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LitCovid-sample-CHEBI

LitCovid-sample-PD-NCBITaxon

{"project":"LitCovid-sample-PD-NCBITaxon","denotations":[{"id":"T173","span":{"begin":73,"end":83},"obj":"Species"},{"id":"T174","span":{"begin":90,"end":95},"obj":"Species"},{"id":"T175","span":{"begin":110,"end":120},"obj":"Species"},{"id":"T176","span":{"begin":2170,"end":2180},"obj":"Species"}],"attributes":[{"id":"A174","pred":"ncbi_taxonomy_id","subj":"T174","obj":"NCBItxid:9606"},{"id":"A173","pred":"ncbi_taxonomy_id","subj":"T173","obj":"NCBItxid:2697049"},{"id":"A175","pred":"ncbi_taxonomy_id","subj":"T175","obj":"NCBItxid:2697049"},{"id":"A176","pred":"ncbi_taxonomy_id","subj":"T176","obj":"NCBItxid:2697049"}],"namespaces":[{"prefix":"NCBItxid","uri":"http://purl.bioontology.org/ontology/NCBITAXON/"}],"text":"3D Structural Modeling and Molecular Dynamics Simulation of Glycosylated SARS-CoV-2 S and Human ACE2 Proteins\nSARS-CoV-2 Spike (S) protein structure and ACE2 co-complex – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB) (Wrapp et al., 2020), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The receptor binding domain (RBD) in the “open” conformation was replaced with the RBD from an ACE2 co-complex (PDB code 6M0J) by grafting residues C336 to V524.\nGlycoform generation – Glycans (detected by glycomics) were selected for installation on glycosylated S and ACE2 sequons (detected by glycoproteomics) based on three sets of criteria designed to reasonably capture different aspects of glycosylation microheterogeneity. We denote the first of these glycoform models as “Abundance.” The glycans selected for installation to generate the Abundance model were chosen because they were identified as the most abundant glycan structure (detected by glycomics) that matched the most abundant glycan composition (detected by glycoproteomics) at each individual site. We denote the second glycoform model as “Oxford Class.” The glycans selected for installation to generate the Oxford Class model were chosen because they were the most abundant glycan structure, (detected by glycomics) that was contained within the most highly represented Oxford classification group (detected by glycoproteomics) at each individual site (Figure S7; Tables S1 and S8). Finally, we denote the third glycoform model as “Processed.” The glycans selected for installation to generate the Processed model were chosen because they were the most highly trimmed, elaborated, or terminally decorated structure (detected by glycomics) that corresponded to a composition (detected by glycoproteomics) which was present at ≥ 1/3rd of the abundance of the most highly represented composition at each site (Table S1). 3D structures of the three glycoforms (Abundance, Oxford Class, Processed) were generated for the SARS-CoV-2 S protein alone, and in complex with the glycosylated ACE2 protein. The glycoprotein builder available at GLYCAM-Web (www.glycam.org) was employed together with an in-house program that adjusts the asparagine side chain torsion angles and glycosidic linkages within known low-energy ranges (Nivedha et al., 2014) to relieve any atomic overlaps with the core protein, as described previously (Grant et al., 2016; Peng et al., 2017).\nEnergy minimization and Molecular dynamics (MD) simulations – Each glycosylated structure was placed in a periodic box of TIP3P water molecules with a 10 Å buffer between the solute and the box edge. Energy minimization of all atoms was performed for 20,000 steps (10,000 steepest decent, followed by 10,000 conjugant gradient) under constant pressure (1 atm) and temperature (300 K) conditions. All MD simulations were performed under nPT conditions with the CUDA implementation of the PMEMD (Götz et al., 2012; Salomon-Ferrer et al., 2013) simulation code, as present in the Amber14 software suite (University of California, San Diego). The GLYCAM06j force field (Kirschner et al., 2008) and Amber14SB force field (Maier et al., 2015) were employed for the carbohydrate and protein moieties, respectively. A Berendsen barostat with a time constant of 1 ps was employed for pressure regulation, while a Langevin thermostat with a collision frequency of 2 ps-1 was employed for temperature regulation. A nonbonded interaction cut-off of 8 Å was employed. Long-range electrostatics were treated with the particle-mesh Ewald (PME) method (Darden and Pedersen, 1993). Covalent bonds involving hydrogen were constrained with the SHAKE algorithm, allowing an integration time step of 2 fs to be employed. The energy minimized coordinates were equilibrated at 300K over 400 ps with restraints on the solute heavy atoms. Each system was then equilibrated with restraints on the Ca atoms of the protein for 1ns, prior to initiating 4 independent 250 ns production MD simulations with random starting seeds for a total time of 1 μs per system, with no restraints applied.\nAntigenic surface analysis. A series of 3D structure snapshots of the simulation were taken at 1 ns intervals and analyzed in terms of their ability to interact with a spherical probe based on the average size of hypervariable loops present in an antibody complementarity determining region (CDR), as described recently (https://www.biorxiv.org/content/10.1101/2020.04.07.030445v2). The percentage of simulation time each residue was exposed to the AbASA probe was calculated and plotted onto both the 3D structure and primary sequence."}

LitCovid-sample-sentences

{"project":"LitCovid-sample-sentences","denotations":[{"id":"T428","span":{"begin":0,"end":109},"obj":"Sentence"},{"id":"T429","span":{"begin":110,"end":412},"obj":"Sentence"},{"id":"T430","span":{"begin":413,"end":479},"obj":"Sentence"},{"id":"T431","span":{"begin":480,"end":641},"obj":"Sentence"},{"id":"T432","span":{"begin":642,"end":910},"obj":"Sentence"},{"id":"T433","span":{"begin":911,"end":1250},"obj":"Sentence"},{"id":"T434","span":{"begin":1251,"end":1636},"obj":"Sentence"},{"id":"T435","span":{"begin":1637,"end":2071},"obj":"Sentence"},{"id":"T436","span":{"begin":2072,"end":2248},"obj":"Sentence"},{"id":"T437","span":{"begin":2249,"end":2612},"obj":"Sentence"},{"id":"T438","span":{"begin":2613,"end":2812},"obj":"Sentence"},{"id":"T439","span":{"begin":2813,"end":3008},"obj":"Sentence"},{"id":"T440","span":{"begin":3009,"end":3251},"obj":"Sentence"},{"id":"T441","span":{"begin":3252,"end":3420},"obj":"Sentence"},{"id":"T442","span":{"begin":3421,"end":3614},"obj":"Sentence"},{"id":"T443","span":{"begin":3615,"end":3667},"obj":"Sentence"},{"id":"T444","span":{"begin":3668,"end":3777},"obj":"Sentence"},{"id":"T445","span":{"begin":3778,"end":3912},"obj":"Sentence"},{"id":"T446","span":{"begin":3913,"end":4026},"obj":"Sentence"},{"id":"T447","span":{"begin":4027,"end":4275},"obj":"Sentence"},{"id":"T448","span":{"begin":4276,"end":4303},"obj":"Sentence"},{"id":"T449","span":{"begin":4304,"end":4658},"obj":"Sentence"},{"id":"T450","span":{"begin":4659,"end":4812},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"3D Structural Modeling and Molecular Dynamics Simulation of Glycosylated SARS-CoV-2 S and Human ACE2 Proteins\nSARS-CoV-2 Spike (S) protein structure and ACE2 co-complex – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB) (Wrapp et al., 2020), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The receptor binding domain (RBD) in the “open” conformation was replaced with the RBD from an ACE2 co-complex (PDB code 6M0J) by grafting residues C336 to V524.\nGlycoform generation – Glycans (detected by glycomics) were selected for installation on glycosylated S and ACE2 sequons (detected by glycoproteomics) based on three sets of criteria designed to reasonably capture different aspects of glycosylation microheterogeneity. We denote the first of these glycoform models as “Abundance.” The glycans selected for installation to generate the Abundance model were chosen because they were identified as the most abundant glycan structure (detected by glycomics) that matched the most abundant glycan composition (detected by glycoproteomics) at each individual site. We denote the second glycoform model as “Oxford Class.” The glycans selected for installation to generate the Oxford Class model were chosen because they were the most abundant glycan structure, (detected by glycomics) that was contained within the most highly represented Oxford classification group (detected by glycoproteomics) at each individual site (Figure S7; Tables S1 and S8). Finally, we denote the third glycoform model as “Processed.” The glycans selected for installation to generate the Processed model were chosen because they were the most highly trimmed, elaborated, or terminally decorated structure (detected by glycomics) that corresponded to a composition (detected by glycoproteomics) which was present at ≥ 1/3rd of the abundance of the most highly represented composition at each site (Table S1). 3D structures of the three glycoforms (Abundance, Oxford Class, Processed) were generated for the SARS-CoV-2 S protein alone, and in complex with the glycosylated ACE2 protein. The glycoprotein builder available at GLYCAM-Web (www.glycam.org) was employed together with an in-house program that adjusts the asparagine side chain torsion angles and glycosidic linkages within known low-energy ranges (Nivedha et al., 2014) to relieve any atomic overlaps with the core protein, as described previously (Grant et al., 2016; Peng et al., 2017).\nEnergy minimization and Molecular dynamics (MD) simulations – Each glycosylated structure was placed in a periodic box of TIP3P water molecules with a 10 Å buffer between the solute and the box edge. Energy minimization of all atoms was performed for 20,000 steps (10,000 steepest decent, followed by 10,000 conjugant gradient) under constant pressure (1 atm) and temperature (300 K) conditions. All MD simulations were performed under nPT conditions with the CUDA implementation of the PMEMD (Götz et al., 2012; Salomon-Ferrer et al., 2013) simulation code, as present in the Amber14 software suite (University of California, San Diego). The GLYCAM06j force field (Kirschner et al., 2008) and Amber14SB force field (Maier et al., 2015) were employed for the carbohydrate and protein moieties, respectively. A Berendsen barostat with a time constant of 1 ps was employed for pressure regulation, while a Langevin thermostat with a collision frequency of 2 ps-1 was employed for temperature regulation. A nonbonded interaction cut-off of 8 Å was employed. Long-range electrostatics were treated with the particle-mesh Ewald (PME) method (Darden and Pedersen, 1993). Covalent bonds involving hydrogen were constrained with the SHAKE algorithm, allowing an integration time step of 2 fs to be employed. The energy minimized coordinates were equilibrated at 300K over 400 ps with restraints on the solute heavy atoms. Each system was then equilibrated with restraints on the Ca atoms of the protein for 1ns, prior to initiating 4 independent 250 ns production MD simulations with random starting seeds for a total time of 1 μs per system, with no restraints applied.\nAntigenic surface analysis. A series of 3D structure snapshots of the simulation were taken at 1 ns intervals and analyzed in terms of their ability to interact with a spherical probe based on the average size of hypervariable loops present in an antibody complementarity determining region (CDR), as described recently (https://www.biorxiv.org/content/10.1101/2020.04.07.030445v2). The percentage of simulation time each residue was exposed to the AbASA probe was calculated and plotted onto both the 3D structure and primary sequence."}

LitCovid-sample-PD-MONDO

{"project":"LitCovid-sample-PD-MONDO","denotations":[{"id":"T105","span":{"begin":73,"end":83},"obj":"Disease"},{"id":"T106","span":{"begin":110,"end":120},"obj":"Disease"},{"id":"T107","span":{"begin":2170,"end":2180},"obj":"Disease"},{"id":"T108","span":{"begin":3737,"end":3740},"obj":"Disease"}],"attributes":[{"id":"A107","pred":"mondo_id","subj":"T107","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A106","pred":"mondo_id","subj":"T106","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A108","pred":"mondo_id","subj":"T108","obj":"http://purl.obolibrary.org/obo/MONDO_0020074"},{"id":"A105","pred":"mondo_id","subj":"T105","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"3D Structural Modeling and Molecular Dynamics Simulation of Glycosylated SARS-CoV-2 S and Human ACE2 Proteins\nSARS-CoV-2 Spike (S) protein structure and ACE2 co-complex – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB) (Wrapp et al., 2020), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The receptor binding domain (RBD) in the “open” conformation was replaced with the RBD from an ACE2 co-complex (PDB code 6M0J) by grafting residues C336 to V524.\nGlycoform generation – Glycans (detected by glycomics) were selected for installation on glycosylated S and ACE2 sequons (detected by glycoproteomics) based on three sets of criteria designed to reasonably capture different aspects of glycosylation microheterogeneity. We denote the first of these glycoform models as “Abundance.” The glycans selected for installation to generate the Abundance model were chosen because they were identified as the most abundant glycan structure (detected by glycomics) that matched the most abundant glycan composition (detected by glycoproteomics) at each individual site. We denote the second glycoform model as “Oxford Class.” The glycans selected for installation to generate the Oxford Class model were chosen because they were the most abundant glycan structure, (detected by glycomics) that was contained within the most highly represented Oxford classification group (detected by glycoproteomics) at each individual site (Figure S7; Tables S1 and S8). Finally, we denote the third glycoform model as “Processed.” The glycans selected for installation to generate the Processed model were chosen because they were the most highly trimmed, elaborated, or terminally decorated structure (detected by glycomics) that corresponded to a composition (detected by glycoproteomics) which was present at ≥ 1/3rd of the abundance of the most highly represented composition at each site (Table S1). 3D structures of the three glycoforms (Abundance, Oxford Class, Processed) were generated for the SARS-CoV-2 S protein alone, and in complex with the glycosylated ACE2 protein. The glycoprotein builder available at GLYCAM-Web (www.glycam.org) was employed together with an in-house program that adjusts the asparagine side chain torsion angles and glycosidic linkages within known low-energy ranges (Nivedha et al., 2014) to relieve any atomic overlaps with the core protein, as described previously (Grant et al., 2016; Peng et al., 2017).\nEnergy minimization and Molecular dynamics (MD) simulations – Each glycosylated structure was placed in a periodic box of TIP3P water molecules with a 10 Å buffer between the solute and the box edge. Energy minimization of all atoms was performed for 20,000 steps (10,000 steepest decent, followed by 10,000 conjugant gradient) under constant pressure (1 atm) and temperature (300 K) conditions. All MD simulations were performed under nPT conditions with the CUDA implementation of the PMEMD (Götz et al., 2012; Salomon-Ferrer et al., 2013) simulation code, as present in the Amber14 software suite (University of California, San Diego). The GLYCAM06j force field (Kirschner et al., 2008) and Amber14SB force field (Maier et al., 2015) were employed for the carbohydrate and protein moieties, respectively. A Berendsen barostat with a time constant of 1 ps was employed for pressure regulation, while a Langevin thermostat with a collision frequency of 2 ps-1 was employed for temperature regulation. A nonbonded interaction cut-off of 8 Å was employed. Long-range electrostatics were treated with the particle-mesh Ewald (PME) method (Darden and Pedersen, 1993). Covalent bonds involving hydrogen were constrained with the SHAKE algorithm, allowing an integration time step of 2 fs to be employed. The energy minimized coordinates were equilibrated at 300K over 400 ps with restraints on the solute heavy atoms. Each system was then equilibrated with restraints on the Ca atoms of the protein for 1ns, prior to initiating 4 independent 250 ns production MD simulations with random starting seeds for a total time of 1 μs per system, with no restraints applied.\nAntigenic surface analysis. A series of 3D structure snapshots of the simulation were taken at 1 ns intervals and analyzed in terms of their ability to interact with a spherical probe based on the average size of hypervariable loops present in an antibody complementarity determining region (CDR), as described recently (https://www.biorxiv.org/content/10.1101/2020.04.07.030445v2). The percentage of simulation time each residue was exposed to the AbASA probe was calculated and plotted onto both the 3D structure and primary sequence."}

LitCovid-sample-UniProt

LitCovid-sample-PD-IDO

{"project":"LitCovid-sample-PD-IDO","denotations":[{"id":"T161","span":{"begin":1245,"end":1249},"obj":"http://purl.obolibrary.org/obo/BFO_0000029"},{"id":"T162","span":{"begin":1601,"end":1605},"obj":"http://purl.obolibrary.org/obo/BFO_0000029"},{"id":"T163","span":{"begin":2055,"end":2059},"obj":"http://purl.obolibrary.org/obo/BFO_0000029"},{"id":"T164","span":{"begin":4158,"end":4168},"obj":"http://purl.obolibrary.org/obo/IDO_0000607"}],"text":"3D Structural Modeling and Molecular Dynamics Simulation of Glycosylated SARS-CoV-2 S and Human ACE2 Proteins\nSARS-CoV-2 Spike (S) protein structure and ACE2 co-complex – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB) (Wrapp et al., 2020), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The receptor binding domain (RBD) in the “open” conformation was replaced with the RBD from an ACE2 co-complex (PDB code 6M0J) by grafting residues C336 to V524.\nGlycoform generation – Glycans (detected by glycomics) were selected for installation on glycosylated S and ACE2 sequons (detected by glycoproteomics) based on three sets of criteria designed to reasonably capture different aspects of glycosylation microheterogeneity. We denote the first of these glycoform models as “Abundance.” The glycans selected for installation to generate the Abundance model were chosen because they were identified as the most abundant glycan structure (detected by glycomics) that matched the most abundant glycan composition (detected by glycoproteomics) at each individual site. We denote the second glycoform model as “Oxford Class.” The glycans selected for installation to generate the Oxford Class model were chosen because they were the most abundant glycan structure, (detected by glycomics) that was contained within the most highly represented Oxford classification group (detected by glycoproteomics) at each individual site (Figure S7; Tables S1 and S8). Finally, we denote the third glycoform model as “Processed.” The glycans selected for installation to generate the Processed model were chosen because they were the most highly trimmed, elaborated, or terminally decorated structure (detected by glycomics) that corresponded to a composition (detected by glycoproteomics) which was present at ≥ 1/3rd of the abundance of the most highly represented composition at each site (Table S1). 3D structures of the three glycoforms (Abundance, Oxford Class, Processed) were generated for the SARS-CoV-2 S protein alone, and in complex with the glycosylated ACE2 protein. The glycoprotein builder available at GLYCAM-Web (www.glycam.org) was employed together with an in-house program that adjusts the asparagine side chain torsion angles and glycosidic linkages within known low-energy ranges (Nivedha et al., 2014) to relieve any atomic overlaps with the core protein, as described previously (Grant et al., 2016; Peng et al., 2017).\nEnergy minimization and Molecular dynamics (MD) simulations – Each glycosylated structure was placed in a periodic box of TIP3P water molecules with a 10 Å buffer between the solute and the box edge. Energy minimization of all atoms was performed for 20,000 steps (10,000 steepest decent, followed by 10,000 conjugant gradient) under constant pressure (1 atm) and temperature (300 K) conditions. All MD simulations were performed under nPT conditions with the CUDA implementation of the PMEMD (Götz et al., 2012; Salomon-Ferrer et al., 2013) simulation code, as present in the Amber14 software suite (University of California, San Diego). The GLYCAM06j force field (Kirschner et al., 2008) and Amber14SB force field (Maier et al., 2015) were employed for the carbohydrate and protein moieties, respectively. A Berendsen barostat with a time constant of 1 ps was employed for pressure regulation, while a Langevin thermostat with a collision frequency of 2 ps-1 was employed for temperature regulation. A nonbonded interaction cut-off of 8 Å was employed. Long-range electrostatics were treated with the particle-mesh Ewald (PME) method (Darden and Pedersen, 1993). Covalent bonds involving hydrogen were constrained with the SHAKE algorithm, allowing an integration time step of 2 fs to be employed. The energy minimized coordinates were equilibrated at 300K over 400 ps with restraints on the solute heavy atoms. Each system was then equilibrated with restraints on the Ca atoms of the protein for 1ns, prior to initiating 4 independent 250 ns production MD simulations with random starting seeds for a total time of 1 μs per system, with no restraints applied.\nAntigenic surface analysis. A series of 3D structure snapshots of the simulation were taken at 1 ns intervals and analyzed in terms of their ability to interact with a spherical probe based on the average size of hypervariable loops present in an antibody complementarity determining region (CDR), as described recently (https://www.biorxiv.org/content/10.1101/2020.04.07.030445v2). The percentage of simulation time each residue was exposed to the AbASA probe was calculated and plotted onto both the 3D structure and primary sequence."}

LitCovid-sample-PD-FMA

LitCovid-sample-PD-GO-BP-0

{"project":"LitCovid-sample-PD-GO-BP-0","denotations":[{"id":"T132","span":{"begin":877,"end":890},"obj":"http://purl.obolibrary.org/obo/GO_0070085"},{"id":"T133","span":{"begin":3497,"end":3507},"obj":"http://purl.obolibrary.org/obo/GO_0065007"},{"id":"T134","span":{"begin":3603,"end":3613},"obj":"http://purl.obolibrary.org/obo/GO_0065007"}],"text":"3D Structural Modeling and Molecular Dynamics Simulation of Glycosylated SARS-CoV-2 S and Human ACE2 Proteins\nSARS-CoV-2 Spike (S) protein structure and ACE2 co-complex – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB) (Wrapp et al., 2020), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The receptor binding domain (RBD) in the “open” conformation was replaced with the RBD from an ACE2 co-complex (PDB code 6M0J) by grafting residues C336 to V524.\nGlycoform generation – Glycans (detected by glycomics) were selected for installation on glycosylated S and ACE2 sequons (detected by glycoproteomics) based on three sets of criteria designed to reasonably capture different aspects of glycosylation microheterogeneity. We denote the first of these glycoform models as “Abundance.” The glycans selected for installation to generate the Abundance model were chosen because they were identified as the most abundant glycan structure (detected by glycomics) that matched the most abundant glycan composition (detected by glycoproteomics) at each individual site. We denote the second glycoform model as “Oxford Class.” The glycans selected for installation to generate the Oxford Class model were chosen because they were the most abundant glycan structure, (detected by glycomics) that was contained within the most highly represented Oxford classification group (detected by glycoproteomics) at each individual site (Figure S7; Tables S1 and S8). Finally, we denote the third glycoform model as “Processed.” The glycans selected for installation to generate the Processed model were chosen because they were the most highly trimmed, elaborated, or terminally decorated structure (detected by glycomics) that corresponded to a composition (detected by glycoproteomics) which was present at ≥ 1/3rd of the abundance of the most highly represented composition at each site (Table S1). 3D structures of the three glycoforms (Abundance, Oxford Class, Processed) were generated for the SARS-CoV-2 S protein alone, and in complex with the glycosylated ACE2 protein. The glycoprotein builder available at GLYCAM-Web (www.glycam.org) was employed together with an in-house program that adjusts the asparagine side chain torsion angles and glycosidic linkages within known low-energy ranges (Nivedha et al., 2014) to relieve any atomic overlaps with the core protein, as described previously (Grant et al., 2016; Peng et al., 2017).\nEnergy minimization and Molecular dynamics (MD) simulations – Each glycosylated structure was placed in a periodic box of TIP3P water molecules with a 10 Å buffer between the solute and the box edge. Energy minimization of all atoms was performed for 20,000 steps (10,000 steepest decent, followed by 10,000 conjugant gradient) under constant pressure (1 atm) and temperature (300 K) conditions. All MD simulations were performed under nPT conditions with the CUDA implementation of the PMEMD (Götz et al., 2012; Salomon-Ferrer et al., 2013) simulation code, as present in the Amber14 software suite (University of California, San Diego). The GLYCAM06j force field (Kirschner et al., 2008) and Amber14SB force field (Maier et al., 2015) were employed for the carbohydrate and protein moieties, respectively. A Berendsen barostat with a time constant of 1 ps was employed for pressure regulation, while a Langevin thermostat with a collision frequency of 2 ps-1 was employed for temperature regulation. A nonbonded interaction cut-off of 8 Å was employed. Long-range electrostatics were treated with the particle-mesh Ewald (PME) method (Darden and Pedersen, 1993). Covalent bonds involving hydrogen were constrained with the SHAKE algorithm, allowing an integration time step of 2 fs to be employed. The energy minimized coordinates were equilibrated at 300K over 400 ps with restraints on the solute heavy atoms. Each system was then equilibrated with restraints on the Ca atoms of the protein for 1ns, prior to initiating 4 independent 250 ns production MD simulations with random starting seeds for a total time of 1 μs per system, with no restraints applied.\nAntigenic surface analysis. A series of 3D structure snapshots of the simulation were taken at 1 ns intervals and analyzed in terms of their ability to interact with a spherical probe based on the average size of hypervariable loops present in an antibody complementarity determining region (CDR), as described recently (https://www.biorxiv.org/content/10.1101/2020.04.07.030445v2). The percentage of simulation time each residue was exposed to the AbASA probe was calculated and plotted onto both the 3D structure and primary sequence."}

LitCovid-sample-GO-BP

{"project":"LitCovid-sample-GO-BP","denotations":[{"id":"T125","span":{"begin":877,"end":890},"obj":"http://purl.obolibrary.org/obo/GO_0070085"},{"id":"T126","span":{"begin":3497,"end":3507},"obj":"http://purl.obolibrary.org/obo/GO_0065007"},{"id":"T127","span":{"begin":3603,"end":3613},"obj":"http://purl.obolibrary.org/obo/GO_0065007"}],"text":"3D Structural Modeling and Molecular Dynamics Simulation of Glycosylated SARS-CoV-2 S and Human ACE2 Proteins\nSARS-CoV-2 Spike (S) protein structure and ACE2 co-complex – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB) (Wrapp et al., 2020), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The receptor binding domain (RBD) in the “open” conformation was replaced with the RBD from an ACE2 co-complex (PDB code 6M0J) by grafting residues C336 to V524.\nGlycoform generation – Glycans (detected by glycomics) were selected for installation on glycosylated S and ACE2 sequons (detected by glycoproteomics) based on three sets of criteria designed to reasonably capture different aspects of glycosylation microheterogeneity. We denote the first of these glycoform models as “Abundance.” The glycans selected for installation to generate the Abundance model were chosen because they were identified as the most abundant glycan structure (detected by glycomics) that matched the most abundant glycan composition (detected by glycoproteomics) at each individual site. We denote the second glycoform model as “Oxford Class.” The glycans selected for installation to generate the Oxford Class model were chosen because they were the most abundant glycan structure, (detected by glycomics) that was contained within the most highly represented Oxford classification group (detected by glycoproteomics) at each individual site (Figure S7; Tables S1 and S8). Finally, we denote the third glycoform model as “Processed.” The glycans selected for installation to generate the Processed model were chosen because they were the most highly trimmed, elaborated, or terminally decorated structure (detected by glycomics) that corresponded to a composition (detected by glycoproteomics) which was present at ≥ 1/3rd of the abundance of the most highly represented composition at each site (Table S1). 3D structures of the three glycoforms (Abundance, Oxford Class, Processed) were generated for the SARS-CoV-2 S protein alone, and in complex with the glycosylated ACE2 protein. The glycoprotein builder available at GLYCAM-Web (www.glycam.org) was employed together with an in-house program that adjusts the asparagine side chain torsion angles and glycosidic linkages within known low-energy ranges (Nivedha et al., 2014) to relieve any atomic overlaps with the core protein, as described previously (Grant et al., 2016; Peng et al., 2017).\nEnergy minimization and Molecular dynamics (MD) simulations – Each glycosylated structure was placed in a periodic box of TIP3P water molecules with a 10 Å buffer between the solute and the box edge. Energy minimization of all atoms was performed for 20,000 steps (10,000 steepest decent, followed by 10,000 conjugant gradient) under constant pressure (1 atm) and temperature (300 K) conditions. All MD simulations were performed under nPT conditions with the CUDA implementation of the PMEMD (Götz et al., 2012; Salomon-Ferrer et al., 2013) simulation code, as present in the Amber14 software suite (University of California, San Diego). The GLYCAM06j force field (Kirschner et al., 2008) and Amber14SB force field (Maier et al., 2015) were employed for the carbohydrate and protein moieties, respectively. A Berendsen barostat with a time constant of 1 ps was employed for pressure regulation, while a Langevin thermostat with a collision frequency of 2 ps-1 was employed for temperature regulation. A nonbonded interaction cut-off of 8 Å was employed. Long-range electrostatics were treated with the particle-mesh Ewald (PME) method (Darden and Pedersen, 1993). Covalent bonds involving hydrogen were constrained with the SHAKE algorithm, allowing an integration time step of 2 fs to be employed. The energy minimized coordinates were equilibrated at 300K over 400 ps with restraints on the solute heavy atoms. Each system was then equilibrated with restraints on the Ca atoms of the protein for 1ns, prior to initiating 4 independent 250 ns production MD simulations with random starting seeds for a total time of 1 μs per system, with no restraints applied.\nAntigenic surface analysis. A series of 3D structure snapshots of the simulation were taken at 1 ns intervals and analyzed in terms of their ability to interact with a spherical probe based on the average size of hypervariable loops present in an antibody complementarity determining region (CDR), as described recently (https://www.biorxiv.org/content/10.1101/2020.04.07.030445v2). The percentage of simulation time each residue was exposed to the AbASA probe was calculated and plotted onto both the 3D structure and primary sequence."}

LitCovid-sample-Glycan

{"project":"LitCovid-sample-Glycan","denotations":[{"id":"T2947","span":{"begin":3009,"end":3012},"obj":"https://glytoucan.org/Structures/Glycans/G05518TD"}],"text":"3D Structural Modeling and Molecular Dynamics Simulation of Glycosylated SARS-CoV-2 S and Human ACE2 Proteins\nSARS-CoV-2 Spike (S) protein structure and ACE2 co-complex – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB) (Wrapp et al., 2020), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The receptor binding domain (RBD) in the “open” conformation was replaced with the RBD from an ACE2 co-complex (PDB code 6M0J) by grafting residues C336 to V524.\nGlycoform generation – Glycans (detected by glycomics) were selected for installation on glycosylated S and ACE2 sequons (detected by glycoproteomics) based on three sets of criteria designed to reasonably capture different aspects of glycosylation microheterogeneity. We denote the first of these glycoform models as “Abundance.” The glycans selected for installation to generate the Abundance model were chosen because they were identified as the most abundant glycan structure (detected by glycomics) that matched the most abundant glycan composition (detected by glycoproteomics) at each individual site. We denote the second glycoform model as “Oxford Class.” The glycans selected for installation to generate the Oxford Class model were chosen because they were the most abundant glycan structure, (detected by glycomics) that was contained within the most highly represented Oxford classification group (detected by glycoproteomics) at each individual site (Figure S7; Tables S1 and S8). Finally, we denote the third glycoform model as “Processed.” The glycans selected for installation to generate the Processed model were chosen because they were the most highly trimmed, elaborated, or terminally decorated structure (detected by glycomics) that corresponded to a composition (detected by glycoproteomics) which was present at ≥ 1/3rd of the abundance of the most highly represented composition at each site (Table S1). 3D structures of the three glycoforms (Abundance, Oxford Class, Processed) were generated for the SARS-CoV-2 S protein alone, and in complex with the glycosylated ACE2 protein. The glycoprotein builder available at GLYCAM-Web (www.glycam.org) was employed together with an in-house program that adjusts the asparagine side chain torsion angles and glycosidic linkages within known low-energy ranges (Nivedha et al., 2014) to relieve any atomic overlaps with the core protein, as described previously (Grant et al., 2016; Peng et al., 2017).\nEnergy minimization and Molecular dynamics (MD) simulations – Each glycosylated structure was placed in a periodic box of TIP3P water molecules with a 10 Å buffer between the solute and the box edge. Energy minimization of all atoms was performed for 20,000 steps (10,000 steepest decent, followed by 10,000 conjugant gradient) under constant pressure (1 atm) and temperature (300 K) conditions. All MD simulations were performed under nPT conditions with the CUDA implementation of the PMEMD (Götz et al., 2012; Salomon-Ferrer et al., 2013) simulation code, as present in the Amber14 software suite (University of California, San Diego). The GLYCAM06j force field (Kirschner et al., 2008) and Amber14SB force field (Maier et al., 2015) were employed for the carbohydrate and protein moieties, respectively. A Berendsen barostat with a time constant of 1 ps was employed for pressure regulation, while a Langevin thermostat with a collision frequency of 2 ps-1 was employed for temperature regulation. A nonbonded interaction cut-off of 8 Å was employed. Long-range electrostatics were treated with the particle-mesh Ewald (PME) method (Darden and Pedersen, 1993). Covalent bonds involving hydrogen were constrained with the SHAKE algorithm, allowing an integration time step of 2 fs to be employed. The energy minimized coordinates were equilibrated at 300K over 400 ps with restraints on the solute heavy atoms. Each system was then equilibrated with restraints on the Ca atoms of the protein for 1ns, prior to initiating 4 independent 250 ns production MD simulations with random starting seeds for a total time of 1 μs per system, with no restraints applied.\nAntigenic surface analysis. A series of 3D structure snapshots of the simulation were taken at 1 ns intervals and analyzed in terms of their ability to interact with a spherical probe based on the average size of hypervariable loops present in an antibody complementarity determining region (CDR), as described recently (https://www.biorxiv.org/content/10.1101/2020.04.07.030445v2). The percentage of simulation time each residue was exposed to the AbASA probe was calculated and plotted onto both the 3D structure and primary sequence."}

2_test

{"project":"2_test","denotations":[{"id":"32841605-32075877-19659565","span":{"begin":340,"end":344},"obj":"32075877"},{"id":"32841605-24375430-19659566","span":{"begin":2488,"end":2492},"obj":"24375430"},{"id":"32841605-28120784-19659567","span":{"begin":2587,"end":2591},"obj":"28120784"},{"id":"32841605-28017661-19659568","span":{"begin":2606,"end":2610},"obj":"28017661"},{"id":"32841605-22582031-19659569","span":{"begin":3120,"end":3124},"obj":"22582031"},{"id":"32841605-26592383-19659570","span":{"begin":3149,"end":3153},"obj":"26592383"},{"id":"32841605-17849372-19659571","span":{"begin":3297,"end":3301},"obj":"17849372"},{"id":"32841605-26574453-19659572","span":{"begin":3344,"end":3348},"obj":"26574453"},{"id":"32841605-8119250-19659573","span":{"begin":3771,"end":3775},"obj":"8119250"}],"text":"3D Structural Modeling and Molecular Dynamics Simulation of Glycosylated SARS-CoV-2 S and Human ACE2 Proteins\nSARS-CoV-2 Spike (S) protein structure and ACE2 co-complex – A 3D structure of the prefusion form of the S protein (RefSeq: YP_009724390.1, UniProt: P0DTC2 SPIKE_SARS2), based on a Cryo-EM structure (PDB code 6VSB) (Wrapp et al., 2020), was obtained from the SWISS-MODEL server (swissmodel.expasy.org). The model has 95% coverage (residues 27 to 1146) of the S protein. The receptor binding domain (RBD) in the “open” conformation was replaced with the RBD from an ACE2 co-complex (PDB code 6M0J) by grafting residues C336 to V524.\nGlycoform generation – Glycans (detected by glycomics) were selected for installation on glycosylated S and ACE2 sequons (detected by glycoproteomics) based on three sets of criteria designed to reasonably capture different aspects of glycosylation microheterogeneity. We denote the first of these glycoform models as “Abundance.” The glycans selected for installation to generate the Abundance model were chosen because they were identified as the most abundant glycan structure (detected by glycomics) that matched the most abundant glycan composition (detected by glycoproteomics) at each individual site. We denote the second glycoform model as “Oxford Class.” The glycans selected for installation to generate the Oxford Class model were chosen because they were the most abundant glycan structure, (detected by glycomics) that was contained within the most highly represented Oxford classification group (detected by glycoproteomics) at each individual site (Figure S7; Tables S1 and S8). Finally, we denote the third glycoform model as “Processed.” The glycans selected for installation to generate the Processed model were chosen because they were the most highly trimmed, elaborated, or terminally decorated structure (detected by glycomics) that corresponded to a composition (detected by glycoproteomics) which was present at ≥ 1/3rd of the abundance of the most highly represented composition at each site (Table S1). 3D structures of the three glycoforms (Abundance, Oxford Class, Processed) were generated for the SARS-CoV-2 S protein alone, and in complex with the glycosylated ACE2 protein. The glycoprotein builder available at GLYCAM-Web (www.glycam.org) was employed together with an in-house program that adjusts the asparagine side chain torsion angles and glycosidic linkages within known low-energy ranges (Nivedha et al., 2014) to relieve any atomic overlaps with the core protein, as described previously (Grant et al., 2016; Peng et al., 2017).\nEnergy minimization and Molecular dynamics (MD) simulations – Each glycosylated structure was placed in a periodic box of TIP3P water molecules with a 10 Å buffer between the solute and the box edge. Energy minimization of all atoms was performed for 20,000 steps (10,000 steepest decent, followed by 10,000 conjugant gradient) under constant pressure (1 atm) and temperature (300 K) conditions. All MD simulations were performed under nPT conditions with the CUDA implementation of the PMEMD (Götz et al., 2012; Salomon-Ferrer et al., 2013) simulation code, as present in the Amber14 software suite (University of California, San Diego). The GLYCAM06j force field (Kirschner et al., 2008) and Amber14SB force field (Maier et al., 2015) were employed for the carbohydrate and protein moieties, respectively. A Berendsen barostat with a time constant of 1 ps was employed for pressure regulation, while a Langevin thermostat with a collision frequency of 2 ps-1 was employed for temperature regulation. A nonbonded interaction cut-off of 8 Å was employed. Long-range electrostatics were treated with the particle-mesh Ewald (PME) method (Darden and Pedersen, 1993). Covalent bonds involving hydrogen were constrained with the SHAKE algorithm, allowing an integration time step of 2 fs to be employed. The energy minimized coordinates were equilibrated at 300K over 400 ps with restraints on the solute heavy atoms. Each system was then equilibrated with restraints on the Ca atoms of the protein for 1ns, prior to initiating 4 independent 250 ns production MD simulations with random starting seeds for a total time of 1 μs per system, with no restraints applied.\nAntigenic surface analysis. A series of 3D structure snapshots of the simulation were taken at 1 ns intervals and analyzed in terms of their ability to interact with a spherical probe based on the average size of hypervariable loops present in an antibody complementarity determining region (CDR), as described recently (https://www.biorxiv.org/content/10.1101/2020.04.07.030445v2). The percentage of simulation time each residue was exposed to the AbASA probe was calculated and plotted onto both the 3D structure and primary sequence."}

PMC:7443692 / 63432-68244 JSONTXT

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PMC:7443692 / 63432-68244 JSON TXT