Multi-output interval type-2 fuzzy logic system for protein secondary structure prediction

A new multi-output interval type-2 fuzzy logic system (MOIT2FLS) is introduced for protein secondary structure prediction in this paper. Three outputs of the MOIT2FLS correspond to three structure classes including helix, strand (sheet) and coil. Quantitative properties of amino acids are employed to characterize twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Three clustering tasks are performed using the adaptive vector quantization method to construct an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS. The genetic fitness function is designed based on the Q3 measure. Experimental results demonstrate the dominance of the proposed approach against the traditional methods that are Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

Structural classification of proteins through amino acid sequence using interval type-2 fuzzy logic system

This paper introduces a new multi-output interval type-2 fuzzy logic system (MOIT2FLS) that is automatically constructed from unsupervised data clustering method and trained using heuristic genetic algorithm for a protein secondary structure classification. Three structure classes are distinguished including helix, strand (sheet) and coil which correspond to three outputs of the MOIT2FLS. Quantitative properties of amino acids are used to characterize the twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Amino acid sequences are parsed into learnable patterns using a local moving window strategy. Three clustering tasks are performed using the adaptive vector quantization method to derive an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS with the purpose of maximizing the Q3 measure. Comprehensive experimental results demonstrate the strong superiority of the proposed approach over the traditional methods including Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

Tuning the mechanical and morphological properties of self-assembled peptide hydrogels via control over the gelation mechanism through regulation of ionic strength and the rate of pH change

Hydrogels formed by the self-assembly of peptides are promising biomaterials. The bioactive and biocompatible molecule Fmoc-FRGDF has been shown to be an efficient hydrogelator via a π-β self-assembly mechanism. Herein, we show that the mechanical properties and morphology of Fmoc-FRGDF hydrogels can be effectively and easily manipulated by tuning both the final ionic strength and the rate of pH change. The increase of ionic strength, and consequent increase in rate of gelation and stiffness, does not interfere with the underlying π-β assembly of this Fmoc-protected peptide. However, by tuning the changing rate of the system's pH through the use of glucono-δ-lactone to form a hydrogel, as opposed to the previously reported HCl methodology, the morphology (nano- and microscale) of the scaffold can be manipulated.

Synthesis and self-assembly behaviour of poly(Nα-Boc-l-tryptophan)-block-poly(ethylene glycol)-block-poly(Nα-Boc-l-tryptophan)

We report for the first time the use of Nα-Boc-l-tryptophan for the synthesis of amphiphilic BAB triblock copolymers for potential drug delivery applications. A library of poly(Nα-Boc-l-tryptophan)-block-poly(ethylene glycol)-block-poly(Nα-Boc-l-tryptophan) (PBoclTrp-b-PEG-b-PBoclTrp) amphiphilic copolymers were synthesized through the ring opening polymerization of Nα-Boc-l-tryptophan Nα-carboxy anhydride as initiated by diamino-terminated PEG of fixed molecular weight (Mn 3350). The influence of the hydrophobic block length over self-assembly was investigated for 4 of the BAB copolymers of molecular weights varying between Mn 5000 and Mn 17000. It was found that an increase in hydrophobic block length led to an increase in hydrodynamic size of aggregates in solution, as well as a decrease in critical micelle concentration. TEM analysis showed the formation of spherical micelles with the largest of the copolymers forming interconnected networks of spherical micelles. The influence of hydrophobic block length over the formation of secondary structure was analyzed using circular dichroism and infrared spectroscopy. Collectively we found that the presence of t-Boc protected l-tryptophan leads to the preferential formation of α-helix secondary structure through hydrogen bonding, which, in a drug delivery vehicle context, could help in controlling drug release. Also, it is believed that the use of novel Nα-Boc-l-tryptophan could improve drug stabilization in the hydrophobic core via π-π interactions between indole rings.

Protein solubilising and stabilising ionic liquids

We report a family of biocompatible ionic liquids (ILs) which are able to dissolve significant amounts of proteins such as cytochrome c and in which ATR-FTIR spectroscopy results show retention of secondary structure to extreme temperatures.

Balsamin, a novel ribosome-inactivating protein from the seeds of Balsam apple Momordica balsamina

Plant seeds, a rich source of proteins, are considered important for their application as functional ingredients in a food system. A novel ribosome-inactivating protein (RIP), balsamin was purified from the seeds of Balsam apple, Momordica balsamina. Balsamin was purified by ion exchange chromatography on CM Sepharose and gel filtration on superdex-75. It has a molecular weight of 28 kDa as shown by SDS-PAGE analysis. Balsamin inhibits protein synthesis in a rabbit reticulocyte lysate-based cell free translation assay with an IC50 of 90.6 ng ml⁻¹. It has RNA N-glycosidase activity and releases a 400-base long fragment termed the Endo fragment from 28S rRNA in the same manner as does saporin-6 from Saponaria officinalis. The N-terminal sequence analysis of the first 12 amino acids of balsamin revealed that it shares 83% similarity with type I RIP α-MMC from Momordica charantia and 50% similarity with β-MMC (from Momordica charantia), bryodin I (from Bryonia dioica) and luffin a (from Luffa cylindrica). Balsamin was further characterized by mass spectrometry. CD spectroscopic studies indicate that secondary structure of balsamin contains helix (23.5%), β-strand (24.6%), turn (20%) and random coil (31.9%). Thus RIPs activity expressed in vegetables like Momordica sp. advocates its usage in diet.

Structure and characteristics of milled silk particles

This study examined the structure, thermal property, and ion adsorption of silk particles. The particles were prepared by attritor-bead mill combination, using alkaline (pH10) charge repulsion and surfactant steric repulsion methods. Both methods produced particles with a dominant β-sheet structure, similar to the silk fibre. There was no significant difference in the decomposition temperatures for either the silk fibre or the micro/nano silk particles. An important finding from this study is clear evidence of reduction of amorphous content during the final stage of powdering using the bead mill. As a result, despite reduction in β-sheet crystallites with the progressive milling, the relative β-sheet content actually increased during this process. However, intermolecular forces between the β-sheets reduced significantly and hence the XRD results showed significant reduction in crystallinity in nano silk particles but crystal forming segments remained with β-sheet conformations after milling. The structural change influenced the ion-adsorption property where particle-size reduction resulted in a significant increase in both the rate and volume of HCrO4- adsorption. © 2014 Elsevier B.V.

Mutational interference mapping experiment (MIME) for studying RNA structure and function

RNA regulates many biological processes; however, identifying functional RNA sequences and structures is complex and time-consuming. We introduce a method, mutational interference mapping experiment (MIME), to identify, at single-nucleotide resolution, the primary sequence and secondary structures of an RNA molecule that are crucial for its function. MIME is based on random mutagenesis of the RNA target followed by functional selection and next-generation sequencing. Our analytical approach allows the recovery of quantitative binding parameters and permits the identification of base-pairing partners directly from the sequencing data. We used this method to map the binding site of the human immunodeficiency virus-1 (HIV-1) Pr55(Gag) protein on the viral genomic RNA in vitro, and showed that, by analyzing permitted base-pairing patterns, we could model RNA structure motifs that are crucial for protein binding.

Complex coacervation between flaxseed protein isolate and flaxseed gum

Flaxseed protein isolate (FPI) and flaxseed gum (FG) were extracted, and the electrostatic complexation between these two biopolymers was studied as a function of pH and FPI-to-FG ratio using turbidimetric and electrophoretic mobility (zeta potential) tests. The zeta potential values of FPI, FG, and their mixtures at the FPI-to-FG ratios of 1:1, 3:1, 5:1, 10:1, 15:1 were measured over a pH range 8.0-1.5. The alteration of the secondary structure of FPI as a function of pH was studied using circular dichroism. The proportion of a-helical structure decreased, whereas both β-sheet structure and random coil structure increased with the lowering of pH from 8.0 to 3.0. The acidic pH affected the secondary structure of FPI and the unfolding of helix conformation facilitated the complexation of FPI with FG. The optimum FPI-to-FG ratio for complex coacervation was found to be 3:1. The critical pH values associated with the formation of soluble (pHc) and insoluble (pHΦ1) complexes at the optimum FPI-to-FG ratio were found to be 6.0 and 4.5, respectively. The optimum pH (pHopt) for the optimum complex coacervation was 3.1. The instability and dissolution of FPI-FG complex coacervates started (pHΦ2) at pH2.1. These findings contribute to the development of FPI-FG complex coacervates as delivery vehicles for unstable albeit valuable nutrients such as omega-3 fatty acids.

Physicochemical and functional properties of protein isolate produced from Australian chia seeds

Protein was isolated from Australian chia seeds and converted to powders using spray, freeze and vacuum drying methods, to investigate the effect of drying methods on physicochemical and functional attributes of chia-seed protein isolate (CPI). It was found that there was no significant difference in the proximate composition; however vacuum dried CPI (VDCPI) had the highest bulk density and oil absorption capacity, whereas spray dried powder (SDCPI) demonstrated the highest solubility, water absorption capacity and lowest surface hydrophobicity. Solubility of all powders was higher at elevated temperature and alkaline pH. Foaming capacity and foam stability of CPI were found to increase with increasing pH and protein concentration. SDCPI was the least denatured and VDCPI the most denatured, demonstrating the poorest solubility and foaming properties of the latter. These findings are expected to be useful in selection of a drying process to yield chia seed protein powders with more desirable functionality.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Mining characteristic relations bind to RNA secondary structures

The identification of RNA secondary structures has been among the most exciting recent developments in biology and medical science. It has been recognized that there is an abundance of functional structures with frameshifting, regulation of translation, and splicing functions. However, the inherent signal for secondary structures is weak and generally not straightforward due to complex interleaving substrings. This makes it difficult to explore their potential functions from various structure data. Our approach, based on a collection of predicted RNA secondary structures, allows us to efficiently capture interesting characteristic relations in RNA and bring out the top-ranked rules for specified association groups. Our results not only point to a number of interesting associations and include a brief biological interpretation to them. It assists biologists in sorting out the most significant characteristic structure patterns and predicting structurefunction relationships in RNA.

Structure and properties of biomedical films prepared from aqueous and acidic silk fibroin solutions

Silk fibroin films are promising materials for a range of biomedical applications. To understand the effects of casting solvents on film properties, we used water (W), formic acid (FA), and trifluoroacetic acid (TFA) as solvents. We characterized molecular weight, secondary structure, mechanical properties, and degradation behavior of cast films. Significant degradation of fibroin was observed for TFA-based film compared to W and TA-based films when analyzed by SDS-PAGE. Fibroin degradation resulted in a significant reduction in tensile strength and modulus of TFA-based films. Compared to water, TFA-based films demonstrated lower water solubility (19.6% vs. 62.5% in 12 h) despite having only a marginal increase in their ß-sheet content (26.9% vs. 23.7%). On the other hand, FA-based films with 34.3% ß-sheet were virtually water insoluble. Following solubility treatment, ß-sheet content in FA-based films increased to 50.9%. On exposure to protease XIV, water-annealed FA-based films lost 74% mass in 22 days compared to only 30% mass loss by ethanol annealed FA films. This study demonstrated that a small variation in the ß-sheet percentage and random coil conformations resulted in a significant change in the rates of enzymatic degradation without alteration to their tensile properties. The film surface roughness changed with the extent of enzymatic hydrolysis.