Algorithmic computation of knot polynomials of secondary structure elements of proteins

The classification of protein structures is an important and still outstanding problem. The purpose of this paper is threefold. First, we utilize a relation between the Tutte and homfly polynomial to show that the Alexander-Conway polynomial can be algorithmically computed for a given planar graph. Second, as special cases of planar graphs, we use polymer graphs of protein structures. More precisely, we use three building blocks of the three-dimensional protein structure-alpha-helix, antiparallel beta-sheet, and parallel beta-sheet-and calculate, for their corresponding polymer graphs, the Tutte polynomials analytically by providing recurrence equations for all three secondary structure elements. Third, we present numerical results comparing the results from our analytical calculations with the numerical results of our algorithm-not only to test consistency, but also to demonstrate that all assigned polynomials are unique labels of the secondary structure elements. This paves the way for an automatic classification of protein structures.

Spray Congealed Lipid Microparticles with High Protein Loading: Preparation and Solid State Characterization

The spray-congealing technique, a solvent-free drug encapsulation process, was successfully employed to obtain lipid-based particulate systems with high (10–20% w/w) protein loading. Bovine serum albumin (BSA) was utilised as model protein and three low melting lipids (glyceryl palmitostearate, trimirystin and tristearin) were employed as carriers. BSA-loaded lipid microparticles were characterised in terms of particle size, morphology and drug loading. The results showed that the microparticles exhibited a spherical shape, mean diameter in the range 150–300 µm and an encapsulation efficiency higher than 90%. Possible changes in the protein structure as a result of the manufacturing process was then investigated for the first time using UV spectrophotometry in fourth derivative mode and FT-Raman spectroscopy. The results suggested that the structural integrity of the protein was maintained within the particles. Thermal analysis indicated that the effect of protein on the thermal properties of the carriers could be detected. Spray-congealing could thus be considered a suitable technique to produce highly BSA-loaded microparticles preserving the structure of the protein.

Resonantly Enhanced Multi-Photon Ionization Spectrum of the Neutral Green Fluorescent Protein Chromophore

The photophysics of the green fluorescent protein is governed by the electronic structure of the chromophore at the heart of its β-barrel protein structure. We present the first two-color, resonance-enhanced, multiphoton ionization spectrum of the isolated neutral chromophore in vacuo with supporting electronic structure calculations. We find the absorption maximum to be 3.65 ± 0.05 eV (340 ± 5 nm), which is blue-shifted by 0.5 eV (55 nm) from the absorption maximum of the protein in its neutral form. Our results show that interactions between the chromophore and the protein have a significant influence on the electronic structure of the neutral chromophore during photoabsorption and provide a benchmark for the rational design of novel chromophores as fluorescent markers or photomanipulators.

The effect of sucrose on protein stability and fibrillation of prion and S6 protein

Aggregation and fibrillation of proteins have a great importance in medicine and industry. Misfolding and aggregation are the basis of many neurodegenerative diseases like Alzheimer and Parkinson. Osmolytes are molecules that can accumulate within cells and act as protective agents and they can inclusively act as protein stabilizers when cells are exposed to stress conditions. Osmolytes can also act as protein stabilizers in vitro. In this work, two different proteins were studied, the ribosomal protein from Thermus thermophilus and the mouse prion protein. The existence of an unstructured N-terminal on the prion protein does not affect its stability. The effect of the osmolyte sucrose on the fibrillation and stabilization of these two proteins was studied through kinectic and equilibrium measurements. It was shown that sucrose is able to compact the native structure of S6 protein in fibrillization conditions. Sucrose affects also folding and unfolding kinetic of S6 protein, delaying unfolding and increasing folding rate constants. The mechanism of stabilization by sucrose is non-specific because it is distributed for all protein structure, as it was demonstrated by a protein engineering approach. Sucrose delays the process of formation and elongation of S6 and prion protein from mouse. This delay is the result of the compaction of the native structure refered above. However, cellular toxicity studies have shown that fibrils formed in the presence of sucrose are more toxic to neuronal cells.

Metal ions modulate the folding and stability of the tumor suppressor protein S100A2

Febs Journal (2009)276:1776-1786

Design of bio-inspired ionic liquids for protein stabilisation

Ionic Liquids (ILs) consist in organic salts that are liquid at/or near room temperature. Since ILs are entirely composed of ions, the formation of ion pairs is expected to be one essential feature for describing solvation in ILs. In recent years, protein - ionic liquid (P-IL) interactions have been the subject of intensive studies mainly because of their capability to promote folding/unfolding of proteins. However, the ion pairs and their lifetimes in ILs in P-IL thematic is dismissed, since the action of ILs is therefore the result of a subtle equilibrium between anion-cation interaction, ion-solvent and ion-protein interaction. The work developed in this thesis innovates in this thematic, once the design of ILs for protein stabilisation was bio-inspired in the high concentration of organic charged metabolites found in cell milieu. Although this perception is overlooked, those combined concentrations have been estimated to be ~300 mM among the macromolecules at concentrations exceeding 300 g/L (macromolecular crowding) and transient ion-pair can naturally occur with a potential specific biological role. Hence the main objective of this work is to develop new bio-ILs with a detectable ion-pair and understand its effects on protein structure and stability, under crowding environment, using advanced NMR techniques and calorimetric techniques. The choline-glutamate ([Ch][Glu]) IL was synthesized and characterized. The ion-pair was detected in water solutions using mainly the selective NOE NMR technique. Through the same technique, it was possible to detect a similar ion-pair promotion under synthetic and natural crowding environments. Using NMR spectroscopy (protein diffusion, HSQC experiments, and hydrogen-deuterium exchange) and differential scanning calorimetry (DSC), the model protein GB1 (production and purification in isotopic enrichment media) it was studied in the presence of [Ch][Glu] under macromolecular crowding conditions (PEG, BSA, lysozyme). Under dilute condition, it is possible to assert that the [Ch][Glu] induces a preferential hydration by weak and non-specific interactions, which leads to a significant stabilisation. On the other hand, under crowding environment, the [Ch][Glu] ion pair is promoted, destabilising the protein by favourable weak hydrophobic interactions , which disrupt the hydration layer of the protein. However, this capability can mitigates the effect of protein crowders. Overall, this work explored the ion-pair existence and its consequences on proteins in conditions similar to cell milieu. In this way, the charged metabolites found in cell can be understood as key for protein stabilisation.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

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.

Predicting protein structures and structural annotation of proteomes

Protein structure prediction methods aim to predict the structures of proteins from their amino acid sequences, utilizing various computational algorithms. Structural genome annotation is the process of attaching biological information to every protein encoded within a genome via the production of three-dimensional protein models.

Towards data warehousing and mining of protein unfolding simulation data

OBJECTIVES: The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. METHODS: To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis. RESULTS: To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse. CONCLUSIONS: Web and grid services, especially pre-defined data mining services that can run on or 'near' the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

Grid warehousing of molecular dynamics protein unfolding data

With the increasing awareness of protein folding disorders, the explosion of genomic information, and the need for efficient ways to predict protein structure, protein folding and unfolding has become a central issue in molecular sciences research. Molecular dynamics computer simulations are increasingly employed to understand the folding and unfolding of proteins. Running protein unfolding simulations is computationally expensive and finding ways to enhance performance is a grid issue on its own. However, more and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. This paper describes efforts to provide a grid-enabled data warehouse for protein unfolding data. We outline the challenge and present first results in the design and implementation of the data warehouse.

Ruminal dry matter and nitrogen degradation in relation to condensed tannin and protein molecular structures in sainfoin (Onobrychis viciifolia) and lucerne (Medicago sativa).

Sainfoin is a temperate legume that contains condensed tannins (CT), i.e. polyphenols that are able to bind proteins and thus reduce protein degradation in the rumen. A reduction in protein degradation in the rumen can lead to a subsequent increase in amino acid flow to the small intestine. The effects of CT in the rumen and the intestine differ according to the amount and structure of CT and the nature of the protein molecular structure. The objective of the present study was to investigate the degradability in the rumen of three CT-containing sainfoin varieties and CT-free lucerne in relation to CT content and structure (mean degree of polymerization, proportion of prodelphinidins and cis-flavanol units) and protein structure (amide I and II bands, ratio of amide I-to-amide II, α-helix, β-sheet, ratio of α-helix-to-β-sheet). Protein molecular structures were identified using Fourier transform/infrared-attenuated total reflectance (FT/IR-ATR) spectroscopy. The in situ degradability of three sainfoin varieties (Ambra, Esparcette and Villahoz) was studied in 2008, during the first growth cycle at two harvest dates (P1 and P2, i.e. 5 May and 2 June, respectively) and at one date (P3) during the second growth cycle (2 June) and these were compared with a tannin-free legume, lucerne (Aubigny). Loss of dry matter (DMDeg) and nitrogen (NDeg) in polyester bags suspended in the rumen was measured using rumen-fistulated cows. The NDeg of lucerne compared with sainfoin was 0·80 v. 0·77 at P1, 0·78 v. 0·65 at P2 and 0·79 v. 0·70 at P3, respectively. NDeg was related to the rapidly disappearing fraction (‘a’) fraction (r=0·76), the rate of degradation (‘c’) (r=0·92), to the content (r=−0·81) and structure of CT. However, the relationship between NDeg and the slowly disappearing fraction (‘b’) was weak. There was a significant effect of date and species×date, for NDeg and ‘a’ fraction. The secondary protein structure varied with harvest date (species×date) and was correlated with the fraction ‘b’. Both tannin and protein structures influenced the NDeg degradation. CT content and structure were correlated to the ‘a’ fraction and to the ‘c’. Features of the protein molecular secondary structure were correlated to the ‘b’ fraction.

Disorder prediction methods, their applicability to different protein targets and their usefulness for guiding experimental studies

The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution.

Whey protein mouth drying influenced by thermal denaturation

Whey proteins are becoming an increasingly popular functional food ingredient. There are, however, sensory properties associated with whey protein beverages that may hinder the consumption of quantities sufficient to gain the desired nutritional benefits. One such property is mouth drying. The influence of protein structure on the mouthfeel properties of milk proteins has been previously reported. This paper investigates the effect of thermal denaturation of whey proteins on physicochemical properties (viscosity, particle size, zeta-potential, pH), and relates this to the observed sensory properties measured by qualitative descriptive analysis and sequential profiling. Mouthcoating, drying and chalky attributes built up over repeated consumption, with higher intensities for samples subjected to longer heating times (p < 0.05). Viscosity, pH, and zeta-potential were found to be similar for all samples, however particle size increased with longer heating times. As the pH of all samples was close to neutral, this implies that neither the precipitation of whey proteins at low pH, nor their acidity, as reported in previous literature, can be the drying mechanisms in this case. The increase in mouth drying with increased heating time suggests that protein denaturation is a contributing factor and a possible mucoadhesive mechanism is discussed.

In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.