Challenging Complexity with Simplicity: Rethinking the Role of Single-Step Models in Computer-Aided Synthesis Planning.

Computer-assisted synthesis planning has become increasingly important in drug discovery. While deep-learning models have shown remarkable progress in achieving high accuracies for single-step retrosynthetic predictions, their performances in retrosynthetic route planning need to be checked. This study compares the intricate single-step models with a straightforward template enumeration approach for retrosynthetic route planning on a real-world drug molecule data set. Despite the superior single-step accuracy of advanced models, the template enumeration method with a heuristic-based retrosynthesis knowledge score was found to surpass them in efficiency in searching the reaction space, achieving a higher or comparable solve rate within the same time frame. This counterintuitive result underscores the importance of efficiency and retrosynthesis knowledge in retrosynthesis route planning and suggests that future research should incorporate a simple template enumeration as a benchmark. It also suggests that this simple yet effective strategy should be considered alongside more complex models to better cater to the practical needs of computer-assisted synthesis planning in drug discovery.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking.

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.

Associations of Sedentary Behavior with Risks of Cardiovascular Disease Events among Chinese Adults.

AIMS: Evidence regarding the modification effects of age, sex, ethnicity, socioeconomic status, or weight status on the associations of sedentary behavior (SB) with cardiovascular diseases (CVDs) is limited. Moreover, the mechanisms for the associations also remain unclear. We aimed to investigate the possible influence of these factors on the associations of SB with CVD events and whether the associations are mediated by metabolic phenotypes. METHODS: This study included 42,619 participants aged 20-74 years, recruited from the Shanghai Suburban Adult Cohort and Biobank study. SB was assessed at baseline and integrated with health information systems to predict future CVD events. Cox proportional hazards models, interaction analyses, restricted cubic splines and causal mediation analyses were used for assessments. RESULTS: Compared to those with ＜3 h/d sedentary time, participants having SB ≥ 5 h/d had significantly higher risks of CVD (HR[95%CI]: 1.27[1.12-1.44]), coronary heart disease (CHD, 1.35[1.14-1.60]), and ischemic stroke (IS, 1.30[1.06-1.60]). The association of CHD was more pronounced in the retired individuals than their counterparts (1.45[1.20-1.76] versus 1.06[0.74-1.52], pinteraction=0.046). When SB was expressed as a continuous variable, a 1 h/d increment in SB was positively associated with risks of CVD (1.03[1.01-1.05]), CHD (1.04[1.01-1.07]), and IS (1.05[1.01-1.08]). High-density lipoprotein cholesterol (HDL-C, proportion mediated: 12.54%, 12.23%, and 11.36%, all p＜0.001), followed by triglyceride (TG, 5.28%, 4.77%, and 4.86%, all p＜0.01) and serum uric acid (SUA, 3.64%, 4.24%, and 2.29%, all p＜0.05) were major mediators through metabolic phenotypes. CONCLUSIONS: Higher SB was associated with elevated risks of CVD events. The detrimental effect of SB on CHD risk was more pronounced among retired individuals. Moreover, HDL-C, TG and SUA partially mediated the relationships between SB and CVD events. Our findings may have implications for preventing and controlling CVD associated with SB.

Association of triglyceride-glucose index with cardiovascular disease among a general population: a prospective cohort study.

BACKGROUND: The impact of triglyceride-glucose (TyG) index, a surrogate marker for insulin resistance, on the risk of cardiovascular disease (CVD) in general populations remains controversial. We aimed to comprehensively study the relationship between TyG index with the risk of incident CVD events in the general population in Shanghai. METHODS: A total of 42,651 participants without previous history of CVD events from Shanghai Suburban Adult Cohort and Biobank (SSACB) were included. SSACB was a community-based natural population cohort study using multistage cluster sampling method. TyG index was calculated as Ln [fasting serum triglyceride (mg/dL) * fasting blood glucose (mg/dL)/2]. Kaplan-Meier curves, log-rank test and cox proportional hazards model were used to calculate the association between TyG index and incident CVD, including stroke and coronary heart disease (CHD). Restricted cubic spline analyses were used to determine whether there was a non-linear relationship between TyG index and CVD events. RESULTS: During a median follow-up of 4.7 years, 1,422 (3.3%) individuals developed CVD, including 674 (1.6%) cases of stroke and 732 (1.7%) cases of CHD. A one unit increment higher TyG index was associated with [HR(95%CI)] 1.16(1.04-1.29) in CVD and with 1.39(1.19-1.61) in stroke. Only linear relationships between TyG and CVD/stroke were observed, while no relationship was observed with CHD after adjustments for confounders. In subgroup analyses, younger (< 50y) and diabetic participants had higher risk of CVD than their counterpart groups, while hypertensive and dyslipidemic participants depicted lower risks than their counterparts. CONCLUSION: Elevated TyG index was associated with a higher risk of incident CVD and stroke. TyG index may help in the early stage of identifying people at high risk of CVD.

CavityPlus 2022 Update: An Integrated Platform for Comprehensive Protein Cavity Detection and Property Analyses with User-friendly Tools and Cavity Databases.

Ligand binding sites provide essential information for uncovering protein functions and structure-based drug discovery. To facilitate cavity detection and property analysis process, we developed a comprehensive web server, CavityPlus in 2018. CavityPlus applies the CAVITY program to detect potential binding sites in a given protein structure. The CavPharmer, CorrSite, and CovCys tools can then be applied to generate receptor-based pharmacophore models, identify potential allosteric sites, or detect druggable cysteine residues for covalent drug design. While CavityPlus has been widely used, the constantly evolving knowledge and methods make it necessary to improve and extend its functions. This study presents a new version of CavityPlus, CavityPlus 2022 through a series of upgrades. We upgraded the CAVITY tool to greatly speed up cavity detection calculation. We optimized the CavPharmer tool for fast speed and more accurate results. We integrated the newly developed CorrSite2.0 into the CavityPlus 2022 web server for its improved performance of allosteric site prediction. We also added a new CavityMatch module for drug repurposing and protein function studies by searching similar cavities to a given cavity from pre-constructed cavity databases. The new version of CavityPlus is freely available at http://pkumdl.cn:8000/cavityplus/.

Prevalence trends and disease burden of diabetes and prediabetes in Chinese adults of Shanghai.

BACKGROUND: To estimate secular trends and disease burden of diabetes and prediabetes among Chinese adults. METHODS: Three population-based surveys were performed among Chinese adults in Shanghai in 2002-2003 (n = 12 302), 2009 (n = 7414), and 2017 (n = 18 960). Diabetes and prediabetes were defined using the 1999 World Health Organization (WHO) criteria. Cochran-Armitage trend test was used to examine the trends in prevalence, awareness, and glycemic control status. Disability adjusted life years (DALYs) were estimated to evaluate the disease burden of diabetes-related complications using the population attribution fraction approach based on published data. RESULTS: The age-adjusted prevalence of diabetes increased during the 15-year period (p for trend <.001) and reached 23.0% (95% CI: 22.1 ~ 24.0%) in men and 15.7% (95% CI: 15.1 ~ 16.4%) among women in 2017. The prevalence of impaired glucose tolerance peaked in 2009, whereas that of impaired fasting glucose increased continuously (p for trend <.001). The awareness of diabetes was found to increase and the glycemic control rates decreased over the three surveys. The estimated DALYs of diabetes complications were found to have increased rapidly due to the increasing prevalence of diabetes and the decreasing glycemic control rates. CONCLUSIONS: Prediabetes and diabetes affect a considerable proportion of Chinese adults in Shanghai. Our results highlight the necessary to strengthen the community healthcare system in China to guarantee extensive management of diabetes and prediabetes.

Specific pupylation as IDEntity reporter (SPIDER) for the identification of protein-biomolecule interactions.

Protein-biomolecule interactions play pivotal roles in almost all biological processes. For a biomolecule of interest, the identification of the interacting protein(s) is essential. For this need, although many assays are available, highly robust and reliable methods are always desired. By combining a substrate-based proximity labeling activity from the pupylation pathway of Mycobacterium tuberculosis and the streptavidin (SA)-biotin system, we developed the Specific Pupylation as IDEntity Reporter (SPIDER) method for identifying protein-biomolecule interactions. Using SPIDER, we validated the interactions between the known binding proteins of protein, DNA, RNA, and small molecule. We successfully applied SPIDER to construct the global protein interactome for m6A and mRNA, identified a variety of uncharacterized m6A binding proteins, and validated SRSF7 as a potential m6A reader. We globally identified the binding proteins for lenalidomide and CobB. Moreover, we identified SARS-CoV-2-specific receptors on the cell membrane. Overall, SPIDER is powerful and highly accessible for the study of protein-biomolecule interactions.

Anxiety prevalence and its association with physical activity in patients with non-communicable diseases during COVID-19 lockdown: a cross-sectional study in Shanghai, China.

BACKGROUND: Quarantine due to the COVID-19 pandemic may have created great psychological stress among vulnerable populations. We aimed to investigate the prevalence of anxiety and explore the association between physical activities (PA) and anxiety risk in people with non-communicable diseases during the period of COVID-19 lockdown. METHODS: We conducted a cross-sectional telephone survey from February 25 to April 20, 2020, the period of COVID-19 lockdown in Shanghai. Up to 8000 patients with type 2 diabetes and/or hypertension were selected using multi-stage cluster random sampling. PA level was measured based on the International Physical Activity Questionnaire using Metabolic Equivalent for Task scores, while symptoms of anxiety were assessed by the 7-item Generalized Anxiety Disorder scale. Multiple logistic regression analyses were performed to evaluate the associations of type and level of PA with the risk of anxiety. RESULTS: Of a total 4877 eligible patients, 2602 (53.4%) reported with anxiety, and 2463 (50.5%), 123 (2.5%) and 16 (0.3%) reported with mild, moderate, and severe anxiety. The prevalence of anxiety was higher in the females, the elders, non-smokers, non-drinkers, and patients with diabetes, and the associations of anxiety with sex, age, smoking, drinking and diagnosis of diabetes were significant. A significant negative association was observed for housework activities (OR 0.53, 95%CI: [0.45, 0.63], p < 0.001) and trip activities (OR 0.55, 95%CI: [0.48, 0.63], p < 0.001) with anxiety, but no significant was found for exercise activities (OR 1.06, 95%CI: [0.94, 1.20], p = 0.321). Compared with patients with a low PA level, those with a moderate (OR 0.53, 95%CI: [0.44, 0.64], p < 0.001) or a high PA level (OR 0.51, 95%CI: [0.43, 0.51], p < 0.001) had a lower prevalence of anxiety. CONCLUSION: This study demonstrates a higher prevalence of anxiety in patients with hypertension, diabetes, or both during the COVID-19 lockdown. The negative associations of housework and trip activities with anxiety highlight the potential benefit of PA among patients with non-communicable diseases.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets.

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence-based self-supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence-based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue-binding weights from the well-trained network using the gradient-weighted class activation mapping (Grad-Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule-binding sites. Thus, our work provides a highly effective sequence-based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule-binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.

A NR2E1-interacting peptide of LSD1 inhibits the proliferation of brain tumour initiating cells.

OBJECTIVES: Elimination of brain tumour initiating cells (BTICs) is important for the good prognosis of malignant brain tumour treatment. To develop a novel strategy targeting BTICs, we studied NR2E1(TLX) involved self-renewal mechanism of BTICs and explored the intervention means. MATERIALS AND METHODS: NR2E1 and its interacting protein-LSD1 in BTICs were studied by gene interference combined with cell growth, tumour sphere formation, co-immunoprecipitation and chromatin immunoprecipitation assays. NR2E1 interacting peptide of LSD1 was identified by Amide Hydrogen/Deuterium Exchange and Mass Spectrometry (HDX-MS) and analysed by in vitro functional assays. The in vivo function of the peptide was examined with intracranial mouse model by transplanting patient-derived BTICs. RESULTS: We found NR2E1 recruits LSD1, a lysine demethylase, to demethylate mono- and di-methylated histone 3 Lys4 (H3K4me/me2) at the Pten promoter and repress its expression, thereby promoting BTIC proliferation. Using Amide Hydrogen/Deuterium Exchange and Mass Spectrometry (HDX-MS) method, we identified four LSD1 peptides that may interact with NR2E1. One of the peptides, LSD1-197-211 that locates at the LSD1 SWIRM domain, strongly inhibited BTIC proliferation by promoting Pten expression through interfering NR2E1 and LSD1 function. Furthermore, overexpression of this peptide in human BTICs can inhibit intracranial tumour formation. CONCLUSION: Peptide LSD1-197-211 can repress BTICs by interfering the synergistic function of NR2E1 and LSD1 and may be a promising lead peptide for brain tumour therapy in future.

In silico protein function prediction: the rise of machine learning-based approaches.

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

A spatiotemporal reconstruction of the C. elegans pharyngeal cuticle reveals a structure rich in phase-separating proteins.

How the cuticles of the roughly 4.5 million species of ecdysozoan animals are constructed is not well understood. Here, we systematically mine gene expression datasets to uncover the spatiotemporal blueprint for how the chitin-based pharyngeal cuticle of the nematode Caenorhabditis elegans is built. We demonstrate that the blueprint correctly predicts expression patterns and functional relevance to cuticle development. We find that as larvae prepare to molt, catabolic enzymes are upregulated and the genes that encode chitin synthase, chitin cross-linkers, and homologs of amyloid regulators subsequently peak in expression. Forty-eight percent of the gene products secreted during the molt are predicted to be intrinsically disordered proteins (IDPs), many of which belong to four distinct families whose transcripts are expressed in overlapping waves. These include the IDPAs, IDPBs, and IDPCs, which are introduced for the first time here. All four families have sequence properties that drive phase separation and we demonstrate phase separation for one exemplar in vitro. This systematic analysis represents the first blueprint for cuticle construction and highlights the massive contribution that phase-separating materials make to the structure.

MolMiner: You Only Look Once for Chemical Structure Recognition.

Molecular structures are commonly depicted in 2D printed forms in scientific documents such as journal papers and patents. However, these 2D depictions are not machine readable. Due to a backlog of decades and an increasing amount of printed literatures, there is a high demand for translating printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR). Most OCSR systems developed over the last three decades use a rule-based approach, which vectorizes the depiction based on the interpretation of vectors and nodes as bonds and atoms. Here, we present a practical software called MolMiner, which is primarily built using deep neural networks originally developed for semantic segmentation and object detection to recognize atom and bond elements from documents. These recognized elements can be easily connected as a molecular graph with a distance-based construction algorithm. MolMiner gave state-of-the-art performance on four benchmark data sets and a self-collected external data set from scientific papers. As MolMiner performed similarly well in real-world OCSR tasks with a user-friendly interface, it is a useful and valuable tool for daily applications. The free download links of Mac and Windows versions are available at https://github.com/iipharma/pharmamind-molminer.

CavitySpace: A Database of Potential Ligand Binding Sites in the Human Proteome.

Location and properties of ligand binding sites provide important information to uncover protein functions and to direct structure-based drug design approaches. However, as binding site detection depends on the three-dimensional (3D) structural data of proteins, functional analysis based on protein ligand binding sites is formidable for proteins without structural information. Recent developments in protein structure prediction and the 3D structures built by AlphaFold provide an unprecedented opportunity for analyzing ligand binding sites in human proteins. Here, we constructed the CavitySpace database, the first pocket library for all the proteins in the human proteome, using a widely-applied ligand binding site detection program CAVITY. Our analysis showed that known ligand binding sites could be well recovered. We grouped the predicted binding sites according to their similarity which can be used in protein function prediction and drug repurposing studies. Novel binding sites in highly reliable predicted structure regions provide new opportunities for drug discovery. Our CavitySpace is freely available and provides a valuable tool for drug discovery and protein function studies.

Advances and Challenges in De Novo Drug Design Using Three-Dimensional Deep Generative Models.

A persistent goal for de novo drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aiming at three-dimensional (3D) molecule generation have also been proposed, gaining attention for their unique advantages and potential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developments in 3D molecular generative models combined with deep learning and discusses future directions for de novo drug design.

COVID-19 outbreak improves attractiveness of medical careers in Chinese senior high school students.

BACKGROUND: The shortage of healthcare workers is becoming a serious global problem. The underlying reasons may be specific to the healthcare system in each country. Over the past decade, medicine has become an increasingly unpopular profession in China due to the heavy workload, long-term training, and inherent risks. The ongoing COVID-19 pandemic has placed the life-saving roles of healthcare professionals under the spotlight. This public health crisis may have a profound impact on career choices in Chinese population. METHODS: We conducted a questionnaire-based online survey among 21,085 senior high school students and 21,009 parents from 24 provinces (or municipalities) of China. We investigated the change of interest in medical study due to the outbreak of COVID-19 and the potential motivational factors based on the expectancy-value theory framework. Pearson correlation analysis was used to assess the correlation of static or dynamic interest in medical career pursuit with the reported number of COVID-19 cases. Logistic regression model was adopted to analyze the main factors associated with students' choices. RESULTS: We observed an increased preference for medical study post the outbreak of COVID-19 in both students (17.5 to 29.6%) and parents (37.1 to 47.3%). Attainment value was found to be the main reason for the choice among students, with the contribution to society rated as the top motivation. On the other hand, the predominant demotivation in high school students was lack of interest, followed by concerns regarding violence against doctors, heavy workload, long-term training and heavy responsibility as a doctor. Additionally, students who were female, in the resit of final year, had highly educated parents and outside of Hubei province were significantly associated with a keen interest in pursuing medical study. CONCLUSIONS: This is the first multi-center cross-sectional study exploring the positive change and motivations of students' preferences in medical study due to the outbreak of COVID-19. Our results may help medical educators, researchers and policymakers to restructure medical education to make it more appealing to high school students, particularly, to develop a more supportive social and working environment for medical professionals to maintain the observed enhanced enthusiasm.

Prediction of liquid-liquid phase separating proteins using machine learning.

BACKGROUND: The liquid-liquid phase separation (LLPS) of biomolecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular function. Dysregulation of LLPS is implicated in a number of diseases. Although the LLPS of biomolecules has been investigated intensively in recent years, the knowledge of the prevalence and distribution of phase separation proteins (PSPs) is still lag behind. Development of computational methods to predict PSPs is therefore of great importance for comprehensive understanding of the biological function of LLPS. RESULTS: Based on the PSPs collected in LLPSDB, we developed a sequence-based prediction tool for LLPS proteins (PSPredictor), which is an attempt at general purpose of PSP prediction that does not depend on specific protein types. Our method combines the componential and sequential information during the protein embedding stage, and, adopts the machine learning algorithm for final predicting. The proposed method achieves a tenfold cross-validation accuracy of 94.71%, and outperforms previously reported PSPs prediction tools. For further applications, we built a user-friendly PSPredictor web server ( http://www.pkumdl.cn/PSPredictor ), which is accessible for prediction of potential PSPs. CONCLUSIONS: PSPredictor could identifie novel scaffold proteins for stress granules and predict PSPs candidates in the human genome for further study. For further applications, we built a user-friendly PSPredictor web server ( http://www.pkumdl.cn/PSPredictor ), which provides valuable information for potential PSPs recognition.

miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.

The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages. Furthermore, we proposed a strategy combing both active learning and ensemble learning to improve the quality of the models. The models achieved satisfactory performance in the internal test datasets and four self-collected external test datasets. We also employed the models as a general index to make an evaluation on a widely known benchmark dataset DEKOIS 2.0, and surprisingly found a powerful ability on virtual screening tasks. Our model system (termed as miDruglikeness) provides a comprehensive drug-likeness prediction tool for drug discovery and development.