Prediction of protein cellular attributes using pseudo-amino acid composition

The cellular attributes of a protein, such as which compartment of a cell it belongs to and how it is associated with the lipid bilayer of an organelle, are closely correlated with its biological functions. The success of human genome project and the rapid increase in the number of protein sequences entering into data bank have stimulated a challenging frontier: How to develop a fast and accurate method to predict the cellular attributes of a protein based on its amino acid sequence? The existing algorithms for predicting these attributes were all based on the 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 for protein sequences is extremely large, which has posed a formidable difficulty for realizing this goal. To deal with such a difficulty, the pseudo‐amino acid composition is introduced. It is a combination of a set of discrete sequence correlation factors and the 20 components of the conventional amino acid composition. A remarkable improvement in prediction quality has been observed by using the pseudo‐amino acid composition. The success rates of prediction thus obtained are so far the highest for the same classification schemes and same data sets. It has not escaped from our notice that the concept of pseudo‐amino acid composition as well as its mathematical framework and biochemical implication may also have a notable impact on improving the prediction quality of other protein features. Proteins 2001;43:246–255. © 2001 Wiley‐Liss, Inc.     

Analysing proteomic data

The rapid growth of proteomics has been made possible by the development of reproducible 2D gels and biological mass spectrometry. However, despite technical improvements 2D gels are still less than perfectly reproducible and gels have to be aligned so spots for identical proteins appear in the same place. Gels can be warped by a variety of techniques to make them concordant. When gels are manipulated to improve registration, information is lost, so direct methods for gel registration which make use of all available data for spot matching are preferable to indirect ones. In order to identify proteins from gel spots a property or combination of properties that are unique to that protein are required. These can then be used to search databases for possible matches. Molecular mass, pI, amino acid composition and short sequence tags can all be used in database searches. Currently the method of choice for protein identification is mass spectrometry. Proteins are eluted from the gels and cleaved with specific endoproteases to produce a series of peptides of different molecular mass. In peptide mass fingerprinting, the peptide profile of the unknown protein is compared with theoretical peptide libraries generated from sequences in the different databases. Tandem mass spectroscopy (MS/MS) generates short amino acid sequence tags for the individual peptides. These partial sequences combined with the original peptide masses are then used for database searching, greatly improving specificity. Increasingly protein identification from MS/MS data is being fully or partially automated. When working with organisms, which do not have sequenced genomes (the case with most helminths), protein identification by database searching becomes problematical. A number of approaches to cross species protein identification have been suggested, but if the organism being studied is only distantly related to any organism with a sequenced genome then the likelihood of protein identification remains small. The dynamic nature of the proteome means that there really is no such thing as a single representative proteome and a complete set of metadata (data about the data) is going to be required if the full potential of database mining is to be realised in the future.     

CyclinPred: a SVM-based method for predicting cyclin protein sequences

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server--CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods.     

Protein simple sequence conservation

Protein simple sequences, a subset of low-complexity sequences, are regions of sequence highly enriched in one or a few residue types. Simple sequences are exceedingly common, the average being more than one per protein sequence. Despite being so common, such sequences are not well-studied. The simple sequences that have been subjected to detailed study are often found to possess important functions. Here we present a survey of protein simple sequences, generally enriched in a single residue type, with the aim of studying their conservation. We find that the majority of such simple sequences are not conserved. However, conserved protein simple sequences are relatively common, with approximately 11% of the surveyed protein families possessing a conserved simple sequence. The data obtained in this study support the idea that simple sequences are conserved for functional reasons. Such functions can range from substrate binding, to mediating protein-protein interactions, to structural integrity. A perhaps surprising finding is that the residue enriching a conserved simple sequence is itself not necessarily conserved. Neither is the length of many of the highly conserved simple sequences. In the few cases where structural and functional data is available it is found that the conserved simple sequences are consistent with both local structure and function. The data presented support the idea that protein simple sequences can be conserved and have important roles in protein structure and function.     

Prediction of protein coding regions by the 3-base periodicity analysis of a DNA sequence

With the exponential growth of genomic sequences, there is an increasing demand to accurately identify protein coding regions (exons) from genomic sequences. Despite many progresses being made in the identification of protein coding regions by computational methods during the last two decades, the performances and efficiencies of the prediction methods still need to be improved. In addition, it is indispensable to develop different prediction methods since combining different methods may greatly improve the prediction accuracy. A new method to predict protein coding regions is developed in this paper based on the fact that most of exon sequences have a 3-base periodicity, while intron sequences do not have this unique feature. The method computes the 3-base periodicity and the background noise of the stepwise DNA segments of the target DNA sequences using nucleotide distributions in the three codon positions of the DNA sequences. Exon and intron sequences can be identified from trends of the ratio of the 3-base periodicity to the background noise in the DNA sequences. Case studies on genes from different organisms show that this method is an effective approach for exon prediction.     

根据蛋白质的氨基酸组成实现其快速鉴定

常规进行蛋白质鉴定的方法是测定其氨基酸顺序,它需要蛋白质顺序分析仪,对蛋白质的纯度要求高,费时和花费大,与之相比,蛋白质的氨基酸组成和分子量是容易实验测定的。本文描述了一个基于蛋白质的组成和分子量进行其快速鉴定的方法。其基本出发点是,通过统计蛋白质序列数据库中每个序列的氨基酸组成和分子量,得到一个含蛋白质长度、组成和分子量的数据库,将靶蛋白质的组成等数据与该数据库进行对比,可以检出组成和分子量与之接近的蛋白质。从而对该蛋白质进行初步鉴定。在有些情况下,甚至能相当准确地确定靶蛋白质与数据库中的某个（些）蛋白质相关。根据这一原理本文设计了根据氨基酸组成检索蛋白质组成数据库的程序,通过对胰岛素原、细胞肿瘤抗原Ｐ５３和泛肽等多种蛋白质的组成分析,证实根据氨基酸组成能较好地进行蛋白质鉴定。     

Estimation of the quality of global alignment of amino acid sequences based on evolution criterion

The aim of the work is to develop a common method for estimating the pairwise alignment quality versus the evolutionary distance (degree of homology) between the sequences being compared and versus the type of alignment procedure. 3D alignments or any data on 3D protein structure are not used in the study. Based on the accepted protein sequences evolution model, it is possible to estimate the capability of the concrete alignment algorithm to recover the genuine alignment. In this study a classical Needleman and Wunsch global alignment algorithm has been tested on a set of sequences from the Prefab database. Accuracy and confidence of a global alignment procedure were calculated as dependent on the shares of insertions/deletions and mutations.     

Identification of latent periodicity in domains of alkaline proteases

Internal repeats in protein sequences have wide-ranging implications for the structure and function of proteins. A keen analysis of the repeats in protein sequences may help us to better understand the structural organization of proteins and their evolutionary relations. In this paper, a mathematical method for searching for latent periodicity in protein sequences is developed. Using this method, we identified simple sequence repeats in the alkaline proteases and found that the sequences could show the same periodicity as their tertiary structures. This result may help us to reduce difficulties in the study of the relationship between sequences and their structures.     

Accurate formula for P-values of gapped local sequence and profile alignments

A simple general approximation for the distribution of gapped local alignment scores is presented, suitable for assessing significance of comparisons between two protein sequences or a sequence and a profile. The approximation takes account of the scoring scheme (i.e. gap penalty and substitution matrix or profile), sequence composition and length. Use of this formula means it is unnecessary to fit an extreme-value distribution to simulations or to the results of databank searches. The method is based on the theoretical ideas introduced by R. Mott and R. Tribe in 1999. Extensive simulation studies show that score-thresholds produced by the method are accurate to within +/-5 % 95 % of the time. We also investigate factors which effect the accuracy of alignment statistics, and show that any method based on asymptotic theory is limited because asymptotic behaviour is not strictly achieved for many real protein sequences, due to extreme composition effects. Consequently, it may not be practicable to find a general formula that is significantly more accurate until the sub-asymptotic behaviour of alignments is better understood.     

A test for nucleotide sequence homology

Two macromolecular sequences which have evolved from a common ancestor sequence will tend to include a large number of elements unaffected by replacement mutations in both sequences, as long as the evolutionary rate is not too high or the divergence time is not too great. The positions of corresponding elements may have changed in either daughter sequence due to deletion/insertion mutations involving other sequence elements, but their order can be expected to be the same in both sequences. These sets of correspondences, called matches, may be computed by a recursive algorithm which incorporates constraints on the number of deletion/insertion mutations hypothesized to have occurred. A test is developed which computes the significance of each deletion/insertion hypothesized, based on Monte-Carlo sampling of random sequences with the same base composition as the experimental sequences being tested. Applying the test to 5 S RNAs confirms the relation of Escherichia coli and KB carcinoma 5 S RNAs and establishes the previously undetected homology between Pseudomonas fluorescens and KB 5 S RNAs.     

Efficient pattern comparative method for selecting functionally important motifs in protein sequences: Application to zinc enzymes

We have developed a pattern comparative method for identifying functionally important motifs in protein sequences. The essence of most standard pattern comparative methods is a comparison of patterns occurring in different sequences using an optimized weight matrix. In contrast, our approach is based on a measure of similarity among all the candidate motifs within the same sequence. This method may prove to be particularly efficient for proteins encoding the same biochemical function, but with different primary sequences, and when tertiary structure information from one or more sequences is available. We have applied this method to a special class of zinc-binding enzymes known as endopeptidases.     

Alignments of DNA and protein sequences containing frameshift errors

Molecular sequences, like all experimental data, are subjectto error. Many current DNA sequencing protocols have very signerror rates and often generate artefactual insertions and deletionsof bases (indels) which corrupt the translation of sequencesand compromise the detection of protein homologies. The impactof these errors on the utility of molecular sequence data isdependent on the analytic technique used to interpret the data.In the presence of frameshift errors, standard algorithms usingsix-frame translation can miss important homologies becauseonly subfragments of the correct translation are available inany given frame. We present a new algorithm which can detectand correct frameshift errors in DNA sequences during comparisonof translated sequences with protein sequences in the databases.This algorithm can recognize homologous proteins sharing 30%identity even in the presence of a 7% frameshift error rate.Our algorithm uses dynamic programming, producing a guaranteedoptimal alignment in the presence of frameshifts, and has asensitivity equivalent to Smith-Waterman. The computationalefficiency of the algorithm is O(nm) where n and m are the sizesof two sequences being compared. The algorithm does not relyon prior knowledge or heuristic rules and performs sign betterthan any previously reported method.     

Random sequences

The comparison of protein or nucleic acid sequences frequently leads to observations whose improbability can be tested only by Monte Carlo techniques that require randomizing the sequences being compared. Two decisions need to be made. One is whether one demands a resulting random sequence to have the properties of the original sequence (a shuffled sequence) or only expects it to have them (a representative sequence). The second decision concerns the properties of the sequence of which two are composition and nearest-neighbor frequencies. It is shown that biased nearest-neighbor frequencies can significantly affect the probability of observing a given result. Methods for producing random sequences according to these decisions are given.     

The uniqueness of protein sequences: A Monte Carlo analysis

Amino acid sequences have already been examined in some detail in order to relate them to structural aspects, homology and gene duplication. This report introduces the concept of internal uniqueness of tripeptides within protein sequences and uses the Monte Carlo method to study this property. Some idea of internal uniqueness may be obtained from such an analysis using only a single sequence if the probability of the random occurrence is about 0.001 or less. This method of analysis is similar to that used in quantitative evaluations of homology. When the probability of the random occurrence is larger than 0.001 a homologous group of sequences is required and the random probabilities may be compared with the real occurrences within the group. From such an examination insulin and cytochrome c are identified as protein sequences with high internal uniqueness. A comparison of data from internal uniqueness and gene duplication analyses shows that these two properties need not be related. Results of the analysis point to internal uniqueness as an additional parameter for inclusion in speculations on why twenty amino acids are coded in protein structure.     

Using amino acid patterns to accurately predict translation initiation sites

The translation initiation site (TIS) prediction problem is about how to correctly identify TIS in mRNA, cDNA, or other types of genomic sequences. High prediction accuracy can be helpful in a better understanding of protein coding from nucleotide sequences. This is an important step in genomic analysis to determine protein coding from nucleotide sequences. In this paper, we present an in silico method to predict translation initiation sites in vertebrate cDNA or mRNA sequences. This method consists of three sequential steps as follows. In the first step, candidate features are generated using k-gram amino acid patterns. In the second step, a small number of top-ranked features are selected by an entropy-based algorithm. In the third step, a classification model is built to recognize true TISs by applying support vector machines or ensembles of decision trees to the selected features. We have tested our method on several independent data sets, including two public ones and our own extracted sequences. The experimental results achieved are better than those reported previously using the same data sets. Our high accuracy not only demonstrates the feasibility of our method, but also indicates that there might be "amino acid" patterns around TIS in cDNA and mRNA sequences.     

A thioredoxin family protein of the apicoplast periphery identifies abundant candidate transport vesicles in Toxoplasma gondii

Toxoplasma gondii, which causes toxoplasmic encephalitis and birth defects, contains an essential chloroplast-related organelle to which proteins are trafficked via the secretory system. This organelle, the apicoplast, is bounded by multiple membranes. In this report we identify a novel apicoplast-associated thioredoxin family protein, ATrx1, which is predominantly soluble or peripherally associated with membranes, and which localizes primarily to the outer compartments of the organelle. As such, it represents the first protein to be identified as residing in the apicoplast intermembrane spaces. ATrx1 lacks the apicoplast targeting sequences typical of luminal proteins. However, sequences near the N terminus are required for proper targeting of ATrx1, which is proteolytically processed from a larger precursor to multiple smaller forms. This protein reveals a population of vesicles, hitherto unrecognized as being highly abundant in the cell, which may serve to transport proteins to the apicoplast.     

COPid: composition based protein identification

In the past, a large number of methods have been developed for predicting various characteristics of a protein from its composition. In order to exploit the full potential of protein composition, we developed the web-server COPid to assist the researchers in annotating the function of a protein from its composition using whole or part of the protein. COPid has three modules called search, composition and analysis. The search module allows searching of protein sequences in six different databases. Search results list database proteins in ascending order of Euclidian distance or descending order of compositional similarity with the query sequence. The composition module allows calculation of the composition of a sequence and average composition of a group of sequences. The composition module also allows computing composition of various types of amino acids (e.g. charge, polar, hydrophobic residues). The analysis module provides the following options; i) comparing composition of two classes of proteins, ii) creating a phylogenetic tree based on the composition and iii) generating input patterns for machine learning techniques. We have evaluated the performance of composition-based (or alignment-free) similarity search in the subcellular localization of proteins. It was found that the alignment free method performs reasonably well in predicting certain classes of proteins. The COPid web-server is available at http://www.imtech.res.in/raghava/copid/.