ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823 391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support.

ADMETlab 3.0 is the second updated version of the web server that provides a comprehensive and efficient platform for evaluating ADMET-related parameters as well as physicochemical properties and medicinal chemistry characteristics involved in the drug discovery process. This new release addresses the limitations of the previous version and offers broader coverage, improved performance, API functionality, and decision support. For supporting data and endpoints, this version includes 119 features, an increase of 31 compared to the previous version. The updated number of entries is 1.5 times larger than the previous version with over 400 000 entries. ADMETlab 3.0 incorporates a multi-task DMPNN architecture coupled with molecular descriptors, a method that not only guaranteed calculation speed for each endpoint simultaneously, but also achieved a superior performance in terms of accuracy and robustness. In addition, an API has been introduced to meet the growing demand for programmatic access to large amounts of data in ADMETlab 3.0. Moreover, this version includes uncertainty estimates in the prediction results, aiding in the confident selection of candidate compounds for further studies and experiments. ADMETlab 3.0 is publicly for access without the need for registration at: https://admetlab3.scbdd.com.

Caspase-11/GSDMD contributes to the progression of hyperuricemic nephropathy by promoting NETs formation.

Hyperuricemia is an independent risk factor for chronic kidney disease (CKD) and promotes renal fibrosis, but the underlying mechanism remains largely unknown. Unresolved inflammation is strongly associated with renal fibrosis and is a well-known significant contributor to the progression of CKD, including hyperuricemia nephropathy. In the current study, we elucidated the impact of Caspase-11/Gasdermin D (GSDMD)-dependent neutrophil extracellular traps (NETs) on progressive hyperuricemic nephropathy. We found that the Caspase-11/GSDMD signaling were markedly activated in the kidneys of hyperuricemic nephropathy. Deletion of Gsdmd or Caspase-11 protects against the progression of hyperuricemic nephropathy by reducing kidney inflammation, proinflammatory and profibrogenic factors expression, NETs generation, α-smooth muscle actin expression, and fibrosis. Furthermore, specific deletion of Gsdmd or Caspase-11 in hematopoietic cells showed a protective effect on renal fibrosis in hyperuricemic nephropathy. Additionally, in vitro studies unveiled the capability of uric acid in inducing Caspase-11/GSDMD-dependent NETs formation, consequently enhancing α-smooth muscle actin production in macrophages. In summary, this study demonstrated the contributory role of Caspase-11/GSDMD in the progression of hyperuricemic nephropathy by promoting NETs formation, which may shed new light on the therapeutic approach to treating and reversing hyperuricemic nephropathy.

LINC00173 silence and estrone supply suppress ER+ breast cancer by estrogen receptor α degradation and LITAF activation.

Persistent activation of estrogen receptor alpha (ERα)-mediated estrogen signaling plays a pivotal role in driving the progression of estrogen receptor positive (ER+) breast cancer (BC). In the current study, LINC00173, a long non-coding RNA, was found to bind both ERα and lipopolysaccharide (LPS)-induced tumor necrosis factor alpha (TNFα) factor (LITAF), then cooperatively to inhibit ERα protein degradation by impeding the nuclear export of ERα. Concurrently, LITAF was found to attenuate TNFα transcription after binding to LINC00173, and this attenuating transcriptional effect was quite significant under lipopolysaccharide stimulation. Distinct functional disparities between estrogen subtypes emerge, with estradiol synergistically promoting ER+ BC cell growth with LINC00173, while estrone (E1) facilitated LITAF-transcriptional activation. In terms of therapeutic significance, silencing LINC00173 alongside moderate addition of E1 heightened TNFα and induced apoptosis, effectively inhibiting ER+ BC progression.

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an essential tool for automatically mining knowledge from an enormous amount of literature. However, existing OCSR methods fall far short of our expectations for realistic requirements due to their poor recovery accuracy. In this paper, we developed a deep neural network model named ABC-Net (Atom and Bond Center Network) to predict graph structures directly. Based on the divide-and-conquer principle, we propose to model an atom or a bond as a single point in the center. In this way, we can leverage a fully convolutional neural network (CNN) to generate a series of heat-maps to identify these points and predict relevant properties, such as atom types, atom charges, bond types and other properties. Thus, the molecular structure can be recovered by assembling the detected atoms and bonds. Our approach integrates all the detection and property prediction tasks into a single fully CNN, which is scalable and capable of processing molecular images quite efficiently. Experimental results demonstrate that our method could achieve a significant improvement in recognition performance compared with publicly available tools. The proposed method could be considered as a promising solution to OCSR problems and a starting point for the acquisition of molecular information in the literature.

BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution.

MOTIVATION: Understanding chemical-gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions. RESULTS: We developed BioNet, a deep biological networkmodel with a graph encoder-decoder architecture. The graph encoder utilizes graph convolution to learn latent information embedded in complex interactions among chemicals, genes, diseases and biological pathways. The learning process is featured by two consecutive steps. Then, embedded information learnt by the encoder is then employed to make multi-type interaction predictions between chemicals and genes with a tensor decomposition decoder based on the RESCAL algorithm. BioNet includes 79 325 entities as nodes, and 34 005 501 relations as edges. To train such a massive deep graph model, BioNet introduces a parallel training algorithm utilizing multiple Graphics Processing Unit (GPUs). The evaluation experiments indicated that BioNet exhibits outstanding prediction performance with a best area under Receiver Operating Characteristic (ROC) curve of 0.952, which significantly surpasses state-of-theart methods. For further validation, top predicted CGIs of cancer and COVID-19 by BioNet were verified by external curated data and published literature.

GSDMD and GSDME synergy in the transition of acute kidney injury to chronic kidney disease.

BACKGROUND AND HYPOTHESIS: Acute kidney injury (AKI) could progress to chronic kidney disease (CKD) and the AKI-CKD transition has major clinical significance. A growing body of evidence has unveiled the role of pyroptosis in kidney injury. We postulate that GSDMD and GSDME exert cumulative effects on the AKI-CKD transition by modulating different cellular responses. METHODS: We established an AKI-CKD transition model induced by folic acid in wildtype (WT), Gsdmd-/-, Gsdme-/-, and Gsdmd-/-Gsdme-/- mice. Tubular injury, renal fibrosis and inflammatory responses were evaluated. In vitro studies were conducted to investigate the interplay among tubular cells, neutrophils, and macrophages. RESULTS: Double deletion of Gsdmd and Gsdme conferred heightened protection against AKI, mitigating inflammatory responses, including the formation of neutrophil extracellular traps (NETs), macrophage polarization and differentiation, and ultimately renal fibrosis, compared with wildtype mice and mice with single deletion of either Gsdmd or Gsdme. Gsdme, but not Gsdmd deficiency, shielded tubular cells from pyroptosis. GSDME-dependent tubular cell death stimulated NETs formation and prompted macrophage polarization towards a pro-inflammatory phenotype. Gsdmd deficiency suppressed NETs formation and subsequently hindered NETs-induced macrophage-to-myofibroblast transition (MMT). CONCLUSION: GSDMD and GSDME collaborate to contribute to AKI and subsequent renal fibrosis induced by folic acid. Synchronous inhibition of GSDMD and GSDME could be an innovative therapeutic strategy for mitigating the AKI-CKD transition.

DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety.

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.

The association between trajectory of serum cholesterol, statin dosage, and the risk of recurrent biliary stone diseases.

BACKGROUND: Statins may reduce the risk of recurrent gallstone disease by decreasing bile cholesterol saturation and pathogenicity. However, limited studies have investigated this issue. This study aimed to assess whether statin doses and serum cholesterol levels were associated with a decreased risk of recurrent biliary stone diseases after the first event index, with a follow-up time of 15 years. METHODS: Based on the Chang Gung Research Database (CGRD) between January 1, 2001, and December 31, 2020, we enrolled 68,384 patients with the International Classification of Diseases, Ninth and Tenth Revision codes of choledocholithiasis. After exclusions, 32,696 patients were divided into non-statin (<28 cDDD, cumulative defined daily doses) (n = 27,929) and statin (≥28 cDDD) (n = 4767) user groups for analysis. Serum cholesterol trajectories were estimated using group-based trajectory modeling (n = 8410). RESULTS: The statin users had higher Charlson Comorbidity Index (CCI) scores than the non-statin users. Time-dependent Cox regression analysis showed that statin use >365 cDDD was associated with a significantly lower risk of recurrent biliary stones (adjusted hazard ratio [aHR] = 0.28, 95% CI, 0.24-0.34; p < 00.0001), acute pancreatitis (aHR = 0.24, 95% CI, 0.17-0.32, p < 00.0001), and cholangitis (aHR = 0.28, 95% CI, 0.25-0.32, p < 00.0001). Cholecystectomy was also a protective factor for recurrent biliary stones (aHR = 0.41, 95% CI, 0.37-0.46; p < 00.0001). The higher trajectory serum cholesterol group (Group 3) had a lower risk trend for recurrent biliary stones (aHR = 0.79, p = 0.0700) and a lower risk of cholangitis (aHR = 0.79, p = 0.0071). CONCLUSION: This study supports the potential benefits of statin use and the role of cholecystectomy in reducing the risk of recurrent biliary stone diseases.

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep learning (DL) methods deliver a data-driven method to automatically learn expressive representations from complex raw data. Inspired by this, researchers have attempted to apply various deep neural network models to simplified molecular input line entry specification (SMILES) strings, which contain all the composition and structure information of molecules. However, current models usually suffer from the scarcity of labeled data. This results in a low generalization ability of SMILES-based DL models, which prevents them from competing with the state-of-the-art computational methods. In this study, we utilized the BiLSTM (bidirectional long short term merory) attention network (BAN) in which we employed a novel multi-step attention mechanism to facilitate the extracting of key features from the SMILES strings. Meanwhile, SMILES enumeration was utilized as a data augmentation method in the training phase to substantially increase the number of labeled data and enlarge the probability of mining more patterns from complex SMILES. We again took advantage of SMILES enumeration in the prediction phase to rectify model prediction bias and provide a more accurate prediction. Combined with the BAN model, our strategies can greatly improve the performance of latent features learned from SMILES strings. In 11 canonical absorption, distribution, metabolism, excretion and toxicity-related tasks, our method outperformed the state-of-the-art approaches.

MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction.

MOTIVATION: Accurate and efficient prediction of molecular properties is one of the fundamental issues in drug design and discovery pipelines. Traditional feature engineering-based approaches require extensive expertise in the feature design and selection process. With the development of artificial intelligence (AI) technologies, data-driven methods exhibit unparalleled advantages over the feature engineering-based methods in various domains. Nevertheless, when applied to molecular property prediction, AI models usually suffer from the scarcity of labeled data and show poor generalization ability. RESULTS: In this study, we proposed molecular graph BERT (MG-BERT), which integrates the local message passing mechanism of graph neural networks (GNNs) into the powerful BERT model to facilitate learning from molecular graphs. Furthermore, an effective self-supervised learning strategy named masked atoms prediction was proposed to pretrain the MG-BERT model on a large amount of unlabeled data to mine context information in molecules. We found the MG-BERT model can generate context-sensitive atomic representations after pretraining and transfer the learned knowledge to the prediction of a variety of molecular properties. The experimental results show that the pretrained MG-BERT model with a little extra fine-tuning can consistently outperform the state-of-the-art methods on all 11 ADMET datasets. Moreover, the MG-BERT model leverages attention mechanisms to focus on atomic features essential to the target property, providing excellent interpretability for the trained model. The MG-BERT model does not require any hand-crafted feature as input and is more reliable due to its excellent interpretability, providing a novel framework to develop state-of-the-art models for a wide range of drug discovery tasks.

DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications.

SUMMARY: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research. To improve the performance of DeepKG, a cascaded hybrid information extraction framework is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144 900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7980 entities and 43 760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform. AVAILABILITY AND IMPLEMENTATION: All the results are publicly available at the website (http://covidkg.ai/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MICER: a pre-trained encoder-decoder architecture for molecular image captioning.

MOTIVATION: Automatic recognition of chemical structures from molecular images provides an important avenue for the rediscovery of chemicals. Traditional rule-based approaches that rely on expert knowledge and fail to consider all the stylistic variations of molecular images usually suffer from cumbersome recognition processes and low generalization ability. Deep learning-based methods that integrate different image styles and automatically learn valuable features are flexible, but currently under-researched and have limitations, and are therefore not fully exploited. RESULTS: MICER, an encoder-decoder-based, reconstructed architecture for molecular image captioning, combines transfer learning, attention mechanisms and several strategies to strengthen effectiveness and plasticity in different datasets. The effects of stereochemical information, molecular complexity, data volume and pre-trained encoders on MICER performance were evaluated. Experimental results show that the intrinsic features of the molecular images and the sub-model match have a significant impact on the performance of this task. These findings inspire us to design the training dataset and the encoder for the final validation model, and the experimental results suggest that the MICER model consistently outperforms the state-of-the-art methods on four datasets. MICER was more reliable and scalable due to its interpretability and transfer capacity and provides a practical framework for developing comprehensive and accurate automated molecular structure identification tools to explore unknown chemical space. AVAILABILITY AND IMPLEMENTATION: https://github.com/Jiacai-Yi/MICER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Open Source Graph-Based Weighted Cycle Closure Method for Relative Binding Free Energy Calculations.

Free energy perturbation-relative binding free energy (FEP-RBFE) prediction has shown its reliability and accuracy in the prediction of protein-ligand binding affinities, which plays a fundamental role in structure-based drug design. In FEP-RBFE predictions, the calculation of each mutation path is associated with a statistical error, and cycle closure (cc) has proven to be an effective method in improving the calculation accuracy by correcting the hysteresis (summation of errors) of each closed cycle to the theoretical value 0. However, a primary hypothesis was made in the current cycle closure method that the hysteresis is evenly distributed to all paths, which is unlikely to be true in practice and may limit the further improvement of the calculation accuracy when better error estimation methods are available. Moreover, being a closed source software makes the current cycle closure method unachievable in many studies. In this paper, a newly implemented open source graph-based weighted cycle closure (wcc) algorithm was developed and introduced, not only including functions from the original cc method but also containing a new wcc method which can consider different error contributions from different paths and further improve the calculation accuracy. The wcc program also provides a new path-independent molecular error calculation method, which can be quite useful in many studies (like structure-activity relationship (SAR)) compared with the path-dependent method of the original cc program.

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties.

Because undesirable pharmacokinetics and toxicity of candidate compounds are the main reasons for the failure of drug development, it has been widely recognized that absorption, distribution, metabolism, excretion and toxicity (ADMET) should be evaluated as early as possible. In silico ADMET evaluation models have been developed as an additional tool to assist medicinal chemists in the design and optimization of leads. Here, we announced the release of ADMETlab 2.0, a completely redesigned version of the widely used AMDETlab web server for the predictions of pharmacokinetics and toxicity properties of chemicals, of which the supported ADMET-related endpoints are approximately twice the number of the endpoints in the previous version, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints and 8 toxicophore rules (751 substructures). A multi-task graph attention framework was employed to develop the robust and accurate models in ADMETlab 2.0. The batch computation module was provided in response to numerous requests from users, and the representation of the results was further optimized. The ADMETlab 2.0 server is freely available, without registration, at https://admetmesh.scbdd.com/.

Free energy perturbation-based large-scale virtual screening for effective drug discovery against COVID-19.

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE-based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

A risk factor attention-based model for cardiovascular disease prediction.

BACKGROUND: Cardiovascular disease (CVD) is a serious disease that endangers human health and is one of the main causes of death. Therefore, using the patient's electronic medical record (EMR) to predict CVD automatically has important application value in intelligent assisted diagnosis and treatment, and is a hot issue in intelligent medical research. However, existing methods based on natural language processing can only predict CVD according to the whole or part of the context information of EMR. RESULTS: Given the deficiencies of the existing research on CVD prediction based on EMRs, this paper proposes a risk factor attention-based model (RFAB) to predict CVD by utilizing CVD risk factors and general EMRs text, which adopts the attention mechanism of a deep neural network to fuse the character sequence and CVD risk factors contained in EMRs text. The experimental results show that the proposed method can significantly improve the prediction performance of CVD, and the F-score reaches 0.9586, which outperforms the existing related methods. CONCLUSIONS: RFAB focuses on the key information in EMR that leads to CVD, that is, 12 risk factors. In the stage of risk factor identification and extraction, risk factors are labeled with category information and time attribute information by BiLSTM-CRF model. In the stage of CVD prediction, the information contained in risk factors and their labels is fused with the information of character sequence in EMR to predict CVD. RFAB makes well use of the fine-grained information contained in EMR, and also provides a reliable idea for predicting CVD.

ParaCopasi: A package for parallel biochemical simulation and analysis.

Biochemical simuflation and analysis play a significant role in systems biology research. Numerous software tools have been developed to serve this area. Using these tools for completing tasks, for example, stochastic simulation, parameter fitting and optimization, usually requires sufficient computational power to make the duration of completion acceptable. COPASI is one of the most powerful tools for quantitative simulation and analysis targeted at biological systems. It supports systems biology markup language and covers multiple categories of tasks. This work develops an open source package ParaCopasi for parallel COPASI tasks and investigates its performance regarding accelerations. ParaCopasi can be installed on platforms equipped with multicore CPU to exploit the cores, scaling from desktop computers to large scale high-performance computing clusters. More cores bring more performance. The results show that the parallel efficiency has a positive correlation with the total workload. The parallel efficiency reaches a level of at least 95% for both homogeneous and heterogenous tasks when computational workload is adequate. An example is illustrated by applicating this package in parameter estimation to calibrate a biochemical kinetics model.

Exploring the Binding Affinity and Mechanism between ACE2 and the Trimers of Delta and Omicron Spike Proteins by Molecular Dynamics Simulation and Bioassay.

Five major variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged and posed challenges in controlling the pandemic. Among them, the current dominant variant, viz., Omicron, has raised serious concerns about its infectiousness and antibody neutralization. However, few studies pay attention to the effect of the mutations on the dynamic interaction network of Omicron S protein trimers binding to the host angiotensin-converting enzyme 2 (ACE2). In this study, we conducted molecular dynamics (MD) simulations and enzyme linked immunosorbent assay (ELISA) to explore the binding strength and mechanism of wild type (WT), Delta, and Omicron S protein trimers to ACE2. The results showed that the binding capacities of both the two variants' S protein trimers to ACE2 are enhanced in varying degrees, indicating possibly higher cell infectiousness. Energy decomposition and protein-protein interaction network analysis suggested that both the mutational and conserved sites make effects on the increase in the overall affinity through a variety of interactions. The experimentally determined KD values by biolayer interferometry (BLI) and the predicted binding free energies of the RBDs of Delta and Omicron to mAb HLX70 revealed that the two variants may have the high risk of immune evasion from the mAb. These results are not only helpful in understanding the binding strength and mechanism of S protein trimer-ACE2 but also beneficial for drug, especially for antibody development.