Estimating the Effects of Public Health Measures by SEIR(MH) Model of COVID-19 Epidemic in Local Geographic Areas

The COVID-19 pandemic of 2020–21 has been a major challenge to public health systems worldwide. Mathematical models of epidemic are useful tools for assessment of the situation and for providing decision-making support for relevant authorities. We developed and implemented SEIR(MH) model that extends the conventional SEIR model with parameters that define public lockdown (the level and start of lockdown) and the medical system capacity to contain patients. Comparative modeling of four regions in Europe that have similar population sizes and age structures, but different public health systems, was performed: Baden-Württemberg, Lombardy, Belgium, and Switzerland. Modeling suggests that the most effective measure for controlling epidemic is early lockdown (exponential effect), followed by the number of available hospital beds (linear effect if the capacity is insufficient, with diminishing returns when the capacity is sufficient). Dynamic management of lockdown levels is likely to produce better outcomes than strict lockdown.

Modeling of relative shear deformation zones before strong earthquakes in Kamchatka from 2018-2021

Представлено сравнительное моделирование зон относительных сдвиговых деформаций для четырех камчатских землетрясений с Mw ≥ 4.8, произошедших в период с декабря 2018 г. по март 2021 г., основанное на статической модели деформационного поля в рамках теории упругости. Земная кора рассмотрена как однородное изотропное упругое полупространство, в котором присутствуют различные источники напряжения, описывающие очаг землетрясения: точечный источник в виде единичной силы, точечный источник в виде комбинации девяти двойных сил, распределенный источник в виде прямоугольной площадки. We present a comparative modeling of the zones of relative shear deformation for four Kamchatka earthquakes Mw≥4.8 that occurred between December 2018 and March 2021. Modeling based on a static model of the deformation field in the framework of the theory of elasticity. The Earth’s crust is considered as a homogeneous isotropic elastic half-space, in which there are different sources of stress describing the source of the earthquake: a point source in the form of a single force, a point source in the form of a combination of nine double forces, a distributed source in the form of a rectangular area.

A Comparative Modeling Study of Thermal Mitigation Strategies in Irreversible Electroporation Treatments

Irreversible electroporation (IRE), otherwise known as non-thermal pulsed field ablation (PFA), is an attractive focal ablation modality due to its ability to destroy aberrant cells with limited disruption of extracellular tissue architecture. Despite its non-thermal cell death mechanism, application of electrical energy results in Joule heating that, if ignored, can cause undesired thermal injury. Engineered thermal mitigation (TM) technologies including phase change materials (PCMs) and active cooling (AC) have been reported and tested in isolated preliminary studies to limit the risk of thermal damage, but their performance compared to one another is relatively unknown. Further, the effects of pulsing paradigm, electrode geometry, PCM composition, and chosen active cooling parameters have not been examined. Here, we develop a computational model of conventional bipolar and monopolar probes with solid, PCM-filled, or actively cooled cores and simulate clinical IRE treatments in pancreatic tissue. We find that probes with integrated PCM cores can be tuned to drastically limit thermal damage compared to traditional solid probes. Actively cooled probes, on the other hand, provide even more control over thermal effects within the probe vicinity and can altogether eliminate thermal damage. In practice, these differences in performance are tempered by the increased time, expense, and effort necessary to use actively cooled probes compared to traditional solid probes or those containing a PCM core.

Structural comparative modeling of multi-domain ΔF508 CFTR

Cystic Fibrosis (CF) is a common genetic disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs - which increases the risk of infection and eventually causes death. CFTR is an ATP binding cassette (ABC) transporter composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 (ΔF508), making ΔF508 CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain ΔF508 CFTR structure has been determined and few studies have modeled ΔF508 using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a ΔF508 model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and ΔF508 CFTR structural models. These models demonstrate the destabilizing effects of ΔF508 on NBD1 and the NBD1/TMD interface in both the closed and open conformation of CFTR. Furthermore, we modeled ΔF508/R1070W and ΔF508 bound to a the CFTR corrector VX-809. Our models reveal the stabilizing effects of R1070W and VX-809 on multi-domain models of ΔF508 CFTR and pave the way for rational design of additional drugs that target ΔF508 CFTR for treatment of CF.

International collaboration in disposal research: comparative modeling of coupled processes in the DECOVALEX project

This presentation gives an overview of an international research collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. The creation of the international DECOVALEX project, now running for more than 25 years, was motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel. DECOVALEX emphasizes joint analysis and comparative modeling of state-of-the-art field and laboratory experiments, across a range of host rock options and repository designs. Participating research teams are from radioactive waste management organizations, national research institutes, regulatory agencies, universities, and consulting groups, providing a wide range of perspectives and solutions to these complex problems. The presentation provides examples of the research contributions made collectively in past DECOVALEX tasks and also touches on the unique modeling challenges tackled in the ongoing project phase, referred to as DECOVALEX-2023. The current phase comprises 17 partner organizations, about 50 modeling teams, and 7 modeling tasks, which cover a broad portfolio from fundamental studies on gas migration to full-scale in situ heater experiments in different host rocks to performance assessment studies. Together, these examples illustrate that the insight and scientific knowledge gained within the DECOVALEX project would not have been possible if one group had studied these complex THMC modeling challenges alone rather than within a truly collaborative setting.

Comparative Modeling And Enzymatic Affinity of Novel Haloacid Dehalogenase From Bacillus Megaterium Strain BHS1 Isolated From Alkaline Blue Lake In Turkey

This is the first structural model of L-haloacid dehalogenase (DehLBHS1) isolated from alkalotolerant Bacillus megaterium BHS1, which has been known to degrading halogenated environmental contaminants. The study suggested five important key amino acid residues of DehLBHS1, namely Arg40, Phe59, Asn118, Asn176 and Trp178 important for catalysis and molecular recognition of haloalkanoic acid. Alkatolerant DehLBHS1was modeled by I-TASSER with the best C-score 1.23. Model validation was carried out utilising PROCHECK to produce the Ramachandran map with 89.2 percent of its residues were found in the most preferred region, indicating that the model was appropriate. The Molecular docking (MD) simulation found that the DehLBHS1 preferred 2,2DCP more than other substrates and formed one hydrogen bond with Arg40 and minimum energy -2.5 kJ/ mol. Molecular dynamics has verified the substrate preference towards 2,2DCP based on RMSD, RMSF, Gyration, Hydrogen bond and Molecular distance. This structural knowledge from DehLBHS1 structural perspective gives insights into substrate specificity and catalytic function to exploit DehLBHS1 of BHS1 strain in degrading 2,2-DCP in the polluted alkaline environments.

Characterization of Inducible HSP70 Genes in an Antarctic Yeast, Glaciozyma antarctica PI12, in Response to Thermal Stress

The induction of highly conserved heat shock protein 70 (HSP70) is often related to a cellular response due to harmful stress or adverse life conditions. In this study, we determined the expression of Hsp70 genes in the Antarctic yeast, Glaciozyma antarctica, under different several thermal treatments for several exposure periods. The main aims of the present study were (1) to determine if stress-induced Hsp70 could be used to monitor the exposure of the yeast species G. antarctica to various types of thermal stress; (2) to analyze the structures of the G. antarctica HSP70 proteins using comparative modeling; and (3) to evaluate the relationship between the function and structure of HSP70 in G. antarctica. In this study, we managed to amplify and clone 2 Hsp70 genes from G. antarctica named GaHsp70-1 and GaHsp70-2. The cells of G. antarctica expressed significantly inducible Hsp70 genes after the heat and cold shock treatments. Interestingly, GaHsp70-1 showed 2–6-fold higher expression than GaHsp70-2 after the heat and cold exposure. ATP hydrolysis analysis on both G. antarctica HSP70s proved that these psychrophilic chaperones can perform activities in a wide range of temperatures, such as at 37, 25, 15, and 4 °C. The 3D structures of both HSP70s revealed several interesting findings, such as the substitution of a β-sheet to loop in the N-terminal ATPase binding domain and some modest residue substitutions, which gave the proteins the flexibility to function at low temperatures and retain their functional activity at ambient temperatures. In conclusion, both analyzed HSP70s played important roles in the physiological adaptation of G. antarctica.

ScanNet: An interpretable geometric deep learning model for structure-based protein binding site prediction

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies sheds light on its function in vivo. Currently, two classes of methods prevail: Machine Learning (ML) models built on top of handcrafted features and comparative modeling. They are respectively limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy - including for unseen protein folds - and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful, and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/