Inter-domain distance prediction based on deep learning for domain assembly.

AlphaFold2 achieved a breakthrough in protein structure prediction through the end-to-end deep learning method, which can predict nearly all single-domain proteins at experimental resolution. However, the prediction accuracy of full-chain proteins is generally lower than that of single-domain proteins because of the incorrect interactions between domains. In this work, we develop an inter-domain distance prediction method, named DeepIDDP. In DeepIDDP, we design a neural network with attention mechanisms, where two new inter-domain features are used to enhance the ability to capture the interactions between domains. Furthermore, we propose a data enhancement strategy termed DPMSA, which is employed to deal with the absence of co-evolutionary information on targets. We integrate DeepIDDP into our previously developed domain assembly method SADA, termed SADA-DeepIDDP. Tested on a given multi-domain benchmark dataset, the accuracy of SADA-DeepIDDP inter-domain distance prediction is 11.3% and 21.6% higher than trRosettaX and trRosetta, respectively. The accuracy of the domain assembly model is 2.5% higher than that of SADA. Meanwhile, we reassemble 68 human multi-domain protein models with TM-score ≤ 0.80 from the AlphaFold protein structure database, where the average TM-score is improved by 11.8% after the reassembly by our method. The online server is at http://zhanglab-bioinf.com/DeepIDDP/.

Pathfinder: Protein folding pathway prediction based on conformational sampling.

The study of protein folding mechanism is a challenge in molecular biology, which is of great significance for revealing the movement rules of biological macromolecules, understanding the pathogenic mechanism of folding diseases, and designing protein engineering materials. Based on the hypothesis that the conformational sampling trajectory contain the information of folding pathway, we propose a protein folding pathway prediction algorithm named Pathfinder. Firstly, Pathfinder performs large-scale sampling of the conformational space and clusters the decoys obtained in the sampling. The heterogeneous conformations obtained by clustering are named seed states. Then, a resampling algorithm that is not constrained by the local energy basin is designed to obtain the transition probabilities of seed states. Finally, protein folding pathways are inferred from the maximum transition probabilities of seed states. The proposed Pathfinder is tested on our developed test set (34 proteins). For 11 widely studied proteins, we correctly predicted their folding pathways and specifically analyzed 5 of them. For 13 proteins, we predicted their folding pathways to be further verified by biological experiments. For 6 proteins, we analyzed the reasons for the low prediction accuracy. For the other 4 proteins without biological experiment results, potential folding pathways were predicted to provide new insights into protein folding mechanism. The results reveal that structural analogs may have different folding pathways to express different biological functions, homologous proteins may contain common folding pathways, and α-helices may be more prone to early protein folding than ß-strands.

Recent Advances and Challenges in Protein Structure Prediction.

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

FePSe3 -Nanosheets-Integrated Cryogenic-3D-Printed Multifunctional Calcium Phosphate Scaffolds for Synergistic Therapy of Osteosarcoma.

Clinical treatment of osteosarcoma encounters great challenges of postsurgical tumor recurrence and extensive bone defect. To develop an advanced artificial bone substitute that can achieve synergistic bone regeneration and tumor therapy for osteosarcoma treatment, a multifunctional calcium phosphate composite enabled by incorporation of bioactive FePSe3 -nanosheets within the cryogenic-3D-printed α-tricalcium phosphate scaffold (TCP-FePSe3 ) is explored. The TCP-FePSe3 scaffold exhibits remarkable tumor ablation ability due to the excellent NIR-II (1064 nm) photothermal property of FePSe3 -nanosheets. Moreover, the biodegradable TCP-FePSe3 scaffold can release selenium element to suppress tumor recurrence by activating of the caspase-dependent apoptosis pathway. In a subcutaneous tumor model, it is demonstrated that tumors can be efficiently eradicated via the combination treatment with local photothermal ablation and the antitumor effect of selenium element. Meanwhile, in a rat calvarial bone defect model, the superior angiogenesis and osteogenesis induced by TCP-FePSe3 scaffold have been observed in vivo. The TCP-FePSe3 scaffold possesses improved capability to promote the repair of bone defects via vascularized bone regeneration, which is induced by the bioactive ions of Fe, Ca, and P released during the biodegradation of the implanted scaffolds. The TCP-FePSe3 composite scaffolds fabricated by cryogenic-3D-printing illustrate a distinctive strategy to construct multifunctional platform for osteosarcoma treatment.

Distance-guided protein folding based on generalized descent direction.

Advances in the prediction of the inter-residue distance for a protein sequence have increased the accuracy to predict the correct folds of proteins with distance information. Here, we propose a distance-guided protein folding algorithm based on generalized descent direction, named GDDfold, which achieves effective structural perturbation and potential minimization in two stages. In the global stage, random-based direction is designed using evolutionary knowledge, which guides conformation population to cross potential barriers and explore conformational space rapidly in a large range. In the local stage, locally rugged potential landscape can be explored with the aid of conjugate-based direction integrated into a specific search strategy, which can improve the exploitation ability. GDDfold is tested on 347 proteins of a benchmark set, 24 template-free modeling (FM) approaches targets of CASP13 and 20 FM targets of CASP14. Results show that GDDfold correctly folds [template modeling (TM) score ≥ = 0.5] 316 out of 347 proteins, where 65 proteins have TM scores that are greater than 0.8, and significantly outperforms Rosetta-dist (distance-assisted fragment assembly method) and L-BFGSfold (distance geometry optimization method). On CASP FM targets, GDDfold is comparable with five state-of-the-art full-version methods, namely, Quark, RaptorX, Rosetta, MULTICOM and trRosetta in the CASP 13 and 14 server groups.

Structural analogue-based protein structure domain assembly assisted by deep learning.

MOTIVATION: With the breakthrough of AlphaFold2, the protein structure prediction problem has made remarkable progress through deep learning end-to-end techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of the full-chain model can be further improved by domain assembly assisted by deep learning. RESULTS: In this article, we developed a structural analogue-based protein structure domain assembly method assisted by deep learning, named SADA. In SADA, a multi-domain protein structure database was constructed for the full-chain analogue detection using individual domain models. Starting from the initial model constructed from the analogue, the domain assembly simulation was performed to generate the full-chain model through a two-stage differential evolution algorithm guided by the energy function with an inter-residue distance potential predicted by deep learning. SADA was compared with the state-of-the-art domain assembly methods on 356 benchmark proteins, and the average TM-score of SADA models is 8.1% and 27.0% higher than that of DEMO and AIDA, respectively. We also assembled 293 human multi-domain proteins, where the average TM-score of the full-chain model after the assembly by SADA is 1.1% higher than that of the model by AlphaFold2. To conclude, we find that the domains often interact in the similar way in the quaternary orientations if the domains have similar tertiary structures. Furthermore, homologous templates and structural analogues are complementary for multi-domain protein full-chain modelling. AVAILABILITY AND IMPLEMENTATION: http://zhanglab-bioinf.com/SADA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

A sequential niche multimodal conformational sampling algorithm for protein structure prediction.

MOTIVATION: Massive local minima on the protein energy landscape often cause traditional conformational sampling algorithms to be easily trapped in local basin regions, because they find it difficult to overcome high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy. RESULTS: A sequential niche multimodal conformational sampling algorithm for protein structure prediction (SNfold) is proposed in this study. In SNfold, a derating function is designed based on the knowledge learned from the previous sampling and used to construct a series of sampling-guided energy functions. These functions then help the sampling algorithm overcome high-energy barriers and avoid the re-sampling of the explored regions. In inaccurate protein energy models, the high-energy conformation that may correspond to the native structure can be sampled with successively updated sampling-guided energy functions. The proposed SNfold is tested on 300 benchmark proteins, 24 CASP13 and 19 CASP14 FM targets. Results show that SNfold correctly folds (TM-score ≥ 0.5) 231 out of 300 proteins. In particular, compared with Rosetta restrained by distance (Rosetta-dist), SNfold achieves higher average TM-score and improves the sampling efficiency by more than 100 times. On several CASP FM targets, SNfold also shows good performance compared with four state-of-the-art servers in CASP. As a plug-in conformational sampling algorithm, SNfold can be extended to other protein structure prediction methods. AVAILABILITY AND IMPLEMENTATION: The source code and executable versions are freely available at https://github.com/iobio-zjut/SNfold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association between well-characterized gene polymorphisms and the hypnosis response caused by sevoflurane-induced anaesthesia.

WHAT IS KNOWN AND OBJECTIVE: Sevoflurane is the most widely used volatile anaesthetic in clinical practice. It exhibits a hypnotic (unconsciousness) effect and causes a loss of reaction to noxious stimuli (immobility). However, to date, the mechanism of action of sevoflurane is poorly understood. In this study, we explored the effects of genetic variations on sevoflurane-induced hypnosis. METHODS: Sixty-six SNPs in 18 candidate genes were genotyped using MALDI-TOF MassARRAY in a discovery cohort containing 161 patients administered sevoflurane. Significant polymorphisms were assessed in a validation cohort containing 265 patients. RESULTS AND DISCUSSION: Three polymorphisms (GRIN1 rs28681971, rs79901440 and CHRNA7 rs72713539) were significantly associated with the time to loss of consciousness in patients treated with sevoflurane in the discovery cohort; among them, GRIN1 rs28681971 showed a significant association even after false discovery rate (FDR) correction (pFDR  = 0.039). Following the validation analysis, GRIN1 rs28681971 and rs79901440 showed statistical efficacy (pFDR  = 0.027, 0.034). Combined assessments and meta-analysis of the results of the two cohorts indicated that the C carriers of rs28681971 and T carriers of rs79901440 in GRIN1 require a longer time to achieve unconsciousness. WHAT IS NEW AND CONCLUSION: These findings suggest that GRIN1 polymorphisms are associated with sevoflurane-induced unconsciousness. Thus, the genotypes of GRIN1 may serve as novel and meaningful biomarkers for sevoflurane-induced unconsciousness.

Reconfigurable nonblocking 4-port silicon thermo-optic optical router based on Mach-Zehnder optical switches.

We demonstrate a reconfigurable nonblocking 4-port silicon thermo-optic optical router based on Mach-Zehnder optical switches. For all optical links in its 9 routing states, the optical signal-to-noise ratios are larger than 15 dB in the wavelength range from 1525 to 1565 nm. Each optical link of the optical router can manipulate 50 wavelength-division-multiplexing channels with the data rate of 32 Gbps for each channel in the same wavelength range. Its average energy efficiency is about 16.3 fJ/bit, and its response time is about 19 µs.

3D Printed Photothermal Scaffold Sandwiching Bacteria Inside and Outside Improves The Infected Microenvironment and Repairs Bone Defects.

Bone infection is one of the most devastating orthopedic outcomes, and overuse of antibiotics may cause drug-resistance problems. Photothermal therapy(PTT) is a promising antibiotic-free strategy for treating infected bone defects. Considering the damage to normal tissues and cells caused by high-temperature conditions in PTT, this study combines the antibacterial property of Cu to construct a multi-functional Cu2 O@MXene/alpha-tricalcium phosphate (α-TCP) scaffold support with internal and external sandwiching through 3D printing technology. On the "outside", the excellent photothermal property of Ti3 C2 MXene is used to carry out the programmed temperature control by the active regulation of 808 nm near-infrared (NIR) light. On the "inside", endogenous Cu ions gradually release and the release accumulates within the safe dose range. Specifically, programmed temperature control includes brief PTT to rapidly kill early bacteria and periodic low photothermal stimulation to promote bone tissue growth, which reduces damage to healthy cells and tissues. Meanwhile, Cu ions are gradually released from the scaffold over a long period of time, strengthening the antibacterial effect of early PTT, and promoting angiogenesis to improve the repair effect. PTT combined with Cu can deliver a new idea forinfected bone defects through in vitro and vivo application.

FoldPAthreader: predicting protein folding pathway using a novel folding force field model derived from known protein universe.

Protein folding has become a tractable problem with the significant advances in deep learning-driven protein structure prediction. Here we propose FoldPAthreader, a protein folding pathway prediction method that uses a novel folding force field model by exploring the intrinsic relationship between protein evolution and folding from the known protein universe. Further, the folding force field is used to guide Monte Carlo conformational sampling, driving the protein chain fold into its native state by exploring potential intermediates. On 30 example targets, FoldPAthreader successfully predicts 70% of the proteins whose folding pathway is consistent with biological experimental data.

Metabolic Control During Macrophage Polarization by a Citrate-Functionalized Scaffold for Maintaining Bone Homeostasis.

Metabolites, as markers of phenotype at the molecular level, can regulate the function of DNA, RNA, and proteins through chemical modifications or interactions with large molecules. Citrate is an important metabolite that affects macrophage polarization and osteoporotic bone function. Therefore, a better understanding of the precise effect of citrate on macrophage polarization may provide an effective alternative strategy to reverse osteoporotic bone metabolism. In this study, a citrate functional scaffold to control the metabolic pathway during macrophage polarization based on the metabolic differences between pro-inflammatory and anti-inflammatory phenotypes for maintaining bone homeostasis, is fabricated. Mechanistically, only outside M1 macrophages are accumulated high concentrations of citrate, in contrast, M2 macrophages consume massive citrate. Therefore, citrate-functionalized scaffolds exert more sensitive inhibitory effects on metabolic enzyme activity during M1 macrophage polarization than M2 macrophage polarization. Citrate can block glycolysis-related enzymes by occupying the binding-site and ensure sufficient metabolic flux in the TCA cycle, so as to turn the metabolism of macrophages to oxidative phosphorylation of M2 macrophage, largely maintaining bone homeostasis. These studies indicate that exogenous citrate can realize metabolic control of macrophage polarization for maintaining bone homeostasis in osteoporosis.

A LIBS spectrum baseline correction method based on the non-parametric prior penalized least squares algorithm.

Laser-induced breakdown spectroscopy (LIBS) has become a popular element analysis technique because of its real-time multi-element detection and non-damage advantages. However, due to factors such as laser-substance interaction and the experimental environment, the measured LIBS spectrum signal contains a continuous background, severely influencing spectrum analysis. In this paper, we propose a LIBS spectrum baseline correction method based on the non-parametric prior penalized least squares (NPPPLS) algorithm. Compared with the traditional Penalized Least Squares (PLS) method, improvements have been made in two aspects. On the one hand, a new weight method with faster convergence is proposed. On the other hand, we combine the Adam algorithm and introduce the RMSE of the baseline correction result at the previous time to constrain the update of the balance parameter, which enables the balance parameter to be adjusted adaptively and no parameter prior is required. The simulation results show that the proposed NPPPLS algorithm can achieve excellent correction results, even with no parametric priors. In addition, the performance of the NPPPLS algorithm is not affected by the initial value of the balance parameter, and the stability and robustness are significantly improved. Finally, we conducted baseline correction of the experimental LIBS spectrum and performed univariate and multivariate analyses. The results show that the quantitative analysis accuracy is improved after baseline correction, and the correlation coefficient R2 of different elements obtained by the extreme learning machine method of multivariate analysis can reach 0.99, demonstrating a better quantitative analysis result. The simulation and experimental results verify the excellent performance of the proposed NPPPLS algorithm, which can be effectively used to improve the accuracy of quantitative analysis. In addition, this method is also expected to be used for baseline correction of the Raman spectrum, near-infrared spectrum and so on.

Recent Advances in Protein Folding Pathway Prediction through Computational Methods.

The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through specific structures, providing insights into the treatment and prevention of diseases. With the advancement of AI technology in the field of protein structure prediction, computational methods have become increasingly important and promising for studying protein folding mechanisms. In this review, we retrospect the current progress in the field of protein folding mechanisms by computational methods from four perspectives: simulation of an inverse folding pathway from native state to unfolded state; prediction of early folding residues by machine learning; exploration of protein folding pathways through conformational sampling; prediction of protein folding intermediates based on templates. Finally, the challenges and future perspectives of the protein folding problem by computational methods are also discussed.

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader.

Recognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we propose a method, PAthreader, to recognize remote templates and explore folding pathways. Firstly, we design a three-track alignment between predicted distance profiles and structure profiles extracted from PDB and AlphaFold DB, to improve the recognition accuracy of remote templates. Secondly, we improve the performance of AlphaFold2 using the templates identified by PAthreader. Thirdly, we explore protein folding pathways based on our conjecture that dynamic folding information of protein is implicitly contained in its remote homologs. The results show that the average accuracy of PAthreader templates is 11.6% higher than that of HHsearch. In terms of structure modelling, PAthreader outperform AlphaFold2 and ranks first on the CAMEO blind test for the latest three months. Furthermore, we predict protein folding pathways for 37 proteins, in which the results of 7 proteins are almost consistent with those of biological experiments, and the other 30 human proteins have yet to be verified by biological experiments, revealing that folding information can be exploited from remote homologous structures.

Interferon lambda modulates proinflammatory cytokines production in PBMCs from patients with chronic kidney disease.

BACKGROUND: CKD is a major cause of morbidity and mortality worldwide. Considerable evidence now indicates that renal inflammation plays a central role in the initiation and progression of CKD. Recent investigations have demonstrated that IFNλ plays an important role in the pathogenesis of autoimmune and inflammatory diseases. However, the association of IFNλ with CKD is still poorly understood. OBJECTIVE: To analyze the correlation between IFNλ levels and pro-inflammatory cytokines, and to investigate the effect of IFNλ on PBMCs in patients with CKD. METHODS: PBMCs were harvested from patients with CKD and healthy controls for measuring the expression level of inflammatory cytokines by RT-qPCR. Spearman correlation test was used to analyze correlation between IFNλ and cytokines as well as eGFR. PBMCs from healthy individuals and CKD patients were subjected to IFNλ protein stimulation. IL6, TNFα, IL10, ISG15 and MX1 mRNA level were measured by RT-PCR, STAT1 and phosphorylated STAT1 protein level were measured by Western blot. RESULTS: Patients with CKD showed higher levels of IFNλ in PBMCs compared to healthy controls. IFNλ mRNA levels were associated with cytokines and eGFR. The transcription of IL6, TNFα, and IL10 was significantly increased in healthy human PBMCs after IFNλ stimulation. In addition, IFNλ acts on PBMCs by p-STAT1 and ISG15 as well as MX1. CONCLUSION: High expression of IFNλ was found in CKD patients and was associated with eGFR and disease-related cytokines. More importantly, IFNλ promoted the expression of pro-inflammatory cytokines in PBMCs, suggesting a potential pro-inflammatory role of IFNλ in CKD.

Current understanding of CTLA-4: from mechanism to autoimmune diseases.

Autoimmune diseases (ADs) are characterized by the production of autoreactive lymphocytes, immune responses to self-antigens, and inflammation in related tissues and organs. Cytotoxic T-lymphocyte antigen 4 (CTLA-4) is majorly expressed in activated T cells and works as a critical regulator in the inflammatory response. In this review, we first describe the structure, expression, and how the signaling pathways of CTLA-4 participate in reducing effector T-cell activity and enhancing the immunomodulatory ability of regulatory T (Treg) cells to reduce immune response, maintain immune homeostasis, and maintain autoimmune silence. We then focused on the correlation between CTLA-4 and different ADs and how this molecule regulates the immune activity of the diseases and inhibits the onset, progression, and pathology of various ADs. Finally, we summarized the current progress of CTLA-4 as a therapeutic target for various ADs.

CYP2A6 and GABRA2 Gene Polymorphisms are Associated With Dexmedetomidine Drug Response.

Background: Dexmedetomidine is a commonly used clinical sedative; however, the drug response varies among individuals. Thus, the purpose of this study was to explore the association between dexmedetomidine response and gene polymorphisms related to drug-metabolizing enzymes and drug response (CYP2A6, UGT2B10, UGT1A4, ADRA2A, ADRA2B, ADRA2C, GABRA1, GABRB2, and GLRA1). Methods: This study was a prospective cohort study. A total of 194 female patients aged 18-60 years, American Society of Anesthesiologists (ASA) score I-II, who underwent laparoscopy at the Third Xiangya Hospital of Central South University, were included. The sedative effect was assessed every 2 min using the Ramsay score, and the patient's heart rate decrease within 20 min was recorded. Peripheral blood was collected from each participant to identify genetic variants in the candidate genes of metabolic and drug effects using the Sequenom MassARRAY® platform. Furthermore, additional peripheral blood samples were collected from the first 99 participants at multiple time points after dexmedetomidine infusion to perform dexmedetomidine pharmacokinetic analysis by Phoenix® WinNonlin 7.0 software. Results: Carriers of the minor allele (C) of CYP2A6 rs28399433 had lower metabolic enzyme efficiency and higher plasma concentrations of dexmedetomidine. In addition, the participants were divided into dexmedetomidine sensitive or dexmedetomidine tolerant groups based on whether they had a Ramsay score of at least four within 20 min, and CYP2A6 rs28399433 was identified to have a significant influence on the dexmedetomidine sedation sensitivity by logistic regression with Plink software [p = 0.003, OR (95% CI): 0.27 (0.11-0.65)]. C allele carriers were more sensitive to the sedative effects of dexmedetomidine than A allele carriers. GABRA2 rs279847 polymorphism was significantly associated with the degree of the heart rate decrease. In particular, individuals with the GG genotype had a 4-fold higher risk of heart rate abnormality than carriers of the T allele (OR = 4.32, 95% CI: 1.96-9.50, p = 0.00027). Conclusion: CYP2A6 rs28399433 polymorphism affects the metabolic rate of dexmedetomidine and is associated with susceptibility to the sedative effects of dexmedetomidine; GABRA2 rs279847 polymorphism is significantly associated with the degree of the heart rate decrease.