Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics.

Markov state models (MSMs) play a key role in studying protein conformational dynamics. A sliding count window with a fixed lag time is widely used to sample sub-trajectories for transition counting and MSM construction. However, sub-trajectories sampled with a fixed lag time may not perform well under different selections of lag time, which requires strong prior practice and leads to less robust estimation. To alleviate it, we propose a novel stochastic method from a Poisson process to generate perturbative lag time for sub-trajectory sampling and utilize it to construct a Markov chain. Comprehensive evaluations on the double-well system, WW domain, BPTI, and RBD-ACE2 complex of SARS-CoV-2 reveal that our algorithm significantly increases the robustness and power of a constructed MSM without disturbing the Markovian properties. Furthermore, the superiority of our algorithm is amplified for slow dynamic modes in complex biological processes.

Improved drug-target interaction prediction with intermolecular graph transformer.

The identification of active binding drugs for target proteins (referred to as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.

Investigation and influencing factors about well-being level of elderly chronic patients during COVID-19 postpandemic period in Beijing.

ABSTRACT: The Corona Virus Disease 2019 (COVID-19) pandemic has huge impacts on the world, including human health and economic decline. The COVID-19 has severe infectivity, especially the elderly with chronic diseases will cause various complications after infection and accelerate the disease process. In addition, COVID-19 will also affect their mental health. Therefore, the mental health of elderly patients with chronic diseases cannot be ignored. The aim of this study was to investigate the well-being level of elderly people with chronic disease during COVID-19 postpandemic period in Beijing and analysis related influencing factors, so as to provide a basis for improving the well-being level of elderly chronic patients during the postpandemic period.Elderly patients with chronic diseases who met the inclusion criteria in 5 different administrative regions in Beijing were selected to carry out a questionnaire survey. The contents of the questionnaire included general data, the Memorial University of Newfoundland Happiness scale and the awareness situation of the COVID-19 pandemic. A total of 500 questionnaires were distributed by WeChat and 486 valid questionnaires were collected. The t test and one-way analysis of variance were used to compare Memorial University of Newfoundland Happiness scores between 2 or more groups, multiple linear regression analysis was used to conduct multiple factor analysis to explore the related factors about well-being level of elderly chronic patients.A total of 109 cases (22.43%) were evaluated high well-being level, 319 cases (65.64%) were evaluated moderate well-being level and 58 cases (11.93%) were evaluated low well-being according to the Memorial University of Newfoundland Happiness (MUNSH) scores rating. The multiple linear regression indicated that the education level, number of chronic diseases, medical expenses, frequency of children's visits, taking care of grandchildren or not, and group activity frequency significantly affected the well-being of patients with chronic diseases during COVID-19 postpandemic period in Beijing (P < .05).Most elderly patients with chronic diseases had moderate or above sense of well-being during postpandemic period, but we should still pay attention to the mental health of those elderly chronic patients with low education level, much comorbidity, more medical expenses, less visits by children, not take care of grandchildren and never participate in group activities.

Exploring the Regulatory Function of the N‐terminal Domain of SARS‐CoV‐2 Spike Protein through Molecular Dynamics Simulation

SARS‐CoV‐2 is what has caused the COVID‐19 pandemic. Early viral infection is mediated by the SARS‐CoV‐2 homo‐trimeric Spike (S) protein with its receptor binding domains (RBDs) in the receptor‐accessible state. Molecular dynamics simulation on the S protein with a focus on the function of its N‐terminal domains (NTDs) is performed. The study reveals that the NTD acts as a “wedge” and plays a crucial regulatory role in the conformational changes of the S protein. The complete RBD structural transition is allowed only when the neighboring NTD that typically prohibits the RBD's movements as a wedge detaches and swings away. Based on this NTD “wedge” model, it is proposed that the NTD–RBD interface should be a potential drug target. The Spike protein of SARS‐CoV‐2 plays a key role in the infection process. The N‐terminal domain (NTD) of the Spike protein plays a regulatory function by the “wedge” model: it typically wedges in to prohibit receptor binding domain's (RBD's) movements and occasionally moves out to allow RBD to tilt downward. Potential drugs are virtually screened for the NTD‐RBD interface.

Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein through Molecular Dynamics Simulation (Adv. Theory Simul. 10/2021)

N-terminal Domain of SARS-CoV-2 Spike Protein In article number 2100152, Yao Li, Tong Wang, Haipeng Gong, and co-workers propose the ?wedge? model to demonstrate the regulatory function of the N-terminal domain (NTD) of SARS-CoV-2 Spike protein. The NTD typically wedges in to prohibit receptor binding domain's (RBD's) movements and it occasionally moves out to allow RBD to tilt downward.

Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features.

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia. Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts. Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively. Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.