Rational proteomics II: electrostatic nature of cofactor preference in the short-chain oxidoreductase (SCOR) enzyme family

The dominant role of long-range electrostatic interatomic interactions in nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide phosphate (NAD/NADP) cofactor recognition has been shown for enzymes of the short-chain oxidoreductase (SCOR) family. An estimation of cofactor preference based only on the contribution of the electrostatic energy term to the total energy of enzyme-cofactor interaction has been tested for approximately 40 known three-dimensional (3D) crystal complexes and approximately 330 SCOR enzymes, with cofactor preference predicted by the presence of Asp or Arg recognition residues at specific 3D positions in the beta2alpha3 loop (Duax et al., Proteins 2003;53:931-943). The results obtained were found to be consistent with approximately 90% reliable cofactor assignments for those subsets. The procedure was then applied to approximately 170 SCOR enzymes with completely uncertain NAD/NADP dependence, due to the lack of Asp and Arg marker residues. The proposed 3D electrostatic approach for cofactor assignment ("3D_DeltaE(el)") has been implemented in an automatic screening procedure, and together with the use of marker residues proposed earlier (Duax et al., Proteins 2003;53:931-943), increases the level of reliable predictions for the putative SCORs from approximately 70% to approximately 90%. It is expected to be applicable for any NAD/NADP-dependent enzyme subset having at least 25-30% sequence identity, with at least one enzyme of known 3D crystal structure.     

Computational fluid dynamic analysis of bioprinted self-supporting perfused tissue models

Natural tissues are incorporated with vasculature, which is further integrated with a cardiovascular system responsible for driving perfusion of nutrient-rich oxygenated blood through the vasculature to support cell metabolism within most cell-dense tissues. Since scaffold-free biofabricated tissues being developed into clinical implants, research models, and pharmaceutical testing platforms should similarly exhibit perfused tissue-like structures, we generated a generalizable biofabrication method resulting in self-supporting perfused (SSuPer) tissue constructs incorporated with perfusible microchannels and integrated with the modular FABRICA perfusion bioreactor. As proof of concept, we perfused an MLO-A5 osteoblast-based SSuPer tissue in the FABRICA. Although our resulting SSuPer tissue replicated vascularization and perfusion observed in situ, supported its own weight, and stained positively for mineral using Von Kossa staining, our in vitro results indicated that computational fluid dynamics (CFD) should be used to drive future construct design and flow application before further tissue biofabrication and perfusion. We built a CFD model of the SSuPer tissue integrated in the FABRICA and analyzed flow characteristics (net force, pressure distribution, shear stress, and oxygen distribution) through five SSuPer tissue microchannel patterns in two flow directions and at increasing flow rates. Important flow parameters include flow direction, fully developed flow, and tissue microchannel diameters matched and aligned with bioreactor flow channels. We observed that the SSuPer tissue platform is capable of providing direct perfusion to tissue constructs and proper culture conditions (oxygenation, with controllable shear and flow rates), indicating that our approach can be used to biofabricate tissue representing primary tissues and that we can model the system in silico.     

Three-dimensional macro-scale assessment of regional and temporal wall shear stress characteristics on aortic valve leaflets

The aortic valve (AV) achieves unidirectional blood flow between the left ventricle and the aorta. Although hemodynamic stresses have been shown to regulate valvular biology, the native wall shear stress (WSS) experienced by AV leaflets remains largely unknown. The objective of this study was to quantify computationally the macro-scale leaflet WSS environment using fluid–structure interaction modeling. An arbitrary Lagrangian–Eulerian approach was implemented to predict valvular flow and leaflet dynamics in a three-dimensional AV geometry subjected to physiologic transvalvular pressure. Local WSS characteristics were quantified in terms of temporal shear magnitude (TSM), oscillatory shear index (OSI) and temporal shear gradient (TSG). The dominant radial WSS predicted on the leaflets exhibited high amplitude and unidirectionality on the ventricularis (TSM>7.50 dyn/cm², OSI < 0.17, TSG>325.54 dyn/cm² s) but low amplitude and bidirectionality on the fibrosa (TSM < 2.73 dyn/cm², OSI>0.38, TSG < 191.17 dyn/cm² s). The radial WSS component computed in the leaflet base, belly and tip demonstrated strong regional variability (ventricularis TSM: 7.50–22.32 dyn/cm², fibrosa TSM: 1.26–2.73 dyn/cm²). While the circumferential WSS exhibited similar spatially dependent magnitude (ventricularis TSM: 1.41–3.40 dyn/cm², fibrosa TSM: 0.42–0.76 dyn/cm²) and side-specific amplitude (ventricularis TSG: 101.73–184.43 dyn/cm² s, fibrosa TSG: 41.92–54.10 dyn/cm² s), its temporal variations were consistently bidirectional (OSI>0.25). This study provides new insights into the role played by leaflet–blood flow interactions in valvular function and critical hemodynamic stress data for the assessment of the hemodynamic theory of AV disease.     

Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks

Genome-scale metabolic models are central in connecting genotypes to metabolic phenotypes. However, even for well studied organisms, such as Escherichia coli, draft networks do not contain a complete biochemical network. Missing reactions are referred to as gaps. These gaps need to be filled to enable functional analysis, and gap-filling choices influence model predictions. To investigate whether functional networks existed where all gap-filling reactions were supported by sequence similarity to annotated enzymes, four draft networks were supplemented with all reactions from the Model SEED database for which minimal sequence similarity was found in their genomes. Quadratic programming revealed that the number of reactions that could partake in a gap-filling solution was vast: 3,270 in the case of E. coli, where 72% of the metabolites in the draft network could connect a gap-filling solution. Nonetheless, no network could be completed without the inclusion of orphaned enzymes, suggesting that parts of the biochemistry integral to biomass precursor formation are uncharacterized. However, many gap-filling reactions were well determined, and the resulting networks showed improved prediction of gene essentiality compared with networks generated through canonical gap filling. In addition, gene essentiality predictions that were sensitive to poorly determined gap-filling reactions were of poor quality, suggesting that damage to the network structure resulting from the inclusion of erroneous gap-filling reactions may be predictable.