Computational Methods to Target Protein-Protein Interactions.

The chapter emphasizes the importance of understanding protein-protein interactions in cellular mechanisms and highlights the role of computational modeling in predicting these interactions. It discusses sequence-based approaches such as evolutionary trace (ET), correlated mutation analysis (CMA), and subtractive correlated mutation (SCM) for identifying crucial amino acid residues, considering interface conservation or evolutionary changes. The chapter also explores methods like differential ET, hidden-site class model, and spatial cluster detection (SCD) for interface specificity and spatial clustering. Furthermore, it examines approaches combining structural and sequential methodologies and evaluates modeled predictions through initiatives like critical assessment of prediction of interactions (CAPRI). Additionally, the chapter provides an overview of various software programs used for molecular docking, detailing their search, sampling, refinement and scoring stages, along with innovative techniques and tools like normal mode analysis (NMA) and adaptive Poisson-Boltzmann solver (APBS) for electrostatic calculations. These computational and experimental approaches are crucial for unraveling protein-protein interactions and aid in developing potential therapeutics for various diseases.

Caspases in Alzheimer's Disease: Mechanism of Activation, Role, and Potential Treatment.

With the aging of the population, treatment of conditions emerging in old age, such as neurodegenerative disorders, has become a major medical challenge. Of these, Alzheimer's disease, leading to cognitive dysfunction, is of particular interest. Neuronal loss plays an important role in the pathophysiology of this condition, and over the years, a great effort has been made to determine the role of various factors in this process. Unfortunately, until now, the exact pathomechanism of this condition remains unknown. However, the most popular theories associate AD with abnormalities in the Tau and ß-amyloid (Aß) proteins, which lead to their deposition and result in neuronal death. Neurons, like all cells, die in a variety of ways, among which pyroptosis, apoptosis, and necroptosis are associated with the activation of various caspases. It is worth mentioning that Tau and Aß proteins are considered to be one of the caspase activators, leading to cell death. Moreover, the protease activity of caspases influences both of the previously mentioned proteins, Tau and Aß, converting them into more toxic derivatives. Due to the variety of ways caspases impact the development of AD, drugs targeting caspases could potentially be useful in the treatment of this condition. Therefore, there is a constant need to search for novel caspase inhibitors and evaluate them in preclinical and clinical trials.

Methods of Lysergic Acid Synthesis-The Key Ergot Alkaloid.

Ergot is the spore form of the fungus Claviceps purpurea. Ergot alkaloids are indole compounds that are biosynthetically derived from L-tryptophan and represent the largest group of fungal nitrogen metabolites found in nature. The common part of ergot alkaloids is lysergic acid. This review shows the importance of lysergic acid as a representative of ergot alkaloids. The subject of ergot and its alkaloids is presented, with a particular focus on lysergic acid. All methods of total lysergic acid synthesis-through Woodward, Hendrickson, and Szantay intermediates and Heck coupling methods-are presented. The topic of biosynthesis is also discussed.

Covasim: An agent-based model of COVID-19 dynamics and interventions.

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

Controlling COVID-19 via test-trace-quarantine.

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.