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[PMID]:29432422
[Au] Autor:Mathur D; Mehta A; Firmal P; Bedi G; Sood C; Gautam A; Raghava GPS
[Ad] Endereço:Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.
[Ti] Título:TopicalPdb: A database of topically delivered peptides.
[So] Source:PLoS One;13(2):e0190134, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:TopicalPdb (http://crdd.osdd.net/raghava/topicalpdb/) is a repository of experimentally verified topically delivered peptides. Data was manually collected from research articles. The current release of TopicalPdb consists of 657 entries, which includes peptides delivered through the skin (462 entries), eye (173 entries), and nose (22 entries). Each entry provides comprehensive information related to these peptides like the source of origin, nature of peptide, length, N- and C-terminal modifications, mechanism of penetration, type of assays, cargo and biological properties of peptides, etc. In addition to natural peptides, TopicalPdb contains information of peptides having non-natural, chemically modified residues and D-amino acids. Besides this primary information, TopicalPdb stores predicted tertiary structures as well as peptide sequences in SMILE format. Tertiary structures of peptides were predicted using state-of-art method PEPstrMod. In order to assist users, a number of web-based tools have been integrated that includes keyword search, data browsing, similarity search and structural similarity. We believe that TopicalPdb is a unique database of its kind and it will be very useful in designing peptides for non-invasive topical delivery.
[Mh] Termos MeSH primário: Bases de Dados de Proteínas
Peptídeos/química
[Mh] Termos MeSH secundário: Administração Tópica
Peptídeos/administração & dosagem
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Nm] Nome de substância:
0 (Peptides)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180309
[Lr] Data última revisão:
180309
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180213
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0190134


  2 / 13881 MEDLINE  
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[PMID]:29244012
[Au] Autor:Jelínek J; Skoda P; Hoksza D
[Ad] Endereço:Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, Prague 2, Czech Republic. jelinek@ksi.mff.cuni.cz.
[Ti] Título:Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.
[So] Source:BMC Bioinformatics;18(Suppl 15):492, 2017 Dec 06.
[Is] ISSN:1471-2105
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. RESULTS: We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. CONCLUSION: In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.
[Mh] Termos MeSH primário: Aminoácidos
Bases de Conhecimento
Mapeamento de Interação de Proteínas/métodos
Proteínas
Software
[Mh] Termos MeSH secundário: Aminoácidos/química
Aminoácidos/metabolismo
Biologia Computacional
Bases de Dados de Proteínas
Modelos Estatísticos
Proteínas/química
Proteínas/metabolismo
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Amino Acids); 0 (Proteins)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180307
[Lr] Data última revisão:
180307
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171216
[St] Status:MEDLINE
[do] DOI:10.1186/s12859-017-1921-4


  3 / 13881 MEDLINE  
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[PMID]:29244010
[Au] Autor:Maruyama O; Kuwahara Y
[Ad] Endereço:Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan. om@imi.kyushu-u.ac.jp.
[Ti] Título:RocSampler: regularizing overlapping protein complexes in protein-protein interaction networks.
[So] Source:BMC Bioinformatics;18(Suppl 15):491, 2017 Dec 06.
[Is] ISSN:1471-2105
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo: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. One is related to small complexes, which contains two or three components. It is relatively difficult to identify them due to their simpler internal structure, but unfortunately complexes of such sizes are dominant in major protein complex databases, such as CYC2008. Another difficulty is how to model overlaps between predicted complexes, that is, how to evaluate different predicted complexes sharing common proteins because CYC2008 and other databases include such protein complexes. Thus, it is critical how to model overlaps between predicted complexes to identify them simultaneously. RESULTS: In this paper, we propose a sampling-based protein complex prediction method, RocSampler (Regularizing Overlapping Complexes), which exploits, as part of the whole scoring function, a regularization term for the overlaps of predicted complexes and that for the distribution of sizes of predicted complexes. We have implemented RocSampler in MATLAB and its executable file for Windows is available at the site, http://imi.kyushu-u.ac.jp/~om/software/RocSampler/ . CONCLUSIONS: We have applied RocSampler to five yeast PPI networks and shown that it is superior to other existing methods. This implies that the design of scoring functions including regularization terms is an effective approach for protein complex prediction.
[Mh] Termos MeSH primário: Bases de Dados de Proteínas
Mapeamento de Interação de Proteínas
Software
[Mh] Termos MeSH secundário: Biologia Computacional
Mapeamento de Interação de Proteínas/métodos
Mapeamento de Interação de Proteínas/normas
Mapas de Interação de Proteínas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180307
[Lr] Data última revisão:
180307
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171216
[St] Status:MEDLINE
[do] DOI:10.1186/s12859-017-1920-5


  4 / 13881 MEDLINE  
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[PMID]:29220436
[Au] Autor:Ni Y; Jensen K; Kouskoumvekaki I; Panagiotou G
[Ad] Endereço:Systems Biology & Bioinformatics Group, School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong.
[Ti] Título:NutriChem 2.0: exploring the effect of plant-based foods on human health and drug efficacy.
[So] Source:Database (Oxford);2017, 2017 Jan 01.
[Is] ISSN:1758-0463
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Database URL: http://sbb.hku.hk/services/NutriChem-2.0/.
[Mh] Termos MeSH primário: Bases de Dados de Produtos Farmacêuticos
Bases de Dados de Proteínas
Interações Alimento-Droga
Plantas Comestíveis
[Mh] Termos MeSH secundário: Biologia Computacional
Mineração de Dados
Seres Humanos
Interface Usuário-Computador
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1801
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171209
[St] Status:MEDLINE
[do] DOI:10.1093/database/bax044


  5 / 13881 MEDLINE  
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[PMID]:29220433
[Au] Autor:Malhotra S; Mugumbate G; Blundell TL; Higueruelo AP
[Ad] Endereço:Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge CB2 1GA, UK.
[Ti] Título:TIBLE: a web-based, freely accessible resource for small-molecule binding data for mycobacterial species.
[So] Source:Database (Oxford);2017, 2017 Jan 01.
[Is] ISSN:1758-0463
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Database URL: http://www-cryst.bioc.cam.ac.uk/tible/.
[Mh] Termos MeSH primário: Antituberculosos/farmacologia
Proteínas de Bactérias
Bases de Dados de Produtos Farmacêuticos
Bases de Dados de Proteínas
Internet
Mycobacterium tuberculosis
[Mh] Termos MeSH secundário: Proteínas de Bactérias/química
Proteínas de Bactérias/metabolismo
Testes de Sensibilidade Microbiana
Mycobacterium tuberculosis/efeitos dos fármacos
Mycobacterium tuberculosis/enzimologia
Mycobacterium tuberculosis/metabolismo
Interface Usuário-Computador
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Antitubercular Agents); 0 (Bacterial Proteins)
[Em] Mês de entrada:1801
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171209
[St] Status:MEDLINE
[do] DOI:10.1093/database/bax041


  6 / 13881 MEDLINE  
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[PMID]:29220432
[Au] Autor:Mottin L; Pasche E; Gobeill J; Rech de Laval V; Gleizes A; Michel PA; Bairoch A; Gaudet P; Ruch P
[Ad] Endereço:Information Science Department, BiTeM Group, HES-SO/HEG Genève, 17 Rue de la Tambourine, Carouge CH-1227, Switzerland.
[Ti] Título:Triage by ranking to support the curation of protein interactions.
[So] Source:Database (Oxford);2017, 2017 Jan 01.
[Is] ISSN:1758-0463
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Database URL: http://candy.hesge.ch/nextA5.
[Mh] Termos MeSH primário: Biologia Computacional/métodos
Curadoria de Dados/métodos
Mineração de Dados/métodos
Bases de Dados de Proteínas
Mapeamento de Interação de Proteínas/métodos
[Mh] Termos MeSH secundário: Interface Usuário-Computador
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1801
[Cu] Atualização por classe:180308
[Lr] Data última revisão:
180308
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171209
[St] Status:MEDLINE
[do] DOI:10.1093/database/bax040


  7 / 13881 MEDLINE  
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[PMID]:29187139
[Au] Autor:Calyseva J; Vihinen M
[Ad] Endereço:Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, BMC B13, SE-22 184, Lund, Sweden.
[Ti] Título:PON-SC - program for identifying steric clashes caused by amino acid substitutions.
[So] Source:BMC Bioinformatics;18(1):531, 2017 Nov 29.
[Is] ISSN:1471-2105
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo: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. Testing structural compatibility of variations has been difficult due to the lack of a dedicated method that could handle vast amounts of variation data produced by next generation sequencing technologies. RESULTS: We developed a method, PON-SC, for detecting protein structural clashes due to amino acid substitutions. The method utilizes side chain rotamer library and tests whether any of the common rotamers can be fitted into the protein structure. The tool was tested both with variants that cause and do not cause clashes and found to have accuracy of 0.71 over five test datasets. CONCLUSIONS: We developed a fast method for residue side chain clash detection. The method provides in addition to the prediction also visualization of the variant in three dimensional structure.
[Mh] Termos MeSH primário: Aminoácidos/química
Proteínas/química
Software
[Mh] Termos MeSH secundário: Algoritmos
Substituição de Aminoácidos
Aminoácidos/metabolismo
Bases de Dados de Proteínas
Conformação Proteica
Engenharia de Proteínas/métodos
Proteínas/genética
Proteínas/metabolismo
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
0 (Amino Acids); 0 (Proteins)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180306
[Lr] Data última revisão:
180306
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171201
[St] Status:MEDLINE
[do] DOI:10.1186/s12859-017-1947-7


  8 / 13881 MEDLINE  
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[PMID]:28460216
[Au] Autor:Mackenzie CO; Grigoryan G
[Ad] Endereço:Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755, United States.
[Ti] Título:Protein structural motifs in prediction and design.
[So] Source:Curr Opin Struct Biol;44:161-167, 2017 06.
[Is] ISSN:1879-033X
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:The Protein Data Bank (PDB) has been an integral resource for shaping our fundamental understanding of protein structure and for the advancement of such applications as protein design and structure prediction. Over the years, information from the PDB has been used to generate models ranging from specific structural mechanisms to general statistical potentials. With accumulating structural data, it has become possible to mine for more complete and complex structural observations, deducing more accurate generalizations. Motif libraries, which capture recurring structural features along with their sequence preferences, have exposed modularity in the structural universe and found successful application in various problems of structural biology. Here we summarize recent achievements in this arena, focusing on subdomain level structural patterns and their applications to protein design and structure prediction, and suggest promising future directions as the structural database continues to grow.
[Mh] Termos MeSH primário: Biologia Computacional/métodos
Desenho de Drogas
Proteínas/química
[Mh] Termos MeSH secundário: Motivos de Aminoácidos
Bases de Dados de Proteínas
[Pt] Tipo de publicação:JOURNAL ARTICLE; REVIEW
[Nm] Nome de substância:
0 (Proteins)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180306
[Lr] Data última revisão:
180306
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170502
[St] Status:MEDLINE


  9 / 13881 MEDLINE  
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[PMID]:28454901
[Au] Autor:Wu S; Han J; Zhang X; Zhong D; Liu R
[Ad] Endereço:School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
[Ti] Título:A computational model for predicting integrase catalytic domain of retrovirus.
[So] Source:J Theor Biol;423:63-70, 2017 Jun 21.
[Is] ISSN:1095-8541
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. Accordingly, it is of great importance to put forward a computational ICD model in this work to annotate these domains in the retroviruses. The proposed model then discovered 11,660 new putative ICDs after scanning sequences without ICD annotations. Subsequently in order to provide much confidence in ICD prediction, it was tested under different cross-validation methods, compared with other database search tools, and verified on independent datasets. Furthermore, an evolutionary analysis performed on the annotated ICDs of retroviruses revealed a tight connection between ICD and retroviral classification. All the datasets involved in this paper and the application software tool of this model can be available for free download at https://sourceforge.net/projects/icdtool/files/?source=navbar.
[Mh] Termos MeSH primário: Domínio Catalítico
Biologia Computacional
Evolução Molecular
Integrases/química
Retroviridae/classificação
Análise de Sequência de Proteína
[Mh] Termos MeSH secundário: Simulação por Computador
Bases de Dados de Proteínas
Anotação de Sequência Molecular
Software
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
EC 2.7.7.- (Integrases)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180305
[Lr] Data última revisão:
180305
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170430
[St] Status:MEDLINE


  10 / 13881 MEDLINE  
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[PMID]:29377907
[Au] Autor:Pezeshgi Modarres H; Mofrad MR; Sanati-Nezhad A
[Ad] Endereço:BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta, Canada.
[Ti] Título:ProtDataTherm: A database for thermostability analysis and engineering of proteins.
[So] Source:PLoS One;13(1):e0191222, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Protein thermostability engineering is a powerful tool to improve resistance of proteins against high temperatures and thereafter broaden their applications. For efficient protein thermostability engineering, different thermostability-classified data sources including sequences and 3D structures are needed for different protein families. However, no data source is available providing such data easily. It is the first release of ProtDataTherm database for analysis and engineering of protein thermostability which contains more than 14 million protein sequences categorized based on their thermal stability and protein family. This database contains data needed for better understanding protein thermostability and stability engineering. Providing categorized protein sequences and structures as psychrophilic, mesophilic and thermophilic makes this database useful for the development of new tools in protein stability prediction. This database is available at http://profiles.bs.ipm.ir/softwares/protdatatherm. As a proof of concept, the thermostability that improves mutations were suggested for one sample protein belonging to one of protein families with more than 20 mesophilic and thermophilic sequences and with known experimentally measured ΔT of mutations available within ProTherm database.
[Mh] Termos MeSH primário: Bases de Dados de Proteínas
Engenharia de Proteínas
Estabilidade Proteica
[Mh] Termos MeSH secundário: Algoritmos
Sequência de Aminoácidos
Proteínas de Bactérias/química
Proteínas de Bactérias/genética
Temperatura Alta
Modelos Moleculares
Mutação
Termodinâmica
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Nm] Nome de substância:
0 (Bacterial Proteins)
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180226
[Lr] Data última revisão:
180226
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180130
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0191222



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