Electrospun Lipid Binding Proteins Composite Nanofibers with Antibacterial Properties

Electrospinning is here used for the first time to prepare nanofibers including a host/guest complex in a keratin/poly(ethylene oxide) matrix. The host is a lipid binding protein and the guest is an insoluble bactericidal molecule, irgasan, bound within the protein internal cavity. The obtained nanofibers, characterized by scanning electron microscopy, exhibit excellent antibacterial activity toward Gram positive and negative bacteria, even with a moderate protein/irgasan cargo. Solution NMR studies, employed to provide molecular information on the cargo system, points to a micromolar affinity, compatible with both the electrospinning process and slow guest release. The versatility of the carrier protein, capable of interacting with a variety of druggable hydrophobic molecules, is exploitable for the development of innovative biomedical devices, whose properties can be tuned by the selected guest.

     

Precisely Designed Isopeptide Bridge‐Crosslinking Endows Artificial Hydrolases with High Stability and Catalytic Activity under Extreme Denaturing Conditions

Enzymes normally lose their activities under extreme conditions due to the dissociation of their active tertiary structure. If an enzyme could maintain its catalytic activity under non‐physiological or denaturing conditions, it might be used in more applications in the pharmaceutical and chemical industries. Recently, we reported a coiled‐coil six‐helical bundle (6HB) structure as a scaffold for designing artificial hydrolytic enzymes. Here, intermolecular isopeptide bonds were incorporated to enhance the stability and activity of such biomolecules under denaturing conditions. These isopeptide bridge‐tethered 6HB enzymes showed exceptional stability against unfolding and retained or even had increased catalytic activity for a model hydrolysis reaction under thermal and chemical denaturing conditions. Thus, isopeptide bond‐tethering represents an efficient route to construct ultrastable artificial hydrolases, with promising potential to maintain biocatalysis under extreme conditions.     

Synthesis of N,N‐Dialkylamino‐nor‐Dihydroxanthene‐Hemicyanine Fused Near‐Infrared Fluorophores and Their First Water‐Soluble and/or Bioconjugatable Analogues

The effective synthesis of extended conjugated N ,N ‐dialkylamino‐nor ‐dihydroxanthene‐based fluorophores is described from diversely functionalized salicylic aldehydes. The access to these original fluorescent derivatives proceeded in two steps through a one‐pot construction of the unusual nor ‐dihydroxanthene (nor ‐DHX) scaffold followed by a diversification step providing a wide variety of nor ‐DHX‐hemicyanine fused dyes emitting in the range of 730–790 nm. The versatility of our approach has enabled a further extension to the late‐stage introduction of negatively/positively charged polar groups onto their terminal nitrogen heterocyclic subunit, thereby giving access to the first water‐soluble and/or bioconjugatable members of this emerging class of NIR fluorophores. Our water‐solubilizing method is easily implementable, and the nor ‐DHX‐hemicyanine skeleton maintains satisfying fluorescence quantum yields (5–20 %) under physiological conditions. Finally, the bioconjugation ability of fluorescent derivatives bearing a free carboxylic acid was demonstrated through the covalent labeling of a model protein, namely, bovine serum albumin.     

Lactic acid bacteria in dairy food: Surface characterization and interactions with food matrix components

This review gives an overview of the importance of interactions occurring in dairy matrices between Lactic Acid Bacteria and milk components. Dairy products are important sources of biological active compounds of particular relevance to human health. These compounds include immunoglobulins, whey proteins and peptides, polar lipids, and lactic acid bacteria including probiotics. A better understanding of interactions between bioactive components and their delivery matrix may successfully improve their transport to their target site of action. Pioneering research on probiotic lactic acid bacteria has mainly focused on their host effects. However, very little is known about their interaction with dairy ingredients. Such knowledge could contribute to designing new and more efficient dairy food, and to better understand relationships between milk constituents. The purpose of this review is first to provide an overview of the current knowledge about the biomolecules produced on bacterial surface and the composition of the dairy matter. In order to understand how bacteria interact with dairy molecules, adhesion mechanisms are subsequently reviewed with a special focus on the environmental conditions affecting bacterial adhesion. Methods dedicated to investigate the bacterial surface and to decipher interactions between bacteria and abiotic dairy components are also detailed. Finally, relevant industrial implications of these interactions are presented and discussed.     

Local protein backbone folds determined by calculated NMR chemical shifts

NMR chemical shifts (CSs: δN(NH), δC(α), δC(β), δC', δH(NH), and δH(α)) were computed for the amino acid backbone conformers (α(L), β(L), γ(L), δ(L), ε(L), α(D), γ(D), δ(D), and ε(D) [Perczel et al., J Am Chem Soc 1991, 113, 6256]) modeled by oligoalanine structures. Topological differences of the extended fold were investigated on single β-strands, hairpins with type I and II β-turns, as well as double- and triple-stranded β-sheet models. The so-called "capping effect" was analyzed: residues at the termini of a homoconformer sequence unit usually have different CSs than the central residues of an adequately long homoconformer model. In heteroconformer sequences capping effect ruins the direct applicability of several chemical shift types (δH(NH), δC', and δN(NH)) for backbone structure determination of the parent residue. Experimental δH(α), δC(α), and δC(β) values retrieved from protein database are in good agreement with the relevant computed data in the case of the common backbone conformers (α(L), β(L), γ(L), and ε(L)), even though neighboring residue effects were not accounted for. Experimental and computed ΔδH(α)-ΔδC(α), ΔδH(α)-ΔδC(β), and ΔδC(α)-ΔδC(β) maps give qualitatively the same picture, that is, the positions of the backbone conformers relative to each other are very similar. This indicates that the H(α), C(α), and C(β) chemical shifts of alanine depend considerably on the backbone fold of the parent residue also in proteins. We provide tabulated CSs of the chiral amino acids that may predict the various structures of the residues.     

A novel graphical representation of protein sequences and its application

On the basis of information on the evolution of the 20 amino acids and their physiochemical characteristics, we propose a new two-dimensional (2D) graphical representation of protein sequences in this article. By this representation method, we use 2D data to represent three-dimensional information constructed by the amino acids' evolution index, the class information of amino acid based on physiochemical characteristics, and the order of the amino acids appearing in the protein sequences. Then, using discrete Fourier transform, the sequence signals with different lengths can be transformed to the frequency domain, in which the sequences are with the same length. A new method is used to analyze the protein sequence similarity and to predict the protein structural class. The experiments indicate that our method is effective and useful.     

SDOCK: a global protein-protein docking program using stepwise force-field potentials

Fast Fourier transform (FFT) method limits the forms of scoring functions in global protein-protein docking. On the other hand, force field potentials can effectively describe the energy hyper surface of biological macromolecules. In this study, we developed a new protein-protein docking program, SDOCK, that incorporates van der Waals attractive potential, geometric collision, screened electrostatic potential, and Lazaridis-Karplus desolvation energy into the scoring function in the global searching process. Stepwise potentials were generated from the corresponding continuous forms to treat the structure flexibility. After optimization of the atom solvation parameters and the weights of different potential terms based on a new docking test set that contains 142 cases with small or moderate conformational changes upon binding, SDOCK slightly outperformed the well-known FFT based global docking program ZDOCK3.0. Among the 142 cases tested, 52.8% gave at least one near-native solutions in the top 100 solutions. SDOCK was also tested on six blind testing cases in Critical Assessment of Predicted Interactions rounds 13 to 18. In all six cases, the near-native solutions could be found within the top 350 solutions. Because the SDOCK approach performs global docking based on force-field potentials, one of its advantages is that it provides global binding free energy surface profiles for further analysis. The efficiency of the program is also comparable with that of other FFT based protein-protein docking programs. SDOCK is available for noncommercial applications at http://mdl.ipc.pku.edu.cn/cgi-bin/down.cgi.     

Improving protein structural class prediction using novel combined sequence information and predicted secondary structural features

Protein structural class prediction solely from protein sequences is a challenging problem in bioinformatics. Numerous efficient methods have been proposed for protein structural class prediction, but challenges remain. Using novel combined sequence information coupled with predicted secondary structural features (PSSF), we proposed a novel scheme to improve prediction of protein structural classes. Given an amino acid sequence, we first transformed it into a reduced amino acid sequence and calculated its word frequencies and word position features to combine novel sequence information. Then we added the PSSF to the combine sequence information to predict protein structural classes. The proposed method was tested on four benchmark datasets in low homology and achieved the overall prediction accuracies of 83.1%, 87.0%, 94.5%, and 85.2%, respectively. The comparison with existing methods demonstrates that the overall improvements range from 2.3% to 27.5%, which indicates that the proposed method is more efficient, especially for low-homology amino acid sequences.     

PROCOS: computational analysis of protein-protein complexes

One of the main challenges in protein-protein docking is a meaningful evaluation of the many putative solutions. Here we present a program (PROCOS) that calculates a probability-like measure to be native for a given complex. In contrast to scores often used for analyzing complex structures, the calculated probabilities offer the advantage of providing a fixed range of expected values. This will allow, in principle, the comparison of models corresponding to different targets that were solved with the same algorithm. Judgments are based on distributions of properties derived from a large database of native and false complexes. For complex analysis PROCOS uses these property distributions of native and false complexes together with a support vector machine (SVM). PROCOS was compared to the established scoring schemes of ZRANK and DFIRE. Employing a set of experimentally solved native complexes, high probability values above 50% were obtained for 90% of these structures. Next, the performance of PROCOS was tested on the 40 binary targets of the Dockground decoy set, on 14 targets of the RosettaDock decoy set and on 9 targets that participated in the CAPRI scoring evaluation. Again the advantage of using a probability-based scoring system becomes apparent and a reasonable number of near native complexes was found within the top ranked complexes. In conclusion, a novel fully automated method is presented that allows the reliable evaluation of protein-protein complexes.     

A comparison of neighbor search algorithms for large rigid molecules

Fast determination of neighboring atoms is an essential step in molecular dynamics simulations or Monte Carlo computations, and there exists a variety of algorithms to efficiently compute neighbor lists. However, most of these algorithms are general, and not specifically designed for a given type of application. As a result, although their average performance is satisfactory, they might be inappropriate in some specific application domains. In this article, we study the case of detecting neighbors between large rigid molecules, which has applications in, e.g., rigid body molecular docking, Monte Carlo simulations of molecular self-assembly or diffusion, and rigid body molecular dynamics simulations. More precisely, we compare the traditional grid-based algorithm to a series of hierarchy-based algorithms that use bounding volumes to rapidly eliminate large groups of irrelevant pairs of atoms during the neighbor search. We compare the performance of these algorithms based on several parameters: the size of the molecules, the average distance between them, the cutoff distance, as well as the type of bounding volume used in the culling hierarchy (AABB, OBB, wrapped, or layered spheres). We demonstrate that for relatively large systems (> 100,000 atoms) the algorithm based on the hierarchy of wrapped spheres shows the best results and the traditional grid-based algorithm gives the worst timings. For small systems, however, the grid-based algorithm and the one based on the wrapped sphere hierarchy are beneficial.