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 protein structural class with pseudo-amino acid composition and support vector machine fusion network

Because a priori knowledge of a protein structural class can provide useful information about its overall structure, the determination of protein structural class is a quite meaningful topic in protein science. However, with the rapid increase in newly found protein sequences entering into databanks, it is both time-consuming and expensive to do so based solely on experimental techniques. Therefore, it is vitally important to develop a computational method for predicting the protein structural class quickly and accurately. To deal with the challenge, this article presents a dual-layer support vector machine (SVM) fusion network that is featured by using a different pseudo-amino acid composition (PseAA). The PseAA here contains much information that is related to the sequence order of a protein and the distribution of the hydrophobic amino acids along its chain. As a showcase, the rigorous jackknife cross-validation test was performed on the two benchmark data sets constructed by Zhou. A significant enhancement in success rates was observed, indicating that the current approach may serve as a powerful complementary tool to other existing methods in this area.     

The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition

The outer membrane proteins (OMPs) are β-barrel membrane proteins that performed lots of biology functions. The discriminating OMPs from other non-OMPs is a very important task for understanding some biochemical process. In this study, a method that combines increment of diversity with modified Mahalanobis Discriminant, called IDQD, is presented to predict 208 OMPs, 206 transmembrane helical proteins (TMHPs) and 673 globular proteins (GPs) by using Chou's pseudo amino acid compositions as parameters. The overall accuracy of jackknife cross-validation is 93.2% and 96.1%, respectively, for three datasets (OMPs, TMHPs and GPs) and two datasets (OMPs and non-OMPs). These predicted results suggest that the method can be effectively applied to discriminate OMPs, TMHPs and GPs. And it also indicates that the pseudo amino acid composition can better reflect the core feature of membrane proteins than the classical amino acid composition.     

Predicting subcellular localization of proteins by hybridizing functional domain composition and pseudo-amino acid composition

Recent advances in large-scale genome sequencing have led to the rapid accumulation of amino acid sequences of proteins whose functions are unknown. Since the functions of these proteins are closely correlated with their subcellular localizations, many efforts have been made to develop a variety of methods for predicting protein subcellular location. In this study, based on the strategy by hybridizing the functional domain composition and the pseudo-amino acid composition (Cai and Chou [2003]: Biochem. Biophys. Res. Commun. 305:407-411), the Intimate Sorting Algorithm (ISort predictor) was developed for predicting the protein subcellular location. As a showcase, the same plant and non-plant protein datasets as investigated by the previous investigators were used for demonstration. The overall success rate by the jackknife test for the plant protein dataset was 85.4%, and that for the non-plant protein dataset 91.9%. These are so far the highest success rates achieved for the two datasets by following a rigorous cross validation test procedure, further confirming that such a hybrid approach may become a very useful high-throughput tool in the area of bioinformatics, proteomics, as well as molecular cell biology.     

基于分段氨基酸组成成分的蛋白质相互作用预测

蛋白质相互作用研究有助于揭示生命过程的许多本质问题,也有助于疾病预防、诊断,对药物研制具有重要的参考价值。文章首先构建出蛋白质作用数据库,提出分段氨基酸组成成分特征提取方法来预测蛋白质相互作用。10CV检验下,基于支持向量机的3段氨基酸组成成分特征提取方法的预测总精度为86.2%,比传统的氨基酸组成成分方法提高2.31个百分点;采用Guo的数据库和检验方法,3段氨基酸组成成分特征提取方法的预测总精度为90.11%,比Guo的自相关函数特征提取方法提高2.75个百分点,从而表明分段氨基酸组成成分特征提取方法可有效地应用于蛋白质相互作用预测。     

Preparative separation and amino acid composition of neurofilament triplet proteins

Abstract: Intact neurofilaments were isolated from bovine spinal cord white matter, washed by sedimentation in 0.1 m -NaCl, and extracted with 8 m -urea. Solubilized neurofilament triplet proteins of molecular weights approximately 68,000 (P68), 150,000 (P150), and 200,000 (P200) were purified by preparative electrophoresis, using an LKB 7900 Uniphor apparatus. The method provides for an enhanced yield of purified protein and has markedly reduced admixture of electrophoresed protein with acrylamide and associated protein contaminants. Amino acid compositions of the purified neurofilament triplet proteins are reported and compared.     

Dietary deficiencies of single amino acids: Whole-body amino acid composition of adult rats

Abstract

To ascertain whether chronic amino acid deficiency alters the amino acid composition of the body, 44 adult female rats were randomly allocated to one of 11 treatments which included one control group, ingesting an adequate diet with balanced protein, and ten deficient groups in which one group received protein-deficient diets and the other groups consumed diets each deficient in a single essential amino acid. The degree of deficiency was adjusted to achieve a gradual decline in body weight to 85% of the initial weight and was then adjusted so that this weight was maintained until the end of the experiment at 93 days, when the rats were killed. Deficient rats had lower absolute weights of liver, gastrointestinal tract and muscle than animals given the adequate diet but greater relative weights (% of body weight) of heart and kidneys. There were no significant differences amongst groups in percentages of lipid, nitrogen, protein plus lipid or dry matter. Chronic marginal amino acid deficiencies did not selectively alter amino acid composition.     

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.     

Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition

Membrane proteins are vitally important for many biological processes and have become an attractive target for both basic research and drug design. Knowledge of membrane protein types often provides useful clues in deducing the functions of uncharacterized membrane proteins. With the unprecedented increasing of newly found protein sequences in the post-genomic era, it is highly demanded to develop an automated method for fast and accurately identifying the types of membrane proteins according to their amino acid sequences. Although quite a few identifiers have been developed in this regard through various approaches, such as covariant discriminant (CD), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN), classifier the way they operate the identification is basically individual. As is well known, wise persons usually take into account the opinions from several experts rather than rely on only one when they are making critical decisions. Likewise, a sophisticated identifier should be trained by several different modes. In view of this, based on the frame of pseudo-amino acid that can incorporate a considerable amount of sequence-order effects, a novel approach called "stacked generalization" or "stacking" has been introduced. Unlike the "bagging" and "boosting" approaches which only combine the classifiers of a same type, the stacking approach can combine several different types of classifiers through a meta-classifier to maximize the generalization accuracy. The results thus obtained were very encouraging. It is anticipated that the stacking approach may also hold a high potential to improve the identification quality for, among many other protein attributes, subcellular location, enzyme family class, protease type, and protein-protein interaction type. The stacked generalization classifier is available as a web-server named "SG-MPt_Pred" at: http://202.120.37.186/bioinf/wangsq/service.htm.     

Correlations between nucleotide frequencies and amino acid composition in 115 bacterial species

We studied the correlations between amino acid composition and mononucleotide and dinucleotide frequencies in 115 bacterial genomes of varying G+C content. Observed amino acid frequencies were compared with those expected from the actual mononucleotide and dinucleotide frequencies. Both mononucleotide and dinucleotide frequencies correlate well with the amino acid frequency, with dinucleotide frequencies doing so better. Despite the strong correlations, some of the observed amino acid frequencies, in particular for Arg, Val, Asp, Glu, Ser, and Cys, were consistently different from predicted values in all genomes. We suggest that this variation from predicted values is a consequence of selection pressure at the level of amino acids, while the close correspondence to the predictions in residues such as Thr, Phe, Lys, and Asn arises only from mutation and selection pressure at the level of the nucleic acid sequences.     

Protein location prediction using atomic composition and global features of the amino acid sequence

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectively used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.     

半滑舌鳎受精卵、卵黄囊仔鱼和开口仔鱼氨基酸及脂肪酸的变化

采用气相色谱仪和氨基酸分析仪测定了半滑舌鳎(Cynoglossus semilaevis)受精卵、卵黄囊仔鱼和开口仔鱼的氨基酸与脂肪酸组成的变化。结果表明:总氨基酸组成在受精卵和卵黄囊仔鱼之间变化明显,但是在卵黄囊仔鱼和开口仔鱼之间只有细微的变化。开口仔鱼与其摄食的轮虫的总必需氨基酸组成相关。受精卵、卵黄囊仔鱼、开口仔鱼的游离氨基酸含量分别为139 mg/g、3.6 mg/g和2.5 mg/g,占总氨基酸含量的22.3%、3.6%和2.5%。饱和脂肪酸的总量从受精卵到卵黄囊仔鱼明显下降,但是发育到开口仔鱼含量无显著变化。单不饱和脂肪酸和多不饱和脂肪酸的总量在不同发育阶段无显著变化,而EPA和DHA的含量从卵黄囊仔鱼到开口仔鱼有明显下降。这表明在早期发育阶段半滑舌鳎主要利用饱和脂肪酸作为能量代谢的基质,对饱和脂肪酸的利用程度大于单不饱和脂肪酸和多不饱和脂肪酸。半滑舌鳎似乎需要长链的多不饱和脂肪酸如EPA、DHA和ARA。     

Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition

In this paper we present a study of classification of the 20 amino acids via a fuzzy clustering technique. In order to calculate distances among the various elements we employ two different distance functions: the Minkowski distance function and the NTV metric. In the clustering procedure we take into account several physical properties of the amino acids. We examine the effect of the number and nature of properties taken into account to the clustering procedure as a function of the degree of similarity and the distance function used. It turns out that one should use the properties that determine in the more important way the behavior of the amino acids and that the use of the appropriate metric can help in defining the separation into groups.     

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor.     

Conservation of the basic pattern of cellular amino acid composition of archaeobacteria during biological evolution and the putative amino acid composition of primitive life forms

Summary. Previous studies showed that the cellular amino acid composition obtained by amino acid analysis of whole cells, differs such as eubacteria, protozoa, fungi and mammalian cells. These results suggest that the difference in the cellular amino acid composition reflects biological changes as the result of evolution. However, the basic pattern of cellular amino acid composition was relatively constant in all organisms examined. In the present study, we examined archaeobacteria, because they are considered important in understanding the relationship between biological evolution and cellular amino acid composition. The cellular amino acid compositions of Archaeoglobus fulgidus, Pyrococcus horikoshii, Methanobacterium thermoautotrophicum and Methanococcus jannaschii differed slightly from each other, but were similar to those determined from codon usage data, based on the complete genomes. Thus, the cellular amino acid composition reflects biological evolution. We suggest that primitive forms of life appearing on earth at the end of prebiotic evolution had a similar-cellular amino acid composition. Received November 28, 2000 Accepted January 30, 2001     

Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine

The amino acid gamma-aminobutyric-acid receptors (GABA_ARs) belong to the ligand-gated ion channels (LGICs) superfamily. GABA_ARs are highly diverse in the central nervous system. These channels play a key role in regulating behavior. As a result, the prediction of GABA_ARs from the amino acid sequence would be helpful for research on these receptors. We have developed a method to predict these proteins using the features obtained from Chou's pseudo-amino acid composition concept and support vector machine as a powerful machine learning approach. The predictor efficiency was assessed by five-fold cross-validation. This method achieved an overall accuracy and Matthew's correlation coefficient (MCC) of 94.12% and 0.88, respectively. Furthermore, to evaluate the effect and power of each feature, the minimum Redundancy and Maximum Relevance (mRMR) feature selection method was implemented. An interesting finding in this study is the presence of all six characters (hydrophobicity, hydrophilicity, side chain mass, pK1, pK2 and pI) or combination of the characters among the 5 higher ranked features (pk2 and pI, hydrophobicity and mass, pk1, hydrophilicity and mass) obtained from the mRMR feature selection method. The results show a biologically justifiable ranked attributes of pk2 and pI; hydrophobicity, hydrophilicity and mass; mass and pk1; pk2 and mass. Based on our results, using the concept of Chou's pseudo-amino acid composition and support vector machine is an effective approach for the prediction of GABA_ARs.     

利用伪氨基酸组分和支持向量机预测抗冻蛋白

抗冻蛋白是一类具有提高生物抗冻能力的蛋白质。抗冻蛋白能够特异性的与冰晶相结合,进而阻止体液内冰核的形成与生长。因此,对抗冻蛋白的生物信息学研究对生物工程发展。提高作物抗冻性有重要的推动作用。本文采用由400条抗冻蛋白序列和400条非抗冻蛋白序列构成数据集,以伪氨基酸组分为特征,利用支持向量机分类算法预测抗冻蛋白,对训练集预测精度达到91．3％,对测试集预测精度达到78．8％。该结果证明伪氨基酸组分能够很好的反映抗冻蛋白特性,并能够用于预测抗冻蛋白。     

Predicting protease types by hybridizing gene ontology and pseudo amino acid composition

Proteases play a vitally important role in regulating most physiological processes. Different types of proteases perform different functions with different biological processes. Therefore, it is highly desired to develop a fast and reliable means to identify the types of proteases according to their sequences, or even just identify whether they are proteases or nonproteases. The avalanche of protein sequences generated in the postgenomic era has made such a challenge become even more critical and urgent. By hybridizing the gene ontology approach and pseudo amino acid composition approach, a powerful predictor called GO-PseAA predictor was introduced to address the problems. To avoid redundancy and bias, demonstrations were performed on a dataset where none of proteins has >/= 25% sequence identity to any other. The overall success rates thus obtained by the jackknife cross-validation test in identifying protease and nonprotease was 91.82%, and that in identifying the protease type was 85.49% among the following five types: (1) aspartic, (2) cysteine, (3) metallo, (4) serine, and (5) threonine. The high jackknife success rates yielded for such a stringent dataset indicate the GO-PseAA predictor is very powerful and might become a useful tool in bioinformatics and proteomics.     

The effects of guanine and cytosine variation on dinucleotide frequency and amino acid composition in the human genome

Summary One hundred twelve human DNA sequences were analyzed with respect to dinucleotide frequency and amino acid composition. The variation in guanine and cytosine (G+C) content revealed: (1) at 2–3 and 3-1 doublet positions CG discrimination is attenuated at high G+C, but TA disfavor is enhanced, and (2) several amino acids are subject to G+C change. These findings have been reported in part for collections of sequences from various species. The present study confirms that in a single organism-the human-the G+C effects do exist. Aspects of the argument that connects G+C with protein thermal stability are also discussed.