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.     

Molecular characterization and sequencing of antifreeze proteins from larvae of the beetle Dendroides canadensis

The deduced amino acid sequences of antifreeze proteins (AFPs) from larvae of the beetle Dendroides canadensis were determined from both complementary DNAs (cDNAs) and from peptide sequencing. These consisted of proteins with a 25-residue signal peptide and mature proteins 83 (Dendroides antifreeze protein; DAFP-1) or 84 (DAFP-2) amino acids in length which differed at only two positions. Peptide sequencing yielded sequences which overlapped exactly with those of the deduced cDNA sequences of DAFP-1 and DAFP-2, while the partial sequence of another AFP (DAFP-3) matched 21 of 28 residues. Seven 12- or 13-mer repeating units are present in these antifreeze proteins with a consensus sequence consisting of: Cys-Thr-X₃-Ser-X₅-X₆-Cys-X₈-X₉-Ala-X₁₁-Thr-X₁₃, where X₃ and X₁₁ tend toward charged residues, X₅ tends toward threonine or serine, X₆ toward asparagine or aspartate, X₉ toward asparagine or lysine, and X₁₃ toward alanine in the 13-mers. The most interesting feature of these proteins is that throughout the length of the mature antifreeze proteins every sixth residue is a cysteine. These sequences are not similar to any of the known fish AFPs, but they are similar to AFPs from the beetle Tenebrio molitor. Accepted: 14 November 1997     

Activation of antifreeze proteins from larvae of the beetle Dendroides canadensis

Summary Purified antifreeze proteins (AFPs) from the larvae of the beetle Dendroides canadensis do not produce the high levels of antifreeze activity seen in the hemolymph of overwintering larvae, even when the purified AFPs are assayed at very high concentrations. However, addition of certain proteins or agar (at concentrations sufficiently low that the gel state does not result) to the Dendroides AFP resulted in a 2–3-fold increase in activity. A 70-kDa protein with AFP-activating capabilities was purified from Dendroides larvae. Addition of this endogenous activator protein to a 4 mg·ml^-1 solution of AFP increased the activity of the AFPs to values comparable to those of the hemolymph of overwintering larvae. Data derived from a modified immunoblot technique demonstrate that the activators bind to the AFP, or vice versa. Formation of this association must allow the AFP to block ice crystal growth by binding to the surface of potential seed crystals in the normal fashion. However, because the AFP-activator complex is much larger than the AFP alone, the complex probably blocks a greater surface area of the crystal and is thus a more efficient antifreeze.Abbreviations AFP antifreeze protein - BSA bovine serum albumine - DEAE diethylaminoethyl - Ig immunoglubolin - LPIN lipoprotein ice nucleator - PIN protein ice nucleator - SDS sodium dodecyl sulfate - PAGE polyacrylamide gel electrophoresis - TH thermal hysteresis     

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.     

Purification and characterization of antifreeze proteins from larvae of the beetle Dendroides canadensis

Summary Four antifreeze proteins (AFPs) were purified from larvae of the beetle Dendroides canadensis. The AFPs are similar in amino acid compositions, having high contents of hydrophilic amino acids (45–55 mol%) and cysteine (16 mol% Cys). Approximately half of the Cys residues form disulfide bridges, and both the disulfide bridges and free sulfhydryls are essential for activity. The N-terminals of the AFPs are blocked. The pH optimum of the AFPs is 7.8, but major loss of activity occurred only at very high pH (12.0). The detergents SDS and Triton X-100 did not inactivate the AFPs. Circular dichroism spectra indicate the presence of both and secondary structures in the AFPs, in addition to a large random structure component.Abbreviations AFP antifreeze protein - CD circular dichroism - DTT dithiothreitol - HPLC high pressure liquid chromatography - PAGE polyacrylamide gel electrophoresis - PAS periodic acid Schiff - SDS sodium dodecyl sulfate - TFA trifluoroacetic acid     

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.     

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.     

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.     

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 cDNA library from the antenna of Monochamus alternatus Hope and binding properties of odorant‐binding proteins

Chemoreception is a key feature in selection of host plants by insects. We performed a preliminary functional characterization of olfactory proteins isolated from an antennal cDNA library of Monochamus alternatus. We identified four olfactory genes, including two encoding putative classic odorant‐binding proteins (OBPs) and two encoding minus‐C OBPs. We expressed two of the four OBPs, MaltOBP3 and MaltOBP5, in a bacterial system and assessed their ligand specificity by measuring the competitive binding of fluorescent probe, N‐phenyl‐1‐naph‐thylamine, in the presence of 17 volatile beetle‐ or host‐plant‐related ligands. The results indicated that although MaltOBP3 and MaltOBP5 bound a distinctly different group of competitors, both had relatively high binding affinities (K_i < 20 μm ) for certain compounds. The differences in their binding affinities towards host‐plant ligands suggest the roles of MaltOBP3 and MaltOBP5 in host‐plant selection.     

Hydrodynamical properties of recombinant spider silk proteins: Effects of pH,salts and shear,and implications for the spinning process

We have investigated the effect of pH, salts and shear on the hydrodynamical diameter of recombinant major ampullate (MA) rMaSpI silk proteins in solution as a function of time using ¹H solution NMR spectroscopy. The results indicate that the silk proteins in solution are composed of two diffusing populations, a high proportion of “native” solubilized proteins and a small amount of high molecular weight oligomers. Similar results are observed with the MA gland content. Salts help maintaining the proteins in a compact form in solution over time and inhibit aggregation, the absence of salts triggering protein assembly leading to a gel state. Moreover, the aggregation kinetics of rMaSpI at low salt concentration accelerates as the pH is close to the isoelectric point of the proteins, suggesting that the pH decrease tends to slow down aggregation. The data also support the strong impact of shear on the spinning process and suggest that the assembly is driven by a nucleation conformational conversion mechanism. Thus, the adjustment of the physicochemical conditions in the ampulla seems to promote a stable, long term storage. In addition, the optimization of protein conformation as well as their unfolding and aggregation propensity in the duct leads to a specifically organized structure. © 2013 Wiley Periodicals, Inc. Biopolymers 99: 582–593, 2013.     

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

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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.     

NAGbinder: An approach for identifying N‐acetylglucosamine interacting residues of a protein from its primary sequence

N‐acetylglucosamine (NAG) belongs to the eight essential saccharides that are required to maintain the optimal health and precise functioning of systems ranging from bacteria to human. In the present study, we have developed a method, NAGbinder, which predicts the NAG‐interacting residues in a protein from its primary sequence information. We extracted 231 NAG‐interacting nonredundant protein chains from Protein Data Bank, where no two sequences share more than 40% sequence identity. All prediction models were trained, validated, and evaluated on these 231 protein chains. At first, prediction models were developed on balanced data consisting of 1,335 NAG‐interacting and noninteracting residues, using various window size. The model developed by implementing Random Forest using binary profiles as the main principle for identifying NAG‐interacting residue with window size 9, performed best among other models. It achieved highest Matthews Correlation Coefficient (MCC) of 0.31 and 0.25, and Area Under Receiver Operating Curve (AUROC) of 0.73 and 0.70 on training and validation data set, respectively. We also developed prediction models on realistic data set (1,335 NAG‐interacting and 47,198 noninteracting residues) using the same principle, where the model achieved MCC of 0.26 and 0.27, and AUROC of 0.70 and 0.71, on training and validation data set, respectively. The success of our method can be appraised by the fact that, if a sequence of 1,000 amino acids is analyzed with our approach, 10 residues will be predicted as NAG‐interacting, out of which five are correct. Best models were incorporated in the standalone version and in the webserver available at https://webs.iiitd.edu.in/raghava/nagbinder/