Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the twilight zone we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our
Experimentally determining the subcellular localization of a protein can be a laborious and time consuming task. Immunolabeling or tagging (such as with a green fluorescent protein) to view localization using fluorescence microscope are often used. A high throughput alternative is to use prediction. Through the development of new approaches in computer science, coupled with an increased dataset of proteins of known localization, computational tools can now provide fast and accurate localization predictions for many organisms. This has resulted in subcellular localization prediction becoming one of the challenges being successfully aided by bioinformatics, and machine learning. Many prediction methods now exceed the accuracy of some high-throughput laboratory methods for the identification of protein subcellular localization.[1] Particularly, some predictors have been developed[2] that can be used to deal with proteins that may simultaneously exist, or move between, two or more different ...
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Applies a random forest algorithm to automatically learn from and then interpret ultraviolet photodissociation (UVPD) mass spectra, passing results to a hidden Markov model for de novo sequence prediction and scoring. We show this combined strategy provides high-performance de novo peptide sequencing, enabling the de novo sequencing of thousands of peptides from an Escherichia coli lysate at high confidence.
TY - JOUR. T1 - NcPred for accurate nuclear protein prediction using n-mer statistics with various classification algorithms. AU - Islam, Md. Saiful. AU - Kabir, Alaol. AU - Sakib, Kazi. AU - Hossain, Alamgir. N1 - 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011) Salamanca, Spain 6-8 April 2011.. PY - 2011. Y1 - 2011. N2 - Prediction of nuclear proteins is one of the major challenges in genome annotation. A method, NcPred is described, for predicting nuclear proteins with higher accuracy exploiting n-mer statistics with different classification algorithms namely Alternating Decision (AD) Tree, Best First (BF) Tree, Random Tree and Adaptive (Ada) Boost. On BaCello dataset [1], NcPred improves about 20% accuracy with Random Tree and about 10% sensitivity with Ada Boost for Animal proteins compared to existing techniques. It also increases the accuracy of Fungal protein prediction by 20% and recall by 4% with AD Tree. In case of Human ...
Extensive study has been conducted on the identification of peptide sequences with mass spectrometry. With the development of computer hardware and algorithms, de novo sequencing has drawn attention from researchers for many years. Because it does not require a protein database, de novo sequencing is able to serve as either a complement of database searching or a stand alone method. As shown by Novor \cite{novor}, the speed of de novo sequencing significantly exceeds the speed of protein database searching. Improving the accuracy of de novo sequencing is essential. Overlapping peptides occur quite frequently in a typical heavy chain proteomics sample. In this thesis, we have proposed an algorithm to efficiently and reliably detect the overlapping peptides. In addition, two strategies named labeling and voting are designed to utilize overlapping peptides so as to improve the accuracy of de novo sequencing. According to the results, the effect of our labeling strategy is not obvious with the ...
MOTIVATION Peptide-sequencing methods by mass spectrum use the following two approaches: database searching and de novo sequencing. The database-searching approach is convenient; however, in cases wherein the corresponding sequences are not included in the databases, the exact identification is difficult. On the other hand, in the case of de novo sequencing, no preliminary information is necessary; however, continuous amino acid sequence peaks and the differentiation of these peaks are required. It is, however, very difficult to obtain and differentiate the peaks of all amino acids by using an actual spectrum. We propose a novel de novo sequencing approach using not only mass-to-charge ratio but also ion peak intensity and amino acid cleavage intensity ratio (CIR). RESULTS Our method compensates for any undetectable amino acid peak intervals by estimating the amino acid set and the probability of peak expression based on amino acid CIR. It provides more accurate identification of sequences than the
Low-complexity regions (LCRs) in proteins are tracts that are highly enriched in one or a few amino acids. Given their high abundance, and their capacity to expand in relatively short periods of time through replication slippage, they can greatly con
SMURFLite (simplified Structural Motifs Using Random Fields) is a web application for protein remote homology detection, specifically in beta-structural proteins.. ::DEVELOPER. Berger Lab. :: SCREENSHOTS. N/A. :: REQUIREMENTS. ...
If you have been following along with the tutorial, by now you have been through several manual de novo sequencing exercises. The one un-blinded, and two blinded sequences have been fairly complete with abundant fragmentation. Just to ground you in reality, this is not always the case, and more often than not the abundance of fragment ions tends to thin near the fringes of the spectrum making it difficult to determine a complete peptide sequence. It also makes it difficult to start a sequence, as your first jump will often be a combination of 2 or 3 amino acids. In addition to this complication, triply charged ions or ions of higher charge states can give fragments of doubly, singly, and triply charge states, making the problem so much more complicated. The de novo problem would seem to lend itself well to a computational solution. Amazingly, until just recently, few if any de novo programs have given satisfactory results leading most experts in the field to say, I can do better by hand. Well, ...
Notice the y ion intensity takes a hit when we encounter glutamic acid, going from y10 to y11 and then again when we cross aspartic acid going from y13 ...
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In a computed protein multiple sequence alignment, the coreness of a column is the fraction of its substitutions that are in so-called core columns of the gold-standard reference alignment of its proteins. In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted. We develop for the first time a predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and
TY - JOUR. T1 - Grouping of amino acid types and extraction of amino acid properties from multiple sequence alignments using variance maximization. AU - Wrabl, James O.. AU - Grishin, Nick V.. PY - 2005/11/15. Y1 - 2005/11/15. N2 - Understanding of amino acid type co-occurrence in trusted multiple sequence alignments is a prerequisite for improved sequence alignment and remote homology detection algorithms. Two objective approaches were used to investigate co-occurrence, both based on variance maximization of the weighted residue frequencies in columns taken from a large alignment database. The first approach discretely grouped amino acid types, and the second approach extracted orthogonal properties of amino acids using principal components analysis. The grouping results corresponded to amino acid physical properties such as side chain hydrophobicity, size, or backbone flexibility, and an optimal arrangement of approximately eight groups was observed. However, interpretation of the orthogonal ...
Jalview hands-on training course is for anyone who works with sequence data and multiple sequence alignments from proteins, RNA and DNA.. Register via the University of Cambridge website.. Jalview is free software for protein and nucleic acid sequence alignment generation, visualisation and analysis. It includes sophisticated editing options and provides a range of analysis tools to investigate the structure and function of macromolecules through a multiple window interface. For example, Jalview supports 8 popular methods for multiple sequence alignment, prediction of protein secondary structure by JPred and disorder prediction by four methods. Jalview also has options to generate phylogenetic trees, and assess consensus and conservation across sequence families. Sequences, alignments and additional annotation can be accessed directly from public databases and journal-quality figures generated for publication.. The course involves of a mixture of talks and hands-on exercises.. Day 1 is an ...
Multiple sequence alignments (MSAs) are essential in most bioinformatics analyses that involve comparing homologous sequences. The exact way of computing an optimal alignment between N sequences has a computational complexity of O(LN) for N sequences of length L making it prohibitive for even small numbers of sequences. Most automatic methods are based on the progressive alignment heuristic (Hogeweg and Hesper, 1984), which aligns sequences in larger and larger subalignments, following the branching order in a guide tree. With a complexity of roughly O(N2), this approach can routinely make alignments of a few thousand sequences of moderate length, but it is tough to make alignments much bigger than this. The progressive approach is a greedy algorithm where mistakes made at the initial alignment stages cannot be corrected later. To counteract this effect, the consistency principle was developed (Notredame et al, 2000). This has allowed the production of a new generation of more accurate ...
Download MSAProbs: Multiple Sequence Alignment for free. One of the most accurate multiple protein sequence aligners. MSAProbs is an open-source protein multiple sequence ailgnment algorithm, achieving the stastistically highest alignment accuracy on popular benchmarks: BALIBASE, PREFAB, SABMARK, OXBENCH, compared to ClustalW, MAFFT, MUSCLE, ProbCons and Probalign.
We reformulate the problem in terms of searching paths in a graph. To this goal, let M P denote the set of ion masses m i in input increased with: their complementary masses m P - m i + 2, the mass of the hydrogen, 1, and of its complementary mass m P - 17. By abuse of notation, M P = {m1,...,m n }, where m i ,m j if i ,j.. We build a directed acyclic graph G P = (V, E) as follows. Let a node v i associate to a member m i of M P , and an edge from v i to v j if m j - m i equals the sum of residue masses.. The de novo sequencing problem consists in determining any path from v1 to v n in the graph G P .. Although there is a unique original protein, the de novo sequencing may have in general more solutions (or none). In order to choose one sequence among the possible solutions, researchers have introduced any scoring function [1-3] depending on the masses of the fragments in the spectra. Our algorithm can determine either the solution of maximum score according to any given function or that of ...
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One way to understand the molecular mechanism of a cell is to understand the function of each protein encoded in its genome. The function of a protein is largely dependent on the three-dimensional structure the protein assumes after folding. Since the determination of three-dimensional structure experimentally is difficult and expensive, an easier and cheaper approach is for one to look at the primary sequence of a protein and to determine its function by classifying the sequence into the corresponding functional family. In this paper, we propose an effective data mining technique for the multi-class protein sequence classification. For experimentations, the proposed technique has been tested with different sets of protein sequences. Experimental results show that it outperforms other existing protein sequence classifiers and can effectively classify proteins into their corresponding functional families ...
One of the core activities of high-throughput proteomics is the identification of peptides from mass spectra. Some peptides can be identified using spectral matching programs like Sequest or Mascot, but many spectra do not produce high quality database matches. De novo peptide sequencing is an approach to determine partial peptide sequences for some of the unidentified spectra. A drawback of de novo peptide sequencing is that it produces a series of ordered and disordered sequence tags and mass tags rather than a complete, non-degenerate peptide amino acid sequence. This incomplete data is difficult to use in conventional search programs such as BLAST or FASTA. DeNovoID is a program that has been specifically designed to use degenerate amino acid sequence and mass data derived from MS experiments to search a peptide database. Since the algorithm employed depends on the amino acid composition of the peptide and not its sequence, DeNovoID does not have to consider all possible sequences, but ...
Protein 3D structures, determined largely by their amino acid sequences, have been considered as an essential factor for better understanding the function of proteins [1-3]. However, it is exceedingly difficult to directly predict proteins 3D structures from amino acid sequences [4]. Identifying structure properties, such as secondary structure, solvent accessibility or contact number can provide useful insights into the 3D structures [5-7]. Accurate prediction of structural characteristics from the primary sequence is a crucial intermediate step in protein 3D structure prediction [8, 9].. The solvent accessibility (solvent accessible surface area) is defined as the surface region of a residue that is accessible to a rounded solvent while probing the surface of that residue [10]. Solvent burial residues have a particularly strong association with packed amino acids during the folding process [11], and exposed residues give a useful insight into protein-protein interactions and protein stability ...
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Protein subcellular localization prediction involves the computational prediction of where a protein resides in a cell. It is an important component of bioinformatics-based prediction of protein function and genome annotation, and can also aid us to identify novel drug targets.. Here we use the subcellular localization dataset of human proteins presented in the study of Chou and Shen (2008) for a demonstration. The complete dataset includes 3,134 protein sequences (2,750 different proteins), classified into 14 human subcellular locations. We selected two classes of proteins as our benchmark dataset. Class 1 contains 325 extracell proteins, and class 2 includes 307 mitochondrion proteins.. First, we load the Rcpi package, then read the protein sequences stored in two separated FASTA files with ...
In order to benefit maximally from large scale molecular biology data generated by recent developments, it is important to proceed in an organized manner by developing databases, interfaces, data visualization and data interpretation tools. Protein subcellular localization and microarray gene expression are two of such fields that require immense computational effort before being used as a roadmap for the experimental biologist. Protein subcellular localization is important for elucidating protein function. We developed an automatically updated searchable and downloadable system called model organisms proteome subcellular localization database (MEP2SL) that hosts predicted localizations and known experimental localizations for nine eukaryotes. MEP2SL localizations highly correlated with high throughput localization experiments in yeast and were shown to have superior accuracies when compared with four other localization prediction tools based on two different datasets. Hence, MEP2SL system may ...
CiteSeerX - Scientific documents that cite the following paper: 119931, A decision graph explanation of protein secondary structure prediction
Multiple sequence alignment for short sequences Kristóf Takács Multiple sequence alignment (MSA) has been one of the most important problems in bioinformatics for more decades and it is still heavily examined by many mathematicians and biologists. However, mostly because of the practical motivation of this problem, the research on this topic is focused on aligning…
Protein-binding sites prediction lays a foundation for functional annotation of protein and structure-based drug design. As the number of available protein structures increases, structural alignment based algorithm becomes the dominant approach for protein-binding sites prediction. However, the present algorithms underutilize the ever increasing numbers of three-dimensional protein-ligand complex structures (bound protein), and it could be improved on the process of alignment, selection of templates and clustering of template. Herein, we built so far the largest database of bound templates with stringent quality control. And on this basis, bSiteFinder as a protein-binding sites prediction server was developed. By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server has been significantly improved. Further, the accuracy was approximately 2-10 % higher than that of other algorithms for the
Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction ...
The solvent accessibility of a residue in a protein is a value that represents the solvent exposed surface area of this residue. It is crucial for understanding protein structure and function. As a result of the completion of whole-genome sequencing projects, the sequence-structure gap is rapidly increasing. Importantly, the knowledge of protein structures is a foundation for understanding the mechanism of diseases of living organisms and facilitating discovery of new drugs. The most reliable methods for identification of protein structure are X-ray crystallography techniques, but they are expensive and time-consuming. This leads to a central, yet unsolved study of protein structure prediction in bioinformatics, especially for sequences which do not have a significant sequence similarity with known structures [1]. To predict protein structure, the role of solvent accessibility has been extensively investigated as it is related to the spatial arrangement and packing of amino acids during the ...