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1.
Zhongguo Zhong Yao Za Zhi ; 45(16): 3883-3889, 2020 Aug.
Artigo em Chinês | MEDLINE | ID: mdl-32893585

RESUMO

Shotgun based proteomics and peptidomics analysis were used to investigate the proteins and peptides in marine traditional Chinese medicine(TCM) Sepiae Endoconcha(cuttlebone). Peptides were extracted from cuttlebone by acidified methanol, and then strong cation exchange(SCX) resin was used to enrich those peptides. Also, proteins from cuttlebone were extracted and digested by trypsin. nano-LC Q Exactive Orbitrap mass spectrometry was used to analyze proteins and peptides from cuttlebone. As a result, a total of 16 proteins and 168 peptides were identified by protein database search, and 328 peptides were identified by De novo sequencing. The identified proteins were hemocyanin, enolase, myosin, actin, calmodulin, etc., and the identified peptides were derived from actin, histone, and tubulin. All these proteins and peptides were important components in cuttlebone, which would provide important theoretical and research basis for marine TCM cuttlebone investigations.


Assuntos
Peptídeos , Proteômica , Cátions , Bases de Dados de Proteínas , Espectrometria de Massas
2.
BMC Bioinformatics ; 21(1): 398, 2020 Sep 09.
Artigo em Inglês | MEDLINE | ID: mdl-32907537

RESUMO

BACKGROUND: Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied. RESULTS: In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations. CONCLUSIONS: Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.


Assuntos
Biomarcadores Tumorais/metabolismo , Neoplasias do Colo/patologia , Proteínas/metabolismo , Neoplasias do Colo/metabolismo , Bases de Dados de Proteínas , Humanos , Imuno-Histoquímica , Aprendizado de Máquina , Proteínas/classificação
3.
BMC Bioinformatics ; 21(1): 400, 2020 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-32912135

RESUMO

BACKGROUND: Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. RESULTS: As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins' marker. CONCLUSIONS: Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens' favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.


Assuntos
Interações Hospedeiro-Patógeno , Mapas de Interação de Proteínas , Proteínas/metabolismo , Doenças Transmissíveis/metabolismo , Doenças Transmissíveis/patologia , Doenças Transmissíveis/virologia , Bases de Dados de Proteínas , Humanos , Interface Usuário-Computador , Vírus/patogenicidade
4.
BMC Bioinformatics ; 21(1): 376, 2020 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-32867673

RESUMO

BACKGROUND: Two-dimensional gel electrophoresis (2-DGE) is a commonly used tool for proteomic analysis. This gel-based technique separates proteins in a sample according to their isoelectric point and molecular weight. 2-DGE images often present anomalies due to the acquisition process, such as: diffuse and overlapping spots, and background noise. This study proposes a joint pre-processing framework that combines the capabilities of nonlinear filtering, background correction and image normalization techniques for pre-processing 2-DGE images. Among the most important, joint nonlinear diffusion filtering, adaptive piecewise histogram equalization and multilevel thresholding were evaluated using both synthetic data and real 2-DGE images. RESULTS: An improvement of up to 46% in spot detection efficiency was achieved for synthetic data using the proposed framework compared to implementing a single technique of either normalization, background correction or filtering. Additionally, the proposed framework increased the detection of low abundance spots by 20% for synthetic data compared to a normalization technique, and increased the background estimation by 67% compared to a background correction technique. In terms of real data, the joint pre-processing framework reduced the false positives up to 93%. CONCLUSIONS: The proposed joint pre-processing framework outperforms results achieved with a single approach. The best structure was obtained with the ordered combination of adaptive piecewise histogram equalization for image normalization, geometric nonlinear diffusion (GNDF) for filtering, and multilevel thresholding for background correction.


Assuntos
Eletroforese em Gel Bidimensional/métodos , Bases de Dados de Proteínas , Humanos , Processamento de Imagem Assistida por Computador , Proteínas/análise , Proteômica/métodos , Razão Sinal-Ruído
5.
Nat Commun ; 11(1): 4414, 2020 09 04.
Artigo em Inglês | MEDLINE | ID: mdl-32887877

RESUMO

CD4+ helper T cells contribute important functions to the immune response during pathogen infection and tumor formation by recognizing antigenic peptides presented by class II major histocompatibility complexes (MHC-II). While many computational algorithms for predicting peptide binding to MHC-II proteins have been reported, their performance varies greatly. Here we present a yeast-display-based platform that allows the identification of over an order of magnitude more unique MHC-II binders than comparable approaches. These peptides contain previously identified motifs, but also reveal new motifs that are validated by in vitro binding assays. Training of prediction algorithms with yeast-display library data improves the prediction of peptide-binding affinity and the identification of pathogen-associated and tumor-associated peptides. In summary, our yeast-display-based platform yields high-quality MHC-II-binding peptide datasets that can be used to improve the accuracy of MHC-II binding prediction algorithms, and potentially enhance our understanding of CD4+ T cell recognition.


Assuntos
Epitopos de Linfócito T/genética , Oligopeptídeos , Sítios de Ligação , Linfócitos T CD4-Positivos/imunologia , Técnicas de Visualização da Superfície Celular , Bases de Dados de Proteínas , Epitopos de Linfócito T/química , Epitopos de Linfócito T/metabolismo , Genes MHC da Classe II , Antígenos de Histocompatibilidade Classe II/metabolismo , Humanos , Oligopeptídeos/química , Oligopeptídeos/genética , Oligopeptídeos/metabolismo , Ligação Proteica/genética , Receptores de Antígenos de Linfócitos T , Proteínas Recombinantes/metabolismo , Saccharomyces cerevisiae/metabolismo
6.
J Transl Med ; 18(1): 319, 2020 08 18.
Artigo em Inglês | MEDLINE | ID: mdl-32811513

RESUMO

In less than 20 years, three deadly coronaviruses, SARS-CoV, MERS-CoV and SARS-CoV-2, have emerged in human population causing hundreds to hundreds of thousands of deaths. Other coronaviruses are causing epizootic representing a significant threat for both domestic and wild animals. Members of this viral family have the longest genome of all RNA viruses, and express up to 29 proteins establishing complex interactions with the host proteome. Deciphering these interactions is essential to identify cellular pathways hijacked by these viruses to replicate and escape innate immunity. Virus-host interactions also provide key information to select targets for antiviral drug development. Here, we have manually curated the literature to assemble a unique dataset of 1311 coronavirus-host protein-protein interactions. Functional enrichment and network-based analyses showed coronavirus connections to RNA processing and translation, DNA damage and pathogen sensing, interferon production, and metabolic pathways. In particular, this global analysis pinpointed overlooked interactions with translation modulators (GIGYF2-EIF4E2), components of the nuclear pore, proteins involved in mitochondria homeostasis (PHB, PHB2, STOML2), and methylation pathways (MAT2A/B). Finally, interactome data provided a rational for the antiviral activity of some drugs inhibiting coronaviruses replication. Altogether, this work describing the current landscape of coronavirus-host interactions provides valuable hints for understanding the pathophysiology of coronavirus infections and developing effective antiviral therapies.


Assuntos
Infecções por Coronavirus/metabolismo , Coronavirus/metabolismo , Interações Hospedeiro-Patógeno/fisiologia , Mapas de Interação de Proteínas , Proteínas Virais/metabolismo , Animais , Betacoronavirus/fisiologia , Coronavirus/química , Infecções por Coronavirus/virologia , Bases de Dados de Proteínas , Humanos , Proteínas Mitocondriais/metabolismo , Pandemias , Pneumonia Viral/metabolismo , Pneumonia Viral/virologia , Fatores de Transcrição/metabolismo , Replicação Viral/genética
7.
Structure ; 28(8): 874-878, 2020 08 04.
Artigo em Inglês | MEDLINE | ID: mdl-32755569

RESUMO

During global pandemics, the spread of information needs to be faster than the spread of the virus in order to ensure the health and safety of human populations worldwide. In our current crisis, the demand for SARS-CoV-2 drugs and vaccines highlights the importance of biological targets and their three-dimensional shape. In particular, structural biology as a field was poised to quickly respond to crises due to previous experience and expertise and because of its early adoption of open access practices.


Assuntos
Betacoronavirus/química , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/virologia , Pandemias , Pneumonia Viral/epidemiologia , Pneumonia Viral/virologia , Proteínas Virais/química , Cisteína Endopeptidases/química , Bases de Dados de Proteínas , Humanos , Modelos Moleculares , Biologia Molecular , Conformação Proteica , RNA Replicase/química , Glicoproteína da Espícula de Coronavírus/química , Proteínas não Estruturais Virais/química
8.
PLoS One ; 15(8): e0233673, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32750050

RESUMO

Computational algorithms are often used to assess pathogenicity of Variants of Uncertain Significance (VUS) that are found in disease-associated genes. Most computational methods include analysis of protein multiple sequence alignments (PMSA), assessing interspecies variation. Careful validation of PMSA-based methods has been done for relatively few genes, partially because creation of curated PMSAs is labor-intensive. We assessed how PMSA-based computational tools predict the effects of the missense changes in the APC gene, in which pathogenic variants cause Familial Adenomatous Polyposis. Most Pathogenic or Likely Pathogenic APC variants are protein-truncating changes. However, public databases now contain thousands of variants reported as missense. We created a curated APC PMSA that contained >3 substitutions/site, which is large enough for statistically robust in silico analysis. The creation of the PMSA was not easily automated, requiring significant querying and computational analysis of protein and genome sequences. Of 1924 missense APC variants in the NCBI ClinVar database, 1800 (93.5%) are reported as VUS. All but two missense variants listed as P/LP occur at canonical splice or Exonic Splice Enhancer sites. Pathogenicity predictions by five computational tools (Align-GVGD, SIFT, PolyPhen2, MAPP, REVEL) differed widely in their predictions of Pathogenic/Likely Pathogenic (range 17.5-75.0%) and Benign/Likely Benign (range 25.0-82.5%) for APC missense variants in ClinVar. When applied to 21 missense variants reported in ClinVar and securely classified as Benign, the five methods ranged in accuracy from 76.2-100%. Computational PMSA-based methods can be an excellent classifier for variants of some hereditary cancer genes. However, there may be characteristics of the APC gene and protein that confound the results of in silico algorithms. A systematic study of these features could greatly improve the automation of alignment-based techniques and the use of predictive algorithms in hereditary cancer genes.


Assuntos
Proteína da Polipose Adenomatosa do Colo/genética , Polipose Adenomatosa do Colo/genética , Genes APC , Mutação de Sentido Incorreto , Algoritmos , Sequência de Aminoácidos , Biologia Computacional/métodos , Simulação por Computador , Bases de Dados de Proteínas , Elementos Facilitadores Genéticos , Evolução Molecular , Éxons , Variação Genética , Humanos , Filogenia , Isoformas de Proteínas/genética , Sítios de Splice de RNA , Alinhamento de Sequência/estatística & dados numéricos
9.
J Chem Inf Model ; 60(8): 3910-3934, 2020 08 24.
Artigo em Inglês | MEDLINE | ID: mdl-32786511

RESUMO

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.


Assuntos
Desenho de Fármacos , Descoberta de Drogas , Mapas de Interação de Proteínas/efeitos dos fármacos , Bibliotecas de Moléculas Pequenas/farmacologia , Algoritmos , Betacoronavirus/efeitos dos fármacos , Betacoronavirus/metabolismo , Infecções por Coronavirus/tratamento farmacológico , Infecções por Coronavirus/metabolismo , Bases de Dados de Proteínas , Humanos , Ligantes , Aprendizado de Máquina , Simulação de Acoplamento Molecular , Terapia de Alvo Molecular , Pandemias , Pneumonia Viral/tratamento farmacológico , Pneumonia Viral/metabolismo , Proteínas/química , Proteínas/metabolismo , Bibliotecas de Moléculas Pequenas/química
10.
Nat Commun ; 11(1): 4065, 2020 08 13.
Artigo em Inglês | MEDLINE | ID: mdl-32792501

RESUMO

Identification of post-translationally or chemically modified peptides in mass spectrometry-based proteomics experiments is a crucial yet challenging task. We have recently introduced a fragment ion indexing method and the MSFragger search engine to empower an open search strategy for comprehensive analysis of modified peptides. However, this strategy does not consider fragment ions shifted by unknown modifications, preventing modification localization and limiting the sensitivity of the search. Here we present a localization-aware open search method, in which both modification-containing (shifted) and regular fragment ions are indexed and used in scoring. We also implement a fast mass calibration and optimization method, allowing optimization of the mass tolerances and other key search parameters. We demonstrate that MSFragger with mass calibration and localization-aware open search identifies modified peptides with significantly higher sensitivity and accuracy. Comparing MSFragger to other modification-focused tools (pFind3, MetaMorpheus, and TagGraph) shows that MSFragger remains an excellent option for fast, comprehensive, and sensitive searches for modified peptides in shotgun proteomics data.


Assuntos
Peptídeos/química , Algoritmos , Animais , Bases de Dados de Proteínas , Humanos , Espectrometria de Massas , Proteômica/métodos
11.
Cell Stress Chaperones ; 25(5): 737-741, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32754823

RESUMO

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2), the cause of COVID-19 disease, has the potential to elicit autoimmunity because mimicry of human molecular chaperones by viral proteins. We compared viral proteins with human molecular chaperones, many of which are heat shock proteins, to determine if they share amino acid-sequence segments with immunogenic-antigenic potential, which can elicit cross-reactive antibodies and effector immune cells with the capacity to damage-destroy human cells by a mechanism of autoimmunity. We identified the chaperones that can putatively participate in molecular mimicry phenomena after SARS-CoV-2 infection, focusing on those for which endothelial cell plasma-cell membrane localization has already been demonstrated. We also postulate that post-translational modifications, induced by physical (shear) and chemical (metabolic) stress caused respectively by the risk factors hypertension and diabetes, might have a role in determining plasma-cell membrane localization and, in turn, autoimmune-induced endothelial damage.


Assuntos
Betacoronavirus/metabolismo , Infecções por Coronavirus/virologia , Proteínas de Choque Térmico , Pneumonia Viral/virologia , Proteínas Virais , Sequência de Aminoácidos , Autoantígenos , Autoimunidade , Bases de Dados de Proteínas , Células Endoteliais/metabolismo , Proteínas de Choque Térmico/química , Proteínas de Choque Térmico/imunologia , Humanos , Epitopos Imunodominantes , Mimetismo Molecular , Pandemias , Proteínas Virais/química , Proteínas Virais/imunologia
12.
BMC Bioinformatics ; 21(Suppl 10): 348, 2020 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-32838733

RESUMO

BACKGROUND: Bioinformatics has pervaded all fields of biology and has become an indispensable tool for almost all research projects. Although teaching bioinformatics has been incorporated in all traditional life science curricula, practical hands-on experiences in tight combination with wet-lab experiments are needed to motivate students. RESULTS: We present a tutorial that starts from a practical problem: finding novel enzymes from marine environments. First, we introduce the idea of metagenomics, a recent approach that extends biotechnology to non-culturable microbes. We presuppose that a probe for the screening of metagenomic cosmid library is needed. The students start from the chemical structure of the substrate that should be acted on by the novel enzyme and end with the sequence of the probe. To attain their goal, they discover databases such as BRENDA and programs such as BLAST and Clustal Omega. Students' answers to a satisfaction questionnaire show that a multistep tutorial integrated into a research wet-lab project is preferable to conventional lectures illustrating bioinformatics tools. CONCLUSION: Experimental biologists can better operate basic bioinformatics if a problem-solving approach is chosen.


Assuntos
Biotecnologia/educação , Biologia Computacional/educação , Biologia Marinha/educação , Metagenômica , Sequência de Aminoácidos , Proteínas de Bactérias/química , Sequência de Bases , Bases de Dados Factuais , Bases de Dados de Proteínas , Objetivos , Humanos , Aprendizagem , Interface Usuário-Computador
13.
PLoS Biol ; 18(8): e3000815, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32760062

RESUMO

Two illustrations integrate current knowledge about severe acute respiratory syndrome (SARS) coronaviruses and their life cycle. They have been widely used in education and outreach through free distribution as part of a coronavirus-related resource at Protein Data Bank (PDB)-101, the education portal of the RCSB PDB. Scientific sources for creation of the illustrations and examples of dissemination and response are presented.


Assuntos
Betacoronavirus/crescimento & desenvolvimento , Pesquisa Biomédica/educação , Infecções por Coronavirus/prevenção & controle , Bases de Dados de Proteínas , Medicina nas Artes , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Animais , Betacoronavirus/fisiologia , Pesquisa Biomédica/métodos , Infecções por Coronavirus/epidemiologia , Infecções por Coronavirus/virologia , Apresentação de Dados , Humanos , Disseminação de Informação/métodos , Estágios do Ciclo de Vida , Pneumonia Viral/epidemiologia , Pneumonia Viral/virologia , Mucosa Respiratória/virologia
14.
Life Sci ; 258: 118170, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32735883

RESUMO

AIMS: Coronavirus disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is a major health concern worldwide. Due to the lack of specific medication and vaccination, drug-repurposing attempts has emerged as a promising approach and identified several human proteins interacting with the virus. This study aims to provide a comprehensive molecular profiling of the immune cell-enriched SARS-CoV-2 interacting protein USP13. MATERIALS AND METHODS: The list of immune cell-enriched proteins interacting with SARS-CoV-2 was retrieved from The Human Protein Atlas. Genomic alterations were identified using cBioPortal. Survival analysis was performed via Kaplan-Meier Plotter. Analyses of protein expression and tumor infiltration levels were carried out by TIMER. KEY FINDINGS: 14 human proteins that interact with SARS-CoV-2 were enriched in immune cells. Among these proteins, USP13 had the highest frequency of genomic alterations. Higher USP13 levels were correlated with improved survival in breast and lung cancers, while resulting in poor prognosis in ovarian and gastric cancers. Furthermore, copy number variations of USP13 significantly affected the infiltration levels of distinct subtypes of immune cells in head & neck, lung, ovarian and stomach cancers. Although our results suggested a tumor suppressor role for USP13 in lung cancer, in other cancers, its role seemed to be context-dependent. SIGNIFICANCE: It is critical to identify and characterize human proteins that interact with SARS-CoV-2 in order to have a better understanding of the disease and to develop better therapies/vaccines. Here, we provided a comprehensive molecular profiling the immune cell-enriched SARS-CoV-2 interacting protein USP13, which will be useful for future studies.


Assuntos
Betacoronavirus/imunologia , Infecções por Coronavirus/imunologia , Endopeptidases/imunologia , Leucócitos/imunologia , Neoplasias/imunologia , Pneumonia Viral/imunologia , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/genética , Infecções por Coronavirus/virologia , Variações do Número de Cópias de DNA , Bases de Dados de Proteínas , Endopeptidases/genética , Humanos , Leucócitos/virologia , Linfócitos do Interstício Tumoral/imunologia , Linfócitos do Interstício Tumoral/virologia , Neoplasias/diagnóstico , Neoplasias/genética , Neoplasias/virologia , Pandemias , Pneumonia Viral/diagnóstico , Pneumonia Viral/genética , Pneumonia Viral/virologia , Prognóstico
15.
PLoS One ; 15(8): e0236894, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32785279

RESUMO

High-quality three-dimensional structural data is of great value for the functional interpretation of biomacromolecules, especially proteins; however, structural quality varies greatly across the entries in the worldwide Protein Data Bank (wwPDB). Since 2008, the wwPDB has required the inclusion of structure factors with the deposition of x-ray crystallographic structures to support the independent evaluation of structures with respect to the underlying experimental data used to derive those structures. However, interpreting the discrepancies between the structural model and its underlying electron density data is difficult, since derived sigma-scaled electron density maps use arbitrary electron density units which are inconsistent between maps from different wwPDB entries. Therefore, we have developed a method that converts electron density values from sigma-scaled electron density maps into units of electrons. With this conversion, we have developed new methods that can evaluate specific regions of an x-ray crystallographic structure with respect to a physicochemical interpretation of its corresponding electron density map. We have systematically compared all deposited x-ray crystallographic protein models in the wwPDB with their underlying electron density maps, if available, and characterized the electron density in terms of expected numbers of electrons based on the structural model. The methods generated coherent evaluation metrics throughout all PDB entries with associated electron density data, which are consistent with visualization software that would normally be used for manual quality assessment. To our knowledge, this is the first attempt to derive units of electrons directly from electron density maps without the aid of the underlying structure factors. These new metrics are biochemically-informative and can be extremely useful for filtering out low-quality structural regions from inclusion into systematic analyses that span large numbers of PDB entries. Furthermore, these new metrics will improve the ability of non-crystallographers to evaluate regions of interest within PDB entries, since only the PDB structure and the associated electron density maps are needed. These new methods are available as a well-documented Python package on GitHub and the Python Package Index under a modified Clear BSD open source license.


Assuntos
Biologia Computacional , Bases de Dados de Proteínas , Elétrons , Proteínas/química
16.
BMC Bioinformatics ; 21(1): 323, 2020 Jul 21.
Artigo em Inglês | MEDLINE | ID: mdl-32693790

RESUMO

BACKGROUND: Protein-protein interactions (PPIs) are central to many biological processes. Considering that the experimental methods for identifying PPIs are time-consuming and expensive, it is important to develop automated computational methods to better predict PPIs. Various machine learning methods have been proposed, including a deep learning technique which is sequence-based that has achieved promising results. However, it only focuses on sequence information while ignoring the structural information of PPI networks. Structural information of PPI networks such as their degree, position, and neighboring nodes in a graph has been proved to be informative in PPI prediction. RESULTS: Facing the challenge of representing graph information, we introduce an improved graph representation learning method. Our model can study PPI prediction based on both sequence information and graph structure. Moreover, our study takes advantage of a representation learning model and employs a graph-based deep learning method for PPI prediction, which shows superiority over existing sequence-based methods. Statistically, Our method achieves state-of-the-art accuracy of 99.15% on Human protein reference database (HPRD) dataset and also obtains best results on Database of Interacting Protein (DIP) Human, Drosophila, Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegan) datasets. CONCLUSION: Here, we introduce signed variational graph auto-encoder (S-VGAE), an improved graph representation learning method, to automatically learn to encode graph structure into low-dimensional embeddings. Experimental results demonstrate that our method outperforms other existing sequence-based methods on several datasets. We also prove the robustness of our model for very sparse networks and the generalization for a new dataset that consists of four datasets: HPRD, E.coli, C.elegan, and Drosophila.


Assuntos
Mapeamento de Interação de Proteínas/métodos , Animais , Caenorhabditis elegans/metabolismo , Simulação por Computador , Bases de Dados de Proteínas , Drosophila/metabolismo , Escherichia coli/metabolismo , Humanos , Aprendizado de Máquina , Redes Neurais de Computação
17.
PLoS One ; 15(7): e0235263, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32639981

RESUMO

Dependent peptide searching is a method for discovering covalently-modified peptides-and therefore proteins-in mass-spectrometry-based proteomics experiments. Being more permissive than standard search methods, it has the potential to discover novel modifications (e.g., post-translational modifications occurring in vivo, or modifications introduced in vitro). However, few studies have explored dependent peptide search results in an untargeted way. In the present study, we sought to evaluate dependent peptide searching as a means of characterising proteins that have been modified in vitro. We generated a model data set by analysing N-ethylmaleimide-treated bovine serum albumin, and performed dependent peptide searches using the popular MaxQuant software. To facilitate interpretation of the search results (hundreds of dependent peptides), we developed a series of visualisation tools (R scripts). We used the tools to assess the diversity of putative modifications in the albumin, and to pinpoint hypothesised modifications. We went on to explore the tools' generality via analyses of public data from studies of rat and human proteomes. Of 19 expected sites of modification (one in rat cofilin-1 and 18 across six different human plasma proteins), eight were found and correctly localised. Apparently, some sites went undetected because chemical enrichment had depleted necessary analytes (potential 'base' peptides). Our results demonstrate (i) the ability of the tools to provide accurate and informative visualisations, and (ii) the usefulness of dependent peptide searching for characterising in vitro protein modifications. Our model data are available via PRIDE/ProteomeXchange (accession number PXD013040).


Assuntos
Peptídeos/análise , Proteínas/química , Proteômica/métodos , Animais , Proteínas Sanguíneas/química , Bovinos , Bases de Dados de Proteínas , Etilmaleimida/análogos & derivados , Humanos , Processamento de Proteína Pós-Traducional , Ratos , Soroalbumina Bovina/química
18.
Proc Natl Acad Sci U S A ; 117(32): 19446-19454, 2020 08 11.
Artigo em Inglês | MEDLINE | ID: mdl-32723829

RESUMO

Antimicrobial peptides are important candidates for developing new classes of antibiotics because of their potency against antibiotic-resistant pathogens. Current research focuses on topical applications and it is unclear how to design peptides with systemic efficacy. To address this problem, we designed two potent peptides by combining database-guided discovery with structure-based design. When bound to membranes, these two short peptides with an identical amino acid composition can adopt two distinct amphipathic structures: A classic horizontal helix (horine) and a novel vertical spiral structure (verine). Their horizontal and vertical orientations on membranes were determined by solid-state 15N NMR data. While horine was potent primarily against gram-positive pathogens, verine showed broad-spectrum antimicrobial activity. Both peptides protected greater than 80% mice from infection-caused deaths. Moreover, horine and verine also displayed significant systemic efficacy in different murine models comparable to conventional antibiotics. In addition, they could eliminate resistant pathogens and preformed biofilms. Significantly, the peptides showed no nephrotoxicity to mice after intraperitoneal or intravenous administration for 1 wk. Our study underscores the significance of horine and verine in fighting drug-resistant pathogens.


Assuntos
Antibacterianos/química , Antibacterianos/farmacologia , Peptídeos Catiônicos Antimicrobianos/química , Peptídeos Catiônicos Antimicrobianos/farmacologia , Sequência de Aminoácidos , Animais , Antibacterianos/metabolismo , Antibacterianos/uso terapêutico , Peptídeos Catiônicos Antimicrobianos/metabolismo , Peptídeos Catiônicos Antimicrobianos/uso terapêutico , Bactérias/efeitos dos fármacos , Bactérias/crescimento & desenvolvimento , Infecções Bacterianas/tratamento farmacológico , Biofilmes/efeitos dos fármacos , Biofilmes/crescimento & desenvolvimento , Membrana Celular/metabolismo , Bases de Dados de Proteínas , Desenho de Fármacos , Farmacorresistência Bacteriana/efeitos dos fármacos , Humanos , Interações Hidrofóbicas e Hidrofílicas , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Testes de Sensibilidade Microbiana , Relação Estrutura-Atividade , Resultado do Tratamento
19.
Int J Med Sci ; 17(11): 1522-1531, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32669955

RESUMO

The outbreak of pneumonia caused by SARS-CoV-2 posed a great threat to global human health, which urgently requires us to understand comprehensively the mechanism of SARS-CoV-2 infection. Angiotensin-converting enzyme 2 (ACE2) was identified as a functional receptor for SARS-CoV-2, distribution of which may indicate the risk of different human organs vulnerable to SARS-CoV-2 infection. Previous studies investigating the distribution of ACE2 mRNA in human tissues only involved a limited size of the samples and a lack of determination for ACE2 protein. Given the heterogeneity among humans, the datasets covering more tissues with a larger size of samples should be analyzed. Indeed, ACE2 is a membrane and secreted protein, while the expression of ACE2 in blood and common blood cells remains unknown. Herein, the proteomic data in HIPED and the antibody-based immunochemistry result in HPA were collected to analyze the distribution of ACE2 protein in human tissues. The bulk RNA-seq profiles from three separate public datasets including HPA tissue Atlas, GTEx, and FANTOM5 CAGE were also obtained to determine the expression of ACE2 in human tissues. Moreover, the abundance of ACE2 in human blood and blood cells was determined by analyzing the data in the PeptideAtlas and the HPA Blood Atlas. We found that the mRNA expression cannot reflect the abundance of ACE2 factor due to the strong differences between mRNA and protein quantities of ACE2 within and across tissues. Our results suggested that ACE2 protein is mainly expressed in the small intestine, kidney, gallbladder, and testis, while the abundance of which in brain-associated tissues and blood common cells is low. HIPED revealed enrichment of ACE2 protein in the placenta and ovary despite a low mRNA level. Further, human secretome shows that the average concentration of ACE2 protein in the plasma of males is higher than those in females. Our research will be beneficial for understanding the transmission routes and sex-based differences in susceptibility of SARS-CoV-2 infection.


Assuntos
Infecções por Coronavirus/metabolismo , Peptidil Dipeptidase A/metabolismo , Pneumonia Viral/metabolismo , Receptores Virais/metabolismo , Betacoronavirus , Bases de Dados de Proteínas , Feminino , Humanos , Imuno-Histoquímica , Masculino , Espectrometria de Massas , Pandemias , Proteômica , RNA Mensageiro/metabolismo , RNA-Seq , Distribuição Tecidual , Transcriptoma
20.
BMC Bioinformatics ; 21(1): 275, 2020 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-32611389

RESUMO

BACKGROUND: Protein engineering has many applications for industry, such as the development of new drugs, vaccines, treatment therapies, food, and biofuel production. A common way to engineer a protein is to perform mutations in functionally essential residues to optimize their function. However, the discovery of beneficial mutations for proteins is a complex task, with a time-consuming and high cost for experimental validation. Hence, computational approaches have been used to propose new insights for experiments narrowing the search space and reducing the costs. RESULTS: In this study, we developed Proteus (an acronym for Protein Engineering Supporter), a new algorithm for proposing mutation pairs in a target 3D structure. These suggestions are based on contacts observed in other known structures from Protein Data Bank (PDB). Proteus' basic assumption is that if a non-interacting pair of amino acid residues in the target structure is exchanged to an interacting pair, this could enhance protein stability. This trade is only allowed if the main-chain conformation of the residues involved in the contact is conserved. Furthermore, no steric impediment is expected between the proposed mutations and the surrounding protein atoms. To evaluate Proteus, we performed two case studies with proteins of industrial interests. In the first case study, we evaluated if the mutations suggested by Proteus for four protein structures enhance the number of inter-residue contacts. Our results suggest that most mutations proposed by Proteus increase the number of interactions into the protein. In the second case study, we used Proteus to suggest mutations for a lysozyme protein. Then, we compared Proteus' outcomes to mutations with available experimental evidence reported in the ProTherm database. Four mutations, in which our results agree with the experimental data, were found. This could be initial evidence that changes in the side-chain of some residues do not cause disturbances that harm protein structure stability. CONCLUSION: We believe that Proteus could be used combined with other methods to give new insights into the rational development of engineered proteins. Proteus user-friendly web-based tool is available at < http://proteus.dcc.ufmg.br >.


Assuntos
Proteínas/química , Interface Usuário-Computador , Algoritmos , Bases de Dados de Proteínas , Muramidase/química , Muramidase/genética , Muramidase/metabolismo , Mutagênese , Engenharia de Proteínas/métodos , Estrutura Terciária de Proteína , Proteínas/genética , Proteínas/metabolismo
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