[Research of mHLA-G and the receptor expression with kidney rejection and CMV active infection following kidney transplantation]

Objective: To study the expression of membrane HLA-G (mHLA-G) and the receptor immunoglobulin-like transcript 2(ILT2) on lymphocyte and find their association with rejection and cytomegalovirus (CMV) infection after renal transplantation. Methods: A total of 88 cases of renal transplant recipients for the first time from February 2014 to February 2016 were studied in this work. Recipients can be divided into rejection group (n=12) and stable renal function group (n=41) according to whether rejection occurred. Recipients only infected CMV not developed rejection were included in the CMV positive group (n=24). CMV negative group (n=11) including CMV negative recipients once infected CMV.The expression of mHLA-G and ILT2 on lymphocytes were detected by flow cytometry, and the differences among different groups were analyzed. Results: The data showed that after renal transplantation, T and B lymphocytes mHLA-G expression rate was the lowest in the rejection group (0.42%±0.35%, 0.88%±0.47%), having significant difference with renal function stable group and CMV positive group (all P<0.01). In CMV positive group the expression of mHLA-G on T and B lymphocytes was the highest (1.31%±0.69%, 2.01%±0.91%), having significant difference with rejection group (P<0.001). The expression of mHLA-G on B cell was statistically significantly different between CMV positive group and CMV negative group (P<0.05). There was no significant difference in ILT2 expression on B cell among the four groups (P>0.05). The expression rate of ILT2 on T cells was higher in the CMV positive group (36.91%±14.91%), having significant difference with the other three groups (P<0.01). Conclusions: Low expression of mHLA-G on T and B lymphocytes may predict rejection after renal transplantation. High expression of mHLA-G and ILT2 on lymphocytes is prone to CMV infection after renal transplantation .

[Characteristics of HBV transmission in families with HBsAg-positive fathers and familial clustering of HBV infection]

OBJECTIVE

To investigate the characteristics of hepatitis B virus (HBV) transmission among family members in families with familial clustering of HBV infection and poor outcomes, as well as the prevalence and distribution characteristics of HBsAg in offspring with different parental HBsAg status.

METHODS

The general information of each member in families with poor outcomes were collected from 2007 to 2010, and serological test was performed to analyze the prevalence and distribution of HBsAg in family members. The chi-square test or Fisher's exact test was used to analyze and compare the sex of offspring and the prevalence of HBsAg in them in 266 nuclear families with different paternal and maternal HBsAg status.

RESULTS

The positive rates of HBsAg in parents, siblings, children, and spouses of the probands were 20%, 88.2%, 76.8%, and 9.5%, respectively. The nuclear families with HBsAg-positive fathers and HBsAg-negative mothers had a significantly increased proportion of male offspring (male/female ratio = 2.02) compared with those with HBsAg-positive mothers and HBsAg-negative fathers (1.22) or those with HBsAg-negative fathers and mothers (0.96). In addition, in the nuclear families with HBsAg-positive fathers and HBsAg-negative mothers, the male offspring had a significantly higher HBsAg positive rate than female offspring (37.4% vs 13.8%), while in those with HBsAg-positive mothers and HBsAg-negative fathers or those with HBsAg-negative fathers and mothers, HBsAg positive rate showed no significant difference between male and female offspring.

CONCLUSION

In families with familial clustering of HBV infection and poor outcomes, mother-to-child transmission is still the major route of HBV transmission, but father-to-child transmission also plays a role in HBV transmission in this special population. Positive HBsAg in fathers is associated with the increased proportion of male offspring, and father-to-son transmission of HBV is higher than father-to-daughter transmission.

[Reconstruct abdominal aortic branch arteries totally by endovascular repair in complex aortic aneurysms: a single-centre experience]

OBJECTIVE

To report experiences and results of complex aortic aneurysms involving the visceral arteries treating by total endovascular repair.

METHODS

Seventy cases of thoracoabdominal aortic lesions treating by total endovascular repair in Department of Vascular Surgery, People's Liberation Army General Hospital from January 2011 to December 2014 were retrospectively analyzed. There were 47 cases underwent chimney technique, 6 underwent sandwich technique, 15 underwent fenestration technique and 2 underwent branched stent grafts technique.

RESULTS

The average follow-up time was 21 months, range from 3 to 47 months. Completion angiography showed that typeⅠ endoleak of chimney, sandwich, fenestration and branched stent grafts group were 9/47, 5/6, 2/15 and 0, respectively. While at 30-day typeⅠ endoleak reduced to 7/47, 2/6, 0 and 0. During follow-up, there were 3 target vessel stents occlusion in chimney group, the patency rate of target vessel stent was 95.1% (58/61). In sandwich, fenestration and branched stent grafts group, all the target vessel stents kept patent.

CONCLUSION

In this study, chimney, sandwich, fenestration and branched stent grafts techniques show good short-term and midterm results, the long-term effects still need further studies.

Blocking NAD(+)/CD38/cADPR/Ca(2+) pathway in sepsis prevents organ damage

BACKGROUND

Although the nicotinamide adenine dinucleotide (NAD(+))/CD38/cyclic ADP ribose (cADPR)/Ca(2+) signaling pathway has been shown to regulate intracellular calcium homeostasis and functions in multiple inflammatory processes, its role in sepsis remains unknown. The aim of this study was to determine whether the NAD(+)/CD38/cADPR/Ca(2+) signaling pathway is activated during sepsis and whether an inhibitor of this pathway, 8-Br-cADPR, protects the organs from sepsis-induced damage.

MATERIALS AND METHODS

Male Sprague-Dawley rats were subjected to cecal ligation and puncture (CLP) or sham laparotomies. NAD(+), cADPR, CD38, and intracellular Ca(2+) levels were measured in the hearts, livers, and kidneys of septic rats at 0, 6, 12, 24, and 48 h after CLP surgery. Rats were also divided into sham, CLP, and CLP+8-Br-cADPR groups, and the hearts, livers, and kidneys were hematoxylin-eosin-stained and assayed for malondialdehyde and superoxide dismutase activities.

RESULTS

NAD(+), cADPR, CD38, and intracellular Ca(2+) levels increased in the hearts, livers, and kidneys of septic rats as early as 6-24 h after CLP surgery. Treatment with 8-Br-cADPR inhibited sepsis-induced intracellular Ca(2+) mobilization, attenuated tissue injury, reduced malondialdehyde levels, and increased superoxide dismutase activity in septic rats.

CONCLUSIONS

The NAD(+)/CD38/cADPR/Ca(2+) signaling pathway was activated during sepsis in the CLP rat model. Blocking this pathway with 8-Br-cADPR protected hearts, livers, and kidneys from sepsis-induced damage.

Effect of baculovirus P35 protein on apoptosis in brain tissue of rats with acute cerebral infarction

We explored the effect of baculovirus P35 protein on apoptosis in the brain tissue of rats with acute cerebral infarction (ACI). A rat model of middle cerebral artery infarction was created. The rats were randomly divided into sham, model, and treatment groups. Baculovirus P35 protein was injected into the intracranial arteries of the treatment group rats. The rats in the model group were given an equal volume of phosphate-buffered saline. The rats were sacrificed after 72 h and the brain tissue was separated. The levels of caspase-3, Bcl-2, and Bax mRNA, the brain cell apoptosis index, and the infarct size were determined. After 72 h, the levels of caspase-3 and Bax mRNA in the model and treatment groups were significantly greater than in the sham group, and the levels of Bcl-2 mRNA were significantly smaller (P < 0.05). The levels of caspase-3 and Bax mRNA were significantly lower in the treatment group than in the model group, and the level of Bcl-2 mRNA was significantly greater (P < 0.05). Compared with the sham group, the brain tissue apoptosis index and the cerebral infarction area increased significantly in the model and treatment groups (P < 0.05). The brain tissue apoptosis index and cerebral infarction area in the treatment group were significantly lower than in the model group (P < 0.05). Baculovirus P35 protein can effectively inhibit brain cell apoptosis in rats with ACI. It delayed apoptosis and necrosis in subjects with ACI tissue and had a protective effect on brain tissue.

Effect of surface free energies on the heterogeneous nucleation of water droplet: a molecular dynamics simulation approach

Heterogeneous nucleation of water droplet on surfaces with different solid-liquid interaction intensities is investigated by molecular dynamics simulation. The interaction potentials between surface atoms and vapor molecules are adjusted to obtain various surface free energies, and the nucleation process and wetting state of nuclei on surfaces are investigated. The results indicate that near-constant contact angles are already established for nano-scale nuclei on various surfaces, with the contact angle decreasing with solid-liquid interaction intensities linearly. Meanwhile, noticeable fluctuation of vapor-liquid interfaces can be observed for the nuclei that deposited on surfaces, which is caused by the asymmetric forces from vapor molecules. The formation and growth rate of nuclei are increasing with the solid-liquid interaction intensities. For low energy surface, the attraction of surface atoms to water molecules is comparably weak, and the pre-existing clusters can depart from the surface and enter into the bulk vapor phase. The distribution of clusters within the bulk vapor phase becomes competitive as compared with that absorbed on surface. For moderate energy surfaces, heterogeneous nucleation predominates and the formation of clusters within bulk vapor phase is suppressed. The effect of high energy particles that embedded in low energy surface is also discussed under the same simulation system. The nucleation preferably initiates on the high energy particles, and the clusters that formed on the heterogeneous particles are trapped around their original positions instead of migrating around as that observed on smooth surfaces. This feature makes it possible for the heterogeneous particles to act as fixed nucleation sites, and simulation results also suggest that the number of nuclei increases monotonously with the number of high energy particles. The growth of nuclei on high energy particles can be divided into three sub-stages, beginning with the formation of a wet-spot, increase of contact angle with near-constant contact line, and finally growth with constant contact angle. The growth rate of nuclei also increases with the size of high energy particles.

PBEF promotes the apoptosis of pulmonary microvascular endothelial cells and regulates the expression of inflammatory factors and AQP1 through the MAPK pathways

Pre-B cell colony-enhancing factor (PBEF) has been shown to have a variety of biological functions. Studies have proven that PBEF plays a functional role in acute lung injury (ALI). Therefore, in this study, we aimed to confirm the importance of PBEF in ALI. The effects of PBEF overexpression on the apoptosis of human pulmonary microvascular endothelial cells (HPMECs) were analyzed by flow cytometry, and the results indicated that PBEF promoted the apoptosis of HPMECs, which aggravated the development of ALI. Comparative experiments involving increasing and decreasing PBEF expression demonstrated that PBEF promoted the expression of inflammatory factors, such as interleukin (IL)‑1β, IL‑6 and IL‑8 in the HPMECs , thus intensifying the inflammatory response. PBEF also inhibited the expression of aquaporin 1 (AQP1), which caused a dysfunction and imbalance in water transport. Moreover, we also found that tumor necrosis factor (TNF)‑α promoted the expression of PBEF in the HPMECs. After blocking the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) pathways, we found that PBEF regulated the expression of inflammatory factors and AQP1, mainly through the MAPK pathways. Taken together, these results demonstrate that the increase in intracellular PBEF expression promoted the apoptosis of HPMECs and the expression of inflammatory factors and thus enhanced the inflammatory response and inhibited the expression of AQP1, which resulted in abnormal water transport, diminishing the regulatory effects of AQP1 on water transport.

Polymorphisms in the delta-like 2 homolog gene and their association with growth and meat-quality traits in Qinchuan cattle

The delta-like 2 homolog (DLK2) modulates adipogenesis, hematopoiesis, osteogenesis, and other cell-differentiation processes. In the present study, we detected potential polymorphisms in the DLK2 gene in 604 individuals of Qinchuan cattle by using PCR-RFLP and DNA-sequencing methods. Herein, we identified five novel single-nucleotide polymorphisms (SNPs) (g.888G>A, g.910A>G, g.995G>A, g.4321A>G, g.4850A>G) and analyzed their association with measured traits. Four of the five analyzed polymorphisms were associated with at least one of the following traits: body weight (BW), chest depth (CD), chest circumference (CC), back fat thickness (BT), and rib-eye area (REA). To the best of our knowledge, our research is the first to report the association of DLK2 gene polymorphisms with growth and meat quality traits in Qinchuan cattle. In summary, the results of our study suggest that the DLK2 gene can be used as a candidate gene in beef cattle breeding.

Predicting targeted polypharmacology for drug repositioning and multi- target drug discovery

Prediction of polypharmacology of known drugs and new molecules against selected multiple targets is highly useful for finding new therapeutic applications of existing drugs (drug repositioning) and for discovering multi-target drugs with improved therapeutic efficacies by collective regulations of primary therapeutic targets, compensatory signalling and drug resistance mechanisms. In this review, we describe recent progresses in exploration of in-silico methods for predicting polypharmacology of known drugs and new molecules by means of structure-based (molecular docking, binding- site structural similarity, receptor-based pharmacophore searching), expression-based (expression profile/signature similarity disease-drug and drug-drug networks), ligand-based (similarity searching, side-effect similarity, QSAR, machine learning), and fragment-based approaches that have shown promising potential in facilitating drug repositioning and the discovery of multi-target drugs.

Virtual screening methods as tools for drug lead discovery from large chemical libraries

Virtual screening methods have been developed and explored as useful tools for searching drug lead compounds from chemical libraries, including large libraries that have become publically available. In this review, we discussed the new developments in exploring virtual screening methods for enhanced performance in searching large chemical libraries, their applications in screening libraries of ~ 1 million or more compounds in the last five years, the difficulties in their applications, and the strategies for further improving these methods.

SNPs at 3'-UTR of the bovine CDIPT gene associated with Qinchuan cattle meat quality traits

The CDIPT is crucial to the fatty acid metabolic pathway, intracellular signal transduction and energy metabolism in eukaryotic cells. We detected three SNPs at 3'-untranslated regions (UTR), named 3'-UTR_108 A > G, 3'-UTR_448 G > A and 3'-UTR_477 C > G, of the CDIPT gene in 618 Qinchuan cattle using PCR-RFLP and DNA sequencing methods. At each of the three SNPs, we found three genotypes named as follows: AA, AB, BB (3'-UTR_108 A > G), CC, CD, DD (3'-UTR_448 G > A) and EE, EF, FF (3'-UTR_477 C > G.). Based on association analysis of these SNPs with ultrasound measurement traits, individuals of genotype BB had a significantly larger loin muscle area than genotype AA. Individuals of genotype CC had significantly thicker back fat than individuals of genotype DD. Individuals of genotype EE also had significantly thicker back fat than did individuals of genotype FF. We conclude that these SNPs of the CDIPT gene could be used as molecular markers for selecting and breeding beef cattle with superior body traits, depending on breeding goals.

Novel SNPs in the exon region of bovine DKK4 gene and their association with body measurement traits in Qinchuan cattle

The aim of this study was to determine whether single nucleotide polymorphisms (SNPs) of bovine Dickkopf homolog 4 (DKK4) are associated with body measurement traits in Qinchuan cattle. By using PCR-SSCP technology and DNA sequencing, we discovered 5 DKK4 SNPs in Qingchuan cattle, including -65G>A and -77G>T in the 5'-untranslated region, 1532C>G and 1533T>C in exon 2, and 2088C>T in exon 3. The sequencing map showed that 1532C>G and 1533T>C were in close linkage disequilibrium and were treated as 1532C>G-1533T>C in this study. Allele frequencies were calculated and analyzed by the chi-square test, which showed that -65G>A and 1532C>G-1533T>C were in Hardy-Weinberg equilibrium (P > 0.05), whereas -77G>T and 2088C>T were not in all 633 tested Qinchuan cattle individuals (P < 0.01). Gene heterozygosity (HE), effective allele number (NE), and polymorphism information content (PIC) were 0.407, 1.686, and 0.324 at -65G>A; 0.472, 1.894, and 0.361 at -77G>T; 0.476, 1.908, and 0.363 at 1532C>G-1533T>C; and 0.218, 1.279, and 0.195 at 2088C>T. We also evaluated the potential association of these SNPs with body measurement traits in all 633 individuals; the results suggest that several SNPs in Qinchuan cattle DKK4 were significantly associated with body length, hip height, rump length, hip width, heart girth, and pin bone width (P < 0.05 and P < 0.01). These results suggest that bovine DKK4 could be used as candidate gene for Qinchuan cattle breeding.

Expression of KLF6 in gastric carcinoma and premalignant gastric lesions

AIM: To detect the expression of the krüppel-like factor 6 (KLF6) in human gastric carcinoma and premalignant gastric lesions to investigate its role in the occurrence and development of gastric cancer.

METHODS: Fresh frozen gastric carcinoma tissues and matched tumor-adjacent normal gastric mucosa tissues were collected from 32 patients with gastric carcinoma. RT-PCR was used to analyze the expression level of KLF6 mRNA in these tissue samples. Meanwhile, the expression of KLF6 protein was detected by immunohistochemistry in 111 formalin-fixed paraffin-embedded samples, including 20 cases of normal gastric mucosa, 45 cases of intestinal metaplasia, 16 cases of dysplasia, and 30 cases of gastric carcinoma. The association of KLF6 expression with clinicopathological characteristics was analyzed.

RESULTS: The positive rate of KLF6 protein expression in normal gastric mucosa was significantly different from those in type Ⅲ intestinal metaplasia, dysplasia and gastric carcinoma (χ² = 6.033, 3.889, 11.092, P = 0.014, 0.049, 0.001). The positive rate of KLF6 expression in gastric carcinoma was associated with tumor differentiation and size (P = 0.009, 0.010). There was no significant difference in the expression of KLF6 mRNA between normal gastric mucosa and gastric carcinoma (P = 0.357).

CONCLUSION: Abnormal expression of KLF6 is closely related to type Ⅲ intestinal metaplasia, dysplasia, and gastric carcinoma. Detection of KLF6 expression may have certain value in the surveillance of premalignant lesions and the discovery of early gastric carcinoma.

Diabetes mellitus and incidence and mortality of gastric cancer: a meta-analysis

AIMS

Epidemiologic studies have examined the possible associations between diabetes mellitus (DM) and gastric cancer (GC), but the results are inconclusive. We conducted a meta-analysis to assess the evidence regarding the associations between diabetes and incidences of, or mortality from, gastric cancer.

METHODS

PubMed, Embase and Web of Science were searched up to Oct 20, 2011. We identified studies that included effects estimates with 95% confidence intervals (CIs) of the associations between GC and diabetes. Summary RRs for the GC incidence and mortality were calculated using random-effects model; subgroup analyses were also performed. Heterogeneity among studies was examined using Q and I2 statistics.

RESULTS

A total of 7 case-control and 18 cohort studies met the inclusion criteria. The summary RR showed a slightly statistical link between history of DM and GC incidence (RR=1.11, 95% CI: 1.00-1.24, p=0.045, I2=79.5%). In the subgroup analyses, a positive association was noted among the studies conducted in Asia (summary RR=1.19, 95% CI: 1.07-1.32, I2=29.8%). Additionally, slight associations between DM and GC were observed by pooling the data of type 2 DM, cohort studies and the studies controlling more confounders, respectively. Furthermore, mortality from GC with diabetes was increased compared with individuals without diabetes (summary RR=1.29, 95% CI: 1.04-1.59). No publication bias was found.

CONCLUSION

Individuals with diabetes have an increased risk of developing gastric cancer, and are positively associated with gastric cancer mortality. Large better-designed cohort studies are needed to verify this conclusion.

Effect of various freezing solutions on cryopreservation of mesenchymal stem cells from different animal species

The objective of this study is to compare the effects of different well defined freezing solutions with a reduced concentration of dimethylsulfoxide (DMSO) combined with polyethylene glycol (PEG) and/or trehalose on cryopreservation of mesenchymal stem cells (MSCs) from mice, rats and calves. Post-thaw cell viability, proliferation capacity and differentiation potential of MSCs from different species were assessed after cryopreservation with the conventional slow freezing method. Although the post-thaw viabilities and metabolic activities varied among the different species, satisfactory results were obtained with 5 percent (v/v) DMSO, 2 percent (w/v) PEG, 3 percent (w/v) trehalose and 2 percent (w/v) bovine serum albumin (BSA) as the freezing solution. Our results showed that mouse MSCs were more robust to cryopreservation compared with rat and bovine MSCs.

Virtual screening of selective multitarget kinase inhibitors by combinatorial support vector machines

Multitarget agents have been increasingly explored for enhancing efficacy and reducing countertarget activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equivalent to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compounds, 12-175 (0.007%-0.104%) of the 168 K MDDR compounds, and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compounds similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.

Virtual screening of Abl inhibitors from large compound libraries by support vector machines

Abl promotes cancers by regulating cell morphogenesis, motility, growth, and survival. Successes of several marketed and clinical trial Abl inhibitors against leukemia and other cancers and appearances of reduced efficacies and drug resistances have led to significant interest in and efforts for developing new Abl inhibitors. In silico methods of pharmacophore, fragment, and molecular docking have been used in some of these efforts. It is desirable to explore other in silico methods capable of searching large compound libraries at high yields and reduced false-hit rates. We evaluated support vector machines (SVM) as a virtual screening tool for searching Abl inhibitors from large compound libraries. SVM trained and tested by 708 inhibitors and 65,494 putative noninhibitors correctly identified 84.4 to 92.3% inhibitors and 99.96 to 99.99% noninhibitors in 5-fold cross validation studies. SVM trained by 708 pre-2008 inhibitors and 65 494 putative noninhibitors correctly identified 50.5% of the 91 inhibitors reported since 2008 and predicted as inhibitors 29,072 (0.21%) of 13.56M PubChem, 659 (0.39%) of 168K MDDR, and 330 (5.0%) of 6638 MDDR compounds similar to the known inhibitors. SVM showed comparable yields and substantially reduced false-hit rates against two similarity based and another machine learning VS methods based on the same training and testing data sets and molecular descriptors. These suggest that SVM is capable of searching Abl inhibitors from large compound libraries at low false-hit rates.

[Preparation of clenbuterol monoclonal antibody with subtractive immunization method]

AIM

To obtain Clenbuterol monoclonal antibodies.

METHODS

Clenbuterol complete antigen was prepared with diazotization method. BALB/c mice was immunized with subtractive immunization, Clenbuterol monoclonal antibody was prepared with rule hybridoma technique.

RESULTS

The mice obtained tolerance to BSA by subtractive immunization. The rate of the hybridoma cell with positive reaction which had obtained was 8.2%, and the specific clenbuterol monoclonal antibody was obtained at last.

CONCLUSION

Monoclonal antibodies to micromolecule contaminant be prepared by subtractive immunization, could decrease the workload in the bolting of monoclonal antibodies, and increase the chance to obtain the antibody of expected.

Prediction of antibiotic resistance proteins from sequence-derived properties irrespective of sequence similarity

Increasing antibiotic resistance has become a worldwide challenge to the clinical treatment of infectious diseases. The identification of antibiotic resistance proteins (ARPs) would be helpful in the discovery of new therapeutic targets and the design of novel drugs to control the potential spread of antibiotic resistance. In this work, a support vector machine (SVM)-based ARP prediction system was developed using 1308 ARPs and 15587 non-ARPs. Its performance was evaluated using 313 ARPs and 7156 non-ARPs. The computed prediction accuracy was 88.5% for ARPs and 99.2% for non-ARPs. A potential application of this method is the identification of ARPs non-homologous to proteins of known function. Further genome screening found that ca. 3.5% and 3.2% of proteins in Escherichia coli and Staphylococcus aureus, respectively, are potential ARPs. These results suggest the usefulness of SVMs for facilitating the identification of ARPs. The software can be accessed at SARPI (Server for Antibiotic Resistance Protein Identification).

Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds

Virtual screening performance of support vector machines (SVM) depends on the diversity of training active and inactive compounds. While diverse inactive compounds can be routinely generated, the number and diversity of known actives are typically low. We evaluated the performance of SVM trained by sparsely distributed actives in six MDDR biological target classes composed of a high number of known actives (983-1645) of high, intermediate, and low structural diversity (muscarinic M1 receptor agonists, NMDA receptor antagonists, thrombin inhibitors, HIV protease inhibitors, cephalosporins, and renin inhibitors). SVM trained by regularly sparse data sets of 100 actives show improved yields at substantially reduced false-hit rates compared to those of published studies and those of Tanimoto-based similarity searching method based on the same data sets and molecular descriptors. SVM trained by very sparse data sets of 40 actives (2.4%-4.1% of the known actives) predicted 17.5-39.5%, 23.0-48.1%, and 70.2-92.4% of the remaining 943-1605 actives in the high, intermediate, and low diversity classes, respectively, 13.8-68.7% of which are outside the training compound families. SVM predicted 99.97% and 97.1% of the 9.997 M PUBCHEM and 167K remaining MDDR compounds as inactive and 2.6%-8.3% of the 19,495-38,483 MDDR compounds similar to the known actives as active. These suggest that SVM has substantial capability in identifying novel active compounds from sparse active data sets at low false-hit rates.

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.