SCOT: Rethinking the classification of secondary structure elements.

A fast method is described for searching and analyzing the protein structure databank. It uses secondary structure followed by residue matching to compare protein structures and is developed from a previous structural alignment method based on dynamic programming. Linear representations of secondary structures are derived and their features compared to identify equivalent elements in two proteins. The secondary structure alignment then constrains the residue alignment, which compares only residues within aligned secondary structures and with similar buried areas and torsional angles. The initial secondary structure alignment improves accuracy and provides a means of filtering out unrelated proteins before the slower residue alignment stage. It is possible to search or sort the protein structure databank very quickly using just secondary structure comparisons. A search through 720 structures with a probe protein of 10 secondary structures required 1.7 CPU hours on a Sun 4/280. Alternatively, combined secondary structure and residue alignments, with a cutoff on the secondary structure score to remove pairs of unrelated proteins from further analysis, took 10.1 CPU hours. The method was applied in searches on different classes of proteins and to cluster a subset of the databank into structurally related groups. Relationships were consistent with known families of protein structure.

A pair of neural network-based algorithms is presented for predicting the tertiary structural class and the secondary structure of proteins. Each algorithm realizes improvements in accuracy based on information provided by the other. Structural class prediction of proteins nonhomologous to any in the training set is improved significantly, from 62.3% to 73.9%, and secondary structure prediction accuracy improves slightly, from 62.26% to 62.64%. A number of aspects of neural network optimization and testing are examined. They include network overtraining and an output filter based on a rolling average. Secondary structure prediction results vary greatly depending on the particular proteins chosen for the training and test sets; consequently, an appropriate measure of accuracy reflects the more unbiased approach of "jackknife" cross-validation (testing each protein in the data-base individually).

A novel method is presented for joint prediction of alignment and common secondary structures of two RNA sequences. The joint consideration of common secondary structures and alignment is accomplished by structural alignment over a search space defined by the newly introduced motif called matched helical regions. The matched helical region formulation generalizes previously employed constraints for structural alignment and thereby better accommodates the structural variability within RNA families. A probabilistic model based on pseudo free energies obtained from precomputed base pairing and alignment probabilities is utilized for scoring structural alignments. Maximum a posteriori (MAP) common secondary structures, sequence alignment and joint posterior probabilities of base pairing are obtained from the model via a dynamic programming algorithm called PARTS. The advantage of the more general structural alignment of PARTS is seen in secondary structure predictions for the RNase P family. For this family, the PARTS MAP predictions of secondary structures and alignment perform significantly better than prior methods that utilize a more restrictive structural alignment model. For the tRNA and 5S rRNA families, the richer structural alignment model of PARTS does not offer a benefit and the method therefore performs comparably with existing alternatives. For all RNA families studied, the posterior probability estimates obtained from PARTS offer an improvement over posterior probability estimates from a single sequence prediction. When considering the base pairings predicted over a threshold value of confidence, the combination of sensitivity and positive predictive value is superior for PARTS than for the single sequence prediction. PARTS source code is available for download under the GNU public license at http://rna.urmc.rochester.edu.

BackgroundProtein pairs that have the same secondary structure packing arrangement but have different topologies have attracted much attention in terms of both evolution and physical chemistry of protein structures. Further investigation of such protein relationships would give us a hint as to how proteins can change their fold in the course of evolution, as well as a insight into physico-chemical properties of secondary structure packing. For this purpose, highly accurate sequence order independent structure comparison methods are needed.ResultsWe have developed a novel protein structure alignment algorithm, MICAN (a structure alignment algorithm that can handle Multiple-chain complexes, Inverse direction of secondary structures, Cα only models, Alternative alignments, and Non-sequential alignments). The algorithm was designed so as to identify the best structural alignment between protein pairs by disregarding the connectivity between secondary structure elements (SSE). One of the key feature of the algorithm is utilizing the multiple vector representation for each SSE, which enables us to correctly treat bent or twisted nature of long SSE. We compared MICAN with other 9 publicly available structure alignment programs, using both reference-dependent and reference-independent evaluation methods on a variety of benchmark test sets which include both sequential and non-sequential alignments. We show that MICAN outperforms the other existing methods for reproducing reference alignments of non-sequential test sets. Further, although MICAN does not specialize in sequential structure alignment, it showed the top level performance on the sequential test sets. We also show that MICAN program is the fastest non-sequential structure alignment program among all the programs we examined here.ConclusionsMICAN is the fastest and the most accurate program among non-sequential alignment programs we examined here. These results suggest that MICAN is a highly effective tool for automatically detecting non-trivial structural relationships of proteins, such as circular permutations and segment-swapping, many of which have been identified manually by human experts so far. The source code of MICAN is freely download-able at http://www.tbp.cse.nagoya-u.ac.jp/MICAN.

Classification of RNA Secondary Structures Using the Techniques of Cluster Analysis

In molecular biology, it is estimated that there is a correlation between the secondary structure of a protein and its functionality. While secondary structure prediction is ultimately possible in wet lab, determining a correlation with the functionality is a hard task which can be facilitated by a computational model. In that context, this paper presents an automated algorithm for the visualization and classification of enzymatic proteins with the aim of examining whether the functionality is correlated to the secondary structure. To that end, up-to-date protein data was acquired from publicly accessible databases in order to construct their secondary structures. The resulting data were injected into a tailored version of a Kohonen Self-Organizing Map (SOM). Part of the work was to determine a proper way of reducing large secondary structures to a common length in order to be able to cope with the constant dimensionality requirement of SOMs. The final contribution consisted in the labeling of the trained nodes. Eventually, we were able to get a visual intuition and some quantified assessment on the nature of this correlation.

Structural alignments of superfamily members often exhibit insertions and deletions of secondary structure elements (SSEs), yet conserved subsets of SSEs appear to be important for maintaining the fold and facilitating common functionalities. A database of aligned SSEs was constructed from the structure-based alignments of protein superfamily members in the CAMPASS database. SSEs were classified into several types on the basis of their length and solvent accessibility and counts were made for the replacements of SSEs in different types at structurally aligned positions. The results, summarized as log-odds substitution matrices, can be used for two types of comparisons: (1) structure against structure, both with secondary structure assignments; and (2) structure against sequence with predicted secondary structures. The conservation of SSEs at each alignment position was defined as the deviation of observed SSE frequencies from the uniform distribution. This offers a useful resource to define and examine the core of superfamily folds. Even when the structure of only a single member of a superfamily is known, the extended method can be used to predict the conservation of SSEs. Such information will be useful when modelling the structure of other members of a superfamily or identifying structurally and functionally important positions in the fold.

A primary and a secondary neural network are applied to secondary structure and structural class prediction for a database of 681 non-homologous protein chains. A new method of decoding the outputs of the secondary structure prediction network is used to produce an estimate of the probability of finding each type of secondary structure at every position in the sequence. In addition to providing a reliable estimate of the accuracy of the predictions, this method gives a more accurate Q3 (74.6%) than the cutoff method which is commonly used. Use of these predictions in jury methods improves the Q3 to 74.8%, the best available at present. On a database of 126 proteins commonly used for comparison of prediction methods, the jury predictions are 76.6% accurate. An estimate of the overall Q3 for a given sequence is made by averaging the estimated accuracy of the prediction over all residues in the sequence. As an example, the analysis is applied to the target beta-cryptogein, which was a difficult target for ab initio predictions in the CASP2 study; it shows that the prediction made with the present method (62% of residues correct) is close to the expected accuracy (66%) for this protein. The larger database and use of a new network training protocol also improve structural class prediction accuracy to 86%, relative to 80% obtained previously. Secondary structure content is predicted with accuracy comparable to that obtained with spectroscopic methods, such as vibrational or electronic circular dichroism and Fourier transform infrared spectroscopy.

This article briefly describes our program Jamsek written in FORTRAN for an ICL 2950/10 computer. Jamsek combines statistical and stereochemical rules most frequently encountered in literature to predict protein secondary structure from its sequence, into a single algorithm. The composite algorithm does not work better than the best existing single algorithms of Garnier et al. (J. Mol. Biol., 120, 97-120, 1978) or Lim (J. Mol. Biol., 88, 873-894, 1974) if percentage of residues with a correctly predicted secondary structure is taken as a criterion. However, it is fairly reliable in predicting the total amount of alpha-helices and beta-sheets in proteins, the secondary structure of highly ordered proteins or their parts and identification of long alpha-helices. It surpasses the previous algorithms by providing a possibility to make a notion about confidence of the prediction of the particular secondary structure elements thanks to the simultaneous availability of four independent predictions of the secondary structure and other relevant data (hydrophobic profile and helical wheel representation). The main body of this article is devoted to a demonstration that output data of Jamsek can simply be used for the prediction of protein topological class, identification of globular proteins containing hydrophobic alpha-helices and, as an auxiliary means, to distinguish between protein coding and non-coding nucleotide sequences.

BackgroundRNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of O(L6)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(L^6)$$\\end{document} for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities.ResultsIn this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency.ConclusionREDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics.

It is a classical result of Stein and Waterman that the asymptotic number S(n) of RNA secondary structures is 1.104366 * pow(n,-3/2) * pow(2.61803,n),where the combinatorial model of RNA concerns a length n homopolymer, such that any base can pair with any other base, subject to the usual convention that hairpin loops must contain at least = 1 unpaired bases. The result of Stein and Waterman is proved by developing recursions,using generating functions and applying Bender's theorem. These recursions form the basis to compute the minimum free energy secondary structure for a given RNA sequence, with respect to the Nussinov energy model, later extended by Zuker to substantially more complicated resursions for the Turner nearest neighbor energy model.In this paper, we study combinatorial asymptotic for two special subclasses of RNA secondary structures - canonical and saturated structures.Canonical secondary structures are defined to have no lonely (isolated) base pairs. This class of secondary structures was introduced b y Bompfuenewerer et al., who noted that the runtime of Vienna RNA Package is substantially decreased when restricting computations to canonical structures. Here we provide an explanation for the speed-up, by proving that the asymptotic number of canonical RNA secondary structures is2.1614 * pow(n,-3/2) * pow(1.96798,n), a result obtained using different methods by Hofacker et al. Saturated secondary structures have the property that no base pairs can be added without violating the definition of secondary structure (i.e. introducing a pseudoknotor base triple). In the Nussinov energy model,where the energy for a base pair is -1, saturated structures correspond to kinetic traps.n prior work, we showed that the asymptotic number of saturated structures of a length n homopolymer is 1.07427 * pow(n,-3/2) * pow(2.35467,n). Here we determine the asymptotic expected number of base pairs in (quasi-) random saturated structures.

BackgroundCatalase (EC 1.11.1.6) is a heme-containing tetrameric enzyme that plays a critical role in signaling and hydrogen peroxide metabolism. It was the first enzyme to be crystallized and isolated. Catalase is a well-known industrial enzyme used in diagnostic and analytical methods in the form of biomarkers and biosensors, as well as in the textile, paper, food, and pharmaceutical industries. In silico analysis of CAT genes and proteins has gained increased interest, emphasizing the development of biomarkers and drug designs. The present work aims to understand the catalase evolutionary relationship of plant species and analyze its physicochemical characteristics, homology, phylogenetic tree construction, secondary structure prediction, and 3D modeling of protein sequences and its validation using a variety of conventional computational methods to assist researchers in better understanding the structure of proteins. ResultsAround 65 plant catalase sequences were computationally evaluated and subjected to bioinformatics assessment for physicochemical characterization, multiple sequence alignment, phylogenetic construction, motif and domain identification, and secondary and tertiary structure prediction. The phylogenetic tree revealed six unique clusters where diversity of plant catalases was found to be the largest for Oryza sativa. The thermostability and hydrophilic nature of these proteins were primarily observed, as evidenced by a relatively high aliphatic index and negative GRAVY value. The distribution of 5 sequence motifs was uniformly distributed with a width length of 50 with the best possible amino residue sequences that resemble the plant catalase PLN02609 superfamily. Using SOPMA, the predicted secondary structure of the protein sequences revealed the predominance of the random coil. The predicted 3D CAT model from Arabidopsis thaliana was a homotetramer, thermostable protein with 59-KDa weight, and its structural validation was confirmed by PROCHECK, ERRAT, Verify3D, and Ramachandran plot. The functional relationships of our query sequence revealed the glutathione reductase as the closest interacting protein of query protein. ConclusionsThis theoretical plant catalases in silico analysis provide insight into its physiochemical characteristics and functional and structural understanding and its evolutionary behavior and exploring protein structure-function relationships when crystal structures are unavailable.

There exist many methods for classifying genomic data by aligning, comparing, and analyzing primary nucleotide sequences using such algorithms as dynamic programming and kinetic folding, etc.. These methods are, however, not always effective as motifs are more conserved in structures than in sequences. Instead of performing classification based on primary sequences, we therefore propose to perform the task from structure, exploiting the phenomenon in which molecules form from a sequence of nucleotides, beginning with a primary sequence that can fold back onto itself to form a secondary structure and then a tertiary structure. The algorithm we propose is able to perform data mining in structural data and is called the Random Multi-Level Attributed (RMLA) graph algorithm for mining and representing secondary genomic structure from such biomolecule as tRNA. The identification of structural similarity is implemented with information measure concept to characterize the resultant class. Experiments are based on known tRNA structural data from database of compilation of tRNA genes. The results show that our approach is able to effectively classify different class of tRNA secondary structure. We also compare our result with other classification algorithms to prove the effectiveness. Our approach shows a better way to classify structural data. In fact, RMLA graph is not suitable only for the classification of genomic data, wherever graphs are used to model data, it is useful for discovering patterns in the databases.

Fourier transform coupled to tryptophan-scanning mutagenesis: Lessons from its application to the prediction of secondary structure in the acetylcholine receptor lipid-exposed transmembrane domains

The solution of binary classification problems using the Support Vector Machine (SVM) method has been well developed. Though multi-class classification is typically solved by combining several binary classifiers, recently, several multi-class methods that consider all classes at once have been proposed. However, these methods require resolving a much larger optimization problem and are applicable to small datasets. Three methods based on binary classifications: one-against-all (OAA), one-against-one (OAO), and directed acyclic graph (DAG), and two approaches for multi-class problem by solving one single optimization problem, are implemented to predict protein secondary structure. Our experiments indicate that multi-class SVM methods are more suitable for protein secondary structure (PSS) prediction than the other methods, including binary SVMs, because their capacity to solve an optimization problem in one step. Furthermore, in this paper, we argue that it is feasible to extend the prediction accuracy by adding a second-stage multi-class SVM to capture the contextual information among secondary structural elements and thereby further improving the accuracies. We demonstrate that two-stage SVMs perform better than single-stage SVM techniques for PSS prediction using two datasets and report a maximum accuracy of 79.5%.