma- and beta,gamma-hybrid peptides, which are composed of two different homologous amino acid constituents in alternate order, are suggested as novel classes of peptide foldamers. On the basis of a systematic conformational search employing the methods of ab initio MO theory, the possibilities for the formation of periodic secondary structures in these systems are described. The conformational analysis provides a great number of helix conformers widely differing in energy, which can be arranged into three groups: (i) helices with all hydrogen bonds formed in forward direction along the sequence, (ii) helices with all hydrogen bonds in backward direction, and (iii) helices with alternate hydrogen-bond directions (mixed or beta-helices). Most stable are representatives of beta-helices, but their stability decreases considerably in more polar environments in comparison to helix conformers from the other two classes. There is a great similarity between the overall topology of the most stable hybrid peptide helices and typical helices of peptides which are exclusively composed of a single type of homologous amino acids. Thus, the helices of the beta,gamma-hybrid peptides mimic perfectly those of the native alpha-peptides as, for instance, the well-known alpha-helix, whereas the most stable helix conformers of alpha,gamma-hybrid peptides correspond well to the overall structure of beta-peptide helices. The two suggested novel hybrid peptide classes expand considerably the pool of peptide foldamers and may be promising tools in peptide design and in material sciences.

Proteins QSAR with Markov average electrostatic potentials

Classic physicochemical and topological indices have been largely used in small molecules QSAR but less in proteins QSAR. In this study, a Markov model is used to calculate, for the first time, average electrostatic potentials xik for an indirect interaction between aminoacids placed at topologic distances k within a given protein backbone. The short-term average stochastic potential xi1 for 53 Arc repressor mutants was used to model the effect of Alanine scanning on thermal stability. The Arc repressor is a model protein of relevance for biochemical studies on bioorganics and medicinal chemistry. A linear discriminant analysis model developed correctly classified 43 out of 53, 81.1% of proteins according to their thermal stability. More specifically, the model classified 20/28, 71.4% of proteins with near wild-type stability and 23/25, 92.0% of proteins with reduced stability. Moreover, predictability in cross-validation procedures was of 81.0%. Expansion of the electrostatic potential in the series xi0, xi1, xi2, and xi3, justified the use of the abrupt truncation approach, being the overall accuracy >70.0% for xi0 but equal for xi1, xi2, and xi3. The xi1 model compared favorably with respect to others based on D-Fire potential, surface area, volume, partition coefficient, and molar refractivity, with less than 77.0% of accuracy [Ramos de Armas, R.; González-Díaz, H.; Molina, R.; Uriarte, E. Protein Struct. Func. Bioinf.2004, 56, 715]. The xi1 model also has more tractable interpretation than others based on Markovian negentropies and stochastic moments. Finally, the model is notably simpler than the two models based on quadratic and linear indices. Both models, reported by Marrero-Ponce et al., use four-to-five time more descriptors. Introduction of average stochastic potentials may be useful for QSAR applications; having xik amenable physical interpretation and being very effective.

Recognition of stable protein mutants with 3D stochastic average electrostatic potentials

As more and more proteins are applied to biochemical research there is increasing interest in studying their stability. In this study, a Markov model has been used to calculate molecular descriptors of the protein structure and these are called the average electrostatic potentials (xi(k)). These descriptors were intended to encode indirect electrostatic pair-wise interactions between amino acids located at Euclidean distance k within a given 3D protein backbone. The different xi(k) values could be calculated for the protein as a whole or for specific protein regions (orbits), which include amino acids that lie within a given range of distances from the center of charge of the protein. In this work we calculated the xi(k) values for 657 mutants of different proteins. A Linear Discriminant Analysis model correctly classified a subset of 435 out of 493 proteins according to their thermal stability - a level of predictability of 88.2%. This experiment was repeated with three additional subsets of proteins selected at random from the initial series of 657. More specifically, the model predicted 314/356 (88.2%) of mutants with higher stability than the corresponding wild-type protein and 264/301 (86.7%) of proteins with near wild-type stability. These results illustrate the possibilities for the average stochastic potentials xi(k) in the study of 3D-structure/property relationships for biochemically relevant proteins.

Side-chain control of folding of the homologous α-, β-, and γ-peptides into “mixed” helices (β-helices)

A systematic analysis of the substituent influence on the formation of the unique secondary structure type of "mixed" helices in the homologous alpha-, beta-, and gamma-peptides was performed on the basis of ab initio molecular orbital theory. Contrary to the common periodic peptide helices, mixed helices have an alternating periodicity and their hydrogen-bonding pattern is similar to those of beta-sheets. They belong, therefore, to the family of beta-helices. It is shown that folding of peptide sequences into mixed helices is energetically preferred over folding into their periodic counterparts in numerous cases. The influence of entropy and solvents on the formation of the various competitive mixed and periodic helix types is discussed. Among the oligomers of the various homologous amino acids, beta-peptides show the highest tendency to form beta-helices. The rules of substituent influence derived from the analysis of a wide variety of backbone substitution patterns might be helpful for a rational design of mixed helix structures, which could be important for mimicking membrane channels.