Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

The effects of channel width on particle sedimentation in fluids using a coupled lattice Boltzmann-discrete element model

Understanding particle settlement in channeled fluids has wide applications, such as fine particulate matter, coronavirus particle transport, and the migration of solid particles in water. Various factors have been investigated but few studies have acknowledged the channel's effect on settlement dynamics. This study developed a coupled interpolated bounce-back lattice Boltzmann-discrete element model and examined how a channel's width affects particle settlement. A factor k denoting the ratio of the channel's width and the particle diameter was defined. The terminal settling velocity for a single particle is inversely proportional to k, and the time that the particle takes to reach the terminal velocity is positively related to k. When k is greater than 15, the channel width's effects are negligible. For dual particles of the same size, the drafting-kissing-tumbling (DKT) process occurs infinitely in a periodic pattern, with the two particles swapping positions and settling around the channel's centerline. The smaller the k, the sooner the DKT process occurs. The particles collide with the channel wall when k <= 10. For dual particles of different sizes, the DKT process occurs once so that the bigger particle leads the settlement. Both particles settle along the channel's centerline in a steady state. The bigger the k, the bigger the difference in their terminal settling velocities until k = 15. The small particle collides with the channel wall if released under the big particle when k = 6. The findings of this study are expected to inform channeling or pipeline design in relevant engineering practices.

EVALUATING THE ETHICAL AND SOCIETAL IMPACTS OF MODELLING PANDEMIC CRISES: EXPERIENCES FROM A WORKSHOP

This paper describes a hybrid (virtual and online) workshop held as part of the EU STAMINA project that aimed to engage project partners to explore ethics and simulation modelling in the context of pandemic preparedness and response. The purpose of the workshop was to consider how the model's design and use in specific pandemic decision-making contexts could have broader implications for issues like transparency, explainability, representativeness, bias, trust, equality, and social injustices. Its outputs will be used as evidence to produce a series of measures that could help mitigate ethical harms and support the greater possible benefit from the use of the models. These include recommendations for policy, data-gathering, training, potential protocols to support end-user engagement, as well as guidelines for designing and using simulation models for pandemic decision-making. This paper presents the methodological approaches taken when designing the workshop, practical concerns raised, initial insights gained, and considers future steps. © SW 2023.All rights reserved

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE

Learning by Designing, Imagination and Programming

The FitPed Project focuses on students' efforts to acquire programming skills in order to become up-to-date professionals and become better life-long learners as well. The current chapter sketches the larger spectrum of learning/teaching paradigms in order to enable more flexible and effective didactic planning in diverse academic curricula. ‘Active Learning' has been coined as one of the best striving to let students regain ‘ownership' of their studying and cognitive development. Simulations, programming, gaming and storytelling are promising candidates for empowering the learning and increasing intrinsic motivation. The chapter will synthesize the various aspects of active learning like: Collaborative, Constructive, Authentic, Situational and Intentional Learning, in order to enable teachers to integrate these instructional ingredients for blended learning even after the Covid-19 era. Learning paradigms have shifted from cognitive acquisition into constructivist approaches, where the learner is encouraged to build more complex concepts from elementary primitives. In this evolution, programming experiences have an important generic role: Students from all major directions need to integrate their thinking in topics like: Algorithmic Thinking, Data Mining, Meta Data, Machine Learning, Deep Learning, Deep Fake, Analytics for Smart Environments, Privacy Issues, etc. For this goal, a basic programming education and experience is useful and necessary. This chapter will highlight how university curricula need to evolve and new teacher roles will develop as well. It will illustrate the transition from the current FitPed Project to its successor. Important additional notion is that the integration of Computer Science and Programming Courses need innovative didactic scenarios as well;Problem-based Learning and Challenge-based Learning are two of the most prominent candidates. After having read this chapter, you will be motivated and equipped to pro-actively design new ICT-oriented courses with your colleagues. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Modeling the epidemic trend of middle eastern respiratory syndrome coronavirus with optimal control

Since the outbreak of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012 in the Middle East, we have proposed a deterministic theoretical model to understand its transmission between individuals and MERS-CoV reservoirs such as camels. We aim to calculate the basic reproduction number (R0) of the model to examine its airborne transmission. By applying stability theory, we can analyze and visualize the local and global features of the model to determine its stability. We also study the sensitivity of R0 to determine the impact of each parameter on the transmission of the disease. Our model is designed with optimal control in mind to minimize the number of infected individuals while keeping intervention costs low. The model includes time -dependent control variables such as supportive care, the use of surgical masks, government campaigns promoting the importance of masks, and treatment. To support our analytical work, we present numerical simulation results for the proposed model.

The impact of vaccination on the dynamics of the Omicron variant of SARS-CoV-2

In the context of vaccination, we develop a novel mathematical model to examine the Omicron type of coronavirus illness. The system's mathematical analysis based on its equilibrium points shall be obtained. The threshold quantity is used to investigate the system's local and global asymptotical analysis. The Omicron vaccination model shown to be stable locally asymptotically if R 0 v < 1 . The system is globally asymptotically stable at the disease-free equilibrium for a special case when η = 1 if R 0 v < 1 . We estimate the model parameters based on the observed data and show that the threshold is R 0 ≈ 2.4894 in the absence of vaccination. The model has the phenomenon of backward bifurcation under certain conditions. Herd immunity analysis is obtained and it turns out that the herd immunity threshold for the South African population is 74%. The impact of vaccination on disease dynamics is also shown and discussed. Further, we have given some graphical results showing the community's disease reduction. [ FROM AUTHOR] Copyright of Waves in Random & Complex Media is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

In silico screening, ADMET analysis and MD simulations of phytochemicals of Onosma bracteata Wall. as SARS CoV-2 inhibitors.

Being attracted with their cardiotonic, antidiabetic, cough relieving activity, treatment of fever, absorbent, anti-asthmatic, etc. activities reported in ancient Ayurvedic literature, phytochemicals of Onosma bracteata wall should be evaluated for their activity against SARS-CoV-2 virus. The main objective of this study is to identify a hit molecule for the inhibition of entry, replication, and protein synthesis of SARS CoV-2 virus into the host. To achieve given objective, computational virtual screening of phytochemicals of Onosma bracteata wall has been performed against three main viral targets: spike, RdRp, and Mpro. Further, the analysis of Lipinski's Ro5 and their estimation of ADMET profiles were performed using computational tools. The MD simulations studies of top hits against each viral target have also been performed for 20 ns to ensure their stability. The analysis of results revealed that Pulmonarioside C (9) and other plant compounds showed better binding affinity towards targets than existing antiviral compounds, making them probable lead compounds against SARS-CoV-2. Structural modifications and studies through in silico analysis provided the founding stone for the establishment of SARS CoV-2 inhibitory potential of phytoconstitutents of Onosma bracteata wall.

Structural basis for the mechanism of interaction of SARS-CoV-2 B.1.640.2 variant RBD with the host receptors hACE2 and GRP78.

The inflicted chaos instigated by the SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) globally continues with the emergence of novel variants. The current global outbreak is aggravated by the manifestation of novel variants, which affect the effectiveness of the vaccine, attachment with hACE2 (human Angiotensin-converting enzyme 2) and immune evasion. Recently, a new variant named University Hospital Institute (IHU) (B.1.640.2) was reported in France in November 2021 and is spreading globally affecting public healthcare. The B.1.640.2 SARS-CoV-2 strain revealed 14 mutations and 9 deletions in spike protein. Thus, it is important to understand how these variations in the spike protein impact the communication with the host. A protein coupling approach along with molecular simulation protocols was used to interpret the variation in the binding of the wild type (WT) and B.1.640.2 variant with hACE2 and Glucose-regulating protein 78 (GRP78) receptors. The initial docking scores revealed a stronger binding of the B.1.640.2-RBD with both the hACE2 and GRP78. To further understand the crucial dynamic changes, we looked at the structural and dynamic characteristics and also explored the variations in the bonding networks between the WT and B.1.640.2-RBD (receptor-binding domain) in association with hACE2 and GRP78, respectively. Our findings revealed that the variant complex demonstrated distinct dynamic properties in contrast to the wild type due to the acquired mutations. Finally, to provide conclusive evidence on the higher binding by the B.1.640.2 variant the TBE was computed for each complex. For the WT with hACE2 the TBE was quantified to be-61.38 ± 0.96 kcal/mol and for B.1.640.2 variant the TBE was estimated to be -70.47 ± 1.00 kcal/mol. For the WT-RBD-GRP78 the TBE -was computed to be 32.32 ± 0.56 kcal/mol and for the B.1.640.2-RBD a TBE of -50.39 ± 0.88 kcal/mol was reported. This show that these mutations are the basis for higher binding and infectivity produced by B.1.640.2 variant and can be targeted for drug designing against it.Communicated by Ramaswamy H. Sarma.

DFT and MD investigations of the biomolecules of phenothiazine derivatives: interactions with gold and water molecules and investigations in search of effective drug for SARS-CoV-2.

Theoretical analyses of two phenothiazine derivatives, 10-[3-(dimethylamino)-2-methylpropyl]phenothiazine-2-carbonitrile (CYM) and 2-[4-[3-(2-chlorophenothiazin-10-yl)propyl]piperazin-1-yl]ethanol (PAZ) are reported using density functional theory (DFT) and molecular dynamics (MD) simulations. Spectroscopic studies, different electronic and chemical parameters are predicted. Red and yellow in electrostatic potential plot is in rings and oxygen atom in PAZ and C≡N and rings in CYM are sensitive to nucleophilic attacks. The blue in hydrogen atoms refer to electrophilic attack in both PAZ and CYM. Stability of the protein-ligand complex formed with these derivatives and angiotensin-converting enzyme 2 (ACE2) was investigated using MD simulation. Radius of gyration of C-alpha atom of 6VW1 displayed the conformational convergence toward a compact structure leading to stable 6VW1-ligand complex which are also in agreement with root mean square fluctuation (RMSF) values. Localized area predicts reactive sites for Au and H2O molecules interaction with these compounds for further practical applications. Charge density is localized on both molecules and also tries to move toward Au-Au dimer and water molecule and such they are expected to contribute to the sensing performance. Communicated by Ramaswamy H. Sarma.

Computational and experimental validation of phthalocyanine and hypericin as effective SARS-CoV-2 fusion inhibitors.

Phthalocyanine and hypericin have been previously identified as possible SARS-CoV-2 Spike glycoprotein fusion inhibitors through a virtual screening procedure. In this paper, atomistic simulations of metal-free phthalocyanines and atomistic and coarse-grained simulations of hypericins, placed around a complete model of the Spike embedded in a viral membrane, allowed to further explore their multi-target inhibitory potential, uncovering their binding to key protein functional regions and their propensity to insert in the membrane. Following computational results, pre-treatment of a pseudovirus expressing the SARS-CoV-2 Spike protein with low compounds concentrations resulted in a strong inhibition of its entry into cells, suggesting the activity of these molecules should involve the direct targeting of the viral envelope surface. The combination of computational and in vitro results hence supports the role of hypericin and phthalocyanine as promising SARS-CoV-2 entry inhibitors, further endorsed by literature reporting the efficacy of these compounds in inhibiting SARS-CoV-2 activity and in treating hospitalized COVID-19 patients.Communicated by Ramaswamy H. Sarma.

Editorial Perspective: When is a 'small effect' actually large and impactful?

Reporting of effect sizes is standard practice in psychology and psychiatry research. However, interpretation of these effect sizes can be meaningless or misleading - in particular, the evaluation of specific effect sizes as 'small', 'medium' and 'large' can be inaccurate depending on the research context. A real-world example of this is research into the mental health of children and young people during the COVID-19 pandemic. Evidence suggests that clinicians and services are struggling with increased demand, yet population studies looking at the difference in mental health before and during the pandemic report effect sizes that are deemed 'small'. In this short review, we utilise simulations to demonstrate that a relatively small shift in mean scores on mental health measures can indicate a large shift in the number of cases of anxiety and depression when scaled up to an entire population. This shows that 'small' effect sizes can in some contexts be large and impactful.

Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach

Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R-2 value of more than 72% average in each year and decreased MAE value, which was better than other studies' deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM2.5 by 14%, PM10 by 18%, NO2 by 14%, and SO2 by 16% during the COVID-B period. This study found an increase in O-3 by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM10 by 42%, PM2.5 by 97%, NO2 by 89%, SO2 by 36%, CO by 58%, O-3 by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.

N'-(3,4-dimethoxybenzylidene)-4-methylbenzenesulfonohydrazide derivatives: Synthesis, Quantum Chemical Method, in silico ADMET, Molecular docking and Molecular dynamic simulations

A new N'-(3,4-dimethoxybenzylidene)-4-methylbenzenesulfonohydrazide derivatives were prepared from a condensation reaction between 4-methylbenzenesulfonohydrazide and 3,4-dimethoxybenzaldehyde. The structure of DMSH was elucidated using various spectral techniques including FT-IR, 1H-NMR and 13C-NMR. The structure of DMSH bond parameters also confirmed by single crystal XRD analysis of related derivatives and optimized bond parameters are calculated by density functional theory (DFT) method at B3LYP/6-311G (d, p) level of theory. The optimized geometrical parameters obtained by DFT calculation are in good agreement with single crystal XRD data. The experimentally observed FT-IR bands were assigned to different normal modes of the molecule. The results show a good agreement with each other when these computed bond parameters are compared to XRD values of related compounds. The stability, chemical reactivity and charge transfer within the molecule was explained by frontier molecular orbital calculations. Atomic charges on the various atoms of DMSH obtained by Mulliken population analysis. Potential reactive sites of the DMSH compound have been identified by MEP which is mapped to the electron density surfaces. The reported molecule is used as a potential NLO material since it has a high μβ0 value. The theoretical UV-vis spectrum of the compound is used to study the visible absorption maxima (λ max). The molecular docking mechanism between DMSH ligand and COVID-19/6WCF and COVID-19/6Y84 receptors were studied to investigate the binding modes of this compound at the active sites. Molecular docking outcomes have shown that the DMSH molecule can be considered as a potential agent against COVID-19/6WCF-6Y84 receptors. In addition, the theoretical parameters of the bioactive molecules were calculated to establish their drug-likeness qualities and ADME/T analysis was carried out to examine the drug properties of the synthesized compound. Molecular dynamics simulation was performed for COVID-19 main protease (Mpro: 6WCF/6Y84) to understand the elements governing the inhibitory effect and the stability of interaction under dynamic conditions. The resultant complex structures were subjected to 100 ns simulation run to estimate their binding stabilities using GROMACS. The molecular dynamics simulation studies provided essential evidence that the systems were stable during the progression of 100 ns simulation run.

A multiconsistent computational methodology to resolve a diffusive epidemiological system with effects of migration, vaccination and quarantine.

BACKGROUND: We provide a compartmental model for the transmission of some contagious illnesses in a population. The model is based on partial differential equations, and takes into account seven sub-populations which are, concretely, susceptible, exposed, infected (asymptomatic or symptomatic), quarantined, recovered and vaccinated individuals along with migration. The goal is to propose and analyze an efficient computer method which resembles the dynamical properties of the epidemiological model. MATERIALS AND METHODS: A non-local approach is utilized for finding approximate solutions for the mathematical model. To that end, a non-standard finite-difference technique is introduced. The finite-difference scheme is a linearly implicit model which may be rewritten using a suitable matrix. Under suitable circumstances, the matrices representing the methodology are M-matrices. RESULTS: Analytically, the local asymptotic stability of the constant solutions is investigated and the next generation matrix technique is employed to calculate the reproduction number. Computationally, the dynamical consistency of the method and the numerical efficiency are investigated rigorously. The method is thoroughly examined for its convergence, stability, and consistency. CONCLUSIONS: The theoretical analysis of the method shows that it is able to maintain the positivity of its solutions and identify equilibria. The method's local asymptotic stability properties are similar to those of the continuous system. The analysis concludes that the numerical model is convergent, stable and consistent, with linear order of convergence in the temporal domain and quadratic order of convergence in the spatial variables. A computer implementation is used to confirm the mathematical properties, and it confirms the ability in our scheme to preserve positivity, and identify equilibrium solutions and their local asymptotic stability.

Under what circumstances would airborne transmission of SARS-CoV-2 occur?

To find out the circumstances under which airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) would happen, we conducted mechanistic and systematic modelling of two Coronavirus disease 2019 (COVID-19) outbreaks, i.e., Hunan 2-bus outbreak and Luk Chuen House outbreak (the horizontal cluster). Computational fluid dynamics (CFD) simulations, multi-zone airflow modelling, multi-route mechanistic modelling, and dose-response estimation were carried out selectively according to the transmission characteristics in each outbreak. Our results revealed that poorly ventilated bus indoor environments bred the Hunan 2-bus outbreak in which airborne transmission predominates;prevailing easterly background wind and probable door opening behaviour led to the secondary infections across the corridor in Luk Chuen House outbreak. Measures to facilitate sufficient ventilation indoors and positive pressure in the housing building corridor may help minimise infection risk. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Calculation of Electrostatic Potential Field of Coronavirus S Proteins for Brownian Dynamics Simulations

The Brownian dynamics method can give insight into the initial stages of the interaction of antiviral drug molecules with the structural components of bacteria or viruses. RAM of conventional personal computer allows calculation of Brownian dynamics of interaction of antiviral drugs with individual coronavirus S protein. However, scaling up this approach for modeling the interaction of antiviral drugs with the whole virion consisting of thousands of proteins and lipids is difficult due to high requirements for computing resources. In the case of the Brownian dynamics method, the main amount of RAM in the calculations is occupied by an array of values of the virion electrostatic potential field. When the system is increased from one S protein to the whole virion, the volume of data increases significantly. The standard protocol for calculating Brownian dynamics uses a three-dimensional grid with a spatial step of 1°A to calculate the electrostatic potential field. In this work, we consider the possibility of increasing the grid spacing parameter for calculating the electrostatic potential field of individual coronavirus S proteins. In this case, the amount of RAM occupied by the electrostatic potential field is reduced, which makes it possible to use personal computers for calculations. We performed Brownian dynamics simulations of interaction of an antiviral photosensitizer molecule with S proteins of three coronaviruses SARS-CoV, MERS-CoV, and SARS-CoV-2, and demonstrated that reduction of detalization of electrostatic potential field does not influence the results of Brownian dynamics much © The Authors 2022. This paper is published with open access at SuperFri.org

Computer-aided comprehensive explorations of RNA structural polymorphism through complementary simulation methods

While RNA folding was originally seen as a simple problem to solve, it has been shown that the promiscuous interactions of the nucleobases result in structural polymorphism, with several competing structures generally observed for non-coding RNA. This inherent complexity limits our understanding of these molecules from experiments alone, and computational methods are commonly used to study RNA. Here, we discuss three advanced sampling schemes, namely Hamiltonian-replica exchange molecular dynamics (MD), ratchet-and-pawl MD and discrete path sampling, as well as the HiRE-RNA coarse-graining scheme, and highlight how these approaches are complementary with reference to recent case studies. While all computational methods have their shortcomings, the plurality of simulation methods leads to a better understanding of experimental findings and can inform and guide experimental work on RNA polymorphism.Copyright ©

The Impact of Vaccination on COVID-19 Pandemic in the Kurdistan Region of Iraq: A Mathematical Modelling

The spreading of COVID-19 became a global issue that had a significant impact on health, life, and economic sectors. Efforts from all over the world are focused on discussing a variety of healthcare approaches to reduce the effect of COVID-19 among individuals. Mathematical tools with numerical simulations are important approaches that help international efforts to determine critical transmission factors as well as controlling the virus spread. In this paper, we develop a mathematical model that considers a vaccination compartment in terms of ordinary differential equations. This study focuses on the real data of confirmed cases in Kurdistan Region of Iraq from July 17th, 2021 to January 1st, 2022. Model results and real data for the total number of infected people were compared using computational tools in MATLAB. Additionally, non-normalization, half-normalization, and full-normalization methods are used to determine the local sensitivities between model variables and parameters. Interestingly, computational results show that the dynamics of model results and real confirmed cases are very close to each other. Accordingly, the elasticity coefficients provide a great understanding of the impact of vaccination on transmissions. The model results here can also help international efforts for further suggestions and improvements to control this disease more effectively. © Palestine Polytechnic University-PPU 2023.

PACAP key interactions with PAC1, VPAC1, and VPAC2 identified by molecular dynamics simulations.

The neuropeptide pituitary adenylate cyclase-activating polypeptide (PACAP) belongs to the glucagon/secretin family. PACAP interacts with the pituitary adenylate cyclase-activating polypeptide receptor type 1 (PAC1) and vasoactive intestinal peptide receptors 1 and 2 (VPAC1 and VPAC2), exhibiting functions in the immune, endocrine, and nervous systems. This peptide is upregulated in numerous instances of brain injury, acting as a neuroprotective agent. It can also suppress HIV-1 and SARS-CoV-2 viral replication in vitro. This work aimed to identify, in each peptide-receptor system, the most relevant residues for complex stability and interaction energy communication via Molecular Dynamics (MD), Free Energy calculations, and Protein-energy networks, thus revealing in detail the underlying mechanisms of activation of these receptors. Hydrogen bond formation, interaction energies, and computational alanine scanning between PACAP and its receptors showed that His1, Asp3, Arg12, Arg14, and Lys15 are crucial to the peptide's stability. Furthermore, several PACAP interactions with structurally conserved positions deemed necessary in GPCR B1 activation, including Arg2.60, Lys2.67, and Glu7.42, were significant for the peptide's stability within the receptors. According to the protein-energy network, the connection between Asp3 of PACAP and the receptors' conserved Arg2.60 represents a critical energy communication hub in all complexes. Additionally, the ECDs of the receptors were also found to function as energy communication hubs for PACAP. Although the overall binding mode of PACAP in the three receptors was found to be highly conserved, Arg12 and Tyr13 of PACAP were more prominent in complex with PAC1, while Ser2 of PACAP was with VPAC2. The detailed analyses performed in this work pave the way for using PACAP and its receptors as therapeutic targets.Communicated by Ramaswamy H. Sarma.