Using Fourier Spectrum Analysis and Pseudo Amino Acid Composition for Prediction of Membrane Protein Types

Membrane proteins are generally classified into the following five types: (1) type I membrane protein, (2) type II membrane protein, (3) multipass transmembrane proteins, (4) lipid chain-anchored membrane proteins, and (5) GPI-anchored membrane proteins. Given the sequence of an uncharacterized membrane protein, how can we identify which one of the above five types it belongs to? This is important because the biological function of a membrane protein is closely correlated with its type. Particularly, with the explosion of protein sequences entering into databanks, it is in high demand to develop an automated method to address this problem. To realize this, the key is to catch the statistical characteristics for each of the five types. However, it is not easy because they are buried in a pile of long and complicated sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. (2001). PROTEINS: Structure, Function, and Genetics 43: 246–255), the technique of Fourier spectrum analysis is introduced. By doing so, the sample of a protein is represented by a set of discrete components that can incorporate a considerable amount of the sequence order effects as well as its amino acid composition information. On the basis of such a statistical frame, the support vector machine (SVM) is introduced to perform predictions. High success rates were yielded by the self-consistency test, jackknife test, and independent dataset test, suggesting that the current approach holds a promising potential to become a high throughput tool for membrane protein type prediction as well as other related areas.     

Geometry preserving projections algorithm for predicting membrane protein types

Given a new uncharacterized protein sequence, a biologist may want to know whether it is a membrane protein or not? If it is, which membrane protein type it belongs to? Knowing the type of an uncharacterized membrane protein often provides useful clues for finding the biological function of the query protein, developing the computational methods to address these questions can be really helpful. In this study, a sequence encoding scheme based on combing pseudo position-specific score matrix (PsePSSM) and dipeptide composition (DC) is introduced to represent protein samples. However, this sequence encoding scheme would correspond to a very high dimensional feature vector. A dimensionality reduction algorithm, the so-called geometry preserving projections (GPP) is introduced to extract the key features from the high-dimensional space and reduce the original high-dimensional vector to a lower-dimensional one. Finally, the K-nearest neighbor (K-NN) and support vector machine (SVM) classifiers are employed to identify the types of membrane proteins based on their reduced low-dimensional features. Our jackknife and independent dataset test results thus obtained are quite encouraging, which indicate that the above methods are used effectively to deal with this complicated problem of predicting the membrane protein type.     

Support vector machines for predicting membrane protein types by using functional domain composition

Membrane proteins are generally classified into the following five types: 1), type I membrane protein; 2), type II membrane protein; 3), multipass transmembrane proteins; 4), lipid chain-anchored membrane proteins; and 5), GPI-anchored membrane proteins. In this article, based on the concept of using the functional domain composition to define a protein, the Support Vector Machine algorithm is developed for predicting the membrane protein type. High success rates are obtained by both the self-consistency and jackknife tests. The current approach, complemented with the powerful covariant discriminant algorithm based on the pseudo-amino acid composition that has incorporated quasi-sequence-order effect as recently proposed by K. C. Chou (2001), may become a very useful high-throughput tool in the area of bioinformatics and proteomics.     

Application of SVM to predict membrane protein types

As a continuous effort to develop automated methods for predicting membrane protein types that was initiated by Chou and Elrod (PROTEINS: Structure, Function, and Genetics, 1999, 34, 137-153), the support vector machine (SVM) is introduced. Results obtained through re-substitution, jackknife, and independent data set tests, respectively, have indicated that the SVM approach is quite a promising one, suggesting that the covariant discriminant algorithm (Chou and Elrod, Protein Eng. 12 (1999) 107) and SVM, if effectively complemented with each other, will become a powerful tool for predicting membrane protein types and the other protein attributes as well.     

Prediction of membrane protein types from sequences and position-specific scoring matrices

Membrane protein plays an important role in some biochemical process such as signal transduction, transmembrane transport, etc. Membrane proteins are usually classified into five types [Chou, K.C., Elrod, D.W., 1999. Prediction of membrane protein types and subcellular locations. Proteins: Struct. Funct. Genet. 34, 137-153] or six types [Chou, K.C., Cai, Y.D., 2005. J. Chem. Inf. Modelling 45, 407-413]. Designing in silico methods to identify and classify membrane protein can help us understand the structure and function of unknown proteins. This paper introduces an integrative approach, IAMPC, to classify membrane proteins based on protein sequences and protein profiles. These modules extract the amino acid composition of the whole profiles, the amino acid composition of N-terminal and C-terminal profiles, the amino acid composition of profile segments and the dipeptide composition of the whole profiles. In the computational experiment, the overall accuracy of the proposed approach is comparable with the functional-domain-based method. In addition, the performance of the proposed approach is complementary to the functional-domain-based method for different membrane protein types.     

Low-frequency Fourier spectrum for predicting membrane protein types

Cell membranes are vitally important to living cells. Although the infrastructure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Knowledge of membrane protein types often provides crucial hints toward determining the function of an uncharacterized membrane protein. With the avalanche of new protein sequences generated in the post-genomic era, it is highly demanded to develop a high throughput tool in identifying the type of newly found membrane proteins according to their primary sequences, so as to timely annotate them for reference usage in both basic research and drug discovery. To realize this, the key is to establish a powerful identifier that can catch their characteristic sequence patterns for different membrane protein types. However, it is not easy because they are buried in a pile of long and complicated sequences. In this paper, based on the concept of the pseudo-amino acid composition [K.C. Chou, PROTEINS: Struct., Funct., Genet. 43 (2001) 246-255], the low-frequency Fourier spectrum analysis is introduced. The merits by doing so are that the sequence pattern information can be more effectively incorporated into a set of discrete components, and that all the existing prediction algorithms can be straightforwardly used on such a formulation for protein samples. High success rates were observed by the re-substitution test, jackknife test, and independent dataset test, indicating that the low-frequency Fourier spectrum approach may become a very useful tool for membrane protein type prediction. The novel approach also holds a high potential for predicting many other attributes of proteins.     

The stability of transmembrane helix interactions measured in a biological membrane

Despite some promising progress in the understanding of membrane protein folding and assembly, there is little experimental information regarding the thermodynamic stability of transmembrane helix interactions and even less on the stability of transmembrane helix-helix interactions in a biological membrane. Here we describe an approach that allows quantitative measurement of transmembrane helix interactions in a biological membrane, and calculation of changes in the interaction free energy resulting from substitution of single amino acids. Dimerization of several variants of the glycophorin A transmembrane domain are characterized and compared to the wild-type (wt) glycophorin A transmembrane helix dimerization. The calculated DeltaDeltaG(app) values are further compared with values found in the literature. In addition, we compare interactions between the wt glycophorin A transmembrane domain and helices in which critical glycine residues are replaced by alanine or serine, respectively. The data demonstrate that replacement of the glycine residues by serine is less destabilizing than replacement by alanine with a DeltaDeltaG(app) value of about 0.4 kcal/mol. Our study comprises the first measurement of a transmembrane helix interaction in a biological membrane, and we are optimistic that it can be further developed and applied.     

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.     

Application of density similarities to predict membrane protein types based on pseudo-amino acid composition

Cell membranes provide integrity of living cells. Although the stability of biological membrane is maintained by the lipid bilayer, membrane proteins perform most of the specific functions such as signal transduction, transmembrane transport, etc. Then it is plausible membrane proteins being attractive drug targets. In this article, based on the concept of using the pseudo-amino acid composition to define a protein, three different density similarities are developed for predicting the membrane protein type. The predicted results showed that the proposed approach can remarkably improve the accuracy, and might become a useful tool for predicting the other attributes of proteins as well.     

Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition

Cell membranes are vitally important to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Membrane proteins are putatively classified into five different types. Identification of their types is currently an important topic in bioinformatics and proteomics. In this paper, based on the concept of representing protein samples in terms of their pseudo-amino acid composition (Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct. Funct. Genet. 43, 246-255), the fuzzy K-nearest neighbors (KNN) algorithm has been introduced to predict membrane protein types, and high success rates were observed. It is anticipated that, the current approach, which is based on a branch of fuzzy mathematics and represents a new strategy, may play an important complementary role to the existing methods in this area. The novel approach may also have notable impact on prediction of the other attributes, such as protein structural class, protein subcellular localization, and enzyme family class, among many others.     

Proteomic profiling of gamma-secretase substrates and mapping of substrate requirements

The presenilin/gamma-secretase complex, an unusual intramembrane aspartyl protease, plays an essential role in cellular signaling and membrane protein turnover. Its ability to liberate numerous intracellular signaling proteins from the membrane and also mediate the secretion of amyloid-beta protein (Abeta) has made modulation of gamma-secretase activity a therapeutic goal for cancer and Alzheimer disease. Although the proteolysis of the prototypical substrates Notch and beta-amyloid precursor protein (APP) has been intensely studied, the full spectrum of substrates and the determinants that make a transmembrane protein a substrate remain unclear. Using an unbiased approach to substrate identification, we surveyed the proteome of a human cell line for targets of gamma-secretase and found a relatively small population of new substrates, all of which are type I transmembrane proteins but have diverse biological roles. By comparing these substrates to type I proteins not regulated by gamma-secretase, we determined that besides a short ectodomain, gamma-secretase requires permissive transmembrane and cytoplasmic domains to bind and cleave its substrates. In addition, we provide evidence for at least two mechanisms that can target a substrate for gamma cleavage: one in which a substrate with a short ectodomain is directly cleaved independent of sheddase association, and a second where a substrate requires ectodomain shedding to instruct subsequent gamma-secretase processing. These findings expand our understanding of the mechanisms of substrate selection as well as the diverse cellular processes to which gamma-secretase contributes.     

Structural and functional determinants in the S5-P region of HCN-encoded pacemaker channels revealed by cysteine-scanning substitutions

Hyperpolarization-activated cyclic nucleotide-modulated (HCN) channels are responsible for the membrane pacemaker current that underlies the spontaneous generation of bioelectrical rhythms. However, their structure-function relationship is poorly understood. Previously, we identified several pore residues that influence HCN gating properties and proposed a pore-to-gate mechanism. Here, we systematically introduced cysteine-scanning substitutions into the descending portion of the P loop (residues 339-345) of HCN1-R (where R is resistance to sulfhydryl-reactive agents) channels, in which all endogenous cysteines except C303 have been removed or replaced. F339C, K340C, A341C, M342C, S343C, and M345C did not produce functional currents. Interestingly, the loss of function phenotype of F339C could be rescued by the reducing agent dithiothreitol (DTT). H344C but not HCN1-R and DTT-treated F339C channels were sensitive to blockade by divalent Cd(2+) (current with 100 microM Cd(2+)/control current at -140 mV = 67.6 +/- 2.9%, 109.3 +/- 3.1%, and 103.8 +/- 1.7%, respectively). Externally applied methanethiosulfate ethylammonium, a covalent sulfhydryl-reactive compound, irreversibly modified H344C by reducing the current at -140 mV (to 43.7 +/- 6.5%), causing a hyperpolarizing steady-state activation shift (change in half-activation voltage: approximately 6 mV) and decelerated gating kinetics (by up to 3-fold). Based on these results, we conclude that pore residues 339-345 are important determinants of the structure-function properties of HCN channels and that the side chain of H344 is externally accessible.     

A 14-amino acid sequence with a beta-turn structure is required for apical membrane sorting of the rat ileal bile acid transporter

The rat ileal sodium-dependent bile acid transporter (Asbt) is a polytopic membrane glycoprotein, which is specifically expressed on the apical domain of the ileal brush-border membrane. In the present study, an essential 14-amino acid (aa 335-348) sorting signal was defined on the cytoplasmic tail of Asbt with two potential phosphorylation sites motifs for casein kinase II ((335)SFQE) and protein kinase C (PKC) ((339)TNK). Two-dimension NMR spectra analysis demonstrated that a tetramer, (340)NKGF, which overlaps with the potential PKC site within the 14-mer signal sequence, adopts a type I beta-turn conformation. Replacement of the potential phosphorylation residue Ser(335) and Thr(339) with alanine or deletion of either the 4 ((335)SFQE) or 10 aa (338-348, containing (339)TNKGF) from the C terminus of Asbt resulted in a significantly decreased initial bile acid transport activity and increased the basolateral distribution of the mutants by 2-3-fold compared with that of wild type Asbt. Deletion of the entire last 14 amino acids (335-348) from the C terminus of Asbt abolished the apical expression of the truncated Asbt. Moreover, replacement of the cytoplasmic tail of the liver basolateral membrane protein, Na(+)/taurocholate cotransporting polypeptide, with the 14-mer peptide tail of Asbt redirected the chimera to the apical domain. In contrast, a chimera consisting of the 14-mer peptide of Asbt fused with green fluorescent protein was expressed in an intracellular transport vesicle-like distribution in transfected Madin-Darby canine kidney and COS 7 cells. This suggests that the apical localization of the 14-mer peptide requires a membrane anchor to support proper targeting. The results from biological reagent treatment and low temperature shift (20 degrees C) suggests that Asbt follows a transport vesicle-mediated apical sorting pathway that is brefeldin A-sensitive and insensitive to protein glycosylation, monensin treatment, and low temperature shift.     

Residues involved in the pore‐forming activity of the Clostridium perfringens iota toxin

Clostridium perfringens iota toxin is a binary toxin that is organized into enzyme (Ia) and binding (Ib) components. Ib forms channels in lipid bilayers and mediates the transport of Ia into the target cells. Here we show that Ib residues 334–359 contain a conserved pattern of alternating hydrophobic and hydrophilic residues forming two amphipathic β‐strands involved in membrane insertion and channel formation. This stretch of amino acids shows remarkable structural and functional analogies with the β‐pore‐forming domain of C. perfringens epsilon toxin. Several mutations within the two amphipathic β‐strands affected pore formation, single‐channel conductance and ion selectivity (S339E‐S341E, Q345H N346E) confirming their involvement in channel formation. F454 of Ib corresponds to the Φ‐clamp F427 of anthrax protective antigen and F428 of C2II binary toxins. The mutation F454A resulted in a loss of cytotoxicity and strong increase in single‐channel conductance (500 pS as compared with 85 pS in 1 M KCl) with a slight decrease in cation selectivity, indicating that the Φ‐clamp is highly conserved and crucial for binary toxin activity. In contrast, the mutants Q367D, N430D, L443E had no or only minor effects on Ib properties, while T360I, T360A and T360W caused a dramatic effect on ion selectivity and single‐channel conductance, indicating gross disturbance of the oligomer structure. This suggests that, at least in the iota toxin family, T360 has a structural role in the pore organization. Moreover, introduction of charged residues within the channel (S339E‐S341E) or in the vestibule (Q367D, N430D and L443E) had virtually no effect on chloroquine or Ia binding, whereas F454A, T360I, T360A and T360W strongly decreased the chloroquine and Ia affinity to Ib. These results support that distinct residues within the vestibule interact with chloroquine and Ia or are responsible for channel structure, while the channel lining amino acids play a less important role.     

Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes

With the rapid increment of protein sequence data, it is indispensable to develop automated and reliable predictive methods for protein function annotation. One approach for facilitating protein function prediction is to classify proteins into functional families from primary sequence. Being the most important group of all proteins, the accurate prediction for enzyme family classes and subfamily classes is closely related to their biological functions. In this paper, for the prediction of enzyme subfamily classes, the Chou's amphiphilic pseudo-amino acid composition [Chou, K.C., 2005. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21, 10-19] has been adopted to represent the protein samples for training the 'one-versus-rest' support vector machine. As a demonstration, the jackknife test was performed on the dataset that contains 2640 oxidoreductase sequences classified into 16 subfamily classes [Chou, K.C., Elrod, D.W., 2003. Prediction of enzyme family classes. J. Proteome Res. 2, 183-190]. The overall accuracy thus obtained was 80.87%. The significant enhancement in the accuracy indicates that the current method might play a complementary role to the exiting methods.     

Predicting membrane protein types with bragging learner

The membrane protein type is an important feature in characterizing the overall topological folding type of a protein or its domains therein. Many investigators have put their efforts to the prediction of membrane protein type. Here, we propose a new approach, the bootstrap aggregating method or bragging learner, to address this problem based on the protein amino acid composition. As a demonstration, the benchmark dataset constructed by K.C. Chou and D.W. Elrod was used to test the new method. The overall success rate thus obtained by jackknife cross-validation was over 84%, indicating that the bragging learner as presented in this paper holds a quite high potential in predicting the attributes of proteins, or at least can play a complementary role to many existing algorithms in this area. It is anticipated that the prediction quality can be further enhanced if the pseudo amino acid composition can be effectively incorporated into the current predictor. An online membrane protein type prediction web server developed in our lab is available at http://chemdata.shu.edu.cn/protein/protein.jsp.     

Using functional domain composition and support vector machines for prediction of protein subcellular location

Proteins are generally classified into the following 12 subcellular locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic reticulum, 5) extracellular, 6) Golgi apparatus, 7) lysosome, 8) mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and 12) vacuole. Because the function of a protein is closely correlated with its subcellular location, with the rapid increase in new protein sequences entering into databanks, it is vitally important for both basic research and pharmaceutical industry to establish a high throughput tool for predicting protein subcellular location. In this paper, a new concept, the so-called "functional domain composition" is introduced. Based on the novel concept, the representation for a protein can be defined as a vector in a high-dimensional space, where each of the clustered functional domains derived from the protein universe serves as a vector base. With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location. High success rates are obtained by the self-consistency test, jackknife test, and independent dataset test, respectively. The current approach not only can play an important complementary role to the powerful covariant discriminant algorithm based on the pseudo amino acid composition representation (Chou, K. C. (2001) Proteins Struct. Funct. Genet. 43, 246-255; Correction (2001) Proteins Struct. Funct. Genet. 44, 60), but also may greatly stimulate the development of this area.     

TMEFF2 shedding is regulated by oxidative stress and mediated by ADAMs and transmembrane serine proteases implicated in prostate cancer

TMEFF2 is a type I transmembrane protein with two follistatin (FS) and one EGF‐like domain over‐expressed in prostate cancer; however its biological role in prostate cancer development and progression remains unclear, which may, at least in part, be explained by its proteolytic processing. The extracellular part of TMEFF2 (TMEFF2‐ECD) is cleaved by ADAM17 and the membrane‐retained fragment is further processed by the gamma‐secretase complex. TMEFF2 shedding is increased with cell crowding, a condition associated with the tumour microenvironment, which was mediated by oxidative stress signalling, requiring jun‐kinase (JNK) activation. Moreover, we have identified that TMEFF2 is also a novel substrate for other proteases implicated in prostate cancer, including two ADAMs (ADAM9 and ADAM12) and the type II transmembrane serine proteinases (TTSPs) matriptase‐1 and hepsin. Whereas cleavage by ADAM9 and ADAM12 generates previously identified TMEFF2‐ECD, proteolytic processing by matriptase‐1 and hepsin produced TMEFF2 fragments, composed of TMEFF2‐ECD or FS and/or EGF‐like domains as well as novel membrane retained fragments. Differential TMEFF2 processing from a single transmembrane protein may be a general mechanism to modulate transmembrane protein levels and domains, dependent on the repertoire of ADAMs or TTSPs expressed by the target cell.     

The Xmrk receptor tyrosine kinase is activated in Xiphophorus malignant melanoma.

Xmrk encodes a putative transmembrane glycoprotein of the tyrosine kinase family and is a melanoma-inducing gene in Xiphophorus. We attempted to investigate the biological function of the putative Xmrk receptor by characterizing its signalling properties. Since a potential ligand for Xmrk has not yet been identified, it has been difficult to analyse the biochemical properties and biological function of this cell surface protein. In an approach towards such analyses, the Xmrk extracellular domain was replaced by the closely related ligand-binding domain sequences of the human epidermal growth factor receptor (HER) and the ligand-induced activity of the chimeric HER-Xmrk protein was examined. We show that the Xmrk protein is a functional receptor tyrosine kinase, is highly active in malignant melanoma and displays a constitutive autophosphorylation activity possibly due to an activating mutation in its extracellular or transmembrane domain. In the focus formation assay the HER-Xmrk chimera is a potent transforming protein equivalent to other tyrosine kinase oncoproteins.     

细菌中I型毒素蛋白与细胞膜相互作用机制的研究进展

I型毒素-抗毒素（TA）系统在细菌基因组中广泛存在,在细菌的生长、生存中发挥多种生物学功能,包括抗菌、红细胞毒性、促进持留菌形成、抑制细菌生长或导致细菌休眠等。绝大部分I型毒素蛋白以细胞膜作为靶标,目前已知的一种作用机制是在细胞膜上形成孔洞结构,造成膜电位的下降或细胞膜的破坏,从而抑制ATP的合成或导致细菌死亡;另一种可能的作用机制是毒素蛋白作用在细胞膜上,改变细胞的形状,导致细胞进入休眠状态。I型毒素蛋白-细胞膜作用机制的复杂性和生物功能的多样性远超预期。因此,解析I型毒素蛋白在不同细胞膜中的组装机制及其所形成结构特征就变得非常重要,这也是揭示其结构-功能关系的关键。本文通过综述已报道的I型TA系统的结构特征与生物学功能,结合对其跨膜结构域的预测,探讨了其可能在细胞膜中形成的不同结构及其对功能的影响,分析了影响作用机制的关键因素。这些研究既给耐药细菌的治疗带来机遇,又为新型抗菌药物的研发带来思路。