1,798 results match your criteria Proteins: Structure Function and Bioinformatics[Journal]


Improving Prediction of Protein Secondary Structure, Backbone Angles, Solvent Accessibility, and Contact Numbers by Using Predicted Contact Maps and an Ensemble of Recurrent and Residual Convolutional Neural Networks.

Bioinformatics 2018 Dec 7. Epub 2018 Dec 7.

School of Information and Communication Technology, Griffith University, Gold Coast, Australia.

Motivation: Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion of protein sequence and structure libraries and advances in deep learning techniques, such as residual convolutional networks (ResNets) and Long-Short-Term Memory Cells in Bidirectional Recurrent Neural Networks (LSTM-BRNNs). Here we leverage an ensemble of LSTM-BRNN and ResNet models, together with predicted residue-residue contact maps, to continue the push towards the attainable limit of prediction for 3- and 8-state secondary structure, backbone angles (θ, τ, ϕ, and ψ), half-sphere exposure, contact numbers, and solvent accessible surface area (ASA). Read More

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http://dx.doi.org/10.1093/bioinformatics/bty1006DOI Listing
December 2018
1 Read

A Structural Homology Approach for Computational Protein Design with Flexible Backbone.

Bioinformatics 2018 Nov 29. Epub 2018 Nov 29.

LISBP, Université de Toulouse, CNRS, INRA, INSA, Toulouse, France.

Motivation: Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Read More

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https://academic.oup.com/bioinformatics/advance-article/doi/
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http://dx.doi.org/10.1093/bioinformatics/bty975DOI Listing
November 2018
5 Reads

Computational discovery of direct associations between GO terms and protein domains.

BMC Bioinformatics 2018 Nov 20;19(Suppl 14):413. Epub 2018 Nov 20.

Université de Lorraine, CNRS, Inria, LORIA, Nancy, F-54500, France.

Background: Families of related proteins and their different functions may be described systematically using common classifications and ontologies such as Pfam and GO (Gene Ontology), for example. However, many proteins consist of multiple domains, and each domain, or some combination of domains, can be responsible for a particular molecular function. Therefore, identifying which domains should be associated with a specific function is a non-trivial task. Read More

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http://dx.doi.org/10.1186/s12859-018-2380-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245584PMC
November 2018
2 Reads

Topology independent structural matching discovers novel templates for protein interfaces.

Bioinformatics 2018 Sep;34(17):i787-i794

Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping SE, Sweden.

Motivation: Protein-protein interactions (PPI) are essential for the function of the cellular machinery. The rapid growth of protein-protein complexes with known 3D structures offers a unique opportunity to study PPI to gain crucial insights into protein function and the causes of many diseases. In particular, it would be extremely useful to compare interaction surfaces of monomers, as this would enable the pinpointing of potential interaction surfaces based solely on the monomer structure, without the need to predict the complete complex structure. Read More

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https://academic.oup.com/bioinformatics/article/34/17/i787/5
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http://dx.doi.org/10.1093/bioinformatics/bty587DOI Listing
September 2018
2 Reads

HITS-PR-HHblits: protein remote homology detection by combining PageRank and Hyperlink-Induced Topic Search.

Brief Bioinform 2018 Nov 7. Epub 2018 Nov 7.

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.

As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database. Read More

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http://dx.doi.org/10.1093/bib/bby104DOI Listing
November 2018
1 Read

Analysis of drug resistance in HIV protease.

BMC Bioinformatics 2018 Oct 22;19(Suppl 11):362. Epub 2018 Oct 22.

Department of Computer Science, 25 Park Place, Atlanta, GA 30303, USA.

Background: Drug resistance in HIV is the major problem limiting effective antiviral therapy. Computational techniques for predicting drug resistance profiles from genomic data can accelerate the appropriate choice of therapy. These techniques can also be used to select protease mutants for experimental studies of resistance and thereby assist in the development of next-generation therapies. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-018-2331-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6196403PMC
October 2018
4 Reads

The EVcouplings Python framework for coevolutionary sequence analysis.

Bioinformatics 2018 Oct 9. Epub 2018 Oct 9.

Department of Systems Biology, Harvard Medical School, Boston, MA, USA.

Summary: Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. Read More

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https://academic.oup.com/bioinformatics/advance-article/doi/
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http://dx.doi.org/10.1093/bioinformatics/bty862DOI Listing
October 2018
2 Reads

Fast design of arbitrary length loops in proteins using InteractiveRosetta.

BMC Bioinformatics 2018 Sep 24;19(1):337. Epub 2018 Sep 24.

Department of Biology, Rensselaer Polytechnic Institute, Troy, NY, USA.

Background: With increasing interest in ab initio protein design, there is a desire to be able to fully explore the design space of insertions and deletions. Nature inserts and deletes residues to optimize energy and function, but allowing variable length indels in the context of an interactive protein design session presents challenges with regard to speed and accuracy.

Results: Here we present a new module (INDEL) for InteractiveRosetta which allows the user to specify a range of lengths for a desired indel, and which returns a set of low energy backbones in a matter of seconds. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-018-2345-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154894PMC
September 2018
3 Reads

ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature.

BMC Bioinformatics 2018 Sep 21;19(1):334. Epub 2018 Sep 21.

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK.

Background: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Read More

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http://dx.doi.org/10.1186/s12859-018-2368-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6150975PMC
September 2018
2 Reads

Phage spanins: diversity, topological dynamics and gene convergence.

BMC Bioinformatics 2018 Sep 15;19(1):326. Epub 2018 Sep 15.

Center for Phage Technology, Department of Biochemistry and Biophysics, Texas A&M University, 2128 TAMU, College Station, TX, 77843-2128, USA.

Background: Spanins are phage lysis proteins required to disrupt the outer membrane. Phages employ either two-component spanins or unimolecular spanins in this final step of Gram-negative host lysis. Two-component spanins like Rz-Rz1 from phage lambda consist of an integral inner membrane protein: i-spanin, and an outer membrane lipoprotein: o-spanin, that form a complex spanning the periplasm. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-018-2342-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6139136PMC
September 2018
4 Reads

Improving Protein Function Prediction Using Protein Sequence and GO-term Similarities.

Bioinformatics 2018 Aug 29. Epub 2018 Aug 29.

Delft Bioinformatics Lab, Delft University of Technology, Mekelweg 4, 2628CD, Delft, the Netherlands.

Motivation: Most automatic functional annotation methods assign Gene Ontology (GO) terms to proteins based on annotations of highly similar proteins. We advocate that proteins that are less similar are still informative. Also, despite their simplicity and structure, GO terms seem to be hard for computers to learn, in particular the Biological Process ontology, which has the most terms (>29,000). Read More

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http://dx.doi.org/10.1093/bioinformatics/bty751DOI Listing

Predicting overlapping protein complexes based on core-attachment and a local modularity structure.

BMC Bioinformatics 2018 Aug 22;19(1):305. Epub 2018 Aug 22.

College of Computer Science and Technology, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, China.

Background: In recent decades, detecting protein complexes (PCs) from protein-protein interaction networks (PPINs) has been an active area of research. There are a large number of excellent graph clustering methods that work very well for identifying PCs. However, most of existing methods usually overlook the inherent core-attachment organization of PCs. Read More

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http://dx.doi.org/10.1186/s12859-018-2309-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6106838PMC
August 2018
1 Read

StructureProfiler: An all-in-one Tool for 3D Protein Structure Profiling.

Bioinformatics 2018 Aug 16. Epub 2018 Aug 16.

ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany.

Motivation: Three-dimensional protein structures are important starting points for elucidating protein function and applications like drug design. Computational methods in this area rely on high quality validation data sets which are usually manually assembled. Due to the increase in published structures as well as the increasing demand for specially tailored validation data sets, automatic procedures should be adopted. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty692DOI Listing
August 2018
1 Read

HFSP: high speed homology-driven function annotation of proteins.

Bioinformatics 2018 Jul;34(13):i304-i312

Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA.

Motivation: The rapid drop in sequencing costs has produced many more (predicted) protein sequences than can feasibly be functionally annotated with wet-lab experiments. Thus, many computational methods have been developed for this purpose. Most of these methods employ homology-based inference, approximated via sequence alignments, to transfer functional annotations between proteins. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty262DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022561PMC
July 2018
1 Read

DisruPPI: structure-based computational redesign algorithm for protein binding disruption.

Bioinformatics 2018 Jul;34(13):i245-i253

Department of Computer Science, Dartmouth, Hanover, NH, USA.

Motivation: Disruption of protein-protein interactions can mitigate antibody recognition of therapeutic proteins, yield monomeric forms of oligomeric proteins, and elucidate signaling mechanisms, among other applications. While designing affinity-enhancing mutations remains generally quite challenging, both statistically and physically based computational methods can precisely identify affinity-reducing mutations. In order to leverage this ability to design variants of a target protein with disrupted interactions, we developed the DisruPPI protein design method (DISRUpting Protein-Protein Interactions) to optimize combinations of mutations simultaneously for both disruption and stability, so that incorporated disruptive mutations do not inadvertently affect the target protein adversely. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty274DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022686PMC

A novel methodology on distributed representations of proteins using their interacting ligands.

Bioinformatics 2018 Jul;34(13):i295-i303

Department of Computer Engineering, Bogazici University, Istanbul, Turkey.

Motivation: The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand-based approach can be utilized in protein representation. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty287DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022674PMC
July 2018
10 Reads

mol2sphere: spherical decomposition of multi-domain molecules for visualization and coarse grained spatial modeling.

Bioinformatics 2018 Nov;34(22):3948-3950

Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, USA.

Motivation: Proteins, especially those involved in signaling pathways are composed of functional modules connected by linker domains with varying degrees of flexibility. To understand the structure-function relationships in these macromolecules, it is helpful to visualize the geometric arrangement of domains. Furthermore, accurate spatial representation of domain structure is necessary for coarse-grain models of the multi-molecular interactions that comprise signaling pathways. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty487DOI Listing
November 2018
1 Read

SSMART: sequence-structure motif identification for RNA-binding proteins.

Bioinformatics 2018 Dec;34(23):3990-3998

Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.

Motivation: RNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty404DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247937PMC
December 2018
8 Reads

Sequence-based prediction of physicochemical interactions at protein functional sites using a function-and-interaction-annotated domain profile database.

BMC Bioinformatics 2018 06 1;19(1):204. Epub 2018 Jun 1.

College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, Biotech Building Room B1-404, 30 South Puzhu Road, Jiangsu, 211816, Nanjing, People's Republic of China.

Background: Identifying protein functional sites (PFSs) and, particularly, the physicochemical interactions at these sites is critical to understanding protein functions and the biochemical reactions involved. Several knowledge-based methods have been developed for the prediction of PFSs; however, accurate methods for predicting the physicochemical interactions associated with PFSs are still lacking.

Results: In this paper, we present a sequence-based method for the prediction of physicochemical interactions at PFSs. Read More

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http://dx.doi.org/10.1186/s12859-018-2206-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984826PMC
June 2018
1 Read

Applying graph theory to protein structures: an Atlas of coiled coils.

Bioinformatics 2018 Oct;34(19):3316-3323

School of Chemistry, University of Bristol, Bristol, UK.

Motivation: To understand protein structure, folding and function fully and to design proteins de novo reliably, we must learn from natural protein structures that have been characterized experimentally. The number of protein structures available is large and growing exponentially, which makes this task challenging. Indeed, computational resources are becoming increasingly important for classifying and analyzing this resource. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty347DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157074PMC
October 2018
1 Read

GapRepairer: a server to model a structural gap and validate it using topological analysis.

Bioinformatics 2018 Oct;34(19):3300-3307

Centre of New Technologies, University of Warsaw, Warsaw, Poland.

Motivation: Over 25% of protein structures possess unresolved fragments. On the other hand, approximately 6% of protein chains have non-trivial topology (and form knots, slipknots, lassos and links). As the topology is fundamental for the proper function of proteins, modeling of topologically correct structures is decisive in various fields, including biophysics, biotechnology and molecular biology. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty334DOI Listing
October 2018
3 Reads

Predicting gene structure changes resulting from genetic variants via exon definition features.

Bioinformatics 2018 Nov;34(21):3616-3623

Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.

Motivation: Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty324DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6198862PMC
November 2018
1 Read

Structural disorder of plasmid-encoded proteins in Bacteria and Archaea.

BMC Bioinformatics 2018 04 25;19(1):158. Epub 2018 Apr 25.

Bio-lab, Institute of General and Physical Chemistry, P.O.B. 45, Studentski trg 12/V, Belgrade, 11001, Serbia.

Background: In the last decade and a half it has been firmly established that a large number of proteins do not adopt a well-defined (ordered) structure under physiological conditions. Such intrinsically disordered proteins (IDPs) and intrinsically disordered (protein) regions (IDRs) are involved in essential cell processes through two basic mechanisms: the entropic chain mechanism which is responsible for rapid fluctuations among many alternative conformations, and molecular recognition via short recognition elements that bind to other molecules. IDPs possess a high adaptive potential and there is special interest in investigating their involvement in organism evolution. Read More

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http://dx.doi.org/10.1186/s12859-018-2158-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5922023PMC

Ultra-fast global homology detection with Discrete Cosine Transform and Dynamic Time Warping.

Bioinformatics 2018 Sep;34(18):3118-3125

Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium.

Motivation: Evolutionary information is crucial for the annotation of proteins in bioinformatics. The amount of retrieved homologs often correlates with the quality of predicted protein annotations related to structure or function. With a growing amount of sequences available, fast and reliable methods for homology detection are essential, as they have a direct impact on predicted protein annotations. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty309DOI Listing
September 2018
2 Reads

SPIDR: small-molecule peptide-influenced drug repurposing.

BMC Bioinformatics 2018 04 16;19(1):138. Epub 2018 Apr 16.

Department of Chemistry and Biochemistry, Boise State University, Boise, USA.

Background: Conventional de novo drug design is costly and time consuming, making it accessible to only the best resourced research organizations. An emergent approach to new drug development is drug repurposing, in which compounds that have already gone through some level of clinical testing are examined for efficacy against diseases divergent than their original application. Repurposing of existing drugs circumvents the time and considerable cost of early stages of drug development, and can be accelerated by using software to screen existing chemical databases to identify suitable drug candidates. Read More

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http://dx.doi.org/10.1186/s12859-018-2153-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5902895PMC
April 2018
2 Reads

COZOID: contact zone identifier for visual analysis of protein-protein interactions.

BMC Bioinformatics 2018 04 6;19(1):125. Epub 2018 Apr 6.

Faculty of Informatics, Masaryk University, Brno, Czech Republic.

Background: Studying the patterns of protein-protein interactions (PPIs) is fundamental for understanding the structure and function of protein complexes. The exploration of the vast space of possible mutual configurations of interacting proteins and their contact zones is very time consuming and requires the proteomic expert knowledge.

Results: In this paper, we propose a novel tool containing a set of visual abstraction techniques for the guided exploration of PPI configuration space. Read More

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http://dx.doi.org/10.1186/s12859-018-2113-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5889581PMC
April 2018
4 Reads

DeepSol: a deep learning framework for sequence-based protein solubility prediction.

Bioinformatics 2018 Aug;34(15):2605-2613

Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar.

Motivation: Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty166DOI Listing
August 2018
2 Reads

Inferring RNA sequence preferences for poorly studied RNA-binding proteins based on co-evolution.

BMC Bioinformatics 2018 03 12;19(1):96. Epub 2018 Mar 12.

Department of Computer Science, University of British Columbia, Vancouver, Canada.

Background: Characterizing the binding preference of RNA-binding proteins (RBP) is essential for us to understand the interaction between an RBP and its RNA targets, and to decipher the mechanism of post-transcriptional regulation. Experimental methods have been used to generate protein-RNA binding data for a number of RBPs in vivo and in vitro. Utilizing the binding data, a couple of computational methods have been developed to detect the RNA sequence or structure preferences of the RBPs. Read More

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http://dx.doi.org/10.1186/s12859-018-2091-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5848454PMC
March 2018
7 Reads

Comparative assessment of strategies to identify similar ligand-binding pockets in proteins.

BMC Bioinformatics 2018 03 9;19(1):91. Epub 2018 Mar 9.

Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.

Background: Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.

Results: We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Read More

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http://dx.doi.org/10.1186/s12859-018-2109-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5845264PMC
March 2018
1 Read

Alignment-free clustering of large data sets of unannotated protein conserved regions using minhashing.

BMC Bioinformatics 2018 03 5;19(1):83. Epub 2018 Mar 5.

School of EECS, Washington State University, 355 NE Spokane St, Pullman, 99164, USA.

Background: Clustering of protein sequences is of key importance in predicting the structure and function of newly sequenced proteins and is also of use for their annotation. With the advent of multiple high-throughput sequencing technologies, new protein sequences are becoming available at an extraordinary rate. The rapid growth rate has impeded deployment of existing protein clustering/annotation tools which depend largely on pairwise sequence alignment. Read More

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http://dx.doi.org/10.1186/s12859-018-2080-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5838936PMC
March 2018
5 Reads

BetaSerpentine: a bioinformatics tool for reconstruction of amyloid structures.

Bioinformatics 2018 02;34(4):599-608

Structural Bioinformatics and Molecular Modeling, Centre de Recherche en Biologie Cellulaire de Montpellier, CNRS, Université Montpellier, Montpellier 34293, France.

Motivation: Numerous experimental studies have suggested that polypeptide chains of large amyloidogenic regions zig-zag in β-serpentine arrangements. These β-serpentines are stacked axially and form the superpleated β-structure. Despite this progress in the understanding of amyloid folds, the determination of their 3D structure at the atomic level is still a problem due to the polymorphism of these fibrils and incompleteness of experimental structural data. Read More

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http://dx.doi.org/10.1093/bioinformatics/btx629DOI Listing
February 2018
4 Reads

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

BMC Bioinformatics 2018 02 6;19(1):35. Epub 2018 Feb 6.

Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, 35131, Italy.

Background: The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. Read More

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http://dx.doi.org/10.1186/s12859-018-2043-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802066PMC
February 2018
3 Reads

Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures.

BMC Bioinformatics 2018 02 1;19(1):29. Epub 2018 Feb 1.

Institute for Glycomics and School of Information and Communication Technology, Griffith University, Parklands Dr, Southport, QLD, 4222, Australia.

Background: Protein structure can be described by backbone torsion angles: rotational angles about the N-Cα bond (φ) and the Cα-C bond (ψ) or the angle between Cα-Cα-Cα (θ) and the rotational angle about the Cα-Cα bond (τ). Thus, their accurate prediction is useful for structure prediction and model refinement. Early methods predicted torsion angles in a few discrete bins whereas most recent methods have focused on prediction of angles in real, continuous values. Read More

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http://dx.doi.org/10.1186/s12859-018-2031-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796405PMC
February 2018
1 Read

Large scale analysis of protein conformational transitions from aqueous to non-aqueous media.

BMC Bioinformatics 2018 01 30;19(1):27. Epub 2018 Jan 30.

Departamento de Ciencia y Tecnología, CONICET, Universidad Nacional de Quilmes, Roque Sáenz Peña 352, B1876BXD, Bernal, Provincia de Buenos Aires, Argentina.

Background: Biocatalysis in organic solvents is nowadays a common practice with a large potential in Biotechnology. Several studies report that proteins which are co-crystallized or soaked in organic solvents preserve their fold integrity showing almost identical arrangements when compared to their aqueous forms. However, it is well established that the catalytic activity of proteins in organic solvents is much lower than in water. Read More

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http://dx.doi.org/10.1186/s12859-018-2044-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5791380PMC
January 2018
1 Read

CONFOLD2: improved contact-driven ab initio protein structure modeling.

BMC Bioinformatics 2018 01 25;19(1):22. Epub 2018 Jan 25.

Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211, MO, USA.

Background: Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed.

Results: We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. Read More

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http://dx.doi.org/10.1186/s12859-018-2032-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5784681PMC
January 2018
1 Read

OPAL: prediction of MoRF regions in intrinsically disordered protein sequences.

Bioinformatics 2018 Jun;34(11):1850-1858

School of Engineering and Physics, The University of the South Pacific, Suva, Fiji.

Motivation: Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty032DOI Listing
June 2018
5 Reads

LocText: relation extraction of protein localizations to assist database curation.

BMC Bioinformatics 2018 01 17;19(1):15. Epub 2018 Jan 17.

Bioinformatics & Computational Biology, Department of Informatics, Technical University of Munich (TUM), Boltzmannstr. 3, Garching, 85748, Germany.

Background: The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Read More

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http://dx.doi.org/10.1186/s12859-018-2021-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773052PMC
January 2018
5 Reads

Mustguseal: a server for multiple structure-guided sequence alignment of protein families.

Bioinformatics 2018 05;34(9):1583-1585

Belozersky Institute of Physicochemical Biology.

Motivation: Comparative analysis of homologous proteins in a functionally diverse superfamily is a valuable tool at studying structure-function relationship, but represents a methodological challenge.

Results: The Mustguseal web-server can automatically build large structure-guided sequence alignments of functionally diverse protein families that include thousands of proteins basing on all available information about their structures and sequences in public databases. Superimposition of protein structures is implemented to compare evolutionarily distant relatives, whereas alignment of sequences is used to compare close homologues. Read More

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http://dx.doi.org/10.1093/bioinformatics/btx831DOI Listing
May 2018
5 Reads

Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks.

BMC Bioinformatics 2017 12 28;18(Suppl 14):497. Epub 2017 Dec 28.

Division of Systems Biology, FDA's National Center for Toxicological Research, Jefferson, AR, 72079, USA.

Background: Blockage of some ion channels and in particular, the hERG (human Ether-a'-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Read More

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http://dx.doi.org/10.1186/s12859-017-1895-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751783PMC
December 2017
3 Reads

RocSampler: regularizing overlapping protein complexes in protein-protein interaction networks.

BMC Bioinformatics 2017 Dec 6;18(Suppl 15):491. Epub 2017 Dec 6.

Graduate School of Mathematics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.

Background: In recent years, protein-protein interaction (PPI) networks have been well recognized as important resources to elucidate various biological processes and cellular mechanisms. In this paper, we address the problem of predicting protein complexes from a PPI network. This problem has two difficulties. Read More

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http://dx.doi.org/10.1186/s12859-017-1920-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5731504PMC
December 2017
4 Reads

Detecting intermediate protein conformations using algebraic topology.

BMC Bioinformatics 2017 Dec 6;18(Suppl 15):502. Epub 2017 Dec 6.

Department of Mathematics, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, 02125, MA, USA.

Background: Understanding protein structure and dynamics is essential for understanding their function. This is a challenging task due to the high complexity of the conformational landscapes of proteins and their rugged energy levels. In particular, it is important to detect highly populated regions which could correspond to intermediate structures or local minima. Read More

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http://dx.doi.org/10.1186/s12859-017-1918-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5731496PMC
December 2017
8 Reads

A boosting approach for prediction of protein-RNA binding residues.

BMC Bioinformatics 2017 Dec 1;18(Suppl 13):465. Epub 2017 Dec 1.

School of Software, Central South University, No.22 Shaoshan South Road, Changsha, 410075, China.

Background: RNA binding proteins play important roles in post-transcriptional RNA processing and transcriptional regulation. Distinguishing the RNA-binding residues in proteins is crucial for understanding how protein and RNA recognize each other and function together as a complex.

Results: We propose PredRBR, an effectively computational approach to predict RNA-binding residues. Read More

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http://dx.doi.org/10.1186/s12859-017-1879-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5773889PMC
December 2017
7 Reads

A proximity-based graph clustering method for the identification and application of transcription factor clusters.

BMC Bioinformatics 2017 Nov 29;18(1):530. Epub 2017 Nov 29.

University of Michigan Department of Computational Medicine and Bioinformatics, 100 Washtenaw Avenue, Ann Arbor, 48109, USA.

Background: Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions. Read More

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http://dx.doi.org/10.1186/s12859-017-1935-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5706350PMC
November 2017
9 Reads

PON-SC - program for identifying steric clashes caused by amino acid substitutions.

BMC Bioinformatics 2017 Nov 29;18(1):531. Epub 2017 Nov 29.

Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, BMC B13, SE-22 184, Lund, Sweden.

Background: Amino acid substitutions due to DNA nucleotide replacements are frequently disease-causing because of affecting functionally important sites. If the substituting amino acid does not fit into the protein, it causes structural alterations that are often harmful. Clashes of amino acids cause local or global structural changes. Read More

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http://dx.doi.org/10.1186/s12859-017-1947-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707825PMC
November 2017
5 Reads

3dRPC: a web server for 3D RNA-protein structure prediction.

Bioinformatics 2018 04;34(7):1238-1240

Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.

RNA-protein interactions occur in many biological processes. To understand the mechanism of these interactions one needs to know three-dimensional (3D) structures of RNA-protein complexes. 3dRPC is an algorithm for prediction of 3D RNA-protein complex structures and consists of a docking algorithm RPDOCK and a scoring function 3dRPC-Score. Read More

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http://dx.doi.org/10.1093/bioinformatics/btx742DOI Listing
April 2018
3 Reads
4.980 Impact Factor

Detecting transitions in protein dynamics using a recurrence quantification analysis based bootstrap method.

Authors:
Wael I Karain

BMC Bioinformatics 2017 Nov 28;18(1):525. Epub 2017 Nov 28.

Department of Physics, Birzeit University, P.O.Box 14, Birzeit, Palestine.

Background: Proteins undergo conformational transitions over different time scales. These transitions are closely intertwined with the protein's function. Numerous standard techniques such as principal component analysis are used to detect these transitions in molecular dynamics simulations. Read More

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http://dx.doi.org/10.1186/s12859-017-1943-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704401PMC
November 2017
5 Reads

Swellix: a computational tool to explore RNA conformational space.

BMC Bioinformatics 2017 Nov 21;18(1):504. Epub 2017 Nov 21.

, 101 Stephenson Parkway, Norman, OK, 73019, USA.

Background: The sequence of nucleotides in an RNA determines the possible base pairs for an RNA fold and thus also determines the overall shape and function of an RNA. The Swellix program presented here combines a helix abstraction with a combinatorial approach to the RNA folding problem in order to compute all possible non-pseudoknotted RNA structures for RNA sequences. The Swellix program builds on the Crumple program and can include experimental constraints on global RNA structures such as the minimum number and lengths of helices from crystallography, cryoelectron microscopy, or in vivo crosslinking and chemical probing methods. Read More

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http://dx.doi.org/10.1186/s12859-017-1910-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5697422PMC
November 2017
10 Reads

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

BMC Bioinformatics 2017 Nov 14;18(1):480. Epub 2017 Nov 14.

Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.

Background: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-C technique can generate genome-wide chromosomal interaction (contact) data, which can be used to investigate the higher-level organization of chromosomes, such as Topologically Associated Domains (TAD), i.e. Read More

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http://dx.doi.org/10.1186/s12859-017-1931-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5686814PMC
November 2017
6 Reads

AlloSigMA: allosteric signaling and mutation analysis server.

Bioinformatics 2017 Dec;33(24):3996-3998

Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore.

Motivation: Allostery is an omnipresent mechanism of the function modulation in proteins via either effector binding or mutations in the exosites. Despite the growing number of online servers and databases devoted to prediction/classification of allosteric sites and their characteristics, there is a lack of resources for an efficient and quick estimation of the causality and energetics of allosteric communication.

Results: The AlloSigMA server implements a unique approach on the basis of the recently introduced structure-based statistical mechanical models of allosteric signaling. Read More

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http://dx.doi.org/10.1093/bioinformatics/btx430DOI Listing
December 2017
9 Reads

TITINdb-a computational tool to assess titin's role as a disease gene.

Bioinformatics 2017 Nov;33(21):3482-3485

Randall Division of Cell and Molecular Biophysics, King's College London BHF Centre of Research Excellence, London SE1 1UL, UK.

Summary: Large numbers of rare and unique titin missense variants have been discovered in both healthy and disease cohorts, thus the correct classification of variants as pathogenic or non-pathogenic has become imperative. Due to titin's large size (363 coding exons), current web applications are unable to map titin variants to domain structures. Here, we present a web application, TITINdb, which integrates titin structure, variant, sequence and isoform information, along with pre-computed predictions of the impact of non-synonymous single nucleotide variants, to facilitate the correct classification of titin variants. Read More

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http://dx.doi.org/10.1093/bioinformatics/btx424DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860166PMC
November 2017
1 Read