Predicting membrane protein types using residue-pair models based on reduced similarity dataset

An algorithm to predict the membrane protein types based on the multi-residue-pair effect in the Markov model is proposed. For a newly constructed dataset of 835 membrane proteins with very low sequence similarity, the overall prediction accuracy has been achieved as high as 81.1% and 71.7% in the resubstitution and jackknife test, respectively, for a prediction of type I single-pass, type II single-pass, multi-pass membrane proteins, lipid chain-anchored and GPI-anchored membrane proteins. The improvement of about 11% in the jackknife test can be achieved compared with the component-coupled algorithm merely based on the amino acid composition (AAC approach). The improvement is also confirmed on a high similarity dataset and the other extrapolating test. The result implies that designing more incisive analysis tools, one should develop algorithms based on the representative dataset with lower sequence similarity. The present algorithm is useful to expedite the determination of the types and functions of new membrane proteins and may be useful for the systematic analysis of functional genome data in a large scale. The computer program is available on request.     

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.     

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.     

A New Multi-label Classifier for Identifying the Functional Types of Singleplex and Multiplex Antimicrobial Peptides

Antimicrobial peptides (AMPs) play an important role in the innate immune system that evolved in most living organisms. As a kind of natural antibiotics, it is promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop a fast and effective computational method for accurately predicting the functional types of AMPs, because the biological functions of AMPs are correlated with the type it belongs to. Although many efforts have been made in this area, to the best of our knowledge, most of the existing predictors only has the ability to deal with whether a peptide is an AMP or not, or a peptide belongs to which one type. However, there are many AMPs have two or more functional types, the phenomenon should worthy of our special notice, because they may have some unique biological functions for new drug design and disease treatment. In this study, in order to reflect the characteristic of multiplex AMPs, a new multi-label classifier based on sequence information and multi-label learning with label-specific features (LIFT) algorithm was developed. It was observed that, the absolute-true with jackknife test by the new predictor on a newly stringent benchmark dataset is 0.5040, and the success rates achieved by the new predictor are 5 % higher than this by iAMP-2L in the same dataset, indicating that our method is quite promising. We hope that the predictor may become a useful high-through tool in identifying the functional types of AMPs.     

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.     

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.     

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.     

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.     

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.     

mPLR-Loc: An adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction

Proteins located in appropriate cellular compartments are of paramount importance to exert their biological functions. Prediction of protein subcellular localization by computational methods is required in the post-genomic era. Recent studies have been focusing on predicting not only single-location proteins but also multi-location proteins. However, most of the existing predictors are far from effective for tackling the challenges of multi-label proteins. This article proposes an efficient multi-label predictor, namely mPLR-Loc, based on penalized logistic regression and adaptive decisions for predicting both single- and multi-location proteins. Specifically, for each query protein, mPLR-Loc exploits the information from the Gene Ontology (GO) database by using its accession number (AC) or the ACs of its homologs obtained via BLAST. The frequencies of GO occurrences are used to construct feature vectors, which are then classified by an adaptive decision-based multi-label penalized logistic regression classifier. Experimental results based on two recent stringent benchmark datasets (virus and plant) show that mPLR-Loc remarkably outperforms existing state-of-the-art multi-label predictors. In addition to being able to rapidly and accurately predict subcellular localization of single- and multi-label proteins, mPLR-Loc can also provide probabilistic confidence scores for the prediction decisions. For readers’ convenience, the mPLR-Loc server is available online (http://bioinfo.eie.polyu.edu.hk/mPLRLocServer).     

Artificial neural network model for predicting membrane protein types

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.     

基于功能域组分的蛋白质折叠类型识别

蛋白质空间结构研究是分子生物学、细胞生物学、生物化学以及药物设计等领域的重要课题.折叠类型反映了蛋白质核心结构的拓扑模式,对折叠类型的识别是蛋白质序列与结构关系研究的重要内容.选取LIFCA数据库中样本量较大的53种折叠类型,应用功能域组分方法进行折叠识别.将Astral 1.65中序列一致性小于95%的样本作为检验集,全库检验结果中平均敏感性为96.42%,特异性为99.91%,马修相关系数(MCC)为0.91,各项统计结果表明:功能域组分方法可以很好地应用在蛋白质折叠识别中,LIFCA相对简单的分类规则可以很好地集中蛋白质的大部分功能特性,反映了结构与功能的对应关系.     

昆虫中肠围食膜蛋白研究进展

围食膜是大多数昆虫中肠内壁附着的一层起润滑和保护作用的半透性粘膜, 按其形成方式不同分为Ⅰ型围食膜和Ⅱ型围食膜。围食膜主要由几丁质和蛋白质构成, 其中蛋白质对于维持围食膜的致密结构至关重要, 对围食膜蛋白的破坏可能会对昆虫的正常生长发育造成干扰, 甚至会导致低龄幼虫的死亡。本文介绍了围食膜的组成与结构, 阐述了昆虫围食膜蛋白研究的新发现、并依据结构特征对它们进行了分类, 总结了以围食膜蛋白为新靶标的害虫防治的可能途径, 讨论了当前围食膜蛋白研究的不足, 最后展望了今后围食膜蛋白研究的发展方向。     

Prediction of the Types of Membrane Proteins Based on Discrete Wavelet Transform and Support Vector Machines

Membrane proteins are crucial for many biological functions and have become attractive targets for both basic research and drug discovery. With the unprecedented increasing of newly found protein sequences in the post-genomic era, it is both time-consuming and expensive to determine the types of newly found membrane proteins solely with traditional experiment, and so it is highly demanded to develop an automatic method for fast and accurately identifying the type of membrane proteins according to their amino acid sequences. In this study, the discrete wavelet transform (DWT) and support vector machine (SVM) have been used for the prediction of the types of membrane proteins. Maximum accuracy has been obtained using SVM with a wavelet function of bior2.4, a decomposition scale j = 4, and Kyte–Doolittle hydrophobicity scales. The results indicate that the proposed method may play an important complementary role to the existing methods in this area.     

Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence

Background  

Knowing the submitochondria localization of a mitochondria protein is an important step to understand its function. We develop a method which is based on an extended version of pseudo-amino acid composition to predict the protein localization within mitochondria. This work goes one step further than predicting protein subcellular location. We also try to predict the membrane protein type for mitochondrial inner membrane proteins.     

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.     

Identify Golgi protein types with modified Mahalanobis discriminant algorithm and pseudo amino acid composition

The Golgi apparatus is an important eukaryotic organelle. Successful prediction of Golgi protein types can provide valuable information for elucidating protein functions involved in various biological processes. In this work, a method is proposed by combining a special mode of pseudo amino acid composition (increment of diversity) with the modified Mahalanobis discriminant for predicting Golgi protein types. The benchmark dataset used to train the predictor thus formed contains 95 Golgi proteins in which none of proteins included has ≥40% pairwise sequence identity to any other. The accuracy obtained by the jackknife test was 74.7%, with the ROC curve of 0.772 in identifying cis-Golgi proteins and trans-Golgi proteins. Subsequently, the method was extended to discriminate cis-Golgi network proteins from cis-Golgi network membrane proteins and trans-Golgi network proteins from trans-Golgi network membrane proteins, respectively. The accuracies thus obtained were 76.1% and 83.7%, respectively. These results indicate that our method may become a useful tool in the relevant areas. As a user-friendly web-server, the predictor is freely accessible at http://immunet.cn/SubGolgi/.     

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.     

Toward the semisynthesis of multidomain transmembrane receptors: modification of Eph tyrosine kinases

Expressed protein ligation (EPL) is a protein engineering approach that allows the modification or assembly of a target protein from multiple recombinant and synthetic polypeptides. EPL has been previously used to modify intracellular proteins and small integral membrane proteins for structural and functional studies. Here we describe the semisynthetic site-specific modification of the complete, multidomain extracellular regions of both A and B classes of Eph receptor tyrosine kinases. We show that the ectodomains of these receptors can be ligated to different peptides under carefully established experimental conditions, while their biological activity is retained. This work extends the boundaries of the EPL technique for semisynthesis of multidomain, extracellular, disulfide-bonded, and glycosylated proteins and highlights its potential application for reconstituting entire single-pass transmembrane proteins.