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1.
Proc Natl Acad Sci U S A ; 119(10): e2202107119, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1758466
2.
Biosensors (Basel) ; 12(2)2022 Feb 07.
Article in English | MEDLINE | ID: covidwho-1715105

ABSTRACT

Rapid detection of proteins is critical in a vast array of diagnostic or monitoring applications [...].


Subject(s)
Proteins/analysis , Humans , Models, Statistical , Proteins/chemistry , Sensitivity and Specificity
3.
Inflamm Res ; 71(2): 183-185, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1611373

ABSTRACT

Growth Hormone-Releasing Hormone (GHRH) is a neuropeptide regulating the release of Growth Hormone (GH) from the anterior pituitary gland, and acts as a growth factor in a diverse variety of tissues. GHRH antagonists (GHRHAnt) have been developed to counteract those events, and the beneficial effects of those peptides toward homeostasis have been associated with anti-inflammatory activities. Our lab is interested in delineating the mechanisms governing endothelial barrier function. Our goal is to establish new grounds on the development of efficient countermeasures against Acute Respiratory Distress Syndrome (ARDS), which has been associated with thousands of deaths worldwide due to COVID-19. Herein we demonstrate in vivo that GHRHAnt suppresses LPS-induced increase in bronchoalveolar lavage fluid (BALF) protein concentration, thus protecting the lungs against edema and inflammation.


Subject(s)
Bronchoalveolar Lavage Fluid/chemistry , Gonadotropin-Releasing Hormone/antagonists & inhibitors , Lipopolysaccharides , Animals , COVID-19/complications , Growth Hormone-Releasing Hormone , Inflammation/etiology , Inflammation/prevention & control , Male , Mice , Mice, Inbred C57BL , Proteins/chemistry , Pulmonary Edema/etiology , Pulmonary Edema/prevention & control , Reactive Oxygen Species , Respiratory Distress Syndrome/drug therapy , Respiratory Distress Syndrome/etiology , SARS-CoV-2
6.
Chemphyschem ; 23(4): e202100704, 2022 02 16.
Article in English | MEDLINE | ID: covidwho-1589144

ABSTRACT

Hadamard encoded saturation transfer can significantly improve the efficiency of NOE-based NMR correlations from labile protons in proteins, glycans and RNAs, increasing the sensitivity of cross-peaks by an order of magnitude and shortening experimental times by ≥100-fold. These schemes, however, fail when tackling correlations within a pool of labile protons - for instance imino-imino correlations in RNAs or amide-amide correlations in proteins. Here we analyze the origin of the artifacts appearing in these experiments and propose a way to obtain artifact-free correlations both within the labile pool as well as between labile and non-labile 1 Hs, while still enjoying the gains arising from Hadamard encoding and solvent repolarizations. The principles required for implementing what we define as the extended Hadamard scheme are derived, and its clean, artifact-free, sensitivity-enhancing performance is demonstrated on RNA fragments derived from the SARS-CoV-2 genome. Sensitivity gains per unit time approaching an order of magnitude are then achieved in both imino-imino and imino-amino/aromatic protons 2D correlations; similar artifact-free sensitivity gains can be observed when carrying out extended Hadamard encodings of 3D NOESY/HSQC-type experiments. The resulting spectra reveal significantly more correlations than their conventionally acquired counterparts, which can support the spectral assignment and secondary structure determination of structured RNA elements.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Magnetic Resonance Spectroscopy/methods , Proteins/chemistry , RNA
7.
J Comput Aided Mol Des ; 35(6): 721-729, 2021 06.
Article in English | MEDLINE | ID: covidwho-1549468

ABSTRACT

We systematically tested the Autodock4 docking program for absolute binding free energy predictions using the host-guest systems from the recent SAMPL6, SAMPL7 and SAMPL8 challenges. We found that Autodock4 behaves surprisingly well, outperforming in many instances expensive molecular dynamics or quantum chemistry techniques, with an extremely favorable benefit-cost ratio. Some interesting features of Autodock4 predictions are revealed, yielding valuable hints on the overall reliability of docking screening campaigns in drug discovery projects.


Subject(s)
Proteins/chemistry , Ligands , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Reproducibility of Results , Retrospective Studies , Software , Solvents/chemistry , Thermodynamics
8.
Viruses ; 13(11)2021 11 15.
Article in English | MEDLINE | ID: covidwho-1524171

ABSTRACT

Angiotensin-converting enzyme 2 (ACE2) is a main receptor for SARS-CoV-2 entry to the host cell. Indeed, the first step in viral entry is the binding of the viral trimeric spike (S) protein to ACE2. Abundantly present in human epithelial cells of many organs, ACE2 is also expressed in the human brain. ACE2 is a type I membrane protein with an extracellular N-terminal peptidase domain and a C-terminal collectrin-like domain that ends with a single transmembrane helix and an intracellular 44-residue segment. This C-terminal segment contains a PDZ-binding motif (PBM) targeting protein-interacting domains called PSD-95/Dlg/ZO-1 (PDZ). Here, we identified the human PDZ specificity profile of the ACE2 PBM using the high-throughput holdup assay and measuring the binding intensities of the PBM of ACE2 against the full human PDZome. We discovered 14 human PDZ binders of ACE2 showing significant binding with dissociation constants' values ranging from 3 to 81 µM. NHERF, SHANK, and SNX27 proteins found in this study are involved in protein trafficking. The PDZ/PBM interactions with ACE2 could play a role in ACE2 internalization and recycling that could be of benefit for the virus entry. Interestingly, most of the ACE2 partners we identified are expressed in neuronal cells, such as SHANK and MAST families, and modifications of the interactions between ACE2 and these neuronal proteins may be involved in the neurological symptoms of COVID-19.


Subject(s)
Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/metabolism , PDZ Domains , Proteins/chemistry , Proteins/metabolism , Receptors, Coronavirus/metabolism , Humans , Microtubule-Associated Proteins/chemistry , Microtubule-Associated Proteins/metabolism , Nerve Tissue Proteins/chemistry , Nerve Tissue Proteins/metabolism , Neurons/metabolism , Phosphoproteins/chemistry , Phosphoproteins/metabolism , /metabolism , Protein Transport , Sodium-Hydrogen Exchangers/chemistry , Sodium-Hydrogen Exchangers/metabolism , Sorting Nexins/chemistry , Sorting Nexins/metabolism
9.
Protein Sci ; 31(1): 283-289, 2022 01.
Article in English | MEDLINE | ID: covidwho-1516798

ABSTRACT

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS-CoV-2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum.


Subject(s)
Databases, Protein , Proteins/chemistry , SARS-CoV-2/chemistry , Viral Proteins/chemistry , Animals , COVID-19/virology , Humans , Models, Molecular , Protein Conformation , Protein Folding , Software
10.
Protein Sci ; 31(1): 173-186, 2022 01.
Article in English | MEDLINE | ID: covidwho-1490887

ABSTRACT

Protein Data Bank Japan (PDBj), a founding member of the worldwide Protein Data Bank (wwPDB) has accepted, processed and distributed experimentally determined biological macromolecular structures for 20 years. During that time, we have continuously made major improvements to our query search interface of PDBj Mine 2, the BMRBj web interface, and EM Navigator for PDB/BMRB/EMDB entries. PDBj also serves PDB-related secondary database data, original web-based modeling services such as Homology modeling of complex structure (HOMCOS), visualization services and utility tools, which we have continuously enhanced and expanded throughout the years. In addition, we have recently developed several unique archives, BSM-Arc for computational structure models, and XRDa for raw X-ray diffraction images, both of which promote open science in the structural biology community. During the COVID-19 pandemic, PDBj has also started to provide feature pages for COVID-19 related entries across all available archives at PDBj from raw experimental data and PDB structural data to computationally predicted models, while also providing COVID-19 outreach content for high school students and teachers.


Subject(s)
Databases, Protein , Proteins/chemistry , Animals , Anniversaries and Special Events , COVID-19/metabolism , Humans , Japan , Models, Molecular , Protein Conformation , Proteins/metabolism , SARS-CoV-2/chemistry , SARS-CoV-2/metabolism , Software , User-Computer Interface , Viral Proteins/chemistry , Viral Proteins/metabolism
11.
Commun Biol ; 4(1): 1240, 2021 10 29.
Article in English | MEDLINE | ID: covidwho-1493232

ABSTRACT

Circular tandem repeat proteins ('cTRPs') are de novo designed protein scaffolds (in this and prior studies, based on antiparallel two-helix bundles) that contain repeated protein sequences and structural motifs and form closed circular structures. They can display significant stability and solubility, a wide range of sizes, and are useful as protein display particles for biotechnology applications. However, cTRPs also demonstrate inefficient self-assembly from smaller subunits. In this study, we describe a new generation of cTRPs, with longer repeats and increased interaction surfaces, which enhanced the self-assembly of two significantly different sizes of homotrimeric constructs. Finally, we demonstrated functionalization of these constructs with (1) a hexameric array of peptide-binding SH2 domains, and (2) a trimeric array of anti-SARS CoV-2 VHH domains. The latter proved capable of sub-nanomolar binding affinities towards the viral receptor binding domain and potent viral neutralization function.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , COVID-19/metabolism , Protein Engineering/methods , Proteins/chemistry , Proteins/metabolism , SARS-CoV-2/metabolism , Tandem Repeat Sequences , Amino Acid Sequence , COVID-19/virology , Computer Simulation , Crystallization , HEK293 Cells , Humans , Models, Molecular , Neutralization Tests , Protein Binding , Protein Domains , Protein Folding , Protein Structure, Secondary , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/metabolism
13.
Cells ; 10(10)2021 10 14.
Article in English | MEDLINE | ID: covidwho-1470797

ABSTRACT

Prediction of linear B cell epitopes is of interest for the production of antigen-specific antibodies and the design of peptide-based vaccines. Here, we present BCEPS, a web server for predicting linear B cell epitopes tailored to select epitopes that are immunogenic and capable of inducing cross-reactive antibodies with native antigens. BCEPS implements various machine learning models trained on a dataset including 555 linearized conformational B cell epitopes that were mined from antibody-antigen protein structures. The best performing model, based on a support vector machine, reached an accuracy of 75.38% ± 5.02. In an independent dataset consisting of B cell epitopes retrieved from the Immune Epitope Database (IEDB), this model achieved an accuracy of 67.05%. In BCEPS, predicted epitopes can be ranked according to properties such as flexibility, accessibility and hydrophilicity, and with regard to immunogenicity, as judged by their predicted presentation by MHC II molecules. BCEPS also detects if predicted epitopes are located in ectodomains of membrane proteins and if they possess N-glycosylation sites hindering antibody recognition. Finally, we exemplified the use of BCEPS in the SARS-CoV-2 Spike protein, showing that it can identify B cell epitopes targeted by neutralizing antibodies.


Subject(s)
COVID-19/prevention & control , Computational Biology/methods , Databases, Factual , Epitopes, B-Lymphocyte/chemistry , SARS-CoV-2 , Animals , Antigens , COVID-19/immunology , Cross Reactions , Glycosylation , Histocompatibility Antigens Class II , Humans , Hydrophobic and Hydrophilic Interactions , Internet , Machine Learning , Mice , Peptides/chemistry , Protein Domains , Proteins/chemistry , Reproducibility of Results , Software , Spike Glycoprotein, Coronavirus/chemistry
14.
Protein Sci ; 31(1): 158-172, 2022 01.
Article in English | MEDLINE | ID: covidwho-1469553

ABSTRACT

Applying simulations with structure-based G o ¯ - like models has proven to be an effective strategy for investigating the factors that control biomolecular dynamics. The common element of these models is that some (or all) of the intra/inter-molecular interactions are explicitly defined to stabilize an experimentally determined structure. To facilitate the development and application of this broad class of models, we previously released the SMOG 2 software package. This suite allows one to easily customize and distribute structure-based (i.e., SMOG) models for any type of polymer-ligand system. The force fields generated by SMOG 2 may then be used to perform simulations in highly optimized MD packages, such as Gromacs, NAMD, LAMMPS, and OpenMM. Here, we describe extensions to the software and demonstrate the capabilities of the most recent version (SMOG v2.4.2). Changes include new tools that aid user-defined customization of force fields, as well as an interface with the OpenMM simulation libraries (OpenSMOG v1.1.0). The OpenSMOG module allows for arbitrary user-defined contact potentials and non-bonded potentials to be employed in SMOG models, without source-code modifications. To illustrate the utility of these advances, we present applications to systems with millions of atoms, long polymers and explicit ions, as well as models that include non-structure-based (e.g., AMBER-based) energetic terms. Examples include large-scale rearrangements of the SARS-CoV-2 Spike protein, the HIV-1 capsid with explicit ions, and crystallographic lattices of ribosomes and proteins. In summary, SMOG 2 and OpenSMOG provide robust support for researchers who seek to develop and apply structure-based models to large and/or intricate biomolecular systems.


Subject(s)
Molecular Dynamics Simulation , Proteins/chemistry , Software , Animals , COVID-19/virology , Humans , Models, Molecular , Protein Conformation , Ribosomes/chemistry , SARS-CoV-2/chemistry , Spike Glycoprotein, Coronavirus/chemistry
15.
Nat Commun ; 12(1): 3023, 2021 05 21.
Article in English | MEDLINE | ID: covidwho-1454758

ABSTRACT

Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of ß-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.


Subject(s)
Computational Biology/methods , Deep Learning , Proteins/chemistry , Proteins/metabolism , Algorithms , Computer Simulation , Molecular Dynamics Simulation , Myosins , Protein Conformation , Software
16.
Structure ; 30(1): 181-189.e5, 2022 01 06.
Article in English | MEDLINE | ID: covidwho-1454541

ABSTRACT

The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances, and active-site boundaries are used for the analysis of active sites. The algorithms derived provide model equations that can predict whether changes in distances, such as contraction or expansion, will result in improved binding affinity. The algorithm is confirmed using kinetic studies of dihydrofolate reductase (DHFR), together with two DHFR-TS crystal structures. Empirical analyses of 881 crystal structures involving 180 ligands are used to interpret protein-ligand binding affinities. MANORAA links to major biological databases for web-based analysis of drug design. The frequency of atoms inside the main protease structures, including those from SARS-CoV-2, shows how the rigid part of the ligand can be used as a probe for molecular design (http://manoraa.org).


Subject(s)
Computational Biology/methods , Databases, Protein , Machine Learning , Protein Domains , Proteins/chemistry , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Crystallography, X-Ray , Drug Design , Humans , Ligands , Models, Molecular , Pandemics , Protein Binding , Proteins/metabolism , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Tetrahydrofolate Dehydrogenase/chemistry , Tetrahydrofolate Dehydrogenase/metabolism , Trimethoprim/chemistry , Trimethoprim/metabolism
17.
Bioanalysis ; 13(19): 1459-1465, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1450902

ABSTRACT

During the first half of 2021, and due to the SARS-CoV-2 pandemic preventing in-person meetings, the European Bioanalysis Forum organized four workshops as live interactive online meetings. The themes discussed at the workshops were carefully selected to match the cyberspace dynamics of the meeting format. The first workshop was a training day on challenges related to immunogenicity. The second one focused on biomarkers and continued the important discussion on integrating the principles of Context of Use (CoU) in biomarker research. The third workshop was dedicated to technology, that is, cutting-edge development in cell-based and ligand-binding assays and automation strategies. The fourth was on progress and the continued scientific and regulatory challenges related to peptide and protein analysis with MS. In all four workshops, the European Bioanalysis Forum included a mixture of scientific and regulatory themes, while reminding the audience of important strategic aspects and our responsibility toward the patient.


Subject(s)
Chemistry Techniques, Analytical , Mass Spectrometry , Proteins/analysis , Proteins/immunology , Automation , Biomarkers/analysis , Humans , Proteins/chemistry
18.
BMC Bioinformatics ; 22(1): 1, 2021 Jan 02.
Article in English | MEDLINE | ID: covidwho-1388726

ABSTRACT

BACKGROUND: Protein-peptide interactions play a fundamental role in a wide variety of biological processes, such as cell signaling, regulatory networks, immune responses, and enzyme inhibition. Peptides are characterized by low toxicity and small interface areas; therefore, they are good targets for therapeutic strategies, rational drug planning and protein inhibition. Approximately 10% of the ethical pharmaceutical market is protein/peptide-based. Furthermore, it is estimated that 40% of protein interactions are mediated by peptides. Despite the fast increase in the volume of biological data, particularly on sequences and structures, there remains a lack of broad and comprehensive protein-peptide databases and tools that allow the retrieval, characterization and understanding of protein-peptide recognition and consequently support peptide design. RESULTS: We introduce Propedia, a comprehensive and up-to-date database with a web interface that permits clustering, searching and visualizing of protein-peptide complexes according to varied criteria. Propedia comprises over 19,000 high-resolution structures from the Protein Data Bank including structural and sequence information from protein-peptide complexes. The main advantage of Propedia over other peptide databases is that it allows a more comprehensive analysis of similarity and redundancy. It was constructed based on a hybrid clustering algorithm that compares and groups peptides by sequences, interface structures and binding sites. Propedia is available through a graphical, user-friendly and functional interface where users can retrieve, and analyze complexes and download each search data set. We performed case studies and verified that the utility of Propedia scores to rank promissing interacting peptides. In a study involving predicting peptides to inhibit SARS-CoV-2 main protease, we showed that Propedia scores related to similarity between different peptide complexes with SARS-CoV-2 main protease are in agreement with molecular dynamics free energy calculation. CONCLUSIONS: Propedia is a database and tool to support structure-based rational design of peptides for special purposes. Protein-peptide interactions can be useful to predict, classifying and scoring complexes or for designing new molecules as well. Propedia is up-to-date as a ready-to-use webserver with a friendly and resourceful interface and is available at: https://bioinfo.dcc.ufmg.br/propedia.


Subject(s)
Database Management Systems , Databases, Protein , Peptides/chemistry , Proteins/chemistry , Algorithms , Humans
19.
Nucleic Acids Res ; 49(W1): W425-W430, 2021 07 02.
Article in English | MEDLINE | ID: covidwho-1387966

ABSTRACT

Methods for estimating the quality of 3D models of proteins are vital tools for driving the acceptance and utility of predicted tertiary structures by the wider bioscience community. Here we describe the significant major updates to ModFOLD, which has maintained its position as a leading server for the prediction of global and local quality of 3D protein models, over the past decade (>20 000 unique external users). ModFOLD8 is the latest version of the server, which combines the strengths of multiple pure-single and quasi-single model methods. Improvements have been made to the web server interface and there has been successive increases in prediction accuracy, which were achieved through integration of newly developed scoring methods and advanced deep learning-based residue contact predictions. Each version of the ModFOLD server has been independently blind tested in the biennial CASP experiments, as well as being continuously evaluated via the CAMEO project. In CASP13 and CASP14, the ModFOLD7 and ModFOLD8 variants ranked among the top 10 quality estimation methods according to almost every official analysis. Prior to CASP14, ModFOLD8 was also applied for the evaluation of SARS-CoV-2 protein models as part of CASP Commons 2020 initiative. The ModFOLD8 server is freely available at: https://www.reading.ac.uk/bioinf/ModFOLD/.


Subject(s)
Computers , Models, Molecular , Neural Networks, Computer , Protein Conformation , Protein Folding , Proteins/chemistry , Software , Reproducibility of Results , Research Design , SARS-CoV-2/chemistry , Viral Proteins/chemistry
20.
Nucleic Acids Res ; 49(D1): D266-D273, 2021 01 08.
Article in English | MEDLINE | ID: covidwho-1387962

ABSTRACT

CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.


Subject(s)
Computational Biology/statistics & numerical data , Databases, Protein/statistics & numerical data , Protein Domains , Proteins/chemistry , Amino Acid Sequence , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Computational Biology/methods , Epidemics , Humans , Internet , Molecular Sequence Annotation , Proteins/genetics , Proteins/metabolism , SARS-CoV-2/genetics , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Sequence Analysis, Protein/methods , Sequence Homology, Amino Acid , Viral Proteins/chemistry , Viral Proteins/genetics , Viral Proteins/metabolism
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