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.     

Prediction whether a human cDNA sequence contains initiation codon by combining statistical information and similarity with protein sequences

MOTIVATION: In the previous works, we developed ATGpr, a computer program for predicting the fullness of a cDNA, i.e. whether it contains an initiation codon or not. Statistical information of short nucleotide fragments was fully exploited in the prediction algorithm. However, sequence similarities to known proteins, which are becoming increasingly available due to recent rapid growth of protein database, were not used in the prediction. In this work, we present a new prediction algorithm based on both statistical and similarity information, which provides better performance in sensitivity and specificity. RESULTS: We evaluated the accuracy of ATGpr for predicting fullness of cDNA sequences from human clustered ESTs of UniGene, and we obtained specificity, sensitivity, and correlation coefficient of this prediction. Specificity and sensitivity crossed at 46% over the ATGpr score threshold of 0.33 and the maximum correlation coefficient of 0.34 was obtained at this threshold. Without ATGpr we found it effective to use alignments with known proteins for predicting the fullness of cDNA sequences. That is, specificity increased monotonously as similarity (identity of the alignments) increased. Specificity was achieved greater than 80% if identity was greater than 40%. For more effective prediction of fullness of cDNA sequences we combined the similarity (identity of query sequence) with known proteins and ATGpr score. As a result, specificity became greater than 80% if identity was greater than 20%. AVAILABILITY: The prediction program, called ATGpr_ sim, is available at http://www.hri.co.jp/atgpr/ATGpr_sim.html CONTACT: nisikawa@crl.hitachi.co.jp     

Tsukuba BB: a branch and bound algorithm for local multiple alignment of DNA and protein sequences.

In this paper we present a branch and bound algorithm for local gapless multiple sequence alignment (motif alignment) and its implementation. The algorithm uses both score-based bounding and a novel bounding technique based on the "consistency" of the alignment. A sequence order independent search tree is used in conjunction with a technique for avoiding redundant calculations inherent in the structure of the tree. This is the first program to exploit the fact that the motif alignment problem is easier for short motifs. Indeed, for a short fixed motif width, the running time of the algorithm is asymptotically linear in the size of the input. We tested the performance of the program on a dataset of 300 E. coli promoter sequences and a dataset of 85 lipocalin protein sequences. For a motif width of 4, the optimal alignment of the entire set of sequences can be found. For the more natural motif width of 6, the program can align 21 sequences of length 100, more than twice the number of sequences which can be aligned by the best previous exact algorithm. The algorithm can relax the constraint of requiring each sequence to be aligned, and align 105 of the 300 promoter sequences with a motif width of 6. For the lipocalin dataset, we introduce a technique for reducing the effective alphabet size with a minimal loss of useful information. With this technique, we show that the program can find meaningful motifs in a reasonable amount of time by optimizing the score over three motif positions.     

TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the C(α)-N bond (Phi) and the C(α)-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/.     

In search of the small ones: improved prediction of short exons in vertebrates, plants, fungi and protists

MOTIVATION: Prediction of the coding potential for stretches of DNA is crucial in gene calling and genome annotation, where it is used to identify potential exons and to position their boundaries in conjunction with functional sites, such as splice sites and translation initiation sites. The ability to discriminate between coding and non-coding sequences relates to the structure of coding sequences, which are organized in codons, and by their biased usage. For statistical reasons, the longer the sequences, the easier it is to detect this codon bias. However, in many eukaryotic genomes, where genes harbour many introns, both introns and exons might be small and hard to distinguish based on coding potential. RESULTS: Here, we present novel approaches that specifically aim at a better detection of coding potential in short sequences. The methods use complementary sequence features, combined with identification of which features are relevant in discriminating between coding and non-coding sequences. These newly developed methods are evaluated on different species, representative of four major eukaryotic kingdoms, and extensively compared to state-of-the-art Markov models, which are often used for predicting coding potential. The main conclusions drawn from our analyses are that (1) combining complementary sequence features clearly outperforms current Markov models for coding potential prediction in short sequence fragments, (2) coding potential prediction benefits from length-specific models, and these models are not necessarily the same for different sequence lengths and (3) comparing the results across several species indicates that, although our combined method consistently performs extremely well, there are important differences across genomes. SUPPLEMENTARY DATA: http://bioinformatics.psb.ugent.be/.     

Characterization of a cDNA coding for sex steroid-binding protein of human plasma

A cDNA (912 nucleotides) coding for human plasma sex steroid-binding protein (SBP) was characterized from a phage clone previously isolated by screening a Charon 21A human liver cDNA library with rat androgen binding protein (ABP) cDNA. The deduced amino acid sequence from the cDNA indicated that the insert was a partial clone coding for 281 amino acids starting with residue 92 (glycine) encompassing the alternating leucyl residues and the carboxyl-end 373 (histidine) as previously reported [(1986) Biochemistry 25, 7584]. The potential polyadenylation signal sequence ATTAAA is present as part of the 3'-coding region and the stop codon TAA. Both are followed by a short 20 untranslated nucleotides and a poly(A) tract of 49 nucleotides. Significant homologous sequences (76%) at the DNA level exist between human SBP and rat ABP which might suggest the possibility that both evolved from a common primordial gene. Demonstration of the presence of an SBP cDNA in a human liver cDNA library provides the first evidence that liver is the site of SBP biosynthesis.     

人腺苷酸环化酶激活多肽cDNA克隆和序列分析

以人睾丸组织总ＲＮＡ为材料,用ＲＴ－ＰＣＲ方法合成了人腺苷酸环化酶激活多肽（ＡＣＡＰ）编码区（５３０ｂｐ）和全长ｃＤＮＡ（１９３０ｂｐ）片段．并分别将这些ｃＤＮＡ片段克隆入ｐＵＣ１８载体的ＳｍａⅠ限制性内切酶位点．对重组质粒分别采用直接ＤＮＡ双链末端终止法和在核酸外切酶Ⅲ和核酸酶Ｓ１作用下连续缺失ＤＮＡ后,相继克隆,构成一系列连续缺失的缺失体的方法,测定了全部核苷酸顺序．结果表明：ＡＣＡＰ编码区的ｃＤＮＡ顺序与已报道的有１２处碱基的改变,其中１１处碱基顺序的改变不引起编码的氨基酸变化,只有第３８５位的Ｔ→Ａ后,才引起其编码的氨基酸由Ｓｅｒ→Ｔｈｒ,但由于Ｓｅｒ和Ｔｈｒ的理化性质极其相似,这一变化可能并不导致蛋白质的生物活性的变化．这些改变可能是由于种族、群体或个体的差异．     

A large scale structural analysis of cDNAs in a unicellular green alga, Chlamydomonas reinhardtii. I. Generation of 3433 non-redundant expressed sequence tags.

To understand genetic information carried in a unicellular green alga, Chlamydomonas reinhardtii, normalized and size-selected cDNA libraries were constructed from cells at photoautotrophic growth, and a total of 11,571 5'-end sequence tags were established. These sequences were grouped into 3433 independent EST species. Similarity search against the public non-redundant protein database indicated that 817 groups showed significant similarity to registered sequences, of which 140 were of previously identified C. reinhardtii genes, but the remaining 2616 species were novel sequences. The coverage of full-length protein coding regions was estimated to be over 60%. These cDNA clones and EST sequence information will provide a powerful source for the future genome-wide functional analysis of uncharacterized genes.     

Detection of protein coding sequences using a mixture model for local protein amino acid sequence.

Locating protein coding regions in genomic DNA is a critical step in accessing the information generated by large scale sequencing projects. Current methods for gene detection depend on statistical measures of content differences between coding and noncoding DNA in addition to the recognition of promoters, splice sites, and other regulatory sites. Here we explore the potential value of recurrent amino acid sequence patterns 3-19 amino acids in length as a content statistic for use in gene finding approaches. A finite mixture model incorporating these patterns can partially discriminate protein sequences which have no (detectable) known homologs from randomized versions of these sequences, and from short (< or = 50 amino acids) non-coding segments extracted from the S. cerevisiea genome. The mixture model derived scores for a collection of human exons were not correlated with the GENSCAN scores, suggesting that the addition of our protein pattern recognition module to current gene recognition programs may improve their performance.     

Gene structure conservation aids similarity based gene prediction

One of the primary tasks in deciphering the functional contents of a newly sequenced genome is the identification of its protein coding genes. Existing computational methods for gene prediction include ab initio methods which use the DNA sequence itself as the only source of information, comparative methods using multiple genomic sequences, and similarity based methods which employ the cDNA or protein sequences of related genes to aid the gene prediction. We present here an algorithm implemented in a computer program called Projector which combines comparative and similarity approaches. Projector employs similarity information at the genomic DNA level by directly using known genes annotated on one DNA sequence to predict the corresponding related genes on another DNA sequence. It therefore makes explicit use of the conservation of the exon–intron structure between two related genes in addition to the similarity of their encoded amino acid sequences. We evaluate the performance of Projector by comparing it with the program Genewise on a test set of 491 pairs of independently confirmed mouse and human genes. It is more accurate than Genewise for genes whose proteins are <80% identical, and is suitable for use in a combined gene prediction system where other methods identify well conserved and non-conserved genes, and pseudogenes.     

A combination of compositional index and genetic algorithm for predicting transmembrane helical segments

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method. Availability: The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.     

An ensemble of support vector machines for predicting the membrane protein type directly from the amino acid sequence

Given a particular membrane protein, it is very important to know which membrane type it belongs to because this kind of information can provide clues for better understanding its function. In this work, we propose a system for predicting the membrane protein type directly from the amino acid sequence. The feature extraction step is based on an encoding technique that combines the physicochemical amino acid properties with the residue couple model. The residue couple model is a method inspired by Chou’s quasi-sequence-order model that extracts the features by utilizing the sequence order effect indirectly. A set of support vector machines, each trained using a different physicochemical amino acid property combined with the residue couple model, are combined by vote rule. The success rate obtained by our system on a difficult dataset, where the sequences in a given membrane type have a low sequence identity to any other proteins of the same membrane type, are quite high, indicating that the proposed method, where the features are extracted directly from the amino acid sequence, is a feasible system for predicting the membrane protein type.     

Retropseudogenes for human chromosomal protein HMG-17

The human genome contains multiple copies of sequences homologous to the cDNA coding for non-histone chromosomal protein HMG-17. To study the mechanism of generation and dispersion of the HMG-17 multigene family a human genomic library was screened and 70 clones isolated and studied by Southern transfer and restriction site analysis. The results suggest that most of the clones contain unique sequences. Sequence analysis of two genomic clones indicates that they contain elements typical of processed retropseudogenes. Even though both sequences contained open reading frames the sequences lacked introns, were flanked by short, direct repeats and lacked elements associated with functional genes. The sequences of the two pseudogenes were 85% homologous to each other and each was 90% homologous to the human cDNA. Based on the sequence difference in the open reading frame between the pseudogenes and the cDNA it can be estimated that the sequences arose approximately ten million years ago from a common precursor. The present paper, which is the first study on genes coding for this nucleosomal binding protein, indicates that the HMG-17 multigene family is the largest known human retropseudogene family.