Comparison of Amino Acids Physico-Chemical Properties and Usage of Late Embryogenesis Abundant Proteins,Hydrophilins and WHy Domain

Late Embryogenesis Abundant proteins (LEAPs) comprise several diverse protein families and are mostly involved in stress tolerance. Most of LEAPs are intrinsically disordered and thus poorly functionally characterized. LEAPs have been classified and a large number of their physico-chemical properties have been statistically analyzed. LEAPs were previously proposed to be a subset of a very wide family of proteins called hydrophilins, while a domain called WHy (Water stress and Hypersensitive response) was found in LEAP class 8 (according to our previous classification). Since little is known about hydrophilins and WHy domain, the cross-analysis of their amino acids physico-chemical properties and amino acids usage together with those of LEAPs helps to describe some of their structural features and to make hypothesis about their function. Physico-chemical properties of hydrophilins and WHy domain strongly suggest their role in dehydration tolerance, probably by interacting with water and small polar molecules. The computational analysis reveals that LEAP class 8 and hydrophilins are distinct protein families and that not all LEAPs are a protein subset of hydrophilins family as proposed earlier. Hydrophilins seem related to LEAP class 2 (also called dehydrins) and to Heat Shock Proteins 12 (HSP12). Hydrophilins are likely unstructured proteins while WHy domain is structured. LEAP class 2, hydrophilins and WHy domain are thus proposed to share a common physiological role by interacting with water or other polar/charged small molecules, hence contributing to dehydration tolerance.     

Effects of decay classes and diameter classes on physico-chemical properties of Pinus koraiensis log in a typical mixed broadleaved-Korean pine forest

AimsLog is an important component for most of forest ecosystems. It plays crucial roles in maintaining soil fertility, sustaining biodiversity and cycling of carbon (C) and nutrient. However, physico-chemical properties of logs vary with decay classes and diameter classes. Our objective was to study effects of decay classes and diameter classes on physico-chemical properties of logs in a typical mixed broadleaved-Korean pine forest in northern China.MethodsIn this study, logs of Pinus koraiensis were chosen as it was the constructive species in the typical mixed broadleaved-Korean pine forest. Logs of P. koraiensis at each decay classes (I-V) were divided into four diameter classes, including diameter class i ≤ 10.0 cm, diameter class ii: 10.1-30.0 cm, diameter class iii: 30.1-50.0 cm, and diameter class iv > 50.0 cm. Then, we explored effects of different decay classes, diameter classes and their interactions on the physico-chemical properties of logs for both the heartwood and sapwood.Important findings The results showed that the physico-chemical properties of heartwood and sapwood generally exhibited similar variations. Their moisture content both increased with an increasing decay class, whereas wood density both decreased with an increased decay class and diameter class. The carbon concentrations of the sapwood showed a trend of gradual increasing during decomposition, and there was an accumulation in nitrogen (N) and phosphorus (P) concentrations of the heartwood and sapwood with an increased decay class, simultaneously. Only N and P concentrations of the heartwood increased and then decreased with an increasing diameter class. The cellulose content decreased with an increasing decay class. In contrast to the cellulose, the lignin content increased with an increased decay class. However, cellulose and lignin contents exhibited no distinct trend among diameter classes. The moisture content of logs had a significant positive correlation with C, N, P concentrations and lignin content (except P concentrations of the heartwood), but had a significant negative correlation with the cellulose content (p< 0.05). The wood density was negatively correlated with C, N, P concentrations and the lignin content, but it was positively correlated with the cellulose content (p< 0.05). Therefore, physico-chemical properties of logs had unique patterns along both decay classes and diameter classes, and the physical properties of logs (the moisture content and wood density) were important factor affecting the variations of their chemical contents.     

腐烂等级、径级对典型阔叶红松林红松倒木物理化学性质的影响

倒木是森林生态系统的重要组成部分, 在地力维护、生物多样性保持以及碳(C)和养分循环等方面具有重要意义, 但倒木物理化学性质随其腐烂等级和径级而变化。为了深入理解腐烂等级和径级对倒木物理化学性质的影响, 该研究以典型阔叶红松林的建群种——红松(Pinus koraiensis)的倒木为研究对象, 将其每个腐烂等级(I-V)下的倒木分为4个径级(径级i ≤ 10.0 cm、径级ii 10.1-30.0 cm、径级iii 30.1-50.0 cm、径级iv >50.0 cm), 研究了不同腐烂等级、径级及两者交互作用对倒木心材和边材物理化学性质的影响。结果表明: 心材和边材具有相似的变化规律。倒木心材和边材含水率随着腐烂等级增加而增加, 而木材密度随腐烂等级和径级的增加均呈下降趋势; 边材C含量以及心材和边材的氮(N)、磷(P)含量随腐烂等级增加呈上升趋势, 心材N、P含量随径级增加呈先增加后减少的趋势; 纤维素含量随腐烂等级增加呈下降趋势, 而木质素含量呈上升趋势, 纤维素和木质素含量随径级增加没有明显变化规律。倒木含水率与C、N、P、木质素含量(除心材P含量)显著正相关, 与纤维素含量显著负相关; 木材密度与C、N、P、木质素含量显著负相关, 与纤维素含量显著正相关。由此可见, 倒木物理化学性质受不同腐烂等级和径级的影响有各自的变化规律, 且倒木的物理性质(含水率和木材密度)是影响化学含量变化的重要因素。     

Comparative computational analysis of ADP Glucose Pyrophosphorylase in plants

ADP-glucose pyrophosphorylase (AGPase), a key enzyme involved in higher plant starch biosynthesis, is composed of pairs of large (LS) and small subunits (SS). Ample evidence has shown that the AGPase catalyzes the rate limiting step in starch biosynthesis in higher plants. In this study, we compiled detailed comparative information about ADP glucose pyrophosphorylase in selected plants by analyzing their structural features e.g. amino acid content, physico-chemical properties, secondary structural features and phylogenetic classification. Functional analysis of these proteins includes identification of important 10 to 20 amino acids long motifs arise because specific residues and regions proved to be important for the biological function of a group of proteins, which are conserved in both structure and sequence during evolution. Phylogenetic analysis depicts two main clusters. Cluster I encompasses large subunits (LS) while cluster II contains small subunits (SS).     

Secondary structure-based assignment of the protein structural classes

Structural class categorizes proteins based on the amount and arrangement of the constituent secondary structures. The knowledge of structural classes is applied in numerous important predictive tasks that address structural and functional features of proteins. We propose novel structural class assignment methods that use one-dimensional (1D) secondary structure as the input. The methods are designed based on a large set of low-identity sequences for which secondary structure is predicted from their sequence (PSSA^sc model) or assigned based on their tertiary structure (SSA^sc). The secondary structure is encoded using a comprehensive set of features describing count, content, and size of secondary structure segments, which are fed into a small decision tree that uses ten features to perform the assignment. The proposed models were compared against seven secondary structure-based and ten sequence-based structural class predictors. Using the 1D secondary structure, SSA^sc and PSSA^sc can assign proteins to the four main structural classes, while the existing secondary structure-based assignment methods can predict only three classes. Empirical evaluation shows that the proposed models are quite promising. Using the structure-based assignment performed in SCOP (structural classification of proteins) as the golden standard, the accuracy of SSA^sc and PSSA^sc equals 76 and 75%, respectively. We show that the use of the secondary structure predicted from the sequence as an input does not have a detrimental effect on the quality of structural class assignment when compared with using secondary structure derived from tertiary structure. Therefore, PSSA^sc can be used to perform the automated assignment of structural classes based on the sequences.     

Structure model of core proteins in photosystem I inferred from the comparison with those in photosystem II and bacteria; an application of principal component analysis to detect the similar regions between distantly related families of proteins.

A principal component analysis based on the physico-chemical properties of amino acid residues is developed to assign similar regions between distantly related families of proteins, taking account of the species diversities in respective families. The most important advantage of this analysis should be that it reflects different physico-chemical properties and thus can predict more detailed structural properties, including the transmembrane helices, than the hydropathy analysis. Its first application reconfirms the similarity between the core proteins of photosynthetic reaction center in purple bacteria and those of photosystem II, indicating that the low percentage of identical amino acid residues estimated previously between them is due to much allowance for amino acid substitutions in purple bacteria. The application of this analysis to the core proteins of photosystem I reveals that any of these proteins includes two domains, each showing high similarity to the amino acid sequences of core proteins in photosystem II and purple bacteria. A core structure model of A1 and A2 proteins folded into four layers of sheets of transmembrane helices is proposed to provide a molecular basis for the electron pathway suggested by spectroscopic experiments as well as for the interaction sites with plastocyanin, 9 kDa protein and LHC proteins.     

Sequence and Structural Features of Enzymes and their Active Sites by EC Class

We have analysed a non-redundant set of 294 enzymes for differences in sequence and structural features between the six main Enzyme Commission (EC) classification groups. This systematic study of enzymes, and their active sites in particular, aims to increase understanding of how the structure of an enzyme relates to its functional role. Many features showed significant differences between the EC classes, including active-site polarity, enzyme size and active-site amino acid propensities. Many attributes correlate with each other to form clusters of related features from which we chose representative features for further analysis. Oxidoreductases have more non-polar active sites, which can be attributed to cofactor binding and a preference for Glu over Asp in active sites in comparison to the other classes. Lyases form a significantly higher proportion of oligomers than any other class, whilst the hydrolases form the largest proportion of monomers. These features were then used in a prediction model that classified each enzyme into its top EC class with an accuracy of 33.1%, which is an increase of 16.4% over random classification. Understanding the link between structure and function is critical to improving enzyme design and the prediction of protein function from structure without transfer of annotation from alignments.     

Bacterial flagella and type III secretion systems

Certain classes of pathogenic bacteria secrete virulence proteins in a Sec-independent manner, by a mechanism known as type III secretion. The main body of the export apparatus specific for virulence proteins is identified as a needle complex, which has a similar structural organization to flagella. The two structures share several proteins with highly homologous amino acid sequences. Even where the sequence identity is low among flagellar proteins from various species, the physico-chemical properties of each protein remain homologous. Therefore, by comparing the physico-chemical properties of unidentified proteins, it is possible to find homologs among type III secretion systems.     

基于序列特征网络的蛋白质结构类型研究

利用复杂网络的方法来探索序列特征因素对蛋白质结构的影响。由于蛋白质的序列对结构具有重要且复杂的影响,因此将蛋白质的结构以及序列特征之间的关系模拟成一个复杂系统,通过利用互相关系数、标准化互信息和传递熵等方法来建立以序列特征为节点的加权网络,进而利用网络中心性的方法来分析不同蛋白质结构类型对应加权网络的中心性分布的差异,探索不同结构类型蛋白质的序列特征差异。发现不同的蛋白质结构类型对应的序列特征网络既有共性又有差异,文章将针对每一种结构类型的网络中心性分布,以及不同结构类型之间的共性与差异进行详细地讨论。研究结果对蛋白质序列与结构之间关系的研究,特别是结构分类研究具有重要的意义。     

Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences

Protein structural class prediction is one of the challenging problems in bioinformatics. Previous methods directly based on the similarity of amino acid (AA) sequences have been shown to be insufficient for low-similarity protein data-sets. To improve the prediction accuracy for such low-similarity proteins, different methods have been recently proposed that explore the novel feature sets based on predicted secondary structure propensities. In this paper, we focus on protein structural class prediction using combinations of the novel features including secondary structure propensities as well as functional domain (FD) features extracted from the InterPro signature database. Our comprehensive experimental results based on several benchmark data-sets have shown that the integration of new FD features substantially improves the accuracy of structural class prediction for low-similarity proteins as they capture meaningful relationships among AA residues that are far away in protein sequence. The proposed prediction method has also been tested to predict structural classes for partially disordered proteins with the reasonable prediction accuracy, which is a more difficult problem comparing to structural class prediction for commonly used benchmark data-sets and has never been done before to the best of our knowledge. In addition, to avoid overfitting with a large number of features, feature selection is applied to select discriminating features that contribute to achieve high prediction accuracy. The selected features have been shown to achieve stable prediction performance across different benchmark data-sets.     

Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences

Protein structural class prediction is one of the challenging problems in bioinformatics. Previous methods directly based on the similarity of amino acid (AA) sequences have been shown to be insufficient for low-similarity protein data-sets. To improve the prediction accuracy for such low-similarity proteins, different methods have been recently proposed that explore the novel feature sets based on predicted secondary structure propensities. In this paper, we focus on protein structural class prediction using combinations of the novel features including secondary structure propensities as well as functional domain (FD) features extracted from the InterPro signature database. Our comprehensive experimental results based on several benchmark data-sets have shown that the integration of new FD features substantially improves the accuracy of structural class prediction for low-similarity proteins as they capture meaningful relationships among AA residues that are far away in protein sequence. The proposed prediction method has also been tested to predict structural classes for partially disordered proteins with the reasonable prediction accuracy, which is a more difficult problem comparing to structural class prediction for commonly used benchmark data-sets and has never been done before to the best of our knowledge. In addition, to avoid overfitting with a large number of features, feature selection is applied to select discriminating features that contribute to achieve high prediction accuracy. The selected features have been shown to achieve stable prediction performance across different benchmark data-sets.     

Dynamics of epigenetic control in plants via SET domain containing proteins: Structural and functional insights

Plants control expression of their genes in a way that involves manipulating the chromatin structural dynamics in order to adapt to environmental changes and carry out developmental processes. Histone modifications like histone methylation are significant epigenetic marks which profoundly and globally modify chromatin, potentially affecting the expression of several genes. Methylation of histones is catalyzed by histone lysine methyltransferases (HKMTs), that features an evolutionary conserved domain known as SET [Su(var)3–9, E(Z), Trithorax]. This methylation is directed at particular lysine (K) residues on H3 or H4 histone. Plant SET domain group (SDG) proteins are categorized into different classes that have been conserved through evolution, and each class have specificity that influences how the chromatin structure operates. The domains discovered in plant SET domain proteins have typically been linked to protein-protein interactions, suggesting that majority of the SDGs function in complexes. Additionally, SDG-mediated histone mark deposition also affects alternative splicing events. In present review, we discussed the diversity of SDGs in plants including their structural properties. Additionally, we have provided comprehensive summary of the functions of the SDG-domain containing proteins in plant developmental processes and response to environmental stimuli have also been highlighted.     

-Crystallin quaternary structure and interactive properties control eye lens transparency

The eye lens is the foremost biological system where function is directly under control of the physico-chemical properties of the cytoplasmic macromolecular solution. Indeed, lens transparency and opacity, lens refractive index gradient and viscosity, are the result of the structural and interactive properties of the crystallins, of their stability, of the fine tuning of their interaction potentials and associations at different levels of organization. Among the different crystallin classes, -crystallins have represented a major challenge for a long time. The -crystallin secondary, tertiary and quaternary structures are still unknown. On the functional side, however, it is established that -crystallin quaternary structure and repulsive interactions determine lens transparency, whereas the -crystallin chaperone effect most probably plays a role in the aging process. In the present paper, we recall the physico-chemical properties and the quaternary structure features of -crystallins that were demonstrated to control light scattering and transparency. The interest of a crystallin mixture for lens function is discussed. Then, a formal approach is proposed to design models for the -crystallin quaternary structure, including the question of whether -crystallins assemble with symmetry. An hypothesis relevant to the fold of the -crystallin C-terminal domain is presented in another paper in this issue.     

Head Proteins from T-Even Bacteriophages II. Physical and Chemical Characterization

Three classes of structural head proteins, having molecular weights of 43,000, 18,000, and 11,000 daltons, were isolated from the T-even bacteriophages and were characterized. Based on electrophoretic studies, the 43,000-dalton class contained one major protein and one (or two) minor components, the 18,000-dalton class contained two protein components, and the 11,000-dalton class contained one major component. The N-terminal residues for the 43,000- and the 11,000-dalton classes were alanine, and the N-terminal residues for the 18,000-dalton class were methionine and alanine. Of the three classes of proteins, the 18,000-dalton proteins were the most acidic, whereas the 11,000-dalton proteins were the most basic. The amino acid composition of the 11,000-dalton class revealed that methionine and cysteine were absent and lysine, histidine, and tryptophan content was higher in the 11,000-dalton class than in the other two classes of proteins. Estimates of the relative number of the three classes of structural proteins were made and indicated that there were between 1,600 and 2,000 subunits of the 43,000-dalton proteins, 100 to 200 of the 18,000-dalton proteins, and 1,000 to 1,500 of the 11,000-dalton proteins. Evidence was presented that the 43,000-dalton proteins and the 11,000-dalton proteins readily formed aggregates with themselves but not with each other. The significance of these interactions to the structure of the T-even phage head was discussed.     

Entropy capacity determines protein folding

Search and study of the general principles that govern kinetics and thermodynamics of protein folding generate a new insight into the factors controlling this process. Here, based on the known experimental data and using theoretical modeling of protein folding, we demonstrate that there exists an optimal relationship between the average conformational entropy and the average energy of contacts per residue-that is, an entropy capacity-for fast protein folding. Statistical analysis of conformational entropy and number of contacts per residue for 5829 protein structures from four general structural classes (all-alpha, all-beta, alpha/beta, alpha+beta) demonstrates that each class of proteins has its own class-specific average number of contacts (class alpha/beta has the largest number of contacts) and average conformational entropy per residue (class all-alpha has the largest number of rotatable angles phi, psi, and chi per residue). These class-specific features determine the folding rates: alpha proteins are the fastest folding proteins, then follow beta and alpha+beta proteins, and finally alpha/beta proteins are the slowest ones. Our result is in agreement with the experimental folding rates for 60 proteins. This suggests that structural and sequence properties are important determinants of protein folding rates.     

Conformational analysis corresponding to intra-chain disulfide bridged peptides in proteins of known three-dimensional structure

We have carried out a systematic analysis in order to evaluate whether Intra-Chain Disulfide Bridged Peptides (ICDBPs) observed in proteins of known three-dimensional structure adopt structurally similar conformations as they may correspond to structural/functional motifs. 406 representative ICDBPs comprising between 3 to 17 amino acid residues could be classified according to peptide sequence length and main-chain secondary structure conformation into 146 classes. ICDBPs comprising 6 amino acid residues are maximally represented in the Protein Data Bank. They also represent the maximum number of main-chain secondary structure conformational classes. Individual ICDBPs in each class represent different protein superfamilies and correspond to different amino acid sequences. We identified 145 ICDBP pairs that had not less-than 0.5 A root mean square deviation value corresponding to their equivalent peptide backbone atoms. We believe these ICDBPs represent structural motifs and possible candidates in order to further explore their structure/function role in the corresponding proteins. The common conformational classes observed for ICDBPs defined according to the main-chain secondary structure conformations; H (helix), B (residue in a isolated beta bridge), C (coil), E (extended beta strand), G (3(10) helix), I (pi helix), S (bend), T (hydrogen-bonded turn) were; "CHHH", "CTTC", "CSSS" and "CSSC" (for ICDBP length 4), "CSSCC" (length 5), "EETTEE", "CCSSCC", "CCSSSC" (length 6), "EETTTEE" (length 7), "EETTTTEE" (length 8), "EEEETTEEEE" (length 10), "EEEETTTEEEE" (length 11) and "EEEETTTTEEEE" (length 12).     

Decomposition of protein tryptophan fluorescence spectra into log-normal components. III. Correlation between fluorescence and microenvironment parameters of individual tryptophan residues

In our previous paper (Reshetnyak, Ya. K., and E. A. Burstein. 2001. Biophys. J. 81:1710-1734) we confirmed the existence of five statistically discrete classes of emitting tryptophan fluorophores in proteins. The differences in fluorescence properties of tryptophan residues of these five classes reflect differences in interactions of excited states of tryptophan fluorophores with their microenvironment in proteins. Here we present a system of describing physical and structural parameters of microenvironments of tryptophan residues based on analysis of atomic crystal structures of proteins. The application of multidimensional statistical methods of cluster and discriminant analyses for the set of microenvironment parameters of 137 tryptophan residues of 48 proteins with known three-dimensional structures allowed us to 1) demonstrate the discrete nature of ensembles of structural parameters of tryptophan residues in proteins; 2) assign spectral components obtained after decomposition of tryptophan fluorescence spectra to individual tryptophan residues; 3) find a correlation between spectroscopic and physico-structural features of the microenvironment; and 4) reveal differences in structural and physical parameters of the microenvironment of tryptophan residues belonging to various spectral classes.     

Optimal relationship between average conformational entropy and average energy of interactions between residues for fast protein folding

Based on the known experimental data and using the theoretical modeling of protein folding, we demonstrate that there exists an optimal relationship between the average conformational entropy and the average energy of contacts per residue, that is an entropy capacity, for fast protein folding. Statistical analysis of conformational entropy and the number of contacts per residue for 5829 protein structures from four general structural classes (all-alpha, all-beta, +/-/beta, alpha+beta) demonstrates that each class of proteins has its own class-specific average number of contacts and average conformational entropy per residue. These class-specific features determine the folding rates: a proteins are the fastest folding proteins, then follow beta and alpha+beta proteins, and finally alpha/beta proteins are the slowest ones.     

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou’s pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246–255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.     

Influence of medium and long range interactions in protein folding.

Protein structures are stabilized by both local and long range interactions. In this work, we analyze the residue-residue contacts and the role of medium- and long-range interactions in globular proteins belonging to different structural classes. The results show that while medium range interactions predominate in all-alpha class proteins, long-range interactions predominate in all-beta class. Based on this, we analyze the performance of several structure prediction methods in different structural classes of globular proteins and found that all the methods predict the secondary structures of all-alpha proteins more accurately than other classes. Also, we observed that the residues occurring in the range of 21-30 residues apart contributes more towards long-range contacts and about 85% of residues are involved in long-range contacts. Further, the preference of residue pairs to the folding and stability of globular proteins is discussed.