Fruit tree crop models: an update

Functional structural plant models of tree crops are useful tools that were introduced more than two decades ago. They can represent the growth and development of a plant through the in silico simulation of the 3D architecture in connection with physiological processes. In tree crops, physiological processes such as photosynthesis, carbon allocation, and growth are usually integrated into these models although other functions such as water and nutrient uptake are often disregarded. The implementation of the 3D architecture involves different techniques such as L-system frameworks, pipe model concepts, and Markovian models to simulate branching processes, bud fates, and elongation of stems based on the production of metamers. The simulation of root architecture is still a challenge for researchers due to a limited amount of information and experimental issues in dealing with roots because root development is not based on the production of metamers. This review aims to focus on functional-structural models of fruit tree crops, highlighting their physiological components. The potential and limits of these tools are reviewed to point out the topics that still need more attention.

Perceiving SARS-CoV2 Mpro and PLpro dual inhibitors from pool of recognized antiviral compounds of endophytic microbes: an in-silico simulation study

The enormous impact of SARS-CoV2 continues and scientific community is seeking to discover the tactics to impede the spread of virus. The essential result is attenuated, and genetically engineered vaccines are being driven into the market with the general effectiveness being around 80%. Therefore, vaccination is not the sole answer for combat this pandemic. The substitute methodology is adapted to target on this virus with a medication in blend with existing vaccines. Papain like protease (nsp-3; nonstructural protein) and Mpro (nsp-5; nonstructural protein) of novel corona virus are the ideal target to develop drugs as they play different roles that are essential for viral development and replication. Utilizing computational methodology, we plan to distinguish a plausible microbial metabolite as analogue of GRL0617 (the well-established inhibitor of PLpro) and X77 (the well-established inhibitor of Mpro) from the pool of known antiviral compounds of endophytic microbes to interact and inhibit PLpro and Mpro as dual inhibitors. In the wake of collecting known antiviral compounds of endophytic microbes and screened them through pharmacophore hypothesis, molecular docking, and dynamics simulation, we perceive Cytonic acid A and Cytonic acid B to be seen as the potent PLpro and Mpro dual inhibitors using rigorous computational methods.

Deep sequencing of DNA from urine of kidney allograft recipients to estimate donor/recipient-specific DNA fractions

Kidney transplantation is the treatment of choice for patients with end-stage kidney failure, but transplanted allograft could be affected by viral and bacterial infections and by immune rejection. The standard test for the diagnosis of acute pathologies in kidney transplants is kidney biopsy. However, noninvasive tests would be desirable. Various methods using different techniques have been developed by the transplantation community. But these methods require improvements. We present here a cost-effective method for kidney rejection diagnosis that estimates donor/recipient-specific DNA fraction in recipient urine by sequencing urinary cell DNA. We hypothesized that in the no-pathology stage, the largest tissue types present in recipient urine are donor kidney cells, and in case of rejection, a larger number of recipient immune cells would be observed. Extensive in-silico simulation was used to tune the sequencing parameters: number of variants and depth of coverage. Sequencing of DNA mixture from 2 healthy individuals showed the method is highly predictive (maximum error < 0.04). We then demonstrated the insignificant impact of familial relationship and ethnicity using an in-house and public database. Lastly, we performed deep DNA sequencing of urinary cell pellets from 32 biopsy-matched samples representing two pathology groups: acute rejection (AR, 11 samples) and acute tubular injury (ATI, 12 samples) and 9 samples with no pathology. We found a significant association between the donor/recipient-specific DNA fraction in the two pathology groups compared to no pathology (P = 0.0064 for AR and P = 0.026 for ATI). We conclude that deep DNA sequencing of urinary cells from kidney allograft recipients offers a noninvasive means of diagnosing acute pathologies in the human kidney allograft.

Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records

ABSTRACTBackgroundDrug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects.MethodsUsing a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect.ResultsWe identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients.ConclusionsTogether, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.