Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image

A novel approach was developed for predicting the structural classes of proteins based on their sequences. It was assumed that proteins belonging to the same structural class must bear some sort of similar texture on the images generated by the cellular automaton evolving rule [Wolfram, S., 1984. Cellular automation as models of complexity. Nature 311, 419-424]. Based on this, two geometric invariant moment factors derived from the image functions were used as the pseudo amino acid components [Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Struct., Funct., Genet. (Erratum: ibid., 2001, vol. 44, 60) 43, 246-255] to formulate the protein samples for statistical prediction. The success rates thus obtained on a previously constructed benchmark dataset are quite promising, implying that the cellular automaton image can help to reveal some inherent and subtle features deeply hidden in a pile of long and complicated amino acid sequences.     

Predicting protein subcellular location using digital signal processing

The biological functions of a protein are closely related to its attributes in a cell. With the rapid accumulation of newly found protein sequence data in databanks, it is highly desirable to develop an automated method for predicting the subcellular location of proteins. The establishment of such a predictor will expedite the functional determination of newly found proteins and the process of prioritizing genes and proteins identified by genomic efforts as potential molecular targets for drug design. The traditional algorithms for predicting these attributes were based solely on amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns in protein sequences is extremely large, posing a formidable difficulty for realizing this goal. To deal with such difficulty, a well-developed tool in digital signal processing named digital Fourier transform (DFT) [1] was introduced. After being translated to a digital signal according to the hydrophobicity of each amino acid, a protein was analyzed by DFT within the frequency domain. A set of frequency spectrum parameters, thus obtained, were regarded as the factors to represent the sequence order effect. A significant improvement in prediction quality was observed by incorporating the frequency spectrum parameters with the conventional amino acid composition. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.     

SLLE for predicting membrane protein types

Introduction of the concept of pseudo amino acid composition (PROTEINS: Structure, Function, and Genetics 43 (2001) 246; Erratum: ibid. 44 (2001) 60) has made it possible to incorporate a considerable amount of sequence-order effects by representing a protein sample in terms of a set of discrete numbers, and hence can significantly enhance the prediction quality of membrane protein type. As a continuous effort along such a line, the Supervised Locally Linear Embedding (SLLE) technique for nonlinear dimensionality reduction is introduced (Science 22 (2000) 2323). The advantage of using SLLE is that it can reduce the operational space by extracting the essential features from the high-dimensional pseudo amino acid composition space, and that the cluster-tolerant capacity can be increased accordingly. As a consequence by combining these two approaches, high success rates have been observed during the tests of self-consistency, jackknife and independent data set, respectively, by using the simplest nearest neighbour classifier. The current approach represents a new strategy to deal with the problems of protein attribute prediction, and hence may become a useful vehicle in the area of bioinformatics and proteomics.     

Predicting protein quaternary structure by pseudo amino acid composition

In the protein universe, many proteins are composed of two or more polypeptide chains, generally referred to as subunits, that associate through noncovalent interactions and, occasionally, disulfide bonds. With the number of protein sequences entering into data banks rapidly increasing, we are confronted with a challenge: how to develop an automated method to identify the quaternary attribute for a new polypeptide chain (i.e., whether it is formed just as a monomer, or as a dimer, trimer, or any other oligomer). This is important, because the functions of proteins are closely related to their quaternary attribute. For example, some critical ligands only bind to dimers but not to monomers; some marvelous allosteric transitions only occur in tetramers but not other oligomers; and some ion channels are formed by tetramers, whereas others are formed by pentamers. To explore this problem, we adopted the pseudo amino acid composition originally proposed for improving the prediction of protein subcellular location (Chou, Proteins, 2001; 43:246-255). The advantage of using the pseudo amino acid composition to represent a protein is that it has paved a way that can take into account a considerable amount of sequence-order effects to significantly improve prediction quality. Results obtained by resubstitution, jack-knife, and independent data set tests, have indicated that the current approach might be quite promising in dealing with such an extremely complicated and difficult problem.     

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.     

PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition

The pseudo amino acid (PseAA) composition can represent a protein sequence in a discrete model without completely losing its sequence-order information, and hence has been widely applied for improving the prediction quality for various protein attributes. However, dealing with different problems may need different kinds of PseAA composition. Here, we present a web-server called PseAAC at http://chou.med.harvard.edu/bioinf/PseAA/, by which users can generate various kinds of PseAA composition to best fit their need.     

Using cellular automata to generate image representation for biological sequences

Summary. A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419–424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their fingerprint. It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246–255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.     

Using complexity measure factor to predict protein subcellular location

Summary. Recent advances in large-scale genome sequencing have led to the rapid accumulation of amino acid sequences of proteins whose functions are unknown. Because the functions of these proteins are closely correlated with their subcellular localizations, it is vitally important to develop an automated method as a high-throughput tool to timely identify their subcellular location. Based on the concept of the pseudo amino acid composition by which a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers (Chou, K. C., Proteins: Structure, Function, and Genetics, 2001, 43: 246–255), the complexity measure approach is introduced. The advantage by incorporating the complexity measure factor as one of the pseudo amino acid components for a protein is that it can more effectively reflect its overall sequence-order feature than the conventional correlation factors. With such a formulation frame to represent the samples of protein sequences, the covariant-discriminant predictor (Chou, K. C. and Elrod, D. W., Protein Engineering, 1999, 12: 107–118) was adopted to conduct prediction. High success rates were obtained by both the jackknife cross-validation test and independent dataset test, suggesting that introduction of the concept of the complexity measure into prediction of protein subcellular location is quite promising, and might also hold a great potential as a useful vehicle for the other areas of molecular biology.     

Using cellular automata images and pseudo amino acid composition to predict protein subcellular location

Summary. The avalanche of newly found protein sequences in the post-genomic era has motivated and challenged us to develop an automated method that can rapidly and accurately predict the localization of an uncharacterized protein in cells because the knowledge thus obtained can greatly speed up the process in finding its biological functions. However, it is very difficult to establish such a desired predictor by acquiring the key statistical information buried in a pile of extremely complicated and highly variable sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246–255), the approach of cellular automata image is introduced to cope with this problem. Many important features, which are originally hidden in the long amino acid sequences, can be clearly displayed through their cellular automata images. One of the remarkable merits by doing so is that many image recognition tools can be straightforwardly applied to the target aimed here. High success rates were observed through the self-consistency, jackknife, and independent dataset tests, respectively.     

基于不同标度伪氨基酸组成预测脂肪酶的类型

从序列出发预测某蛋白质是否为脂肪酶以及属于哪种脂肪酶具有重要的理论和应用价值.提出了基于Z标度和T标度的伪氨基酸组成方法提取序列特征值,采用了k-近邻算法回答上述问题.经参数选择后,三种方法在各自最优运行参数下,其1倍交叉验证的结果为:对脂肪酶和非脂肪酶预测精度分别为92.8%、91.4%和91.3%;对脂肪酶类型预测的精度分别为92.3%、90.3%和89.7%.其中基于Z标度伪氨基酸组成效果最佳.基于T标度的次之,但均明显优于其他6种常见的特征值提取方法,并对其可能的原因进行了探讨.     

Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition

Apoptosis proteins are very important for understanding the mechanism of programmed cell death. The apoptosis protein localization can provide valuable information about its molecular function. The prediction of localization of an apoptosis protein is a challenging task. In our previous work we proposed an increment of diversity (ID) method using protein sequence information for this prediction task. In this work, based on the concept of Chou's pseudo-amino acid composition [Chou, K.C., 2001. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins: Struct. Funct. Genet. (Erratum: Chou, K.C., 2001, vol. 44, 60) 43, 246-255, Chou, K.C., 2005. Using amphiphilic pseudo-amino acid composition to predict enzyme subfamily classes. Bioinformatics 21, 10-19], a different pseudo-amino acid composition by using the hydropathy distribution information is introduced. A novel ID_SVM algorithm combined ID with support vector machine (SVM) is proposed. This method is applied to three data sets (317 apoptosis proteins, 225 apoptosis proteins and 98 apoptosis proteins). The higher predictive success rates than the previous algorithms are obtained by the jackknife tests.     

A simple probabilistic model of multibody interactions in proteins

Protein structure prediction methods typically use statistical potentials, which rely on statistics derived from a database of know protein structures. In the vast majority of cases, these potentials involve pairwise distances or contacts between amino acids or atoms. Although some potentials beyond pairwise interactions have been described, the formulation of a general multibody potential is seen as intractable due to the perceived limited amount of data. In this article, we show that it is possible to formulate a probabilistic model of higher order interactions in proteins, without arbitrarily limiting the number of contacts. The success of this approach is based on replacing a naive table‐based approach with a simple hierarchical model involving suitable probability distributions and conditional independence assumptions. The model captures the joint probability distribution of an amino acid and its neighbors, local structure and solvent exposure. We show that this model can be used to approximate the conditional probability distribution of an amino acid sequence given a structure using a pseudo‐likelihood approach. We verify the model by decoy recognition and site‐specific amino acid predictions. Our coarse‐grained model is compared to state‐of‐art methods that use full atomic detail. This article illustrates how the use of simple probabilistic models can lead to new opportunities in the treatment of nonlocal interactions in knowledge‐based protein structure prediction and design. Proteins 2013; 81:1340–1350. © 2013 Wiley Periodicals, Inc.     

Using pseudo amino acid composition to predict protein subcellular location: Approached with Lyapunov index, Bessel function, and Chebyshev filter

Summary. With the avalanche of new protein sequences we are facing in the post-genomic era, it is vitally important to develop an automated method for fast and accurately determining the subcellular location of uncharacterized proteins. In this article, based on the concept of pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43: 246–255), three pseudo amino acid components are introduced via Lyapunov index, Bessel function, Chebyshev filter that can be more efficiently used to deal with the chaos and complexity in protein sequences, leading to a higher success rate in predicting protein subcellular location.     

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.     

Prediction of protein secondary structure content using amino acid composition and evolutionary information

Knowing protein structure and inferring its function from the structure are one of the main issues of computational structural biology, and often the first step is studying protein secondary structure. There have been many attempts to predict protein secondary structure contents. Previous attempts assumed that the content of protein secondary structure can be predicted successfully using the information on the amino acid composition of a protein. Recent methods achieved remarkable prediction accuracy by using the expanded composition information. The overall average error of the most successful method is 3.4%. Here, we demonstrate that even if we only use the simple amino acid composition information alone, it is possible to improve the prediction accuracy significantly if the evolutionary information is included. The idea is motivated by the observation that evolutionarily related proteins share the similar structure. After calculating the homolog-averaged amino acid composition of a protein, which can be easily obtained from the multiple sequence alignment by running PSI-BLAST, those 20 numbers are learned by a multiple linear regression, an artificial neural network and a support vector regression. The overall average error of method by a support vector regression is 3.3%. It is remarkable that we obtain the comparable accuracy without utilizing the expanded composition information such as pair-coupled amino acid composition. This work again demonstrates that the amino acid composition is a fundamental characteristic of a protein. It is anticipated that our novel idea can be applied to many areas of protein bioinformatics where the amino acid composition information is utilized, such as subcellular localization prediction, enzyme subclass prediction, domain boundary prediction, signal sequence prediction, and prediction of unfolded segment in a protein sequence, to name a few.     

随机森林方法预测膜蛋白类型

膜蛋白的类型与其功能是密切相关的,因此膜蛋白类型的预测是研究其功能的重要手段,从蛋白质的氨基酸序列出发对膜蛋白的类型进行预测有重要意义。文章基于蛋白质的氨基酸序列,将组合离散增量和伪氨基酸组分信息共同作为预测参数,采用随机森林分类器,对8类膜蛋白进行了预测。在Jackknife检验下的预测精度为86.3%,独立检验的预测精度为93.8%,取得了好于前人的预测结果。     

Design of multispecific protein sequences using probabilistic graphical modeling

In nature, proteins partake in numerous protein– protein interactions that mediate their functions. Moreover, proteins have been shown to be physically stable in multiple structures, induced by cellular conditions, small ligands, or covalent modifications. Understanding how protein sequences achieve this structural promiscuity at the atomic level is a fundamental step in the drug design pipeline and a critical question in protein physics. One way to investigate this subject is to computationally predict protein sequences that are compatible with multiple states, i.e., multiple target structures or binding to distinct partners. The goal of engineering such proteins has been termed multispecific protein design. We develop a novel computational framework to efficiently and accurately perform multispecific protein design. This framework utilizes recent advances in probabilistic graphical modeling to predict sequences with low energies in multiple target states. Furthermore, it is also geared to specifically yield positional amino acid probability profiles compatible with these target states. Such profiles can be used as input to randomly bias high‐throughput experimental sequence screening techniques, such as phage display, thus providing an alternative avenue for elucidating the multispecificity of natural proteins and the synthesis of novel proteins with specific functionalities. We prove the utility of such multispecific design techniques in better recovering amino acid sequence diversities similar to those resulting from millions of years of evolution. We then compare the approaches of prediction of low energy ensembles and of amino acid profiles and demonstrate their complementarity in providing more robust predictions for protein design. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

The predominant roles of the sequence periodicity in the self‐assembly of collagen‐mimetic mini‐fibrils

Collagen fibrils represent a unique case of protein folding and self‐association. We have recently successfully developed triple‐helical peptides that can further self‐assemble into collagen‐mimetic mini‐fibrils. The 35 nm axially repeating structure of the mini‐fibrils, which is designated the d‐period, is highly reminiscent of the well‐known 67 nm D‐period of native collagens when examined using TEM and atomic force spectroscopy. We postulate that it is the pseudo‐identical repeating sequence units in the primary structure of the designed peptides that give rise to the d‐period of the quaternary structure of the mini‐fibrils. In this work, we characterize the self‐assembly of two additional designed peptides: peptide Col877 and peptide Col108rr. The triple‐helix domain of Col877 consists of three pseudo‐identical amino acid sequence units arranged in tandem, whereas that of Col108rr consists of three sequence units identical in amino acid composition but different in sequence. Both peptides form stable collagen triple helices, but only triple helices Col877 self‐associate laterally under fibril forming conditions to form mini‐fibrils having the predicted d‐period. The Co108rr triple helices, however, only form nonspecific aggregates having no identifiable structural features. These results further accentuate the critical involvement of the repeating sequence units in the self‐assembly of collagen mini‐fibrils; the actual amino acid sequence of each unit has only secondary effects. Collagen is essential for tissue development and function. This novel approach to creating collagen‐mimetic fibrils can potentially impact fundamental research and have a wide range of biomedical and industrial applications.     

Using ensemble classifier to identify membrane protein types

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.     

Swfoldrate: Predicting protein folding rates from amino acid sequence with sliding window method

Protein folding is the process by which a protein processes from its denatured state to its specific biologically active conformation. Understanding the relationship between sequences and the folding rates of proteins remains an important challenge. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. In this study, the long‐range and short‐range contact in protein were used to derive extended version of the pseudo amino acid composition based on sliding window method. This method is capable of predicting the protein folding rates just from the amino acid sequence without the aid of any structural class information. We systematically studied the contributions of individual features to folding rate prediction. The optimal feature selection procedures are adopted by means of combining the forward feature selection and sequential backward selection method. Using the jackknife cross validation test, the method was demonstrated on the large dataset. The predictor was achieved on the basis of multitudinous physicochemical features and statistical features from protein using nonlinear support vector machine (SVM) regression model, the method obtained an excellent agreement between predicted and experimentally observed folding rates of proteins. The correlation coefficient is 0.9313 and the standard error is 2.2692. The prediction server is freely available at http://www.jci‐bioinfo.cn/swfrate/input.jsp . Proteins 2013. © 2012 Wiley Periodicals, Inc.