scholarly journals Towards a barrier height benchmark set for biologically relevant systems

2016 ◽  
Author(s):  
Jimmy C Kromann ◽  
Anders S Christensen ◽  
Qiang Cui ◽  
Jan H. Jensen
Keyword(s):  
Structural Model ◽  
Geometry Optimization ◽  
System Size ◽  
Model Systems ◽  
Maximum Deviation ◽  
Enzymatic Reactions ◽  
Data Set ◽  
Biologically Relevant ◽  
Enzyme Model ◽  
Reaction Energies

We have collected computed barrier heights and reaction energies (and associated model structures) for five enzymes from studies published by Himo and co-workers. Using this data, obtained at the B3LYP/6- 311+G(2d,2p)[LANL2DZ]//B3LYP/6-31G(d,p) level of theory, we then benchmark PM6, PM7, PM7-TS, and DFTB3 and discuss the influence of system size, bulk solvation, and geometry re-optimization on the error. The mean absolute differences (MADs) observed for these five enzyme model systems are similar to those observed for PM6 and PM7 for smaller systems (10-15 kcal/mol), while DFTB results in a MAD that is significantly lower (6 kcal/mol). The MADs for PMx and DFTB3 are each dominated by large errors for a single system and if the system is disregarded the MADs fall to 4-5 kcal/mol. Overall, results for the condensed phase are neither more or less accurate relative to B3LYP than those in the gas phase. With the exception of PM7-TS, the MAD for small and large structural models are very similar, with a maximum deviation of 3 kcal/mol for PM6. Geometry optimization with PM6 shows that for one system this method predicts a different mechanism compared to B3LYP/6-31G(d,p). For the remaining systems geometry optimization of the large structural model increases the MAD relative to single points, by 2.5 and 1.8 kcal/mol for barriers and reaction energies. For the small structural model the corresponding MADs decrease by 0.4 and 1.2 kcal/mol, respectively. However, despite these small changes, significant changes in the structures are observed for some systems, such as proton transfer and hydrogen bonding rearrangements. The paper represents the first step in the process of creating a benchmark set of barriers computed for systems that are relatively large and representative of enzymatic reactions, a considerable challenge for any one research group but possible through a concerted effort by the community. We end by outlining steps needed to expand and improve the data set and how other researchers can contribute to the process.

scholarly journals Towards a barrier height benchmark set for biologically relevant systems

2016 ◽  
Author(s):  
Jimmy C Kromann ◽  
Anders S Christensen ◽  
Qiang Cui ◽  
Jan H. Jensen
Keyword(s):  
Structural Model ◽  
Geometry Optimization ◽  
System Size ◽  
Model Systems ◽  
Maximum Deviation ◽  
Enzymatic Reactions ◽  
Data Set ◽  
Biologically Relevant ◽  
Enzyme Model ◽  
Reaction Energies

We have collected computed barrier heights and reaction energies (and associated model structures) for five enzymes from studies published by Himo and co-workers. Using this data, obtained at the B3LYP/6- 311+G(2d,2p)[LANL2DZ]//B3LYP/6-31G(d,p) level of theory, we then benchmark PM6, PM7, PM7-TS, and DFTB3 and discuss the influence of system size, bulk solvation, and geometry re-optimization on the error. The mean absolute differences (MADs) observed for these five enzyme model systems are similar to those observed for PM6 and PM7 for smaller systems (10-15 kcal/mol), while DFTB results in a MAD that is significantly lower (6 kcal/mol). The MADs for PMx and DFTB3 are each dominated by large errors for a single system and if the system is disregarded the MADs fall to 4-5 kcal/mol. Overall, results for the condensed phase are neither more or less accurate relative to B3LYP than those in the gas phase. With the exception of PM7-TS, the MAD for small and large structural models are very similar, with a maximum deviation of 3 kcal/mol for PM6. Geometry optimization with PM6 shows that for one system this method predicts a different mechanism compared to B3LYP/6-31G(d,p). For the remaining systems geometry optimization of the large structural model increases the MAD relative to single points, by 2.5 and 1.8 kcal/mol for barriers and reaction energies. For the small structural model the corresponding MADs decrease by 0.4 and 1.2 kcal/mol, respectively. However, despite these small changes, significant changes in the structures are observed for some systems, such as proton transfer and hydrogen bonding rearrangements. The paper represents the first step in the process of creating a benchmark set of barriers computed for systems that are relatively large and representative of enzymatic reactions, a considerable challenge for any one research group but possible through a concerted effort by the community. We end by outlining steps needed to expand and improve the data set and how other researchers can contribute to the process.

scholarly journals Towards a barrier height benchmark set for biologically relevant systems

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1994 ◽  
Author(s):  
Jimmy C. Kromann ◽  
Anders S. Christensen ◽  
Qiang Cui ◽  
Jan H. Jensen
Keyword(s):  
Structural Model ◽  
Geometry Optimization ◽  
System Size ◽  
Model Systems ◽  
Maximum Deviation ◽  
Enzymatic Reactions ◽  
Data Set ◽  
Biologically Relevant ◽  
Enzyme Model ◽  
Reaction Energies

We have collected computed barrier heights and reaction energies (and associated model structures) for five enzymes from studies published by Himo and co-workers. Using this data, obtained at the B3LYP/6- 311+G(2d,2p)[LANL2DZ]//B3LYP/6-31G(d,p) level of theory, we then benchmark PM6, PM7, PM7-TS, and DFTB3 and discuss the influence of system size, bulk solvation, and geometry re-optimization on the error. The mean absolute differences (MADs) observed for these five enzyme model systems are similar to those observed for PM6 and PM7 for smaller systems (10–15 kcal/mol), while DFTB results in a MAD that is significantly lower (6 kcal/mol). The MADs for PMx and DFTB3 are each dominated by large errors for a single system and if the system is disregarded the MADs fall to 4–5 kcal/mol. Overall, results for the condensed phase are neither more or less accurate relative to B3LYP than those in the gas phase. With the exception of PM7-TS, the MAD for small and large structural models are very similar, with a maximum deviation of 3 kcal/mol for PM6. Geometry optimization with PM6 shows that for one system this method predicts a different mechanism compared to B3LYP/6-31G(d,p). For the remaining systems, geometry optimization of the large structural model increases the MAD relative to single points, by 2.5 and 1.8 kcal/mol for barriers and reaction energies. For the small structural model, the corresponding MADs decrease by 0.4 and 1.2 kcal/mol, respectively. However, despite these small changes, significant changes in the structures are observed for some systems, such as proton transfer and hydrogen bonding rearrangements. The paper represents the first step in the process of creating a benchmark set of barriers computed for systems that are relatively large and representative of enzymatic reactions, a considerable challenge for any one research group but possible through a concerted effort by the community. We end by outlining steps needed to expand and improve the data set and how other researchers can contribute to the process.

scholarly journals Towards a barrier height benchmark set for biologically relevant systems

2016 ◽  
Author(s):  
Jimmy C Kromann ◽  
Anders S Christensen ◽  
Jan H. Jensen
Keyword(s):  
Structural Model ◽  
Geometry Optimization ◽  
System Size ◽  
Model Systems ◽  
Maximum Deviation ◽  
Enzymatic Reactions ◽  
Data Set ◽  
Biologically Relevant ◽  
Enzyme Model ◽  
Reaction Energies

We have collected computed barrier heights and reaction energies (and associated model structures) for five enzymes from studies published by Himo and co-workers. Using this data, obtained at the B3LYP/6- 311+G(2d,2p)[LANL2DZ]//B3LYP/6-31G(d,p) level of theory, we then benchmark PM6, PM7, PM7-TS, and DFTB3 and discuss the influence of system size, bulk solvation, and geometry re-optimization on the error. The mean absolute differences (MADs) observed for these five enzyme model systems are similar to those observed for PM6 and PM7 for smaller systems (10-15 kcal/mol), while DFTB results in a MAD that is significantly lower (6 kcal/mol). The MADs for PMx and DFTB3 are each dominated by large errors for a single system and if the system is disregarded the MADs fall to 4-5 kcal/mol. Overall, results for the condensed phase are neither more or less accurate relative to B3LYP than those in the gas phase. With the exception of PM7-TS, the MAD for small and large structural models are very similar, with a maximum deviation of 3 kcal/mol for PM6. Geometry optimization with PM6 shows that for one system this method predicts a different mechanism compared to B3LYP/6-31G(d,p). For the remaining systems geometry optimization of the large structural model increases the MAD relative to single points, by 2.5 and 1.8 kcal/mol for barriers and reaction energies. For the small structural model the corresponding MADs decrease by 0.4 and 1.2 kcal/mol, respectively. However, despite these small changes, significant changes in the structures are observed for some systems, such as proton transfer and hydrogen bonding rearrangements. The paper represents the first step in the process of creating a benchmark set of barriers computed for systems that are relatively large and representative of enzymatic reactions, a considerable challenge for any one research group but possible through a concerted effort by the community. We end by outlining steps needed to expand and improve the data set and how other researchers can contribute to the process.

scholarly journals Selecting single cell clustering parameter values using subsampling-based robustness metrics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan B. Patterson-Cross ◽  
Ariel J. Levine ◽  
Vilas Menon
Keyword(s):  
Single Cell ◽  
Optimal Parameter ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Parameter Selection ◽  
Data Set ◽  
Biologically Relevant ◽  
Cell Clustering ◽  
Parameter Values ◽  
Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

scholarly journals Modelling head loss along emitting pipes using dimensional analysis

2015 ◽  
Vol 35 (3) ◽  
pp. 442-457 ◽  
Author(s):  
Acácio Perboni ◽  
Jose A. Frizzone ◽  
Antonio P. de Camargo ◽  
Marinaldo F. Pinto
Keyword(s):  
Dimensional Analysis ◽  
Excellent Result ◽  
Head Loss ◽  
Maximum Deviation ◽  
Maximum Relative Error ◽  
Data Set ◽  
Head Losses ◽  
Model Based ◽  
Precision And Accuracy ◽  
Relative Errors

Local head losses must be considered in estimating properly the maximum length of drip irrigation laterals. The aim of this work was to develop a model based on dimensional analysis for calculating head loss along laterals accounting for in-line drippers. Several measurements were performed with 12 models of emitters to obtain the experimental data required for developing and assessing the model. Based on the Camargo & Sentelhas coefficient, the model presented an excellent result in terms of precision and accuracy on estimating head loss. The deviation between estimated and observed values of head loss increased according to the head loss and the maximum deviation reached 0.17 m. The maximum relative error was 33.75% and only 15% of the data set presented relative errors higher than 20%. Neglecting local head losses incurred a higher than estimated maximum lateral length of 19.48% for pressure-compensating drippers and 16.48% for non pressure-compensating drippers.

Modular Distributed Models of Engineering Structures

2003 ◽  
Author(s):  
Clark J. Radcliffe ◽  
Jon Sticklen
Keyword(s):  
Engineering Design ◽  
Degrees Of Freedom ◽  
Structural Model ◽  
Integrated Design ◽  
Model Systems ◽  
Structural Stiffness ◽  
Engineering Structures ◽  
Distributed Modeling ◽  
Communications Network ◽  
Component Models

Approaches to engineering design and manufacturing such as integrated design and manufacture and just in time fabrication depend on interaction with and among component supply companies that most often use very diverse technologies. The Internet Engineering Design Agents (i-EDA) software system uses a distributed, component-based, agent methodology that is realized following a strong black box approach to modeling. An individual Design Agent (DA) is a virtual product capable of encapsulating both descriptive and model based information about the product it represents. Hierarchically recursive agents for sub-systems and/or components are linked via a communications network to form larger integrated model systems. A two dimensional bridge system structural model is used as an example to illustrate the distributed assembly of structural models from components registered as DA’s on a communications network. Modular Distributed Modeling (MDM) of engineering structures performs static deflection analysis using traditional, fixed causality, structural stiffness models. This paper presents the methodology required to assemble traditional structural stiffness models provided by internet agents representing structural components. The methodology discussed assembles these component models into the structural stiffness model of an assembly distributed by an agents represent that physical assembly of components. Using this modular distributed modeling method; models of complex assemblies can be built and distributed while hiding the topology and characteristics of their structural subassemblies. The automated, modular, assembly of structural stiffness models will be derived for discrete physical connections. Discrete connections are important to the assembly of components such as truss and shaft structures where the relationship between component displacements involve discrete, matching, degrees of freedom on components to be assembled. Specific examples of discrete assembly of truss bridge component models will be presented.

scholarly journals Template-dependent inhibition of coronavirus RNA-dependent RNA polymerase by remdesivir reveals a second mechanism of action

2020 ◽  
Vol 295 (47) ◽  
pp. 16156-16165 ◽  
Author(s):  
Egor P. Tchesnokov ◽  
Calvin J. Gordon ◽  
Emma Woolner ◽  
Dana Kocinkova ◽  
Jason K. Perry ◽  
...  
Keyword(s):  
Rna Polymerase ◽  
Structural Model ◽  
Antiviral Agent ◽  
Product Formation ◽  
Chain Termination ◽  
Rna Dependent Rna Polymerase ◽  
Biologically Relevant ◽  
Direct Acting Antiviral ◽  
Dependent Inhibition ◽  
Direct Acting

Remdesivir (RDV) is a direct-acting antiviral agent that is used to treat patients with severe coronavirus disease 2019 (COVID-19). RDV targets the viral RNA-dependent RNA polymerase (RdRp) of severe acute respiratory syndrome coronavirus 2 (SARS–CoV-2). We have previously shown that incorporation of the active triphosphate form of RDV (RDV-TP) at position i causes delayed chain termination at position i + 3. Here we demonstrate that the S861G mutation in RdRp eliminates chain termination, which confirms the existence of a steric clash between Ser-861 and the incorporated RDV-TP. With WT RdRp, increasing concentrations of NTP pools cause a gradual decrease in termination and the resulting read-through increases full-length product formation. Hence, RDV residues could be embedded in copies of the first RNA strand that is later used as a template. We show that the efficiency of incorporation of the complementary UTP opposite template RDV is compromised, providing a second opportunity to inhibit replication. A structural model suggests that RDV, when serving as the template for the incoming UTP, is not properly positioned because of a significant clash with Ala-558. The adjacent Val-557 is in direct contact with the template base, and the V557L mutation is implicated in low-level resistance to RDV. We further show that the V557L mutation in RdRp lowers the nucleotide concentration required to bypass this template-dependent inhibition. The collective data provide strong evidence to show that template-dependent inhibition of SARS–CoV-2 RdRp by RDV is biologically relevant.

Psychological well-being in mid to late life: The role of generativity development and parent–child relationships across the lifespan

2006 ◽  
Vol 30 (5) ◽  
pp. 410-421 ◽  
Author(s):  
Jeong Shin An ◽  
Teresa M. Cooney
Keyword(s):  
Psychosocial Development ◽  
Structural Model ◽  
Late Life ◽  
Well Being ◽  
Direct Effects ◽  
Data Set ◽  
Psychological Well Being ◽  
Parental Experiences ◽  
The Impact

This study examined the association between generativity and psychological well-being for a subsample of 1882 mid- to late-life parents using the MIDUS data set. Guided by Erikson's theory of psychosocial development, we tested a structural model of psychological well-being that also included direct and indirect effects (via generativity) of remembered pre-adult relationships with parents and current parental experiences with offspring on well-being. Respondents who recalled positive, trusting relationships with parents in childhood reported more positive parental experiences with their adult offspring and better psychological well-being. Current parental experiences had both indirect and direct effects on well-being too, but generativity had the strongest direct effects. Thus, it appears that the achievement of generativity plays a substantial role in well-being in mid- and late life. Findings also reveal that the impact of generativity on well-being is stronger for females than males. Implications for intervention with older adults, such as promoting volunteer work, are discussed.

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