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Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein–ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery. [ABSTRACT FROM AUTHOR]

Introduction: Efficient epidemiological monitoring of virus diseases is crucial in evaluating general public health, the prevalence of specific diseases, the pattern of spread, and implementing preventative and control strategies into action. Methods: This study analyzed data obtained from the Field Epidemiology Program (FETP) which is part of the Ministry of Health (MOH) in Saudi Arabia, which contained reported cases of infectious diseases over four years, from January 2018 to December 2021, to investigate and highlight the significant trend and incidence rate for each viral infectious disease. Results: Of the reported viral infectious diseases, hepatitis B and C, dengue fever (DF), influenza, chickenpox, and measles were the highest reported viral cases over four years. For the aforementioned diseases, males were often more susceptible to viral infections than females. Except for DF, this viral infection was more common in Saudi citizens. Viral illnesses like hand, foot, and mouth disease were less prevalent, while neurological viral disorders such as acute flaccid paralysis were rarely detected. There was an overall reduction in viral cases recorded during 2020–2021, which may be attributed to the implementation of preventive measures during the Coronavirus Disease 2019 (COVID–19) pandemic or an underreporting of cases during the lockdown of that time. Conclusion: The prevalence of these common viral infections in the Saudi population suggests that understanding the mechanisms influencing changes in these viruses, methods of transmission, and the burden of these diseases is a priority for health policy. This understanding is necessary to develop effective intervention and preventive strategies. [ABSTRACT FROM AUTHOR]

Introduction: Bacterial infections have a significant impact on human health; they can cause severe morbidity and mortality, particularly in susceptible populations. Epidemiological surveillance is a critical tool for monitoring the population's health and facilitate the prevention and control of infectious disease outbreaks. Knowing the burden of bacterial communicable diseases is an initial core step toward public health goals. Methods: Saudi epidemiology surveillance data were utilized to depict the changing epidemiology of bacterial infectious diseases in Saudi Arabia from 2018 to 2021. The cumulative numbers of cases, demographics, and incidence rates were analyzed and visualized. Parametric tests were used to compare the difference in the mean values between categorical variables. Regression analysis was employed to estimate trends in disease rates over time. Statistical significance was set at p value ≤ 0.05. Results: The results revealed that brucellosis, tuberculosis, and salmonellosis were the most frequently reported bacterial infectious diseases in Saudi Arabia. Males were more significantly affected by brucellosis and tuberculosis infections than females. Salmonellosis infections were more significant among Saudi citizens, while pulmonary tuberculosis was more significant in non-Saudis. Interestingly, there was a decline in the incidence rates of numerous bacterial infectious diseases during the Coronavirus Disease 2019 (COVID-19) pandemic and COVID-19 restrictions. Some bacterial infectious diseases were rarely reported in Saudi Arabia, including syphilis and diphtheria. Conclusions: The future perspective of this research is to enhance disease surveillance reporting by including different variables, such as the source of infection, travel history, hospitalization, and mortality rates. The aim is to improve the sensitivity and specificity of surveillance data and focus on the mortality associated with bacterial pathogens to identify the most significant threats and set a public health priority. [ABSTRACT FROM AUTHOR]