A novel protein structural classes prediction method based on predicted secondary structure

Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E–H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between α/β class and α + β class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate α/β and α + β classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html.     

Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation

In this paper, we intend to predict protein structural classes (α, β, α+β, or α/β) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.     

Dynameomics: A Consensus View of the Protein Unfolding/Folding Transition State Ensemble across a Diverse Set of Protein Folds

The Dynameomics project aims to simulate a representative sample of all globular protein metafolds under both native and unfolding conditions. We have identified protein unfolding transition state (TS) ensembles from multiple molecular dynamics simulations of high-temperature unfolding in 183 structurally distinct proteins. These data can be used to study individual proteins and individual protein metafolds and to mine for TS structural features common across all proteins. Separating the TS structures into four different fold classes (all proteins, all-α, all-β, and mixed α/β and α + β) resulted in no significant difference in the overall protein properties. The residues with the most contacts in the native state lost the most contacts in the TS ensemble. On average, residues beginning in an α-helix maintained more structure in the TS ensemble than did residues starting in β-strands or any other conformation. The metafolds studied here represent 67% of all known protein structures, and this is, to our knowledge, the largest, most comprehensive study of the protein folding/unfolding TS ensemble to date. One might have expected broad distributions in the average global properties of the TS relative to the native state, indicating variability in the amount of structure present in the TS. Instead, the average global properties converged with low standard deviations across metafolds, suggesting that there are general rules governing the structure and properties of the TS.     

Effects of Topology and Sequence in Protein Folding Linked via Conformational Fluctuations

Experiments have compared the folding of proteins with different amino acid sequences but the same basic structure, or fold. Results indicate that folding is robust to sequence variations for proteins with some nonlocal folds, such as all-β, whereas the folding of more local, all-α proteins typically exhibits a stronger sequence dependence. Here, we use a coarse-grained model to systematically study how variations in sequence perturb the folding energy landscapes of three model sequences with 3α, 4β + α, and β-barrel folds, respectively. These three proteins exhibit folding features in line with experiments, including expected rank order in the cooperativity of the folding transition and stability-dependent shifts in the location of the free-energy barrier to folding. Using a generalized-ensemble simulation approach, we determine the thermodynamics of around 2000 sequence variants representing all possible hydrophobic or polar single- and double-point mutations. From an analysis of the subset of stability-neutral mutations, we find that folding is perturbed in a topology-dependent manner, with the β-barrel protein being the most robust. Our analysis shows, in particular, that the magnitude of mutational perturbations of the transition state is controlled in part by the size or “width” of the underlying conformational ensemble. This result suggests that the mutational robustness of the folding of the β-barrel protein is underpinned by its conformationally restricted transition state ensemble, revealing a link between sequence and topological effects in protein folding.     

Type I and II β-turns prediction using NMR chemical shifts

A method for predicting type I and II β-turns using nuclear magnetic resonance (NMR) chemical shifts is proposed. Isolated β-turn chemical-shift data were collected from 1,798 protein chains. One-dimensional statistical analyses on chemical-shift data of three classes β-turn (type I, II, and VIII) showed different distributions at four positions, (i) to (i + 3). Considering the central two residues of type I β-turns, the mean values of C_ο, C_α, H_N, and N_H chemical shifts were generally (i + 1) > (i + 2). The mean values of C_β and H_α chemical shifts were (i + 1) < (i + 2). The distributions of the central two residues in type II and VIII β-turns were also distinguishable by trends of chemical shift values. Two-dimensional cluster analyses on chemical-shift data show positional distributions more clearly. Based on these propensities of chemical shift classified as a function of position, rules were derived using scoring matrices for four consecutive residues to predict type I and II β-turns. The proposed method achieves an overall prediction accuracy of 83.2 and 84.2 % with the Matthews correlation coefficient values of 0.317 and 0.632 for type I and II β-turns, indicating that its higher accuracy for type II turn prediction. The results show that it is feasible to use NMR chemical shifts to predict the β-turn types in proteins. The proposed method can be incorporated into other chemical-shift based protein secondary structure prediction methods.     

A high-accuracy protein structural class prediction algorithm using predicted secondary structural information

One major problem with the existing algorithm for the prediction of protein structural classes is low accuracies for proteins from α/β and α+β classes. In this study, three novel features were rationally designed to model the differences between proteins from these two classes. In combination with other rational designed features, an 11-dimensional vector prediction method was proposed. By means of this method, the overall prediction accuracy based on 25PDB dataset was 1.5% higher than the previous best-performing method, MODAS. Furthermore, the prediction accuracy for proteins from α+β class based on 25PDB dataset was 5% higher than the previous best-performing method, SCPRED. The prediction accuracies obtained with the D675 and FC699 datasets were also improved.     

基于位置特异性谱和输入加权神经网络的蛋白质亚细胞定位预测

蛋白质必须处于正确的亚细胞位置才能行使其功能。文章利用PSI-BLAST工具搜索蛋白质序列,提取位点特异性谱中的位点特异性得分矩阵作为蛋白质的一类特征,并计算4等分序列的氨基酸含量以及1～7阶二肽含量作为另外两类特征,由这三类特征一共得到蛋白质序列的12个特征向量。通过设计一个简单加权函数对各类特征向量加权处理,作为神经网络预测器的输入,并使用Levenberg-Marquardt算法代替传统的EBP算法来调整网络权值和阈值,大大提高了训练速度。对具有4类亚细胞位置和12类亚细胞位置的两种蛋白质数据集分别进行＂留一法＂测试和5倍交叉验证测试,总体预测精度分别达到88.4%和83.3%。其中,对4类亚细胞位置数据集的预测效果优于普通BP神经网络、隐马尔可夫模型、模糊K邻近等预测方法,对12类亚细胞位置数据集的预测效果优于支持向量机分类方法。最后还对三类特征采取不同加权比例对预测精度的影响进行了讨论,对选择的八种加权比例的预测结果表明,分别给予三类特征合适的权值系数可以进一步提高预测精度。     

Prediction of Beta-Turn in Protein Using E-SSpred and Support Vector Machine

β-Turn is a secondary protein structure type that plays an important role in protein configuration and function. Here, we introduced an approach of β-turn prediction that used the support vector machine (SVM) algorithm combined with predicted secondary structure information. The secondary structure information was obtained by using E-SSpred, a new secondary protein structure prediction method. A 7-fold cross validation based on the benchmark dataset of 426 non-homologous protein chains was used to evaluate the performance of our method. The prediction results broke the 80% Q _total barrier and achieved Q _total = 80.9%, MCC = 0.44, and Q _predicted higher 0.9% when compared with the best method. The results in our research are coincident with the conclusion that β-turn prediction accuracy can be improved by inclusion of secondary structure information.     

Carotenoid differences in isogenic lines of tomato fruit colour mutants

The main pigment present in fruits of tomato lines isogenic with the cultivar ‘Ailsa-Craig’, but with different fruit colours, is all-trans-β-carotene. Most of the tomato lines also contain cis-phytoene, all-trans-phytofluene, all-trans-ζ-carotene, all-trans-neurosporene, all-trans-lycopene and all-trans-α-carotene. Delta-del fruits accumulate all-trans-δ-carotene as the major pigment, and Tangerine coloured fruits contain massive amounts of the intriguing di-cis-ζ-carotene, tri-cis-neurosporene and tetra-cis-lycopene (also known as ‘prolycopene’); smaller amounts of cis-phytoene and di-cis-phytofluene are also found in Tangerine tomato fruits.     

SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids

The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.     

Inhibition of martentoxin on neuronal BK channel subtype (alpha+beta4): implications for a novel interaction model

Martentoxin as a 37-residue peptide was capable of blocking large-conductance Ca²⁺-activated K⁺ (BK) channels in adrenal medulla chromaffin cells. This study investigated the pharmacological discrimination of martentoxin on BK channel subtypes. The results showed that the iberiotoxin-insensitive neuronal BK channels (α+β4) could be potently blocked by martentoxin (IC₅₀ = ∼80 nM). In contrast, the iberiotoxin-sensitive BK channel consisting of only α-subunit was less sensitive to martentoxin. Distinctively, martentoxin inhibited neuronal BK channels (α+β4) with a novel interaction mode. Two possible interaction sites of neuronal BK channels (α+β4) might be responsible for the binding with martentoxin: one for trapping and the other located at the pore region for blocking. In addition, the inhibition of martentoxin on neuronal BK channels (α+β4) depended on cytoplasmic Ca²⁺ concentration. On the other hand, in vivo experiments from EEG recordings suggested that neuronal BK channels (α+β4) were the primary target of martentoxin. Therefore, this research not only sheds light on a unique ligand for neuronal BK channels (α+β4), but also highlights a novel model approach for the interaction between K⁺ channels and specific-ligands.     

Prediction of protein folding class from amino acid composition

An empirical relation between the amino acid composition and three-dimensional folding pattern of several classes of proteins has been determined. Computer simulated neural networks have been used to assign proteins to one of the following classes based on their amino acid composition and size: (1) 4α-helical bundles, (2) parallel (α/β)₈ barrels, (3) nucleotide binding fold, (4) immunoglobulin fold, or (5) none of these. Networks trained on the known crystal structures as well as sequences of closely related proteins are shown to correctly predict folding classes of proteins not represented in the training set with an average accuracy of 87%. Other folding motifs can easily be added to the prediction scheme once larger databases become available. Analysis of the neural network weights reveals that amino acids favoring prediction of a folding class are usually over represented in that class and amino acids with unfavorable weights are underrepresented in composition. The neural networks utilize combinations of these multiple small variations in amino acid composition in order to make a prediction. The favorably weighted amino acids in a given class also form the most intramolecular interactions with other residues in proteins of that class. A detailed examination of the contacts of these amino acids reveals some general patterns that may help stabilize each folding class. © 1993 Wiley-Liss, Inc.     

Prediction of the β-Hairpins in Proteins Using Support Vector Machine

By using of the composite vector with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting β-hairpin motifs is proposed. The prediction is done on a dataset of 3,088 non homologous proteins containing 6,027 β-hairpins. The overall accuracy of prediction and Matthew’s correlation coefficient are 79.9% and 0.59 for the independent testing dataset. In addition, a higher accuracy of 83.3% and Matthew’s correlation coefficient of 0.67 in the independent testing dataset are obtained on a dataset previously used by Kumar et al. (Nuclic Acid Res 33:154–159). The performance of the method is also evaluated by predicting the β-hairpins of in the CASP6 proteins, and the better results are obtained. Moreover, this method is used to predict four kinds of supersecondary structures. The overall accuracy of prediction is 64.5% for the independent testing dataset.     

Analysis and prediction of the packing of α-helices against a β-sheet in the tertiary structure of globular proteins

The packing of α-helices and β-sheets in six $\text{α}\text{β}$ proteins (e.g. flavodoxin) has been analysed. The results provide the basis for a computer algorithm to predict the tertiary structure of an $\text{α}\text{β}$ protein from its amino acid sequence and actual assignment of secondary structure.The packing of an individual α-helix against a β-sheet generally involves two adjacent ± 4 rows of non-polar residues on the α-helix at the positions i, i + 4, i + 8, i + 1, i + 5, i + 9. The pattern of interacting β-sheet residues results from the twisted nature of the sheet surface and the attendant rotation of the side-chains. At a more detailed level, four of the α-helical residues (i + 1, i + 4, i + 5 and i + 8) form a diamond that surrounds one particular β-sheet residue, generally isoleucine, leucine or valine. In general, the α-helix sits 10 Å above the sheet and lies parallel to the strand direction.The prediction follows a combinational approach. First, a list of possible β-sheet structures (10⁶ to 10¹⁴) is constructed by the generation of all β-sheet topologies and β-strand alignments. This list is reduced by constraints on topology and the location of non-polar residues to mediate the sheet/helix packing, and then rank-ordered on the extent of hydrogen bonding. This algorithm was uniformly applied to 16 $\text{α}\text{β}$ domains in 13 proteins. For every structure, one member of the reduced list was close to the crystal structure; the root-mean-square deviation between equivalenced C^α atoms averaged 5.6 Å for 100 residues. For the $\text{α}\text{β}$ proteins with pure parallel β-sheets, the total number of structures comparable to or better than the native in terms of hydrogen bonds was between 1 and 148. For proteins with mixed β-sheets, the worst case is glyceraldehyde-3-phosphate dehydrogenase, where as many as 3800 structures would have to be sampled. The evolutionary significance of these results as well as the potential use of a combinatorial approach to the protein folding problem are discussed.     

Reconstruction and Stability of Secondary Structure Elements in the Context of Protein Structure Prediction

Efficient and accurate reconstruction of secondary structure elements in the context of protein structure prediction is the major focus of this work. We present a novel approach capable of reconstructing α-helices and β-sheets in atomic detail. The method is based on Metropolis Monte Carlo simulations in a force field of empirical potentials that are designed to stabilize secondary structure elements in room-temperature simulations. Particular attention is paid to lateral side-chain interactions in β-sheets and between the turns of α-helices, as well as backbone hydrogen bonding. The force constants are optimized using contrastive divergence, a novel machine learning technique, from a data set of known structures. Using this approach, we demonstrate the applicability of the framework to the problem of reconstructing the overall protein fold for a number of commonly studied small proteins, based on only predicted secondary structure and contact map. For protein G and chymotrypsin inhibitor 2, we are able to reconstruct the secondary structure elements in atomic detail and the overall protein folds with a root mean-square deviation of <10 Å. For cold-shock protein and the SH3 domain, we accurately reproduce the secondary structure elements and the topology of the 5-stranded β-sheets, but not the barrel structure. The importance of high-quality secondary structure and contact map prediction is discussed.     

Determination of the Stoichiometry between α- and γ1 Subunits of the BK Channel Using LRET

Two families of accessory proteins, β and γ, modulate BK channel gating and pharmacology. Notably, in the absence of internal Ca²⁺, the γ1 subunit promotes a large shift of the BK conductance-voltage curve to more negative potentials. However, very little is known about how α- and γ1 subunits interact. In particular, the association stoichiometry between both subunits is unknown. Here, we propose a method to answer this question using lanthanide resonance energy transfer. The method assumes that the kinetics of lanthanide resonance energy transfer-sensitized emission of the donor double-labeled α/γ1 complex is the linear combination of the kinetics of the sensitized emission in single-labeled complexes. We used a lanthanide binding tag engineered either into the α- or the γ1 subunits to bind Tb⁺³ as the donor. The acceptor (BODIPY) was attached to the BK pore-blocker iberiotoxin. We determined that γ1 associates with the α-subunit with a maximal 1:1 stoichiometry. This method could be applied to determine the stoichiometry of association between proteins within heteromultimeric complexes.     

Recognition of super-secondary structure in proteins

A procedure to recognize super-secondary structure in protein sequences is described. An idealized template, derived from known super-secondary structures, is used to locate probable sites by matching with secondary structure probability profiles. We applied the method to the identification of βαβ units in β/α type proteins with 75% accuracy. The location of super-secondary structure was then used to refine the original (Garnier et al., 1978) secondary structure prediction resulting in an 8.8% improvement, which correctly assigned 83% of secondary structure elements in 14 proteins. Slight modifications to the Garnier et al. method arc suggested, producing a more accurate identification of protein class and a better prediction for β/α. type proteins. A method for the incorporation of hydrophobic information into the prediction is also described.     

Mechanical Resistance in Unstructured Proteins

Single-molecule pulling experiments on unstructured proteins linked to neurodegenerative diseases have measured rupture forces comparable to those for stable folded proteins. To investigate the structural mechanisms of this unexpected force resistance, we perform pulling simulations of the amyloid β-peptide (Aβ) and α-synuclein (αS), starting from simulated conformational ensembles for the free monomers. For both proteins, the simulations yield a set of rupture events that agree well with the experimental data. By analyzing the conformations occurring shortly before rupture in each event, we find that the mechanically resistant structures share a common architecture, with similarities to the folds adopted by Aβ and αS in amyloid fibrils. The disease-linked Arctic mutation of Aβ is found to increase the occurrence of highly force-resistant structures. Our study suggests that the high rupture forces observed in Aβ and αS pulling experiments are caused by structures that might have a key role in amyloid formation.     

20α-hydroxysteroid dehydrogenase activity in the rat corpus luteum; A quantitative cytochemical study

A quantitative cytochemical method for the demonstration of 20α-hydroxysteroid dehydrogenase activity (20α-HSD) in the regressing corpora lutea of the adult rat ovary is described. The method employs unfixed tissue sections and relies upon the oxidation of 20α-hydroxy-4-pregnen-3-one (20α-OH-P) with nitro blue tetrazolium as the hydrogen acceptor. The enzyme was dependent upon NADP⁺ for its activity and was inactive when 20β-hydroxy-4-pregnen-3-one (20β-OH-P) was used as a substrate. The apparent K_m values for 20α-OH-P and NADP⁺ were 3 × 10⁻⁴M and 2.5 × 10⁻⁵M respectively. Inhibition of 20α-HSD activity by steroids was demonstrable at pH 8. Androstenedione was by far the most potent inhibitor, followed by progesterone (the product of the enzyme activity) 17α-hydroxyprogesterone. Compound S and 20β-OH-P. At pH 6.8, a pH more favourable to the progesterone → 20α-OH-P reaction, only progesterone and 17α-hydroxyprogesterone were inhibitory. Testosterone was without demonstrable effect at either pH.