Solution self-assembly of hybrid block copolymers containing poly(ethylene glycol) and amphiphilic beta-strand peptide sequences

The self-assembly in aqueous solution of hybrid block copolymers consisting of amphiphilic β-strand peptide sequences flanked by one or two PEG chains was investigated by means of circular dichroism spectroscopy, small-angle X-ray scattering, and transmission electron microscopy. In comparison with the native peptide sequence, it was found that the peptide secondary structure was stabilized against pH variation in the di-and tri-block copolymers with PEG. Small-angle X-ray scattering indicated the presence of fibrillar structures, the dimensions of which are comparable to the estimated width of a β-strand (with terminal PEG chains in the case of the copolymers). Transmission electron microscopy on selectively stained and dried specimens shows directly the presence of fibrils. It is proposed that these fibrils result from the hierarchical self-assembly of peptide β-strands into helical tapes, which then stack into fibrils.

Interactions of bovine serum albumin with ethylene oxide/butylene oxide copolymers in aqueous solution

The interactions of bovine serum albumin (BSA) with three ethylene oxide/butylene oxide (E/B) copolymers having different block lengths and varying molecular architectures is examined in this study in aqueous solutions. Dynamic light scattering (DLS) indicates the absence of BSA-polymer binding in micellar systems of copolymers with lengthy hydrophilic blocks. On the contrary, stable protein-polyrner aggregates were observed in the case of E18B10 block copolymer. Results from DLS and SAXS suggest the dissociation of E/B copolymer micelles in the presence of protein and the absorption of polymer chains to BSA surface. At high protein loadings, bound BSA adopts a more compact conformation in solution. The secondary structure of the protein remains essentially unaffected even at high polymer concentrations. Raman spectroscopy was used to give insight to the configurations of the bound molecules in concentrated solutions. In the vicinity of the critical gel concentration of E18B10 introduction of BSA can dramatically modify the phase diagram, inducing a gel-sol-gel transition. The overall picture of the interaction diagram of the E18B10-BSA reflects the shrinkage of the suspended particles due to destabilization of micelles induced by BSA and the gelator nature of the globular protein. SAXS and rheology were used to further characterize the structure and flow behavior of the polymer-protein hybrid gels and sols.

Self-assembly of peptide nanotubes in an organic solvent

The self-assembly of a modified fragment of the amyloid beta peptide, based on sequence A beta(16-20), KLVFF, extended to give AAKLVFF is studied in methanol. Self-assembly into peptide nanotubes is observed, as confirmed by electron microscopy and small-angle X-ray scattering. The secondary structure of the peptide is probed by FTIR and circular dichroism, and UV/visible spectroscopy provides evidence for the important role of aromatic interactions between phenylalanine residues in driving beta-sheet self-assembly. The beta-sheets wrap helically to form the nanotubes, the nanotube wall comprising four wrapped beta-sheets. At higher concentration, the peptide nanotubes form a nematic phase that exhibits spontaneous flow alignment as observed by small-angle neutron scattering.

The effect of PEG crystallization on the morphology of PEG/peptide block copolymers containing amyloid beta-peptide fragments

Ordered nanostructures are observed in the melt and solid state for a series of three peptide/PEG conjugates containing fragments of amyloid beta-peptides. These are conjugated to PEG with (M) over bar (n) = 3 300 g.mol(-1) and a melting temperature T-m = 45-50 degrees C. The morphology at room temperature is examined by AFM and POM. This shows spherulite formation for the weakly fibrillizing KLVFF-PEG sample but fibril formation for FFKLVFF-PEG. The fibrillization tendency of the latter is enhanced by multiple phenylalanine residues. Simultaneous SAXS and WAXS was used to investigate the morphology as a function of temperature. The secondary structure is probed by FTIR.

The adsorbed conformation of globular proteins at the air/water interface

External reflection FTIR spectroscopy and surface pressure measurements were used to compare conformational changes in the adsorbed structures of three globular proteins at the air/water interface. Of the three proteins studied, lysozyme, bovine serum albumin and P-lactoglobulin, lysozyme was unique in its behaviour. Lysozyme adsorption was slow, taking approximately 2.5 h to reach a surface pressure plateau (from a 0.07 mM solution), and led to significant structural change. The FTIR spectra revealed that lysozyme formed a highly networked adsorbed layer of unfolded protein with high antiparallel beta-sheet content and that these changes occurred rapidly (within 10 min). This non-native secondary structure is analogous to that of a 3D heat-set protein gel, suggesting that the adsorbed protein formed a highly networked interfacial layer. Albumin and P-lactoglobulin adsorbed rapidly (reaching a plateau within 10 min) and with little chance to their native secondary structure.

Complex formation of bovine serum albumin with a poly(ethylene glycol) lipid conjugate

In this work, we report the formation of complexes by self-assembly of bovine serum albumin (BSA) with a poly(ethylene glycol) lipid conjugate (PEG(2000)-PE) in phosphate saline buffer solution (pH 7.4). Three different sets of samples have been studied. The BSA concentration remained fixed (1, 0.01, or 0.001 wt % BSA) within each set of samples, while the PEG(2000)-PE concentration was varied. Dynamic light scattering (DLS), rheology, and small-angle X-ray scattering (SAXS) were used to study samples with 1 wt % BSA. DLS showed that BSA/PEG(2000)-PE aggregates have a size intermediate between a BSA monomer and a PEG(2000)-PE micelle. Rheology suggested that BSA/PEG(2000)-PE complexes might be surrounded by a relatively compact PEG-lipid shell, while SAXS results showed that depletion forces do not take an important role in the stabilization of the complexes. Samples containing 0.01 wt % BSA were studied by circular dichroism (CD) and ultraviolet fluorescence spectroscopy (UV). UV results showed that at low concentrations of PEG-lipid, PEG(2000)-PE binds to tryptophan (Trp) groups in BSA, while at high concentrations of PEG-lipid the Trp groups are exposed to water. CD results showed that changes in Trp environment take place with a minimal variation of the BSA secondary structure elements. Finally, samples containing 0.001 wt % BSA were studied by zeta-potential experiments. Results showed that steric interactions might play an important role in the stabilization of the BSA/PEG(2000)-PE complexes.

Self-assembly of an amyloid peptide fragment–PEG conjugate: lyotropic phase formation and influence of PEG crystallization

The self-assembly of PEGylated peptides containing a modified sequence from the amyloid beta peptide, YYKLVFF, has been studied in aqueous solution. Two PEG molar masses, PEG1k and PEG3k, were used in the conjugates. It is shown that both YYKLVFF–PEG hybrids form fibrils comprising a peptide core and a PEG corona. The fibrils are much longer for YYKLVFF–PEG1k, pointing to an influence of PEG chain length. The beta-sheet secondary structure of the peptide is retained in the conjugate. Lyotropic liquid crystal phases, specifically nematic and hexagonal columnar phases, are formed at sufficiently high concentration. Flow alignment of these mesophases was investigated by small-angle neutron scattering with in situ steady shearing in a Couette cell. On drying, PEG crystallization occurs leading to characteristic peaks in the X-ray diffraction pattern, and to lamellar structures imaged by atomic force microscopy. The X-ray diffraction pattern retains features of the cross-beta pattern from the beta-sheet structure, showing that this is not disrupted by PEG crystallization.

Self-assembly of PEGylated peptide conjugates containing a modified amyloid β-peptide fragment

The self-assembly of PEGylated peptides containing a modified sequence from the amyloid beta peptide, FEK LVFF, has been studied in aqueous solution. PEG molar masses PEG1k, PEG2k, and PEG10k were used in the conjugates. It is shown that the three FFK LVFF-PEG hybrids form fibrils comprising a FFKLVFF core and a PEG corona. The beta-sheet secondary structure of the peptide is retained in the FFK LVFF fibril core. At sufficiently high concentrations, FEK LVFF-PEG1k and FEK LVFF-PEG2k form a nema tic phase, while PEG10k-FEK LVFF exhibits a hexagonal columnar phase. Simultaneous small angle neutron scattering/shear flow experiments were performed to study the shear flow alignment of the nematic and hexagonal liquid crystal phases. On drying, PEG crystallization occurs without disruption of the FFK LVFF beta-sheet structure leading to characteristic peaks in the X-ray diffraction pattern and FTIR spectra. The stability of beta-sheet structures was also studied in blends of FFKLVFF-PEG conjugates with poly(acrylic acid) (PAA). While PEG crystallization is only observed up to 25% PAA content in the blends, the FFK LVFF beta-sheet structure is retained up to 75% PAA.

A β-amino acid modified heptapeptide containing a designed recognition element disrupts fibrillization of the amyloid β-peptide

We study the complex formation of a peptide betaAbetaAKLVFF, previously developed by our group, with Abeta(1–42) in aqueous solution. Circular dichroism spectroscopy is used to probe the interactions between betaAbetaAKLVFF and Abeta(1–42), and to study the secondary structure of the species in solution. Thioflavin T fluorescence spectroscopy shows that the population of fibers is higher in betaAbetaAKLVFF/Abeta(1–42) mixtures compared to pure Abeta(1–42) solutions. TEM and cryo-TEM demonstrate that co-incubation of betaAbetaAKLVFF with Abeta(1–42) causes the formation of extended dense networks of branched fibrils, very different from the straight fibrils observed for Abeta(1–42) alone. Neurotoxicity assays show that although betaAbetaAKLVFF alters the fibrillization of Abeta(1–42), it does not decrease the neurotoxicity, which suggests that toxic oligomeric Abeta(1–42) species are still present in the betaAbetaAKLVFF/Abeta(1–42) mixtures. Our results show that our designed peptide binds to Abeta(1–42) and changes the amyloid fibril morphology. This is shown to not necessarily translate into reduced toxicity.

Self-assembly of amphiphilic peptides

The self-assembly of amphiphilic peptides is reviewed. The review covers surfactant-like peptides with amphiphilicity arising from the sequence of natural amino acids, and also peptide amphiphiles (PAs) in which lipid chains are attached to hydrophilic peptide sequences containing charged residues. The influence of the secondary structure on the self-assembled structure and vice versa is discussed. For surfactant-like peptides structures including fibrils, nanotubes, micelles and vesicles have been reported. A particularly common motif for PAs is beta-sheet based fibrils, although other structures have been observed. In these structures, the peptide epitope is presented at the surface of the nanostructure, providing remarkable bioactivity. Recent discoveries of potential, and actual, applications of these materials in biomedicine and bionanotechnology are discussed.

Predicting metal-binding site residues in low-resolution structural models

The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality. The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%. The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site.

Improvement of the GenTHREADER method for genomic fold recognition

Motivation: In order to enhance genome annotation, the fully automatic fold recognition method GenTHREADER has been improved and benchmarked. The previous version of GenTHREADER consisted of a simple neural network which was trained to combine sequence alignment score, length information and energy potentials derived from threading into a single score representing the relationship between two proteins, as designated by CATH. The improved version incorporates PSI-BLAST searches, which have been jumpstarted with structural alignment profiles from FSSP, and now also makes use of PSIPRED predicted secondary structure and bi-directional scoring in order to calculate the final alignment score. Pairwise potentials and solvation potentials are calculated from the given sequence alignment which are then used as inputs to a multi-layer, feed-forward neural network, along with the alignment score, alignment length and sequence length. The neural network has also been expanded to accommodate the secondary structure element alignment (SSEA) score as an extra input and it is now trained to learn the FSSP Z-score as a measurement of similarity between two proteins. Results: The improvements made to GenTHREADER increase the number of remote homologues that can be detected with a low error rate, implying higher reliability of score, whilst also increasing the quality of the models produced. We find that up to five times as many true positives can be detected with low error rate per query. Total MaxSub score is doubled at low false positive rates using the improved method.

What are the baselines for protein fold recognition?

What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein—ranging from the length of the sequence to a knowledge of its secondary structure—to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications?

Improvement of 3D protein models using multiple templates guided by single-template model quality assessment

Motivation: Modelling the 3D structures of proteins can often be enhanced if more than one fold template is used during the modelling process. However, in many cases, this may also result in poorer model quality for a given target or alignment method. There is a need for modelling protocols that can both consistently and significantly improve 3D models and provide an indication of when models might not benefit from the use of multiple target-template alignments. Here, we investigate the use of both global and local model quality prediction scores produced by ModFOLDclust2, to improve the selection of target-template alignments for the construction of multiple-template models. Additionally, we evaluate clustering the resulting population of multi- and single-template models for the improvement of our IntFOLD-TS tertiary structure prediction method. Results: We find that using accurate local model quality scores to guide alignment selection is the most consistent way to significantly improve models for each of the sequence to structure alignment methods tested. In addition, using accurate global model quality for re-ranking alignments, prior to selection, further improves the majority of multi-template modelling methods tested. Furthermore, subsequent clustering of the resulting population of multiple-template models significantly improves the quality of selected models compared with the previous version of our tertiary structure prediction method, IntFOLD-TS.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.