1,826 results match your criteria Proteins: Structure Function and Bioinformatics[Journal]


parMATT: parallel multiple alignment of protein 3D-structures with translations and twists for distributed-memory systems.

Bioinformatics 2019 Mar 27. Epub 2019 Mar 27.

Faculty of Computational Mathematics and Cybernetics, Vorobjev hills, Moscow, Russia.

Motivation: Accurate structural alignment of proteins is crucial at studying structure-function relationship in evolutionarily distant homologues. Various software tools were proposed to align multiple protein 3D-structures utilizing one CPU and thus are of limited productivity at large-scale analysis of protein families/superfamilies.

Results: The parMATT is a hybrid MPI/pthreads/OpenMP parallel re-implementation of the MATT algorithm to align multiple protein 3D-structures by allowing translations and twists. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz224DOI Listing

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications.

Brief Bioinform 2019 Mar 21. Epub 2019 Mar 21.

School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. Read More

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http://dx.doi.org/10.1093/bib/bbz029DOI Listing

SodaPop: A Forward Simulation Suite for the Evolutionary Dynamics of Asexual Populations on Protein Fitness Landscapes.

Bioinformatics 2019 Mar 13. Epub 2019 Mar 13.

Département de Biochimie, Université de Montréal, Montréal, Québec, Canada.

Motivation: Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz175DOI Listing

eBDIMS server: protein transition pathways with ensemble analysis in 2D-motion spaces.

Bioinformatics 2019 Feb 19. Epub 2019 Feb 19.

Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.

Summary: Understanding how proteins transition between different conformers, and how conformers relate to each other in terms of structure and function, is not trivial. Here we present an online tool for transition pathway generation between two protein conformations using eBDIMS, a coarse-grained simulation algorithm, which spontaneously predicts transition intermediates trapped experimentally. In addition to path-generation, the server provides an interactive 2D-motion landscape graphical representation of the transitions or any additional conformers to explore their structural relationships. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz104DOI Listing
February 2019
2 Reads

Predicting protein-ligand binding residues with deep convolutional neural networks.

BMC Bioinformatics 2019 Feb 26;20(1):93. Epub 2019 Feb 26.

The High School Affiliated of Liaoning Normal University, Dalian, China.

Background: Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categorized as sequence-based or 3D-structure-based methods. Read More

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http://dx.doi.org/10.1186/s12859-019-2672-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6390579PMC
February 2019
1 Read

BioVR: a platform for virtual reality assisted biological data integration and visualization.

BMC Bioinformatics 2019 Feb 15;20(1):78. Epub 2019 Feb 15.

Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, One Lomb Memorial Drive, Rochester, NY, 14623, USA.

Background: Functional characterization of single nucleotide variants (SNVs) involves two steps, the first step is to convert DNA to protein and the second step is to visualize protein sequences with their structures. As massively parallel sequencing has emerged as a leading technology in genomics, resulting in a significant increase in data volume, direct visualization of SNVs together with associated protein sequences/structures in a new user interface (UI) would be a more effective way to assess their potential effects on protein function.

Results: We have developed BioVR, an easy-to-use interactive, virtual reality (VR)-assisted platform for integrated visual analysis of DNA/RNA/protein sequences and protein structures using Unity3D and the C# programming language. Read More

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http://dx.doi.org/10.1186/s12859-019-2666-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6376704PMC
February 2019
2.576 Impact Factor

MultiDomainBenchmark: a multi-domain query and subject database suite.

BMC Bioinformatics 2019 Feb 14;20(1):77. Epub 2019 Feb 14.

National Center for Biotechnology Information, Bethesda, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894, MD, USA.

Background: Genetic sequence database retrieval benchmarks play an essential role in evaluating the performance of sequence searching tools. To date, all phylogenetically diverse benchmarks known to the authors include only query sequences with single protein domains. Domains are the primary building blocks of protein structure and function. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-019-2660-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6376684PMC
February 2019
4 Reads

Identification and analysis of structurally critical fragments in HopS2.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):552. Epub 2019 Feb 4.

Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam, 784028, India.

Background: Among the diverse roles of the Type III secretion-system (T3SS), one of the notable functions is that it serves as unique nano machineries in gram-negative bacteria that facilitate the translocation of effector proteins from bacteria into their host. These effector proteins serve as potential targets to control the pathogenicity conferred to the bacteria. Despite being ideal choices to disrupt bacterial systems, it has been quite an ordeal in the recent times to experimentally reveal and establish a concrete sequence-structure-function relationship for these effector proteins. Read More

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http://dx.doi.org/10.1186/s12859-018-2551-1DOI Listing
February 2019
1 Read

Understanding the evolutionary trend of intrinsically structural disorders in cancer relevant proteins as probed by Shannon entropy scoring and structure network analysis.

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):549. Epub 2019 Feb 4.

Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts, 02138, USA.

Background: Malignant diseases have become a threat for health care system. A panoply of biological processes is involved as the cause of these diseases. In order to unveil the mechanistic details of these diseased states, we analyzed protein families relevant to these diseases. Read More

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http://dx.doi.org/10.1186/s12859-018-2552-0DOI Listing
February 2019
1 Read
2.576 Impact Factor

Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data.

Authors:
Y-H Taguchi

BMC Bioinformatics 2019 Feb 4;19(Suppl 13):388. Epub 2019 Feb 4.

Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan.

Background: Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug-protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene-gene interactions. Read More

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http://dx.doi.org/10.1186/s12859-018-2395-8DOI Listing
February 2019

Disentangling the complexity of low complexity proteins.

Brief Bioinform 2019 Jan 30. Epub 2019 Jan 30.

Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, Mainz, Germany.

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. Read More

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https://academic.oup.com/bib/advance-article/doi/10.1093/bib
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http://dx.doi.org/10.1093/bib/bbz007DOI Listing
January 2019
12 Reads
9.617 Impact Factor

A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction.

Brief Bioinform 2019 01;20(1):330-346

School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, China.

Intrinsically disordered proteins and regions are widely distributed in proteins, which are associated with many biological processes and diseases. Accurate prediction of intrinsically disordered proteins and regions is critical for both basic research (such as protein structure and function prediction) and practical applications (such as drug development). During the past decades, many computational approaches have been proposed, which have greatly facilitated the development of this important field. Read More

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http://dx.doi.org/10.1093/bib/bbx126DOI Listing
January 2019
24 Reads

KORP: Knowledge-based 6D potential for fast protein and loop modeling.

Bioinformatics 2019 Jan 14. Epub 2019 Jan 14.

Department of Biological Chemical Physics, Rocasolano Institute of Physical Chemistry C.S.I.C., Serrano 119, Madrid, Spain.

Motivation: Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking, and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the 6-dimensional (6D) nature of residue-residue interactions from known protein structures using a simple backbone-based representation.

Results: We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz026DOI Listing
January 2019
2 Reads

Optimising orbit counting of arbitrary order by equation selection.

BMC Bioinformatics 2019 Jan 15;20(1):27. Epub 2019 Jan 15.

Ghent University - imec, IDLab, Technologiepark 15, Ghent, 9052, Belgium.

Background: Graphlets are useful for bioinformatics network analysis. Based on the structure of Hočevar and Demšar's ORCA algorithm, we have created an orbit counting algorithm, named Jesse. This algorithm, like ORCA, uses equations to count the orbits, but unlike ORCA it can count graphlets of any order. Read More

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http://dx.doi.org/10.1186/s12859-018-2483-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6334470PMC
January 2019
1 Read

CoExpresso: assess the quantitative behavior of protein complexes in human cells.

BMC Bioinformatics 2019 Jan 9;20(1):17. Epub 2019 Jan 9.

Department of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark.

Background: Translational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. Read More

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http://dx.doi.org/10.1186/s12859-018-2573-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327379PMC
January 2019
2 Reads

A model to predict the function of hypothetical proteins through a nine-point classification scoring schema.

BMC Bioinformatics 2019 Jan 8;20(1):14. Epub 2019 Jan 8.

Bioclues.org, Kukatpally, Hyderabad, 500072, India.

Background: Hypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-018-2554-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6325861PMC
January 2019
12 Reads
2.576 Impact Factor

Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.

BMC Bioinformatics 2018 Dec 31;19(Suppl 19):526. Epub 2018 Dec 31.

Department of Biosciences and Informatics, Keio University, Yokohama, 223-8522, Japan.

Background: Previous studies have suggested deep learning to be a highly effective approach for screening lead compounds for new drugs. Several deep learning models have been developed by addressing the use of various kinds of fingerprints and graph convolution architectures. However, these methods are either advantageous or disadvantageous depending on whether they (1) can distinguish structural differences including chirality of compounds, and (2) can automatically discover effective features. Read More

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http://dx.doi.org/10.1186/s12859-018-2523-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311897PMC
December 2018
1 Read

PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.

BMC Bioinformatics 2018 Dec 31;19(Suppl 19):522. Epub 2018 Dec 31.

Lab of Information Management, Changzhou University, Changzhou, 213164, China.

Background: Identifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approaches have been developed, there is still significant room for improvement concerning overall performance and availability.

Results: Here, we present an efficient approach termed PDRLGB that uses a light gradient boosting machine (LightGBM) to predict binding residues in protein-DNA complexes. Read More

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http://dx.doi.org/10.1186/s12859-018-2527-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311926PMC
December 2018
1 Read

A nearest-neighbors network model for sequence data reveals new insight into genotype distribution of a pathogen.

BMC Bioinformatics 2018 Dec 12;19(1):475. Epub 2018 Dec 12.

School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA.

Background: Sequence similarity networks are useful for classifying and characterizing biologically important proteins. Threshold-based approaches to similarity network construction using exact distance measures are prohibitively slow to compute and rely on the difficult task of selecting an appropriate threshold, while similarity networks based on approximate distance calculations compromise useful structural information.

Results: We present an alternative network representation for a set of sequence data that overcomes these drawbacks. Read More

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http://dx.doi.org/10.1186/s12859-018-2453-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6291930PMC
December 2018
2 Reads

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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https://academic.oup.com/bioinformatics/advance-article/doi/
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http://dx.doi.org/10.1093/bioinformatics/bty1006DOI Listing
December 2018
2 Reads

Representativeness of variation benchmark datasets.

BMC Bioinformatics 2018 Nov 29;19(1):461. Epub 2018 Nov 29.

Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, BMC B13, SE-221 84, Lund, Sweden.

Background: Benchmark datasets are essential for both method development and performance assessment. These datasets have numerous requirements, representativeness being one. In the case of variant tolerance/pathogenicity prediction, representativeness means that the dataset covers the space of variations and their effects. Read More

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http://dx.doi.org/10.1186/s12859-018-2478-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6267811PMC
November 2018
2 Reads
2.576 Impact Factor

Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification.

BMC Bioinformatics 2018 Nov 30;19(Suppl 15):438. Epub 2018 Nov 30.

Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.

Background: Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Read More

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http://dx.doi.org/10.1186/s12859-018-2414-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6266931PMC
November 2018
3 Reads

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
17 Reads

Computational prediction of inter-species relationships through omics data analysis and machine learning.

BMC Bioinformatics 2018 Nov 20;19(Suppl 14):420. Epub 2018 Nov 20.

School of Business and Engineering Vaud (HEIG-VD), University of Applied Sciences Western Switzerland (HES-SO), Route. de Cheseaux 1, Yverdon-Les-Bains, 1400, Switzerland.

Background: Antibiotic resistance and its rapid dissemination around the world threaten the efficacy of currently-used medical treatments and call for novel, innovative approaches to manage multi-drug resistant infections. Phage therapy, i.e. Read More

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http://dx.doi.org/10.1186/s12859-018-2388-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245486PMC
November 2018
3 Reads

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia.

BMC Bioinformatics 2018 Nov 20;19(Suppl 14):414. Epub 2018 Nov 20.

Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Harilaou-Thermi Road, Thessaloniki, Greece.

Background: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-018-2381-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245605PMC
November 2018
13 Reads

The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins.

BMC Bioinformatics 2018 Nov 20;19(Suppl 14):410. Epub 2018 Nov 20.

School of Computer Science, University of Windsor, Windsor, Ontario, Canada.

Background: The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins. Read More

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http://dx.doi.org/10.1186/s12859-018-2378-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245490PMC
November 2018
9 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
14 Reads

Learning protein binding affinity using privileged information.

BMC Bioinformatics 2018 Nov 15;19(1):425. Epub 2018 Nov 15.

Biomedical Informatics Research Laboratory (BIRL), Department of Computer and Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, ISL, 45650, Pakistan.

Background: Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data. Read More

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https://bmcbioinformatics.biomedcentral.com/articles/10.1186
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http://dx.doi.org/10.1186/s12859-018-2448-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6238365PMC
November 2018
19 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
11 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
4 Reads

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
9 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
19 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
6 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
4 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
9 Reads

Improving protein function prediction using protein sequence and GO-term similarities.

Bioinformatics 2019 Apr;35(7):1116-1124

Department of Intelligent Systems, Delft Bioinformatics Lab, Delft University of Technology, 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
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449755PMC
April 2019
2 Reads

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
5 Reads

StructureProfiler: an all-in-one tool for 3D protein structure profiling.

Bioinformatics 2019 Mar;35(5):874-876

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 datasets which are usually manually assembled. Due to the increase in published structures as well as the increasing demand for specially tailored validation datasets, automatic procedures should be adopted. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty692DOI Listing
March 2019
6 Reads

BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.

BMC Bioinformatics 2018 07 3;19(1):253. Epub 2018 Jul 3.

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.

Background: Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.

Results: BROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Read More

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http://dx.doi.org/10.1186/s12859-018-2255-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6029352PMC
July 2018
11 Reads

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

Bioinformatics 2018 07;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
3 Reads

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

Bioinformatics 2018 07;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
July 2018
2 Reads

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

Bioinformatics 2018 07;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
18 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
3 Reads

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
10 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
4 Reads

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
3 Reads

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
5 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
5 Reads

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
April 2018
4 Reads

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
5 Reads