Metformin induces tolerogenicity of dendritic cells by promoting metabolic reprogramming.

Dendritic cells (DCs) can mediate immune responses or immune tolerance depending on their immunophenotype and functional status. Remodeling of DCs' immune functions can develop proper therapeutic regimens for different immune-mediated diseases. In the immunopathology of autoimmune diseases (ADs), activated DCs notably promote effector T-cell polarization and exacerbate the disease. Recent evidence indicates that metformin can attenuate the clinical symptoms of ADs due to its anti-inflammatory properties. Whether and how the therapeutic effects of metformin on ADs are associated with DCs remain unknown. In this study, metformin was added to a culture system of LPS-induced DC maturation. The results revealed that metformin shifted DC into a tolerant phenotype, resulting in reduced surface expression of MHC-II, costimulatory molecules and CCR7, decreased levels of proinflammatory cytokines (TNF-α and IFN-γ), increased level of IL-10, upregulated immunomodulatory molecules (ICOSL and PD-L) and an enhanced capacity to promote regulatory T-cell (Treg) differentiation. Further results demonstrated that the anti-inflammatory effects of metformin in vivo were closely related to remodeling the immunophenotype of DCs. Mechanistically, metformin could mediate the metabolic reprogramming of DCs through FoxO3a signaling pathways, including disturbing the balance of fatty acid synthesis (FAS) and fatty acid oxidation (FAO), increasing glycolysis but inhibiting the tricarboxylic acid cycle (TAC) and pentose phosphate pathway (PPP), which resulted in the accumulation of fatty acids (FAs) and lactic acid, as well as low anabolism in DCs. Our findings indicated that metformin could induce tolerance in DCs by reprogramming their metabolic patterns and play anti-inflammatory roles in vitro and in vivo.

IGTCM: An integrative genome database of traditional Chinese medicine plants.

Fully understanding traditional Chinese medicines (TCMs) is still challenging because of the extreme complexity of their chemical components and mechanisms of action. The TCM Plant Genome Project aimed to obtain genetic information, determine gene functions, discover regulatory networks of herbal species, and elucidate the molecular mechanisms involved in the disease prevention and treatment, thereby accelerating the modernization of TCMs. A comprehensive database that contains TCM-related information will provide a vital resource. Here, we present an integrative genome database of TCM plants (IGTCM) that contains 14,711,220 records of 83 annotated TCM-related herb genomes, including 3,610,350 genes, 3,534,314 proteins and corresponding coding sequences, and 4,032,242 RNAs, as well as 1033 non-redundant component records for 68 herbs, downloaded and integrated from the GenBank and RefSeq databases. For minimal interconnectivity, each gene, protein, and component was annotated using the eggNOG-mapper tool and Kyoto Encyclopedia of Genes and Genomes database to acquire pathway information and enzyme classifications. These features can be linked across several species and different components. The IGTCM database also provides visualization and sequence similarity search tools for data analyses. These annotated herb genome sequences in IGTCM database are a necessary resource for systematically exploring genes related to the biosynthesis of compounds that have significant medicinal activities and excellent agronomic traits that can be used to improve TCM-related varieties through molecular breeding. It also provides valuable data and tools for future research on drug discovery and the protection and rational use of TCM plant resources. The IGTCM database is freely available at http://yeyn.group:96/.

Using Fully Convolutional Network to Locate Transcription Factor Binding Sites Based on DNA Sequence and Conservation Information.

Transcription factors (TFs) play a part in gene expression. TFs can form complex gene expression regulation system by combining with DNA. Thereby, identifying the binding regions has become an indispensable step for understanding the regulatory mechanism of gene expression. Due to the great achievements of applying deep learning (DL) to computer vision and language processing in recent years, many scholars are inspired to use these methods to predict TF binding sites (TFBSs), achieving extraordinary results. However, these methods mainly focus on whether DNA sequences include TFBSs. In this paper, we propose a fully convolutional network (FCN) coupled with refinement residual block (RRB) and global average pooling layer (GAPL), namely FCNARRB. Our model could classify binding sequences at nucleotide level by outputting dense label for input data. Experimental results on human ChIP-seq datasets show that the RRB and GAPL structures are very useful for improving model performance. Adding GAPL improves the performance by 9.32% and 7.61% in terms of IoU (Intersection of Union) and PRAUC (Area Under Curve of Precision and Recall), and adding RRB improves the performance by 7.40% and 4.64%, respectively. In addition, we find that conservation information can help locate TFBSs.

Development and validation of a nomogram model for cancer-specific survival of patients with poorly differentiated thyroid carcinoma: A SEER database analysis.

Background: This study aimed to establish and validate an accurate prognostic model, based on demographic and clinical parameters, for predicting the cancer-specific survival (CSS) of patients with poorly differentiated thyroid carcinoma (PDTC). Materials and methods: Patients diagnosed with PDTC between 2004 to 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Randomly split the data into training and validation sets. Kaplan-Meier analysis with the log-rank test was performed to compare the survival distribution among cases. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent prognostic factors, which were subsequently utilized to construct a nomogram for predicting the 5- and 10-year cancer-specific survival of patients with PDTC. The discriminative ability and calibration of the nomogram model were assessed using the concordance index and calibration plots, respectively. In addition, we performed a decision curve analysis to assess the clinical value of the nomogram. Simultaneously, we compared the predictive performance of the nomogram model against that of the American Joint Committee on Cancer (AJCC) T-, N-, M-stage. Results: A total of 970 eligible patients were randomly assigned to either a training cohort (n = 679) or a validation cohort (n = 291). The Kaplan-Meier analysis revealed that there were no significant differences in cumulative survival based on the race, radiation, and marital status of patients. The stepwise Cox regression model showed that the model was optimal when the following five variables were included: age, tumor size, T-, N-, and M-stage. A nomogram was developed as a graphical representation of the model and exhibited good calibration and discriminative ability in the study. Compared to the T-, N-, and M-stage, the C-index of nomogram (training group: 0.807, validation group: 0.802), the areas under the receiver operating characteristic curve of the training set (5-year AUC: 0.843, 10-year AUC:0.834) and the validation set (5-year AUC:0.878, 10-year AUC:0.811), and the calibration plots of this model all exhibited better performance. At last, compared with T-, N-, and M-stage, the decision curve analysis indicated that the nomogram had excellent clinical net benefit. Conclusions: The nomogram developed by us can accurately predict the CSS of PDTC patients. It can help clinicians determine appropriate treatment strategies for poorly differentiated thyroid carcinoma patients.

Differences Between Cancer-Specific Survival of Patients With Anaplastic and Primary Squamous Cell Thyroid Carcinoma and Factors Influencing Prognosis: A SEER Database Analysis.

Purpose: Anaplastic thyroid carcinoma (ATC) and primary squamous cell carcinoma of the thyroid (PSCCTh) have similar histological findings and are currently treated using the same approaches; however, the characteristics and prognosis of these cancers are poorly researched. The objective of this study was to determine the differences in characteristics between ATC and PSCCTh and establish prognostic models. Patients and Methods: All variables of patients with ATC and PSCCTh, diagnosed from 2004-2015, were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) database. Percentage differences for categorical data were compared using the Chi-square test. Kaplan-Meier curves, log-rank test, and Cox-regression for survival analysis, and C-index value was used to evaluate the performance of the prognostic models. Results: After application of the inclusion and exclusion criteria, a total of 1164 ATC and 124 PSCCTh patients, diagnosed from 2004 to 2015, were included in the study. There were no differences in sex, ethnicity, age, marital status, or percentage of proximal metastases between the two cancers; however, radiotherapy, chemotherapy, incidence of surgical treatment, and presence of multiple primary tumors were higher in patients with ATC than those with PSCCTh. Further cancer-specific survival (CSS) of patients with PSCCTh was better than that of patients with ATC. Prognostic factors were not identical for the two cancers. Multivariate Cox model analysis indicated that age, sex, radiotherapy, chemotherapy, surgery, multiple primary tumors, marital status, and distant metastasis status are independent prognostic factors for CSS in patients with ATC, while for patients with PSCCTh, the corresponding factors are age, radiotherapy, multiple primary tumors, and surgery. The C-index values of the two models were both > 0.8, indicating that the models exhibited good discriminative ability. Conclusion: Prognostic factors influencing CSS were not identical in patients with ATC and PSCCTh. These findings indicate that different clinical treatment and management plans are required for patients with these two types of thyroid cancer.

Regulation of biomaterial implantation-induced fibrin deposition to immunological functions of dendritic cells.

The performance of implanted biomaterials is largely determined by their interaction with the host immune system. As a fibrous-like 3D network, fibrin matrix formed at the interfaces of tissue and material, whose effects on dendritic cells (DCs) remain unknown. Here, a bone plates implantation model was developed to evaluate the fibrin matrix deposition and DCs recruitment in vivo. The DCs responses to fibrin matrix were further analyzed by a 2D and 3D fibrin matrix model in vitro. In vivo results indicated that large amount of fibrin matrix deposited on the interface between the tissue and bone plates, where DCs were recruited. Subsequent in vitro testing denoted that DCs underwent significant shape deformation and cytoskeleton reorganization, as well as mechanical property alteration. Furthermore, the immune function of imDCs and mDCs were negatively and positively regulated, respectively. The underlying mechano-immunology coupling mechanisms involved RhoA and CDC42 signaling pathways. These results suggested that fibrin plays a key role in regulating DCs immunological behaviors, providing a valuable immunomodulatory strategy for tissue healing, regeneration and implantation.

Quantitative elucidation of associations between nucleotide identity and physicochemical properties of amino acids and the functional insight.

Studies on codon property would deepen our understanding of the origin of primitive life and enlighten biotechnical application. Here, we proposed a quantitative measurement of codon-amino acid association and found that seven out of 13 physicochemical properties have stronger associations with the nucleotide identity at the second codon position, indicating that protein structure and function may associate more closely with it than the other two sites. When extending the effect of codon-amino acid association to protein level, it was found that the correlation between the second codon position (measured by the relative frequencies of nucleobase T and A at this codon site) and hydrophobicity (by the form of GRAVY value) became stronger with 96% genomes having R > 0.90 and p < 1e-60. Furthermore, we revealed that informational genes encoding proteins have lower GRAVY values than operational proteins (p < 3e-37) in both prokaryotic and eukaryotic genomes. The above results reveal a complete link from codon identity (A2 versus T2) to amino acid property (hydrophilic versus hydrophobic) and then to protein functions (informational versus operational). Hence, our work may help to understand how the nucleotide sequence determines protein function.

Spectroscopic and Theoretical Investigation of ß-Lactoglobulin Interactions with Hematoporphyrin and Protoporphyrin IX.

Hematoporphyrin (HP) and protoporphyrin IX (PPIX) are useful porphyrin photosensitizers with significant application values in photodynamic therapy. Currently, many strategies have been developed to improve their clinical performance, such as incorporating them with nanoparticle (NP) carriers. In this work, we studied the possibility of using ß-lactoglobulin (BLG) as a potential NP carrier due to their hydrophobic affinity, pH sensitivity, and low cost of extraction and preservation. The interaction mechanisms of BLG with HP and PPIX were investigated using spectroscopic techniques and molecular docking methods. The molecular docking results agree well with the experimental results, which demonstrate that the formations of HP-BLG and PPIX-BLG complexes are endothermic processes and the main acting force is hydrophobic force. Furthermore, the opening-closure states of EF loop have a great influence on the HP-BLG complex formation, where the central hydrophobic cavity of ß-barrel is available for HP binding at pH 7.4 but not available at pH 6.2. However, the formation of the PPIX-BLG complex is less dependent on the states of the EF loop, and the binding sites of PPIX are both located on the external surface of BLG under both pH 7.4 and 6.2 conditions. All of our results would provide new insight into the mechanisms of noncovalent interactions between BLG and HP/PPIX. It is believed that this work indicated the potential application values of BLG in designing pH-sensitive carriers for the delivery of HP and PPIX, as well as other poorly soluble drugs.

Development of an Immunogenomic Landscape-Based Prognostic Index of Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the eighth leading cancer by incidence worldwide, with approximately 700,000 new cases in 2018 (accounting for 11% of all cancers). The occurrence and development of tumors are closely related to the immunological function of the body and sensitivity to treatment schemes as well as prognosis. It is urgent for clinicians to systematically study patients' immune gene maps to help select a treatment plan and analyze the potential to cure HNSCC. Here, the transcriptomic data of HNSCC samples were downloaded from The Cancer Genome Atlas (TCGA), and 4,793 genes differentially expressed in normal and cancer tissues of HNSCC were identified, including 1,182 downregulated and 3,611 upregulated genes. From these genes, 400 differentially expressed immune-related genes (IRGs) were extracted, including 95 downregulated genes and 305 upregulated genes. The prognostic values of IRGs were evaluated by univariate Cox analysis, and 236 genes that were significantly related to the overall survival (OS) of patients were identified. The signaling pathways that play roles in the prognosis of IRGs were investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and the expression profiles of IRGs and OS in 499 HNSCC patients based on TCGA dataset were integrated. Potential molecular mechanisms and characteristics of these HNSCC-specific IRGs were further explored with the help of a new prognostic index based on IRGs developed by least absolute shrinkage and selection operator (LASSO) Cox analysis. A total of 64 hub genes (IRGs associated with prognosis) were markedly associated with the clinical outcome of HNSCC patients. KEGG functional enrichment analysis revealed that these genes were actively involved in several pathways, e.g., cytokine-cytokine receptor interaction, T-cell receptor signaling, and natural killer cell-mediated cytotoxicity. IRG-based prognostic signatures performed moderately in prognostic predictions. Interestingly, the prognostic index based on IRGs reflected infiltration by several types of immune cells. These data screened several IRGs of clinical significance and revealed drivers of the immune repertoire, demonstrating the importance of a personalized IRG-based immune signature in the recognition, surveillance, and prognosis of HNSCC.

CEG 2.0: an updated database of clusters of essential genes including eukaryotic organisms.

Essential genes are key elements for organisms to maintain their living. Building databases that store essential genes in the form of homologous clusters, rather than storing them as a singleton, can provide more enlightening information such as the general essentiality of homologous genes in multiple organisms. In 2013, the first database to store prokaryotic essential genes in clusters, CEG (Clusters of Essential Genes), was constructed. Afterward, the amount of available data for essential genes increased by a factor >3 since the last revision. Herein, we updated CEG to version 2, including more prokaryotic essential genes (from 16 gene datasets to 29 gene datasets) and newly added eukaryotic essential genes (nine species), specifically the human essential genes of 12 cancer cell lines. For prokaryotes, information associated with drug targets, such as protein structure, ligand-protein interaction, virulence factor and matched drugs, is also provided. Finally, we provided the service of essential gene prediction for both prokaryotes and eukaryotes. We hope our updated database will benefit more researchers in drug targets and evolutionary genomics. Database URL: http://cefg.uestc.cn/ceg.

A Mathematical Modelling of Initiation of Dendritic Cells-Induced T Cell Immune Response.

Dendritic cells (DCs) are the most potent specialized antigen-presenting cells as now known, which play a crucial role in initiating and amplifying both the innate and adaptive immune responses. Immunologically, the motilities and T cell activation capabilities of DCs are closely related to the resulting immune responses. However, due to the complexity of the immune system, the dynamic changes in the number of cells during the peripheral tissue (e.g. skin and mucosa) immune response induced by DCs are still poorly understood. Therefore, this study simulated dynamic number changes of DCs and T cells in this process by constructing several ordinary differential equations and setting the initial conditions of the functions and parameters. The results showed that these equations could simulate dynamic numerical changes of DCs and T cells in peripheral tissue and lymph node, which was in accordance with the physiological conditions such as the duration of immune response, the proliferation rates and the motilities of DCs and T cells. This model provided a theoretical reference for studying the immunologic functions of DCs and practical guidance for the clinical DCs-based therapy against immune-related diseases.

Vascular endothelial growth factor (VEGF) impairs the motility and immune function of human mature dendritic cells through the VEGF receptor 2-RhoA-cofilin1 pathway.

Dendritic cells (DCs) are potent and specialized antigen presenting cells, which play a crucial role in initiating and amplifying both the innate and adaptive immune responses against cancer. Tumor cells can escape from immune attack by secreting suppressive cytokines that solely or cooperatively impair the immune function of DCs. However, the underlying mechanisms are not fully defined. Vascular endothelial growth factor (VEGF) has been identified as a major cytokine in the tumor microenvironment. To elucidate the effects of VEGF on the motility and immune function of mature DCs (mDCs), the cells were treated with 50 ng/mL VEGF and investigated by proteomics and molecular biological technologies. The results showed that VEGF can impair the migration capacity and immune function of mDCs through the RhoA-cofilin1 pathway mediated by the VEGF receptor 2, suggesting impaired motility of mDCs by VEGF is one of the aspects of immune escape mechanisms of tumors. It is clinically important to understand the biological behavior of DCs and the immune escape mechanisms of tumor as well as how to improve the efficiency of antitumor therapy based on DCs.

Mutation Landscape of Base Substitutions, Duplications, and Deletions in the Representative Current Cholera Pandemic Strain.

Pandemic cholera is a major concern for public health because of its high mortality and morbidity. Mutation accumulation (MA) experiments were performed on a representative strain of the current cholera pandemic. Although the base-pair substitution mutation rates in Vibrio cholerae (1.24 × 10-10 per site per generation for wild-type lines and 3.29 × 10-8 for mismatch repair deficient lines) are lower than that previously reported in other bacteria using MA analysis, we discovered specific high rates (8.31 × 10-8 site/generation for wild-type lines and 1.82 × 10-6 for mismatch repair deficient lines) of base duplication or deletion driven by large-scale copy number variations (CNVs). These duplication-deletions are located in two pathogenic islands, IMEX and the large integron island. Each element of these islands has discrepant rate in rapid integration and excision, which provides clues to the pandemicity evolution of V. cholerae. These results also suggest that large-scale structural variants such as CNVs can accumulate rapidly during short-term evolution. Mismatch repair deficient lines exhibit a significantly increased mutation rate in the larger chromosome (Chr1) at specific regions, and this pattern is not observed in wild-type lines. We propose that the high frequency of GATC sites in Chr1 improves the efficiency of MMR, resulting in similar rates of mutation in the wild-type condition. In addition, different mutation rates and spectra were observed in the MA lines under distinct growth conditions, including minimal media, rich media and antibiotic treatments.

IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models.

Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and in silico data to gain an additional insight into microbial physiology. The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.

SSER: Species specific essential reactions database.

BACKGROUND: Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction would be a better option than targeting the enzymes or the corresponding enzyme-encoding gene. The existing databases such as BRENDA, BiGG, KEGG, Bio-models, Biosilico, and many others offer useful and comprehensive information on biochemical reactions. But none of these databases especially focus on essential reactions. Therefore, building a centralized repository for this class of reactions would be of great value. DESCRIPTION: Here, we present a species-specific essential reactions database (SSER). The current version comprises essential biochemical and transport reactions of twenty-six organisms which are identified via flux balance analysis (FBA) combined with manual curation on experimentally validated metabolic network models. Quantitative data on the number of essential reactions, number of the essential reactions associated with their respective enzyme-encoding genes and shared essential reactions across organisms are the main contents of the database. CONCLUSION: SSER would be a prime source to obtain essential reactions data and related gene and metabolite information and it can significantly facilitate the metabolic network models reconstruction and analysis, and drug target discovery studies. Users can browse, search, compare and download the essential reactions of organisms of their interest through the website http://cefg.uestc.edu.cn/sser .

Laribacter hongkongensis anaerobic adaptation mediated by arginine metabolism is controlled by the cooperation of FNR and ArgR.

Laribacter hongkongensis is a fish-borne pathogen associated with invasive infections and gastroenteritis. Its adaptive mechanisms to oxygen-limiting conditions in various environmental niches remain unclear. In this study, we compared the transcriptional profiles of L. hongkongensis under aerobic and anaerobic conditions using RNA-sequencing. Expression of genes involved in arginine metabolism significantly increased under anoxic conditions. Arginine was exploited as the sole energy source in L. hongkongensis for anaerobic respiration via the arginine catabolism pathway: specifically via the arginine deiminase (ADI) pathway. A transcriptional regulator FNR was identified to coordinate anaerobic metabolism by tightly regulating the expression of arginine metabolism genes. FNR executed its regulatory function by binding to FNR boxes in arc operons promoters. Survival of isogenic fnr mutant in macrophages decreased significantly when compared with wild-type; and expression level of fnr increased 8 h post-infection. Remarkably, FNR directly interacted with ArgR, another regulator that influences the biological fitness and intracellular survival of L. hongkongensis by regulating arginine metabolism genes. Our results demonstrated that FNR and ArgR work in coordination to respond to oxygen changes in both extracellular and intracellular environments, by finely regulating the ADI pathway and arginine anabolism pathways, thereby optimizing bacterial fitness in various environmental niches.

A novel proposal of a simplified bacterial gene set and the neo-construction of a general minimized metabolic network.

A minimal gene set (MGS) is critical for the assembly of a minimal artificial cell. We have developed a proposal of simplifying bacterial gene set to approximate a bacterial MGS by the following procedure. First, we base our simplified bacterial gene set (SBGS) on experimentally determined essential genes to ensure that the genes included in the SBGS are critical. Second, we introduced a half-retaining strategy to extract persistent essential genes to ensure stability. Third, we constructed a viable metabolic network to supplement SBGS. The proposed SBGS includes 327 genes and required 431 reactions. This report describes an SBGS that preserves both self-replication and self-maintenance systems. In the minimized metabolic network, we identified five novel hub metabolites and confirmed 20 known hubs. Highly essential genes were found to distribute the connecting metabolites into more reactions. Based on our SBGS, we expanded the pool of targets for designing broad-spectrum antibacterial drugs to reduce pathogen resistance. We also suggested a rough semi-de novo strategy to synthesize an artificial cell, with potential applications in industry.

Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots.

Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics. Inspired by the principle of Z curve theory, we improved PseDNC to give the phase-specific PseDNC (psPseDNC). In this study, we used the prediction of recombination spots as a case to illustrate the capability of psPseDNC and also PseDNC fused with Z curve theory based on a novel machine learning method named large margin distribution machine (LDM). We verified that combining the two widely used approaches could generate better performance compared to only using PseDNC with a support vector machine based (SVM-based) model. The best Mathew's correlation coefficient (MCC) achieved by our LDM-based model was 0.7037 through the rigorous jackknife test and improved by ∼6.6%, ∼3.2%, and ∼2.4% compared with three previous studies. Similarly, the accuracy was improved by 3.2% compared with our previous iRSpot-PseDNC web server through an independent data test. These results demonstrate that the joint use of PseDNC and Z curve enhances performance and can extract more information from a biological sequence. To facilitate research in this area, we constructed a user-friendly web server for predicting hot/cold spots, HcsPredictor, which can be freely accessed from . In summary, we provided a united algorithm by integrating Z curve with PseDNC. We hope this united algorithm could be extended to other classification issues in DNA elements.

ACFIS: a web server for fragment-based drug discovery.

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/.