On the Importance of Amino Acid Sequence and Spatial Proximity of Interacting Residues for Protein Folding

Abstract

Membrane proteins can be classified among 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. T. Kohonen's self-organization model which is a typical neural network is applied for predicting the type of a given membrane protein based on its amino acid composition. As a result, the high rates of self-consistency (94.80%) and cross-validation (77.76%), and stronger fault-tolerant ability were obtained.     

Fishing new proteins in the twilight zone of genomes: the test case of outer membrane proteins in Escherichia coli K12, Escherichia coli O157:H7, and other Gram-negative bacteria

We address the problem of clustering the whole protein content of genomes into three different categories-globular, all-alpha, and all-beta membrane proteins-with the aim of fishing new membrane proteins in the pool of nonannotated proteins (twilight zone). The focus is then mainly on outer membrane proteins. This is performed by using an integrated suite of programs (Hunter) specifically developed for predicting the occurrence of signal peptides in proteins of Gram-negative bacteria and the topography of all-alpha and all-beta membrane proteins. Hunter is tested on the well and partially annotated proteins (2160 and 760, respectively) of Escherichia coli K 12 scoring as high as 95.6% in the correct assignment of each chain to the category. Of the remaining 1253 nonannotated sequences, 1099 are predicted globular, 136 are all-alpha, and 18 are all-beta membrane proteins. In Escherichia coli 0157:H7 we filtered 1901 nonannotated proteins. Our analysis classifies 1564 globular chains, 327 inner membrane proteins, and 10 outer membrane proteins. With Hunter, new membrane proteins are added to the list of putative membrane proteins of Gram-negative bacteria. The content of outer membrane proteins per genome (nine are analyzed) ranges from 1.5% to 2.4%, and it is one order of magnitude lower than that of inner membrane proteins. The finding is particularly relevant when it is considered that this is the first large-scale analysis based on validated tools that can predict the content of outer membrane proteins in a genome and can allow cross-comparison of the same protein type between different species.     

A Multi-label Classifier for Prediction Membrane Protein Functional Types in Animal

Membrane protein is an important composition of cell membrane. Given a membrane protein sequence, how can we identify its type(s) is very important because the type keeps a close correlation with its functions. According to previous studies, membrane protein can be divided into the following eight types: single-pass type I, single-pass type II, single-pass type III, single-pass type IV, multipass, lipid-anchor, GPI-anchor, peripheral membrane protein. With the avalanche of newly found protein sequences in the post-genomic age, it is urgent to develop an automatic and effective computational method to rapid and reliable prediction of the types of membrane proteins. At present, most of the existing methods were based on the assumption that one membrane protein only belongs to one type. Actually, a membrane protein may simultaneously exist at two or more different functional types. In this study, a new method by hybridizing the pseudo amino acid composition with multi-label algorithm called LIFT (multi-label learning with label-specific features) was proposed to predict the functional types both singleplex and multiplex animal membrane proteins. Experimental result on a stringent benchmark dataset of membrane proteins by jackknife test show that the absolute-true obtained was 0.6342, indicating that our approach is quite promising. It may become a useful high-through tool, or at least play a complementary role to the existing predictors in identifying functional types of membrane proteins.     

Using GO-PseAA predictor to identify membrane proteins and their types

Cell membranes are crucial to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, most of the specific functions are carried out by membrane proteins. Knowledge of membrane protein type often offers important clues toward determining the function of an uncharacterized protein. Therefore, predicting the type of a membrane protein from its primary sequence, or even just identifying whether the uncharacterized protein belongs to a membrane protein or not, is an important and challenging problem in bioinformatics and proteomics. To deal with these problems, the GO-PseAA predictor is introduced that is operated in a hybridization space by combining the gene ontology and pseudo amino acid composition. Meanwhile, to test the prediction quality, a dataset was constructed that contains 6476 non-membrane proteins and 5122 membrane proteins classified into five different types. To avoid redundancy and bias, none of the proteins included has > or = 40% sequence identity to any other. It has been observed that the overall success rate by the jackknife cross-validation test in identifying non-membrane proteins and membrane proteins was 94.76%, and that in identifying the five membrane protein types was 95.84%. The high success rates suggest that the GO-PseAA predictor can catch the core feature of the statistical samples concerned and may become an automated high throughput toll in molecular and cell biology.     

The transmembrane domain of the respiratory syncytial virus F protein is an orientation-independent apical plasma membrane sorting sequence

The processes that facilitate transport of integral membrane proteins though the secretory pathway and subsequently target them to particular cellular membranes are relevant to almost every field of biology. These transport processes involve integration of proteins into the membrane of the endoplasmic reticulum (ER), passage from the ER to the Golgi, and post-Golgi trafficking. The respiratory syncytial virus (RSV) fusion (F) protein is a type I integral membrane protein that is uniformly distributed on the surface of infected nonpolarized cells and localizes to the apical plasma membrane of polarized epithelial cells. We expressed wild-type or altered RSV F proteins to gain a better understanding of secretory transport and plasma membrane targeting of type I membrane proteins in polarized and nonpolarized epithelial cells. Our findings reveal a novel, orientation-independent apical plasma membrane targeting function for the transmembrane domain of the RSV F protein in polarized epithelial cells. This work provides a basis for a more complete understanding of the role of the transmembrane domain and cytoplasmic tail of viral type I integral membrane proteins in secretory transport and plasma membrane targeting in polarized and nonpolarized cells.     

Regulation of protein kinase C after stimulation of alpha 1-adrenoceptors in rat hippocampus.

Endogenous inhibitor of protein kinases (type II inhibitor, GABA-modulin) blocks the phosphorylation catalyzed by cAMP-dependent protein kinase (PKA) and protein kinase C (PKC) as a competitive inhibitor of substrate proteins when histone is used as a substrate. Moreover, type II inhibitor blocks the phosphorylation of endogenous membrane proteins by PKC. Stimulation of alpha 1-adrenoceptors induced rapid redistribution of PKC from cytosol to membrane fraction which lasted at least 3 h, accompanied by rapid and short-lasting translocation of type II inhibitor from membrane to cytosol fraction. The cytosol content of type II inhibitor reached maximal level 10 and 20 min and became normal again 40 min after i.p. administration of methoxamine. The above actions of methoxamine were completely blocked by pretreatment with prazosin. It seems that short-lasting redistribution of type II inhibitor from membrane to cytosol fraction allows the effective phosphorylation of membrane proteins by PKC after stimulation of alpha 1-adrenoceptors.     

Predicting membrane protein type by functional domain composition and pseudo-amino acid composition

Given the sequence of a protein, how can we predict whether it is a membrane protein or non-membrane protein? If it is, what membrane protein type it belongs to? Since these questions are closely relevant to the function of an uncharacterized protein, their importance is self-evident. Particularly, with the explosion of protein sequences entering into databanks and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to give a fast answers to these questions. By hybridizing the functional domain (FunD) and pseudo-amino acid composition (PseAA), a new strategy called FunD-PseAA predictor was introduced. To test the power of the predictor, a highly non-homologous data set was constructed where none of proteins has 25% sequence identity to any other. The overall success rates obtained with the FunD-PseAA predictor on such a data set by the jackknife cross-validation test was 85% for the case in identifying membrane protein and non-membrane protein, and 91% in identifying the membrane protein type among the following 5 categories: (1) type-1 membrane protein, (2) type-2 membrane protein, (3) multipass transmembrane protein, (4) lipid chain-anchored membrane protein, and (5) GPI-anchored membrane protein. These rates are much higher than those obtained by the other methods on the same stringent data set, indicating that the FunD-PseAA predictor may become a useful high throughput tool in bioinformatics and proteomics.     

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.     

Specific and azurophilic granules from rabbit polymorphonuclear leukocytes. I. Isolation and characterization of membrane and content subfractions

The specific and azurophilic granules of rabbit polymorphonuclear heterophils (PMNs) have been isolated and fractionated into membrane and extractable subfractions. Analysis by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS PAGE) revealed several features of the protein composition of the two granules: (a) Whereas each type of granule had 40-60 proteins separable on one-dimensional gradient gels, few of the proteins were common to both granules. (b) The proteins of the extractable fractions (which comprised approximately 98% of the total granule protein) of each granule were distinct from the proteins of the membrane fractions (which comprised approximately 2% of the total granule protein). (c) The extractable proteins co- migrated with those collected from the medium of ionophore-treated, degranulating PMNs and therefore were defined as content proteins. These results were confirmed by radiolabeling studies. Lactoperoxidase- catalyzed iodination of intact granules did not label the content proteins but did label proteins that co-migrated with major granule membrane proteins. Moreover, disruption of the granules before iodination led to labeling of both content and membrane proteins. We conclude that the membranes of specific and azurophilic granules, which arise from different faces of the Golgi complex, are composed of unique sets of membrane proteins some of which are exposed on the cytoplasmic face of the granules.     

Discriminating lysosomal membrane protein types using dynamic neural network

This work presents a dynamic artificial neural network methodology, which classifies the proteins into their classes from their sequences alone: the lysosomal membrane protein classes and the various other membranes protein classes. In this paper, neural networks-based lysosomal-associated membrane protein type prediction system is proposed. Different protein sequence representations are fused to extract the features of a protein sequence, which includes seven feature sets; amino acid (AA) composition, sequence length, hydrophobic group, electronic group, sum of hydrophobicity, R-group, and dipeptide composition. To reduce the dimensionality of the large feature vector, we applied the principal component analysis. The probabilistic neural network, generalized regression neural network, and Elman regression neural network (RNN) are used as classifiers and compared with layer recurrent network (LRN), a dynamic network. The dynamic networks have memory, i.e. its output depends not only on the input but the previous outputs also. Thus, the accuracy of LRN classifier among all other artificial neural networks comes out to be the highest. The overall accuracy of jackknife cross-validation is 93.2% for the data-set. These predicted results suggest that the method can be effectively applied to discriminate lysosomal associated membrane proteins from other membrane proteins (Type-I, Outer membrane proteins, GPI-Anchored) and Globular proteins, and it also indicates that the protein sequence representation can better reflect the core feature of membrane proteins than the classical AA composition.     

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.     

A Multilabel Model Based on Chou’s Pseudo–Amino Acid Composition for Identifying Membrane Proteins with Both Single and Multiple Functional Types

Predicting membrane protein type is a meaningful task because this kind of information is very useful to explain the function of membrane proteins. Due to the explosion of new protein sequences discovered, it is highly desired to develop efficient computation tools for quickly and accurately predicting the membrane type for a given protein sequence. Even though several membrane predictors have been developed, they can only deal with the membrane proteins which belong to the single membrane type. The fact is that there are membrane proteins belonging to two or more than two types. To solve this problem, a system for predicting membrane protein sequences with single or multiple types is proposed. Pseudo–amino acid composition, which has proven to be a very efficient tool in representing protein sequences, and a multilabel KNN algorithm are used to compose this prediction engine. The results of this initial study are encouraging.     

Physicochemical factors for discriminating between soluble and membrane proteins: hydrophobicity of helical segments and protein length

The average hydrophobicity of a polypeptide segment is considered to be the most important factor in the formation of transmembrane helices, and the partitioning of the most hydrophobic (MH) segment into the alternative nonpolar environment, a membrane or hydrophobic core of a globular protein may determine the type of protein produced. In order to elucidate the importance of the MH segment in determining which of the two types of protein results from a given amino acid sequence, we statistically studied the characteristics of MH helices, longer than 19 residues in length, in 97 membrane proteins whose three-dimensional structure or topology is known, as well as 397 soluble proteins selected from the Protein Data Bank. The average hydrophobicity of MH helices in membrane proteins had a characteristic relationship with the length of the protein. All MH helices in membrane proteins that were longer than 500 residues had a hydrophobicity greater than 1.75 (Kyte and Doolittle scale), while the MH helices in membrane proteins smaller than 100 residues could be as hydrophilic as 0.1. The possibility of developing a method to discriminate membrane proteins from soluble ones, based on the effect of size on the type of protein produced, is discussed.     

Topology of eukaryotic type II membrane proteins: importance of N-terminal positively charged residues flanking the hydrophobic domain

We have tested the role of different charged residues flanking the sides of the signal/anchor (S/A) domain of a eukaryotic type II (N(cyt)C(exo)) integral membrane protein in determining its topology. The removal of positively charged residues on the N-terminal side of the S/A yields proteins with an inverted topology, while the addition of positively charged residues to only the C-terminal side has very little effect on orientation. Expression of chimeric proteins composed of domains from a type II protein (HN) and the oppositely oriented membrane protein M2 indicates that the HN N-terminal domain is sufficient to confer a type II topology and that the M2 N-terminal ectodomain can direct a type II topology when modified by adding positively charged residues. These data suggest that eukaryotic membrane protein topology is governed by the presence or absence of an N-terminal signal for retention in the cytoplasm that is composed in part of positive charges.     

Prediction of membrane protein types and subcellular locations.

Membrane proteins are classified according to two different schemes. In scheme 1, they are discriminated among the following five types: (1) type I single-pass transmembrane, (2) type II single-pass transmembrane, (3) multipass transmembrane, (4) lipid chain-anchored membrane, and (5) GPI-anchored membrane proteins. In scheme 2, they are discriminated among the following nine locations: (1) chloroplast, (2) endoplasmic reticulum, (3) Golgi apparatus, (4) lysosome, (5) mitochondria, (6) nucleus, (7) peroxisome, (8) plasma, and (9) vacuole. An algorithm is formulated for predicting the type or location of a given membrane protein based on its amino acid composition. The overall rates of correct prediction thus obtained by both self-consistency and jackknife tests, as well as by an independent dataset test, were around 76-81% for the classification of five types, and 66-70% for the classification of nine cellular locations. Furthermore, classification and prediction were also conducted between inner and outer membrane proteins; the corresponding rates thus obtained were 88-91%. These results imply that the types of membrane proteins, as well as their cellular locations and other attributes, are closely correlated with their amino acid composition. It is anticipated that the classification schemes and prediction algorithm can expedite the functionality determination of new proteins. The concept and method can be also useful in the prioritization of genes and proteins identified by genomics efforts as potential molecular targets for drug design.     

α-螺旋跨膜蛋白拓扑结构预测方法的评价

在基因组数据中,有20%～30%的产物被预测为跨膜蛋白,本文通过对膜蛋白拓扑结构预测方法进行分析,并评价其结果,为选择更合适的拓扑结构预测方法预测膜蛋白结构。通过对目前已有的拓扑结构预测方法的评价分析,可以为我们在实际工作中提供重要的参考。比如对一个未知拓扑结构的跨膜蛋白序列,我们可以先进行是否含有信号肽的预测,参考Polyphobius和SignalP两种方法,若两种方法预测结果不一致,综合上述对两种方法的评价,Polyphobius预测的综合能力较好,可取其预测的结果,一旦确定含有信号肽,则N端必然位于膜外侧。然后结合序列的长度,判断蛋白是单跨膜还是多重跨膜,即可参照上述评价结果,选择合适的拓扑结构预测方法进行预测。     

Selective membrane protein internalization accompanies movement from the endoplasmic reticulum to the protein storage vacuole pathway in Arabidopsis

In plant cells, certain membrane proteins move by unknown mechanisms directly from the endoplasmic reticulum (ER) to prevacuolar or vacuole-like organelles where membrane is internalized to form a dense, lattice-like structure. Here, we identify a sequence motif, PIEPPPHH, in the cytoplasmic tail of a membrane protein that directs the protein from the ER to vacuoles where it is internalized. A type II membrane protein in Arabidopsis thaliana, (At)SRC2 (for Soybean Gene Regulated by Cold-2), binds specifically to PIEPPPHH and moves from the ER to the same vacuoles where it is internalized. Not all proteins that move in this pathway are internalized because another Arabidopsis type II membrane protein, (At)VAP (for Vesicle-Associated Protein), localizes to the same organelles but remains exposed on the limiting membrane. The identification of (At)SRC2 and its preference for interaction with a targeting motif specific for the ER-to-vacuole pathway may provide tools for future dissection of mechanisms involved in this unique trafficking system.     

Proteins of the Inner Membrane of Escherichia coli: Identification of Succinate Dehydrogenase by Polyacrylamide Gel Electrophoresis with sdh Amber Mutants

The inner or cytoplasmic membrane fraction of the cell envelope of Escherichia coli was isolated by isopycnic centrifugation on sucrose gradients. The membrane proteins were analyzed by electrophoresis in sodium dodecyl sulfate-polyacrylamide gels (8.5%), and up to 56 bands were resolved. Different preparations gave very similar patterns of proteins. Succinate dehydrogenase mutants (sdh) were isolated which could not grow on succinate minimal medium, although growth on fumarate was unimpaired. The protein patterns of inner membrane preparations from sdh amber mutants were compared with the wild type, and one major band was greatly reduced in the mutants. This component, which represented approximately 5% of the inner membrane protein, was restored by introducing an amber suppressor gene (supU), which also restored the Sdh(+) phenotype. The band corresponded to a protein with a molecular weight of 67,000 daltons, which is close to that for the large subunits of the succinate dehydrogenases of Rhodospirillum rubrum and beef heart mitochondria.     

Sorting of the human folate receptor in MDCK cells

The human folate receptor (hFR) is a glycosylphosphatidy-linositol (GPI) linked plasma membrane protein that mediates delivery of folates into cells. We studied the sorting of the hFR using transfection of the hFR cDNA into MDCK cells. MDCK cells are polarized epithelial cells that preferentially sort GPI-linked proteins to their apical membrane. Unlike other GPI-tailed proteins, we found that in MDCK cells, hFR is functional on both the apical and basolateral surfaces. We verified that the same hFR cDNA that transfected into CHO cells produces the hFR protein that is GPI-linked. We also measured the hFR expression on the plasma membrane of type III paroxysmal nocturnal hemoglobinuria (PNH) human erythrocytes. PNH is a disease that is characterized by the inability of cells to express membrane proteins requiring a GPI anchor. Despite this defect, and different from other GPI-tailed proteins, we found similar levels of hFR in normal and type III PNH human erythrocytes. The results suggest the hypothesis that there may be multiple mechanisms for targeting hFR to the plasma membrane.