Genetic perturbation of IL-6 receptor signaling pathway and risk of multiple respiratory diseases.

Dysregulation of inflammation can lead to multiple chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. Interleukin-6 (IL6) is crucial in regulating the inflammatory cascade, but the causal link between IL6 signaling downregulation and respiratory diseases risk is unclear. This study uses Mendelian randomization to examine the effects of IL6R blockade on respiratory diseases. Analyzing data from 522,681 Europeans, 26 genetic variants were obtained to mimic IL6R inhibition. Our findings show that IL6R blockade significantly reduces the risk of COPD (OR = 0.71, 95% CI = 0.60-9.84) and asthma (OR = 0.82, 95% CI = 0.74-0.90), with protective trends for bronchitis, pulmonary embolism, and lung cancer. Results were consistent across methods, with no significant heterogeneity or pleiotropy. These insights suggest IL6R downregulation as a potential therapeutic target for respiratory diseases, meriting further clinical investigation.

The association between testosterone and serum soluble klotho in the females: evidence from the NHANES database.

Objective: This research aimed to elucidate the relationship between testosterone levels and serum soluble klotho (S-klotho) concentrations in females aged 40-79 years using the National Health and Nutrition Examination Survey (NHANES) dataset. Design: Associations between testosterone and S-klotho were assessed through multivariable linear regression methodologies, spanning nonadjusted, minimally adjusted, and fully adjusted models. Settings: The investigation was conducted as a cross-sectional analysis utilizing the NHANES database. Participants: From 20,146 NHANES participants between 2013 and 2016, 2,444 females met the stipulated inclusion and exclusion criteria. Results: Free androgen index (FAI) showcased a negative correlation with S-klotho levels across all regression models (nonadjusted: ß -7.08, 95% CI -13.39- -0.76; minimally adjusted: ß -9.73, 95% CI -16.6- -2.84; fully adjusted: ß -7.63, 95% CI -14.75-0.51). Conversely, total testosterone did not exhibit significant associations with S-klotho across the models. In the nonadjusted model, estradiol was positively associated with S-klotho concentrations (ß 0.14, 95% CI 0.05-0.23), but this significance was not retained in subsequent regression models. Conclusion: Findings suggest that in U.S. females aged 40-79 years, FAI negatively correlates with S-klotho concentrations, while there is the lack of significant associations for total testosterone and estradiol.

AbImmPred: An immunogenicity prediction method for therapeutic antibodies using AntiBERTy-based sequence features.

Due to the unnecessary immune responses induced by therapeutic antibodies in clinical applications, immunogenicity is an important factor to be considered in the development of antibody therapeutics. To a certain extent, there is a lag in using wet-lab experiments to test the immunogenicity in the development process of antibody therapeutics. Developing a computational method to predict the immunogenicity at once the antibody sequence is designed, is of great significance for the screening in the early stage and reducing the risk of antibody therapeutics development. In this study, a computational immunogenicity prediction method was proposed on the basis of AntiBERTy-based features of amino sequences in the antibody variable region. The AntiBERTy-based sequence features were first calculated using the AntiBERTy pre-trained model. Principal component analysis (PCA) was then applied to reduce the extracted feature to two dimensions to obtain the final features. AutoGluon was then used to train multiple machine learning models and the best one, the weighted ensemble model, was obtained through 5-fold cross-validation on the collected data. The data contains 199 commercial therapeutic antibodies, of which 177 samples were used for model training and 5-fold cross-validation, and the remaining 22 samples were used as an independent test dataset to evaluate the performance of the constructed model and compare it with other prediction methods. Test results show that the proposed method outperforms the comparison method with 0.7273 accuracy on the independent test dataset, which is 9.09% higher than the comparison method. The corresponding web server is available through the official website of GenScript Co., Ltd., https://www.genscript.com/tools/antibody-immunogenicity.

ProtT5 and random forests-based viscosity prediction method for therapeutic mAbs.

Viscosity is a key characteristic of therapeutic antibodies for subcutaneous administration which requires low volume and high concentration formulations. It would be highly beneficial to accurately predict the viscosity of newly developed therapeutic antibodies in the early stages of development. In this work, a ProtT5-XL-UniRef50 (ProtT5) and Random Forests (RF)-based prediction method was proposed for accurately predicting the viscosity of monoclonal antibodies, with only corresponding sequences needed. Starting from the given heavy and light chain V-region sequences, corresponding features were first extracted from the ProtT5 pretrained model. Kernel principal analysis (Kernel-PCA) was then used for reducing the extracted 2048-D (1024-D for each sequence) feature vector to a reasonable level for efficient training of the RF-regressor. Then, the RF model was constructed on 40 commercially available therapeutic antibodies and tested with 3-folds cross-validation. Test results show that the model could reproduce the viscosity value at a high level (Pearson correlation coefficient (PCC) = 0.928). Performance on classifying high (>30 cP) and low (<30 cP) viscosity is much more satisfactory, the Accuracy (ACC) and the area under precision-recall curve (AUC) of the classification model from validation tests are 0.975 and 1.000, respectively. Compared to 5 existing state-of-the-art viscosity prediction methods, the proposed method performs best which would facilitate high concentration antibody viscosity high-throughput screening.

Coevolutionary insights between promoters and transcription factors in the plant and animal kingdoms.

The divergence and continuous evolution of plants and animals contribute to ecological diversity. Promoters and transcription factors (TFs) are key determinants of gene regulation and transcription throughout life. However, the evolutionary trajectories and relationships of promoters and TFs are still poorly understood. Here, we conducted extensive analysis of large-scale multi-omics sequences in 420 animal species and 223 plant species spanning nearly a billion years of evolutionary history. Results showed that promoter GC-content and TF isoelectric points, as features/signatures that accompany long biological evolution, exhibited increasing growth in animal cells but a decreasing trend in plant cells. Furthermore, the evolutionary trajectories of promoter and TF signatures in the animal kingdom provided further evidence that Mammalia as well as Aves evolved directly from the ancestor Reptilia. The strong correlation between promoter and TF signatures indicates that promoters and TFs formed antagonistic coevolution in the animal kingdom, but mutualistic coevolution in the plant kingdom. The distinct coevolutionary patterns potentially drive the plant-animal divergence，divergent evolution and ecological diversity.

Targeting STT3A produces an anti-tumor effect in lung adenocarcinoma by blocking the MAPK and PI3K/AKT signaling pathway.

Background: Glycosylation is crucial for the stability and biological functions of proteins. The aberrant glycosylation of critical proteins plays an important role in multiple cancers, including lung adenocarcinoma (LUAD). STT3 oligosaccharyltransferase complex catalytic subunit A (STT3A) is a major isoform of N-linked glycosyltransferase that catalyzes the glycosylation of various proteins. However, the functions of STT3A in LUAD are still unclear. Methods: The expression profiles of STT3A were initially analyzed in public data sets and then validated by quantitative real-time polymerase chain reaction, Western blot and immunohistochemistry assays in clinical LUAD samples. The overall survival (OS) between patients with high and low STT3A expression was compared using a Kaplan-Meier curve with a log-rank analysis. STT3A was knocked-out using CRISPR/Cas9 and inhibited by NGI-1. Cell Counting Kit-8, colony formation assay, wound-healing, transwell assay, and flow cytometry were performed to assess the cellular functions of STT3A in vitro. A mice xenograft model was established to investigate the effects of STT3A on tumor growth in vivo. Further, the downstream signaling pathways of STT3A were screened by mass spectrometry with a bioinformatics analysis, and the activation of the target pathways were subsequently validated by Western blot. Results: The expression of STT3A was frequently upregulated in LUAD tissues than normal lung tissues. The high expression of STT3A was significantly associated with poor OS in LUAD patients. The knockout or inhibition of STT3A suppressed proliferation, migration, and invasion, and arrested the cell cycle of LUAD cell lines in vitro. Similarly, the knockout or inhibition of STT3A suppressed tumor growth in vivo. In terms of molecular mechanism, STT3A may promote LUAD progression by activating the mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-kinase and protein kinase B (PI3K/AKT) pathways and regulating the epithelial-mesenchymal transition. Conclusions: STT3A promotes LUAD progression via the MAPK and PI3K/AKT signaling pathways and could serve as a novel prognostic biomarker and potential therapeutic target for LUAD patients.

Risk factors for acute exacerbation of interstitial lung disease following lung cancer resection: a systematic review and meta-analysis.

OBJECTIVES: The aim of this study was to investigate the risk factors for acute exacerbation (AE) of interstitial lung disease (ILD) following lung cancer resection. METHODS: We performed a literature screening on the databases including PubMed, Embase, Ovid MEDLINE® and the Web of Science for related studies published up to January 2021. Eligible studies were included and data on risk factors related to postoperative AE were extracted. All analyses were performed with random-effect model. RESULTS: A total of 12 studies of 2655 lung cancer patients with ILD were included in this article. The meta-analysis indicated that male [odds ratios (ORs) = 1.78, 95% confidence interval (CI): 1.02-3.11, P = 0.041], usually interstitial pneumonia pattern on CT (OR = 1.52, 95% CI: 1.06-2.17, P = 0.021), Krebs von den Lungen-6 [standardized mean difference (SMD) = 0.50, 95% CI: 0.06-0.94, P = 0.027], white blood cell (SMD = 0.53, 95% CI: 0.12-0.93, P = 0.010), lactate dehydrogenase (SMD = 0.47, 95% CI: 0.04-0.90, P = 0.032), partial pressure of oxygen (weighted mean difference = -3.09, 95% CI: -5.99 to -0.19, P = 0.037), surgery procedure (OR = 2.31, 95% CI: 1.42-3.77, P < 0.001) and operation time (weighted mean difference = 28.26, 95% CI: 1.13-55.39, P = 0.041) were risk factors for AE of ILD following lung cancer resection. CONCLUSIONS: We found that males, usually interstitial pneumonia pattern on CT, higher levels of Krebs von den Lungen-6, lactate dehydrogenase, white blood cell, lower partial pressure of oxygen, greater scope of operation and longer operation time were risk factors for AE of ILD following lung cancer resection. Patients with these risk factors should be more prudently selected for surgical treatment and be monitored more carefully after surgery.

Electrocautery vs. Stapler in Comparing Safety for Segmentectomy of Lung Cancer: A Meta-Analysis.

Background: Electrocautery and staplers are regarded as the two most common surgical instruments for dissecting the intersegmental plane in segmentectomy. We performed a meta-analysis to compare electrocautery and staplers in terms of their safety and effects. Methods: A systematic search strategy was performed using PubMed, and the retrieval time was up to April 1, 2020. Odds ratio (OR) and mean differences (MDs) with 95% CI were applied to determine the effectiveness of dichotomous or continuous variables, respectively. Results: Six studies including 385 patients were included. The electrocautery had a higher incidence rate of postoperative complication [OR= 1.92, 95% CI (1.12, 3.28), P = 0.02)] and air leak [OR: 3.91, 95% CI (1.64, 9.35), P = 0.002)]. No significant difference was found in the comparison of surgery time, blood loss, and duration of tube days or hospitality days. Conclusions: Our study indicated that patients under segmentectomy were associated with better safety by using stapler than electrocautery in the reduction of postoperative complications.

Lobe-Specific Node Dissection Can Be a Suitable Alternative to Systematic Lymph Node Dissection in Highly Selective Early-Stage Non-Small-Cell Lung Cancer Patients: A Meta-Analysis.

PURPOSE: Whether the lobe-specific lymph node dissection is an alternative to systematic lymph node dissection for early-stage non-small-cell lung cancer remains controversial. An elaborate meta-analysis was conducted to evaluate the effects of lobe-specific lymph node dissection in early-stage patients. METHODS: A systematic literature search was conducted up to February 19, 2020 in PubMed, Ovid, Web of Science, and China National Knowledge Infrastructure databases. The outcomes including overall survival (OS), complications, and recurrence rate were extracted and analyzed. RESULTS: Nine studies including one randomized controlled trial (RCT) and eight retrospective cohort studies with 8499 non-small-cell lung cancer patients were included. The results indicated that lobe-specific lymph node had a lower rate of postoperative complication (relative risk [RR]: 0.83, 95% confidence interval [CI]: 0.72-0.95, P = 0.006). No significant difference was observed between lobe-specific lymph node and systematic lymph node dissection in OS (hazard rate = 1.12, 95% CI: 0.81-1.54, P = 0.501) with high heterogeneity (I2 = 71.9%). CONCLUSION: Lobe-specific lymph node can reach a comparable long-term prognosis in some highly selected patients. However, these results should be viewed cautiously with the existence of high heterogeneity. Due to the high heterogeneity, a strict patient selection process by experienced thoracic surgeons was recommended before validating lobe-specific lymph node.

The accuracy of Raman spectroscopy in the diagnosis of lung cancer: a systematic review and meta-analysis.

BACKGROUND: To investigate the overall performance of Raman Spectroscopy (RS) in the diagnosis of lung cancer. METHODS: We systematic searched databases including PubMed, EMBASE, CNKI and Web of science for studies up to May 2020 with no start date limited. Then we extracted data of true positives, true negatives, false positives and false negatives from the included studies to calculate the pooled sensitivity, specificity, accuracy, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR), with 95% confidence intervals to evaluate the diagnostic value of Raman spectroscopy. We plotted the summary receiver operator characteristics (SROC) and the area under the curve (AUC) to evaluate the overall performance of Raman spectroscopy. Quality assessments and Publication bias were evaluated by QUADAS-2 checklist and Stata software version 12.0. RESULTS: Totally, 12 studies were included in our meta-analysis. The pooled diagnostic sensitivity and specificity of Raman Spectroscopy in lung cancer were 0.90 (95% CI, 0.87-0.92, P<0.05) and 0.76 (95% CI, 0.72-0.79, P<0.05). The pooled PLR and NLR were 5.87 (95% CI, 3.45-9.97) and 0.14 (95% CI, 0.10-0.22) respectively. And the AUC of SROC curve was 0.9453. DISCUSSION: Raman spectroscopy had excellent accuracy with high sensitivity and considerable specificity in the diagnosis of lung cancer.

Prognostic significance of the N1 classification pattern: a meta-analysis of different subclassification methods.

OBJECTIVES: The number of positive lymph node stations has been viewed as a subclassification in the N1 category in the new revision of tumour node metastasis (TNM) staging. However, the survival curve of these patients overlapped with that of some patients in the N2 categories. Our study focused on the prognostic significance of different subclassifications for N1 patients. METHODS: We systematically searched PubMed, Ovid, Web of Science and the Cochrane Library on the topic of N1 lymph node dissection. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were used to assess the prognostic significance of N1 metastases. I2 statistics was used to evaluate heterogeneity among the studies: If significant heterogeneity existed (P ≤ 0.10; I2 >50%), a random effect model was adopted. RESULTS: After a careful investigation, a total of 17 articles were included in the analysis. The results showed that patients with non-small-cell lung cancer with multistation N1 disease have worse survival compared with those with single-station N1 disease (HR 1.53, 95% CI 1.32-1.77; P < 0.001; I2 = 5.1%). No significant difference was observed between groups when we assessed the number of positive lymph nodes (single or multiple) (HR 1.25, 95% CI 0.96-1.64; P = 0.097; I2 = 72.5%). Patients with positive hilar zone lymph nodes had poorer survival than those limited to the intrapulmonary zone (HR 1.80, 95% CI 1.57-2.07; P < 0.001; I2 = 0%). A subgroup analysis conducted according to the different validated lymph node maps showed a stable result. CONCLUSIONS: Our result confirmed the prognostic significance of the N1 subclassification based on station number. Meanwhile, location-based classifications, especially zone-based, were also identified as prognostically significant, which may need further confirmation and validation in the staged population.

The Favorable Prognostic Factors for Superior Sulcus Tumor: A Systematic Review and Meta-Analysis.

Background: Superior sulcus tumor is a rare non-small cell lung cancer with poor prognosis. Exploring the potential prognostic factors of patients with superior sulcus tumor and adopting individualized treatment for patients with different prognostic factors are of great significance for the prolongation of patients' lives. To figure out the prognostic factors of upper sulcus tumors, a meta-analysis was conducted. Method: We searched all the articles published until January 2020 in PubMed, Embase, and Web of Science databases, and the search strategy included the following terms, combining superior sulcus tumor and prognosis. Hazard ratio (HR) with associated confidential interval (CI) was evaluated for the purpose of investigating prognostic factors for superior sulcus tumor. STATA 16.0 was used for analysis of extracted data and assessment of publication bias. Result: Fifteen eligible studies, which had 1,009 patients with superior sulcus tumor, were included in this meta-analysis. The studies were published between 1994 and 2018, and the patient recruitment periods ranged from 1974 to 2016. The median follow-up time ranged from 18 to 95 months. The meta-analysis indicated that lower T stage (HR, 1.63; 95% CI, 1.35-1.97), lower N stage (HR, 3.08; 95% CI: 2.37-3.99), negative surgical margin (HR, 0.25; 95% CI, 0.17-0.38), and pathologic complete response (HR, 0.55; 95% CI, 0.39-0.77) were favorable prognostic factors. Conclusion: We found that T stage, N stage, surgical margin, and pathologic complete response are prognostic factors for superior sulcus tumor. To reach a better long-term survival, patients with these negative prognostic factors may need a more aggressive treatment, while more studies should be conducted to further validate these results and explore a more effective treatment.

Chylothorax after Lung Cancer Surgery: A Key Factor Influencing Prognosis and Quality of Life.

Chylothorax is caused by the accumulation of chylous fluid in the pleural cavity due to the injury of the thoracic duct or its tributaries. Chylothorax following lung cancer surgery, especially pulmonary resection and mediastinal lymph node dissection, is a raw potential postoperative complication as previously reported. Chylothorax might lead to a high mortality rate if not addressed in a timely fashion. This article reviews the anatomy of the thoracic duct, risk factors of postoperative chylothorax, diagnoses and management with chylothorax, and intraoperative prevention of chylothorax. With the development of researches on postoperative chylothorax, more effective treatment and prevention measures need to be proposed to better solve this clinical problem.

Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line.

Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs, but attempts to combine different algorithms and optimize them on large data sets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein, we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in the identification of 540 SAAVs in the mass spectrometry data. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using the novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53, and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.

Loop Enhanced Conformational Resampling Method for Protein Structure Prediction.

Protein structure prediction has been a long-standing problem for the past decades. In particular, the loop region structure remains an obstacle in forming an accurate protein tertiary structure because of its flexibility. In this study, Rama torsion angle and secondary structure feature-guided differential evolution named RSDE is proposed to predict three-dimensional structure with the exploitation on the loop region structure. In RSDE, the structure of the loop region is improved by the following: loop-based cross operator, which interchanges configuration of a randomly selected loop region between individuals, and loop-based mutate operator, which considers torsion angle feature into conformational sampling. A stochastic ranking selective strategy is designed to select conformations with low energy and near-native structure. Moreover, the conformational resampling method, which uses previously learned knowledge to guide subsequent sampling, is proposed to improve the sampling efficiency. Experiments on a total of 28 test proteins reveals that the proposed RSDE is effective and can obtain native-like models.

Guiding exploration in conformational feature space with Lipschitz underestimation for ab-initio protein structure prediction.

Computing conformations which are essential to associate structural and functional information with gene sequences, is challenging due to the high dimensionality and rugged energy surface of the protein conformational space. Consequently, the dimension of the protein conformational space should be reduced to a proper level, and an effective exploring algorithm should be proposed. In this paper, a plug-in method for guiding exploration in conformational feature space with Lipschitz underestimation (LUE) for ab-initio protein structure prediction is proposed. The conformational space is converted into ultrafast shape recognition (USR) feature space firstly. Based on the USR feature space, the conformational space can be further converted into Underestimation space according to Lipschitz estimation theory for guiding exploration. As a consequence of the use of underestimation model, the tight lower bound estimate information can be used for exploration guidance, the invalid sampling areas can be eliminated in advance, and the number of energy function evaluations can be reduced. The proposed method provides a novel technique to solve the exploring problem of protein conformational space. LUE is applied to differential evolution (DE) algorithm, and metropolis Monte Carlo(MMC) algorithm which is available in the Rosetta; When LUE is applied to DE and MMC, it will be screened by the underestimation method prior to energy calculation and selection. Further, LUE is compared with DE and MMC by testing on 15 small-to-medium structurally diverse proteins. Test results show that near-native protein structures with higher accuracy can be obtained more rapidly and efficiently with the use of LUE.

Conformational Space Sampling Method Using Multi-Subpopulation Differential Evolution for De novo Protein Structure Prediction.

Protein structure prediction can be considered as a multimodal optimization problem for sampling the protein conformational space associated with an extremely complex energy landscape. To address this problem, a conformational space sampling method using multi-subpopulation differential evolution, MDE, is proposed. MDE first devotes to generate given numbers of concerned modal under the ultrafast shape recognition-based modal identification protocol, which regards each individual as one modal at beginning. Then, differential evolution is used for keeping the preserved modal survival in the evolution process. Meanwhile, a local descent direction used to sample along with is constructed based on the abstract convex underestimate technique for modal enhancement, which could enhance the ability of sampling in the region with lower energy. Through the sampling process of evolution, several certain clusters contain a series of conformations in proportion to the energy score will be obtained. Representative conformations in the generated clusters can be directly picked out as decoy conformations for further refinement with no extra clustering operation needs. A total of 20 target proteins are tested, in which ten target proteins are tested for comparison with Rosetta and three evolutionary algorithms, and ten easy/hard target proteins in CASP 11 are tested for further verifying the effectiveness of MDE. Test results show strong sampling ability that MDE holds, and near-native conformations can be effectively obtained.

Enhancing Protein Conformational Space Sampling Using Distance Profile-Guided Differential Evolution.

De novo protein structure prediction aims to search for low-energy conformations as it follows the thermodynamics hypothesis that places native conformations at the global minimum of the protein energy surface. However, the native conformation is not necessarily located in the lowest-energy regions owing to the inaccuracies of the energy model. This study presents a differential evolution algorithm using distance profile-based selection strategy to sample conformations with reasonable structure effectively. In the proposed algorithm, besides energy, the residue-residue distance is considered another measure of the conformation. The average distance errors of decoys between the distance of each residue pair and the corresponding distance in the distance profiles are first calculated when the trial conformation yields a larger energy value than that of the target. Then, the distance acceptance probability of the trial conformation is designed based on distance profiles if the trial conformation obtains a lower average distance error compared with that of the target conformation. The trial conformation is accepted to the next generation in accordance with its distance acceptance probability. By using the dual constraints of energy and distance in guiding sampling, the algorithm can sample conformations with lower energies and more reasonable structures. Experimental results of 28 benchmark proteins show that the proposed algorithm can effectively predict near-native protein structures.

A Novel Method Using Abstract Convex Underestimation in Ab-Initio Protein Structure Prediction for Guiding Search in Conformational Feature Space.

To address the searching problem of protein conformational space in ab-initio protein structure prediction, a novel method using abstract convex underestimation (ACUE) based on the framework of evolutionary algorithm was proposed. Computing such conformations, essential to associate structural and functional information with gene sequences, is challenging due to the high-dimensionality and rugged energy surface of the protein conformational space. As a consequence, the dimension of protein conformational space should be reduced to a proper level. In this paper, the high-dimensionality original conformational space was converted into feature space whose dimension is considerably reduced by feature extraction technique. And, the underestimate space could be constructed according to abstract convex theory. Thus, the entropy effect caused by searching in the high-dimensionality conformational space could be avoided through such conversion. The tight lower bound estimate information was obtained to guide the searching direction, and the invalid searching area in which the global optimal solution is not located could be eliminated in advance. Moreover, instead of expensively calculating the energy of conformations in the original conformational space, the estimate value is employed to judge if the conformation is worth exploring to reduce the evaluation time, thereby making computational cost lower and the searching process more efficient. Additionally, fragment assembly and the Monte Carlo method are combined to generate a series of metastable conformations by sampling in the conformational space. The proposed method provides a novel technique to solve the searching problem of protein conformational space. Twenty small-to-medium structurally diverse proteins were tested, and the proposed ACUE method was compared with It Fix, HEA, Rosetta and the developed method LEDE without underestimate information. Test results show that the ACUE method can more rapidly and more efficiently obtain the near-native protein structure.