Estimation of diffusion coefficients of proteins

A correlation for estimating the diffusion coefficients of protein molecules is presented. The correlation is based upon literature values of the protein diffusion coefficients and molal volumes for 143 proteins. The correlation can be used for the estimation of diffusion coefficients using only molecular weight. Accuracy is such that a linear regression on 301 proteins showed 75% of the diffusion coefficients estimated fell within 20% of the experimental values. The relationship between this correlation, the Stokes–Einstein equation, and the Wilke–Chang correlation is discussed.     

An ensemble of reduced alphabets with protein encoding based on grouped weight for predicting DNA-binding proteins

It is well known in the literature that an ensemble of classifiers obtains good performance with respect to that obtained by a stand-alone method. Hence, it is very important to develop ensemble methods well suited for bioinformatics data. In this work, we propose to combine the feature extraction method based on grouped weight with a set of amino-acid alphabets obtained by a Genetic Algorithm. The proposed method is applied for predicting DNA-binding proteins. As classifiers, the linear support vector machine and the radial basis function support vector machine are tested. As performance indicators, the accuracy and Matthews's correlation coefficient are reported. Matthews's correlation coefficient obtained by our ensemble method is approximately 0.97 when the jackknife cross-validation is used. This result outperforms the performance obtained in the literature using the same dataset where the features are extracted directly from the amino-acid sequence.     

Hierarchical clustering of the correlation patterns: new method of domain identification in proteins

New method of identification of dynamical domains in proteins - Hierarchical Clustering of the Correlation Patterns (HCCP) is proposed. HCCP allows to identify the domains using single three-dimensional structure of the studied proteins and does not require any adjustable parameters that can influence the results. The method is based on hierarchical clustering performed on the matrices of correlation patterns, which are obtained by the transformation of ordinary pairwise correlation matrices. This approach allows to extract additional information from the correlation matrices, which increases reliability of domain identification. It is shown that HCCP is insensitive to small variations of the pairwise correlation matrices. Particularly it produces identical results if the data obtained for the same protein crystallized with different spatial positions of domains are used for analysis. HCCP can utilize correlation matrices obtained by any method such as normal mode or essential dynamics analysis, Gaussian network or anisotropic network models, etc. These features make HCCP an attractive method for domain identification in proteins.     

Partitioning of α-lactalbumin and β-lactoglobulin in aqueous two-phase systems of polyvinylpyrrolidone and potassium phosphate

In the present study, the partitioning of α-lactalbumin, β-lactoglobulin, and cheese whey proteins in aqueous two-phase system of polyvinylpyrrolidone-potassium phosphate is investigated. The partitioning of proteins in this system depends on the polymer and salt weight percents in feed, temperature, and pH. The orthogonal central composite design is used to study the effects of different parameters on partitioning of α-lactalbumin and β-lactoglobulin. A second order model is proposed to determine the impact of these parameters. The results of the model show that the weight percent of the salt in feed has a large effect on the protein partitioning. The weight percent of polyvinylpyrrolidone in the feed increases the partitioning coefficients. By increasing the temperature, the viscosity of polyvinylpyrrolidone is reduced and the protein can easily be transferred from one phase to the other phase. The pH of the aqueous two phase system can alter the protein partitioning coefficient through the variation of the protein net charge.     

Comparison between long-range interactions and contact order in determining the folding rate of two-state proteins: application of long-range order to folding rate prediction.

The contact order is believed to be an important factor for understanding protein folding mechanisms. In our earlier work, we have shown that the long-range interactions play a vital role in protein folding. In this work, we analyzed the contribution of long-range contacts to determine the folding rate of two-state proteins. We found that the residues that are close in space and are separated by at least ten to 15 residues in sequence are important determinants of folding rates, suggesting the presence of a folding nucleus at an interval of approximately 25 residues. A novel parameter "long-range order" has been proposed to predict protein folding rates. This parameter shows as good a relationship with the folding rate of two-state proteins as contact order. Further, we examined the minimum limit of residue separation to determine the long-range contacts for different structural classes. We observed an excellent correlation between long-range order and folding rate for all classes of globular proteins. We suggest that in mixed-class proteins, a larger number of residues can serve as folding nuclei compared to all-alpha and all-beta proteins. A simple statistical method has been developed to predict the folding rates of two-state proteins using the long-range order that produces an agreement with experimental results that is better or comparable to other methods in the literature.     

Constrained analysis of fluorescence anisotropy decay:application to experimental protein dynamics

Hydrodynamic properties as well as structural dynamics of proteins can be investigated by the well-established experimental method of fluorescence anisotropy decay. Successful use of this method depends on determination of the correct kinetic model, the extent of cross-correlation between parameters in the fitting function, and differences between the timescales of the depolarizing motions and the fluorophore's fluorescence lifetime. We have tested the utility of an independently measured steady-state anisotropy value as a constraint during data analysis to reduce parameter cross correlation and to increase the timescales over which anisotropy decay parameters can be recovered accurately for two calcium-binding proteins. Mutant rat F102W parvalbumin was used as a model system because its single tryptophan residue exhibits monoexponential fluorescence intensity and anisotropy decay kinetics. Cod parvalbumin, a protein with a single tryptophan residue that exhibits multiexponential fluorescence decay kinetics, was also examined as a more complex model. Anisotropy decays were measured for both proteins as a function of solution viscosity to vary hydrodynamic parameters. The use of the steady-state anisotropy as a constraint significantly improved the precision and accuracy of recovered parameters for both proteins, particularly for viscosities at which the protein's rotational correlation time was much longer than the fluorescence lifetime. Thus, basic hydrodynamic properties of larger biomolecules can now be determined with more precision and accuracy by fluorescence anisotropy decay.     

QBES: predicting real values of solvent accessibility from sequences by efficient, constrained energy optimization

Solvent accessibility, one of the key properties of amino acid residues in proteins, can be used to assist protein structure prediction. Various approaches such as neural network, support vector machines, probability profiles, information theory, Bayesian theory, logistic function, and multiple linear regression have been developed for solvent accessibility prediction. In this article, a much simpler quadratic programming method based on the buriability parameter set of amino acid residues is developed. The new method, called QBES (Quadratic programming and Buriability Energy function for Solvent accessibility prediction), is reasonably accurate for predicting the real value of solvent accessibility. By using a dataset of 30 proteins to optimize three parameters, the average correlation coefficients between the predicted and actual solvent accessibility are about 0.5 for all four independent test sets ranging from 126 to 513 proteins. The method is efficient. It takes only 20 min for a regular PC to obtain results of 30 proteins with an average length of 263 amino acids. Although the proposed method is less accurate than a few more sophisticated methods based on neural network or support vector machines, this is the first attempt to predict solvent accessibility by energy optimization with constraints. Possible improvements and other applications of the method are discussed.     

Using amino acid correlation and community detection algorithms to identify functional determinants in protein families

Correlated mutation analysis has a long history of interesting applications, mostly in the detection of contact pairs in protein structures. Based on previous observations that, if properly assessed, amino acid correlation data can also provide insights about functional sub-classes in a protein family, we provide a complete framework devoted to this purpose. An amino acid specific correlation measure is proposed, which can be used to build networks summarizing all correlation and anti-correlation patterns in a protein family. These networks can be submitted to community structure detection algorithms, resulting in subsets of correlated amino acids which can be further assessed by specific parameters and procedures that provide insight into the relationship between different communities, the individual importance of community members and the adherence of a given amino acid sequence to a given community. By applying this framework to three protein families with contrasting characteristics (the Fe/Mn-superoxide dismutases, the peroxidase-catalase family and the C-type lysozyme/α-lactalbumin family), we show how our method and the proposed parameters and procedures are related to biological characteristics observed in these protein families, highlighting their potential use in protein characterization and gene annotation.     

Filamentous protein model for cyclic AMP-mediated cell regulatory mechanisms.

A striking correlation exists in the literature between cell regulatory phenomena mediated by cyclic AMP and the presence of filamentous proteins. By filamentous proteins is meant microtubules, microfilaments, and actinlike protein, three general classes of proteins which can be grouped on structural and functional grounds. These proteins comprise a significant portion of the protein of all eucaryotic cells and appear to be evolutionary related and quite constant. The cell events discussed include regulation of growth, differentiation, responses to hormones, secretion, including neurotransmitter release, and membrane permeability. These phenomena share a role for cyclic AMP and an involvement of filamentous proteins. The filamentous protein model for cyclic AMP-mediated cell regulatory mechanisms is proposed, in which a common aspect of many cyclic AMP-mediated processes is the regulation by cyclic AMP of filamentous protein function. The filamentous proteins would, by controlling some aspect of motility in the cell, provide the necessary and sufficient means to effect the cell response regulated by cyclic nucleotide levels. A further aim of this article is to bring attention to the emerging importance of filamentous proteins in biological sciences.     

Deriving protein dynamical properties from weighted protein contact number

It has recently been shown that in proteins the atomic mean-square displacement (or B-factor) can be related to the number of the neighboring atoms (or protein contact number), and that this relationship allows one to compute the B-factor profiles directly from protein contact number. This method, referred to as the protein contact model, is appealing, since it requires neither trajectory integration nor matrix diagonalization. As a result, the protein contact model can be applied to very large proteins and can be implemented as a high-throughput computational tool to compute atomic fluctuations in proteins. Here, we show that this relationship can be further refined to that between the atomic mean-square displacement and the weighted protein contact-number, the weight being the square of the reciprocal distance between the contacting pair. In addition, we show that this relationship can be utilized to compute the cross-correlation of atomic motion (the B-factor is essentially the auto-correlation of atomic motion). For a nonhomologous dataset comprising 972 high-resolution X-ray protein structures (resolution <2.0 A and sequence identity <25%), the mean correlation coefficient between the X-ray and computed B-factors based on the weighted protein contact-number model is 0.61, which is better than those of the original contact-number model (0.51) and other methods. We also show that the computed correlation maps based on the weighted contact-number model are globally similar to those computed through normal model analysis for some selected cases. Our results underscore the relationship between protein dynamics and protein packing. We believe that our method will be useful in the study of the protein structure-dynamics relationship.     

Predicting protein folding rate from amino acid sequence

Predicting protein folding rate from amino acid sequence is an important challenge in computational and molecular biology. Over the past few years, many methods have been developed to reflect the correlation between the folding rates and protein structures and sequences. In this paper, we present an effective method, a combined neural network--genetic algorithm approach, to predict protein folding rates only from amino acid sequences, without any explicit structural information. The originality of this paper is that, for the first time, it tackles the effect of sequence order. The proposed method provides a good correlation between the predicted and experimental folding rates. The correlation coefficient is 0.80 and the standard error is 2.65 for 93 proteins, the largest such databases of proteins yet studied, when evaluated with leave-one-out jackknife test. The comparative results demonstrate that this correlation is better than most of other methods, and suggest the important contribution of sequence order information to the determination of protein folding rates.     

Molecular weight estimation of proteins by electrophoresis in linear polyacrylamide gradient gels in the absence of denaturing agents.

It was shown that in linear polyacrylamide gradient gels migration distance of a given protein increases as a function of the square root of the time of electrophoresis. The linearity between these two parameters is demonstrated by the statistical analysis of experimental data obtained with proteins of different shapes and a wide range of electrophoretic mobility. The slopes of the regression lines calculated by this method can be utilized to determine the molecular weight of a nondenatured protein. In fact, there is a linear relationship between the log of the molecular weights and the log of the slopes for proteins with M_rs between 20,000 and 950,000.     

Identifying the singleplex and multiplex proteins based on transductive learning for protein subcellular localization prediction

A new method is proposed to identify whether a query protein is singleplex or multiplex for improving the quality of protein subcellular localization prediction. Based on the transductive learning technique, this approach utilizes the information from the both query proteins and known proteins to estimate the subcellular location number of every query protein so that the singleplex and multiplex proteins can be recognized and distinguished. Each query protein is then dealt with by a targeted single-label or multi-label predictor to achieve a high-accuracy prediction result. We assess the performance of the proposed approach by applying it to three groups of protein sequences datasets. Simulation experiments show that the proposed approach can effectively identify the singleplex and multiplex proteins. Through a comparison, the reliably of this method for enhancing the power of predicting protein subcellular localization can also be verified.     

Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

An hypothesis of the mechanism controlling proteolytic digestive enzyme production levels in Stomoxys calcitrans

Flies fed a human blood meal and sacrificed 9 h later were assayed to give information on unfed fly weight, meal weight, total midgut protein, total midgut proteolytic activity, anterior midgut protein, anterior midgut proteolytic activity, posterior midgut protein, and posterior midgut proteolytic activity; correlation coefficients were calculated for all pairings of these parameters. Posterior midgut protein showed a positive correlation with posterior midgut proteolytic activity and on this evidence it is concluded that proteolytic digestive enzyme secretion in the midgut of Stomoxys calcitrans is controlled by a secretogogue mechanism.It is proposed that the only direct stimulus the food supplies in the control of digestive enzyme production is that for digestive enzyme release from the production cells. It is also proposed that the basis of the secretogogue mechanism is that digestive enzymes are produced in direct proportion to the quantities of amino-acids available for their synthesis and that this is a consequence of the quantities of amino acids released from the food during digestion.     

Characterization of dynamic behavior of side groups of globular proteins by spin-label, NMR and luminescence polarization methods

In this work we suggest a quantitative estimation of a complicated motion of side groups of globular proteins. In the general case, three basic parameters determine the motion: (a) rotational correlation time of a side unit under study, or covalently bound spin label, or dye, (b) parameter S that reflects sterical restrictions for re-orientation of the given unit (these two parameters depending on the side-chain structure and its conformational change within the immediate dynamic protein surrounding whereas correlation times of side units on microviscosity in addition), (c) rotational correlation time of protein globule. These parameters can be measured by spin-label, NMR and fluorescence polarization techniques. An attempt to describe a complicated dynamic behaviour of side units of protein macromolecules with a single dynamics parameter--rotational correlation time--not only leads to a loss of part of information about the local structural dynamics of macromolecules but also can diminish the tau value.     

从氨基酸序列预测蛋白质折叠速率

蛋白质折叠速率预测是当今生物物理学最具挑战性的课题之一．近年来,许多科研工作者开展了大量的研究工作来探索折叠速率的决定因素,许多参数和方法被相继提出．但氨基酸残基间的相互作用、氨基酸的序列顺序等信息对折叠速率的影响从未被提及．采用伪氨基酸组成的方法提取氨基酸的序列顺序信息,利用蒙特卡洛方法选择最佳特征因子,建立线性回归模型进行折叠速率预测．该方法能在不需要任何(显示)结构信息的情况下,直接从蛋白质的氨基酸序列出发对折叠速率进行预测．在Jackknife交互检验方法的验证下,对含有99个蛋白质的数据集,发现折叠速率的预测值与实验值有很好的相关性,相关系数能达到0.81,预测误差仅为2.54．这一精度明显优于其他基于序列的方法,充分说明蛋白质的序列顺序信息是影响蛋白质折叠速率的重要因素．     

A calcium-43 NMR study of calcium binding to an acidic proline-rich phosphoprotein from human saliva

The 43Ca NMR line width measured for Ca2+ bound to protein A, an acidic proline-rich salivary protein, is 1 order of magnitude narrower than has previously been observed for other proteins of similar molecular weight. The correlation times, quadrupole coupling constants, and chemical shifts estimated for Ca2+ ions bound to the intact protein (Mr approximately 10 000) and its 30 amino acid residue long acidic N-terminal TX peptide were indistinguishable within experimental error. These results--as well as the outcome of 1H NMR relaxation rate measurements--are indicative of extensive motions for the protein residues, which in turn give rise to a high degree of flexibility for the protein-bound Ca2+. Ca2+ titration and pH-dependent measurements on protein A, the TX peptide, and the dephosphorylated TX peptide established the importance of the two phosphoserine residues in the binding of Ca2+. Moreover, a comparison of the 43Ca NMR parameters with those obtained for other Ca2+-binding proteins suggests the presence of Ca2+-binding sites of similar symmetry in all these proteins. No evidence was found for a proposed interaction between the highly acidic N-terminal and the weakly basic C-terminal regions of protein A. In contrast, the high pH inflection that was observed in the pH titration curve for the intact protein was also found for the phospho and dephospho TX peptides, thus suggesting that basic moieties in the N-terminal region rather than those in the C-terminal region may be responsible for this observation.