Ice restructuring inhibition activities in antifreeze proteins with distinct differences in thermal hysteresis

Antifreeze proteins (AFPs) share two related properties: the ability to depress the freezing temperature below the melting point of ice (thermal hysteresis; TH); and the ability to inhibit the restructuring of ice into larger crystals. Since the ‘hyperactive’ AFPs, which have been more recently discovered, show an order of magnitude more TH than previously characterized AFPs, we have now determined their activities in ice restructuring inhibition (IrI) assays. IrI activities of three TH-hyperactive AFPs and three less TH-active AFPs varied over an 8-fold range. There was no obvious correlation between high TH activity and high IrI activity. However, the use of mutant AFPs demonstrated that severe disruption of ice-binding residues diminished both TH and IrI similarly, revealing that that the same ice-binding residues are crucial for both activities. In addition, bicarbonate ions, which are known to enhance the TH activity of AFPs, also enhanced their IrI activity. We suggest that these seemingly contradictory observations can be partially explained by differences in the coverage of ice by TH-hyperactive and non-hyperactive AFPs, and by differences in the stability of AFP-bound ice under supercooled and recrystallization conditions.     

A facile method for determining ice recrystallization inhibition by antifreeze proteins

Ice recrystallization, the growth of large ice crystals at the expense of small ones, stresses freeze tolerant organisms and causes spoilage of frozen foods. This process is inhibited by antifreeze proteins (AFPs). Here, we present a simple method for determining the ice recrystallization inhibition (RI) activity of an AFP under physiological conditions using 10microl glass capillaries. Serial dilutions were prepared to determine the concentration below which RI activity was no longer detected, termed the RI endpoint. For type III AFP this was 200nM. The capillary method allows samples to be aligned and viewed simultaneously, which facilitates RI endpoint determination. Once prepared, the samples can be used reproducibly in subsequent RI assays and can be archived in a freezer for future reference. This method was used to detect the elution of type III AFP from a Sephadex G-75 size-exclusion column. RI activity was found at the expected V(e) for a 7kDa protein and also unexpectedly in the void volume.     

The basis for hyperactivity of antifreeze proteins

Antifreeze proteins (AFPs) bind to the surface of ice crystals and lower the non-equilibrium freezing temperature of the icy solution below its melting point. We have recently reported the discovery of three novel hyperactive AFPs from a bacterium, a primitive insect and a fish, which, like two hyperactive AFPs previously recognized in beetles and moths, are considerably better at depressing the freezing point than most fish AFPs. When cooled below the non-equilibrium freezing temperature, ice crystals formed in the presence of any of five distinct, moderately active fish AFPs grow suddenly along the c-axis. Ice crystals formed in the presence of any of the five evolutionarily and structurally distinct hyperactive AFPs remain stable to lower temperatures, and then grow explosively in a direction normal to the c-axis when cooled below the freezing temperature. We argue that this one consistent distinction in the behaviour of these two classes of AFPs is the key to hyperactivity. Whereas both AFP classes bind irreversibly to ice, the hyperactive AFPs are better at preventing ice growth out of the basal planes.     

A machine learning approach for the identification of odorant binding proteins from sequence-derived properties

Background  

Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as odorant presenters. Although several sequence based search methods have been exploited for protein family prediction, less effort has been devoted to the prediction of OBPs from sequence data and this area is more challenging due to poor sequence identity between these proteins.     

A comparative in silico characterization of functional and physicochemical properties of 3FTx (three finger toxin) proteins from four venomous snakes

Snake venom is an abundant resource of diverse pharmacologically bioactive proteins and peptides and a good natural source of drug lead compounds and used as important research tools in the field of toxicology, pharmacology and neuroscience. Three finger toxins (3FTx) is an important super-family of snake venom proteins which has a conserved three finger like appearance in three dimensional structures. Members of 3FTx family show a wide array of pharmacological effects by targeting different receptors and ion channels with high specificity and many of them are being investigated as potential drug target. Therefore, with a vision to verdict a new edge and attempt we determined the amino acid compositional (%) profile, physiochemical properties, secondary structural and functional analysis and phylogenetic relationship of three finger toxins present in four different elapid snake species namely, Naja naja, Astrotia stokesii, Hydrophis cyanocintus and Pelamis platura using different bioinformatics tools. From the outcome of the current studies, it will be possible to know about a range of biological functions which are responsible mainly for the glowing amino acid composition profile of these proteins. Amino acid composition (%) profile although represents differential amount of different amino acid residues which encompasses a family precise model but all the protein sequence have a conserved amount of cysteine. The analysis of physicochemical properties can be used as a basic approach to contribute in developing rational drug through protein engineering and understanding different physiological function which will be beneficial for the welfare of human being.     

ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa

Background

Computational identification of apicoplast-targeted proteins is important in drug target determination for diseases such as malaria. While there are established methods for identifying proteins with a bipartite signal in multiple species of Apicomplexa, not all apicoplast-targeted proteins possess this bipartite signature. The publication of recent experimental findings of apicoplast membrane proteins, called transmembrane proteins, that do not possess a bipartite signal has made it feasible to devise a machine learning approach for identifying this new class of apicoplast-targeted proteins computationally.

Methodology/principal findings

In this work, we develop a method for predicting apicoplast-targeted transmembrane proteins for multiple species of Apicomplexa, whereby several classifiers trained on different feature sets and based on different algorithms are evaluated and combined in an ensemble classification model to obtain the best expected performance. The feature sets considered are the hydrophobicity and composition characteristics of amino acids over transmembrane domains, the existence of short sequence motifs over cytosolically disposed regions, and Gene Ontology (GO) terms associated with given proteins. Our model, ApicoAMP, is an ensemble classification model that combines decisions of classifiers following the majority vote principle. ApicoAMP is trained on a set of proteins from 11 apicomplexan species and achieves 91% overall expected accuracy.

Conclusions/significance

ApicoAMP is the first computational model capable of identifying apicoplast-targeted transmembrane proteins in Apicomplexa. The ApicoAMP prediction software is available at http://code.google.com/p/apicoamp/ and http://bcb.eecs.wsu.edu.     

Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study

Background

State-of-the-art protein-ligand docking methods are generally limited by the traditionally low accuracy of their scoring functions, which are used to predict binding affinity and thus vital for discriminating between active and inactive compounds. Despite intensive research over the years, classical scoring functions have reached a plateau in their predictive performance. These assume a predetermined additive functional form for some sophisticated numerical features, and use standard multivariate linear regression (MLR) on experimental data to derive the coefficients.

Results

In this study we show that such a simple functional form is detrimental for the prediction performance of a scoring function, and replacing linear regression by machine learning techniques like random forest (RF) can improve prediction performance. We investigate the conditions of applying RF under various contexts and find that given sufficient training samples RF manages to comprehensively capture the non-linearity between structural features and measured binding affinities. Incorporating more structural features and training with more samples can both boost RF performance. In addition, we analyze the importance of structural features to binding affinity prediction using the RF variable importance tool. Lastly, we use Cyscore, a top performing empirical scoring function, as a baseline for comparison study.

Conclusions

Machine-learning scoring functions are fundamentally different from classical scoring functions because the former circumvents the fixed functional form relating structural features with binding affinities. RF, but not MLR, can effectively exploit more structural features and more training samples, leading to higher prediction performance. The future availability of more X-ray crystal structures will further widen the performance gap between RF-based and MLR-based scoring functions. This further stresses the importance of substituting RF for MLR in scoring function development.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-291) contains supplementary material, which is available to authorized users.     

Human hnRNP protein A1: A model polypeptide for a structural and genetic investigation of a broad family of RNA binding proteins

The hnRNP fiber is the substrate on which pre-mRNA processing occurs. The protein moiety of the fiber (hnRNP proteins) constitutes a broad family of RNA binding proteins that revealed, upon molecular analysis, a number of interesting features.Heterogeneous nuclear ribonucleoprotein A1 is a major component of the human hnRNP complex. In recent years this protein has attracted great attention because of several emerging evidences of its direct involvement in pre-mRNA processing and it has become one of the best characterized RNA binding proteins. Detailed knowledge of the structure of protein A1 has laid the basis for the understanding of its function, and for this reason A1 can be considered as a model polypeptide for the investigation of a large number of RNA binding proteins.In this work we report recent findings regarding the binding properties of protein A1 as well as new data on the gene structure of A1 and of its closely related hnRNP protein A2. Our results show that a single A1 molecule contains the determinants for simultaneous binding of two single-stranded nucleic acid molecules and we demonstrate that the glycine-rich domain of A1, isolated from the rest of the molecule, is capable of sustaining protein-protein interactions. These features probably account for the reannealing activity of the protein and for its capacity to modulate the binding of snRNPs to intron sequencesin vitro. Comparison of A1 and A2 gene sequences revealed a remarkable conservation of the overall structural organization, suggesting important functions for the different structural elements.     

A new method for concentration of proteins in the calcareous corpuscles separated from the spargana of Spirometra erinacei

Calcareous corpuscles are a characteristic structure found in larval and adult stage cestodes. These corpuscles are known to contain several protein components and to possess protein-binding activity. However, the proteins bound to calcareous corpuscles in situ have not been studied. The present study was undertaken to identify the proteins on calcareous corpuscles. Calcareous corpuscles were purified from the plerocercoids (= spargana) of Spirometra erinacei, and serially dissolved using 0.1 M sulfamic acid solution. Collected supernatants were examined by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and silver staining. The results showed that only the fraction remaining after the 19th dissolved fraction contained proteins. A total of 20 protein molecules were detected in gel, with major bands at 56, 53, 46, 40, 35, 29, 28, 24.5, 21, 19, 16, 13, 10 and 8 kDa. In particular, the proteins corresponding to the 21 and 16 kDa bands were most abundant. Our results demonstrated for the first time the protein contents of the calcareous corpuscles of spargana. Further studies on the functions of these proteins are required.     

SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes

Eukaryotic protein secretion generally occurs via the classical secretory pathway that traverses the ER and Golgi apparatus. Secreted proteins usually contain a signal sequence with all the essential information required to target them for secretion. However, some proteins like fibroblast growth factors (FGF-1, FGF-2), interleukins (IL-1 alpha, IL-1 beta), galectins and thioredoxin are exported by an alternative pathway. This is known as leaderless or non-classical secretion and works without a signal sequence. Most computational methods for the identification of secretory proteins use the signal peptide as indicator and are therefore not able to identify substrates of non-classical secretion. In this work, we report a random forest method, SPRED, to identify secretory proteins from protein sequences irrespective of N-terminal signal peptides, thus allowing also correct classification of non-classical secretory proteins. Training was performed on a dataset containing 600 extracellular proteins and 600 cytoplasmic and/or nuclear proteins. The algorithm was tested on 180 extracellular proteins and 1380 cytoplasmic and/or nuclear proteins. We obtained 85.92% accuracy from training and 82.18% accuracy from testing. Since SPRED does not use N-terminal signals, it can detect non-classical secreted proteins by filtering those secreted proteins with an N-terminal signal by using SignalP. SPRED predicted 15 out of 19 experimentally verified non-classical secretory proteins. By scanning the entire human proteome we identified 566 protein sequences potentially undergoing non-classical secretion. The dataset and standalone version of the SPRED software is available at http://www.inb.uni-luebeck.de/tools-demos/spred/spred.     

A high-resolution 1H NMR approach for structure determination of membrane peptides and proteins in non-deuterated detergent: Application to mastoparan X solubilized in n-octylglucoside

Summary Application of ¹H 2D NMR methods to solubilized membrane proteins and peptides has up to now required the use of selectively deuterated detergents. The unavailability of any of the common biochemical detergents in deuterated form has therefore limited to some extent the scope of this approach. Here a ¹H NMR method is described which allows structure determination of membrane peptides and small membrane proteins by ¹H 2D NMR in any type of non-deuterated detergent. The approach is based on regioselective excitation of protein resonances with DANTE-Z or spin-pinging pulse trains. It is shown that regioselective excitation of the amide-aromatic region of solubilized membrane proteins and peptides leads to an almost complete suppression of the two orders of magnitude higher contribution of the protonated detergent to the ¹H NMR spectrum. Consistently TOCSY, COSY and NOESY sequences incorporating such regioselective excitation in the F2 dimension yield protein ¹H 2D NMR spectra of quality comparable to those obtained in deuterated detergents. Regioselective TOCSY and NOESY spectra display all through-bond and through-space correlations within amide-aromatic protons and between these protons and aliphatic and -protons. Regioselective COSY spectra provide scalar coupling constants between amide and -protons. Application of the method to the membrane-active peptide mastoparan X, solubilized in n-octylglucoside, yields complete sequence-specific assignments and extensive secondary structure-related spatial proximities and coupling constants. It is shown that mastoparan adopts an -helical conformation when bound to nonionic detergent micelles. The present method is expected to increase the applicability of ¹H solution NMR methods to membrane proteins and peptides.Abbreviations 2D NMR two-dimensional NMR - COSY correlated spectroscopy - DANTE delays alternating nutations for tailored excitation - NOESY nuclear Overhauser enhancement spectroscopy - TOCSY total correlation spectroscopy     

A proteomic-based approach for the characterization of some major structural proteins involved in host-parasite relationships from the silkworm parasite Nosema bombycis (Microsporidia)

Nosema bombycis is the causative agent of the silkworm Bombyx mori pebrine disease which inflicts severe worldwide economical losses in sericulture. Little is known about host-parasite interactions at the molecular level for this spore-forming obligate intracellular parasite which belongs to the fungi-related Microsporidia phylum. Major microsporidian structural proteins from the spore wall (SW) and the polar tube (PT) are known to be involved in host invasion. We developed a proteomic-based approach to identify few N. bombycis proteins belonging to these cell structures. Protein extraction protocols were optimized and four N. bombycis spore protein extracts were compared by SDS-PAGE and 2-DE to establish complementary proteomic profiles. Three proteins were shown to be located at the parasite SW. Moreover, 17 polyclonal antibodies were raised against major N. bombycis proteins from all extracts, and three spots were shown to correspond to polar tube proteins (PTPs) by immunofluorescent assay and transmission electron microscopy immunocytochemistry on cryosections. Specific patterns for each PTP were obtained by MALDI-TOF-MS and MS/MS. Peptide sequence tags were deduced by de novo sequencing using Peaks Online and DeNovoX, then evaluated by MASCOT and SEQUEST searches. Identification parameters were higher than false-positive hits, strengthening our strategy that could be enlarged to a nongenomic context.     

Isolation and biochemical characterization of heparin-binding proteins from boar seminal plasma: A dual role for spermadhesins in fertilization

Sperm surface-coated heparin-binding proteins originating from secretions of the male sexual accessory glands, are known to play a pivotal role as extrinsic regulatory factors during sperm capacitation in many mammalian species. They interact with glycosaminoglycans present in the female genital tract and enhance the subsequent zona pellucida-induced acrosome reaction. We have isolated heparin-binding proteins from boar seminal plasma by affinity chromatography on heparin–Sepharose and reverse-phase HPLC. N-Terminal sequence analysis of these proteins identified a boar counterpart of the bovine capacitation factors BSP-A1/2 (also called PDC-109) and BSP-A3. Several carbohydrate- and zona pellucida-binding proteins, which belong to the newly described spermadhesin family, were also identified as heparin-binding proteins. Our results imply that, besides other capacitation factors, members of the spermadhesin family may play a dual role in sperm capacitation and fertilization in the pig. © 1993 Wiley-Liss, Inc.     

A survey on cytosolic non-enzymic proteins involved in the metabolism of lipophilic compounds: from organic anion binders to new protein families

This review deals with recent advances in the research of cytosolic non-enzymic proteins involved in the metabolism of lipophilic compounds. Emphasis is given to the important contribution of structural data in the understanding of the functional properties of these proteins and in the emergence of new protein families. The possibility that many of the 'cytosolic' proteins might be structure-bound and structure-forming in the living cell is discussed, with references to so far available structural data and to recent investigations on the architecture and biochemical composition of the cytoplasm. The aim of this review is to present in a condensed form (227 references) the evolution in the study of cytosolic proteins binding and transferring lipophilic compounds and to enable interested investigators to become aware of current concepts and perspectives in this active and steadily growing area of research.