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Extensive efforts have been devoted to determining the binding specificity of Src homology 3 (SH3) domains usually in a case-by-case manner. A generic structure-based model is necessary to decipher the protein recognition code of the entire domain family. In this study, we have developed a general framework that combines molecular modeling and a machine learning algorithm to capture the energetic characteristics of the domain-peptide interactions and predict the binding specificity of the SH3 domain family. Our model is not trained for individual SH3 domains; rather it is a generic model for the entire domain family. Our model not only achieved satisfactory prediction accuracy but also provided structural insights into which residues are important for the binding specificity 1 domain (4) that recognizes proline-rich peptides with a core motif of PXXP (P is a proline and X is any amino acid) (5, 6). Peptides can bind to SH3 domains in two opposite orientations and are referred as class I and II peptides, which often contain ϩXXPXXP and PXXPXϩ (where X refers to any residue and ϩ refers to a positively charged residue) motifs, respectively. The binding specificity of an SH3 domain is determined by the amino acids in the flanking regions of the core motif, which has been investigated extensively for individual domains. However, a universal model was lacking to decipher the protein recognition code of the SH3 domain family.A generic model for the entire domain family needs to 1) provide a general framework to characterize the domainpeptide interaction and 2) reliably predict the binding specificity of each member in the domain family. Previous experimental and computational studies can only satisfy one of these requirements. For example, peptide library and peptide or protein array technologies are commonly used to determine the peptide motifs recognized by a domain, often represented as a position-specific scoring matrix (7-13). These approaches have limited coverage of the peptide space because the peptides tested in the experiments usually only represent a small portion of all the possible peptides of a given length. In addition, the prediction power of a sequence motif on interacting partners of a domain is often unsatisfactory. Along that line, a survey of protein-protein interaction interfaces (14) also suggested that a sophisticated model, rather than a set of well defined rules, is needed to decipher the specificity of protein recognition.On the other hand, high throughput technologies, such as yeast two-hybrid assay and complex purification followed by mass spectrometry, have been used to identify protein-protein interactions. However, these methods often miss the weak and transient domain-peptide interactions (15). Various computational methods have also been developed to predict the interacting partners of modular domains (16 -20). For example, the SH3-SPOT method builds a position-specific contact frequency matrix based on the protein-peptide contacts in a number of crystal structures of SH3-peptide and ...

BackgroundBreast cancer (breast Ca) is recognised as a major public health problem in the world. Data on reproductive factors associated with breast Ca in the Central African Republic (CAR) is very limited. This study aimed to identify reproductive variables as risk factors for breast Ca in CAR women.MethodsA case–control study was conducted among 174 cases of breast Ca confirmed at the Pathology Unit of the National Laboratory in Bangui between 2003 and 2015 and 348 age-matched controls. Data collection tools included a questionnaire, interviews and a review of medical records of patients. Data were analysed using SPSS software version 20. Odd ratios and 95% confidence intervals (CI) for the likelihood of developing breast Ca were obtained using unconditional logistic regression.ResultsIn total, 522 women with a mean age of 45.8 (SD = 13.4) years were enrolled. Women with breast Ca were more likely to have attained little or no education (AOR = 11.23, CI: 4.65–27.14 and AOR = 2.40, CI: 1.15–4.99), to be married (AOR = 2.09, CI: 1.18–3.71), to have had an abortion (AOR = 5.41, CI: 3.47–8.44), and to be nulliparous (AOR = 1.98, CI: 1.12–3.49). Decreased odds of breast Ca were associated with being employed (AOR = 0.32, CI: 0.19–0.56), living in urban areas (AOR = 0.16, CI: 0.07–0.37), late menarche (AOR = 0.18, CI: 0.07–0.44), regular menstrual cycles (AOR = 0.44, CI: 0.23–0.81), term pregnancy (AOR = 0.26, CI: 0.13–0.50) and hormonal contraceptive use (AOR = 0.62, CI: 0.41–0.93).ConclusionBreast Ca risk factors in CAR did not appear to be significantly different from that observed in other populations. This study highlighted the risk factors of breast Ca in women living in Bangui to inform appropriate control measures.

BackgroundBreast cancer is recognised as a major public health problem in developing countries; however, there is very limited evidence about its epidemiology in the Central African Republic. The aim of this study was to investigate the epidemiological and histopathological characteristics of breast cancer in Bangui.MethodsThis is a retrospective study based on the data collected from pathological anatomy records from 2003 to 2015 in Bangui. A questionnaire was designed to collect information and data was analysed using descriptive and inferential statistical methods.ResultsThe mean age was 45.83 (SD = 13.5) years. The age group of 45–54 years represented the majority of the study population (29.3%). Over 69.5% of the women were housewives with a moderate economic status (56.9%). Less than 14% of the study population had a level of academic degree and 85.6% lived in cities. The breast cancer prevalence was 15.27%. The age-standardized incidence and death by world population (ASW) were 11.19/100,000 and 9.97/100,000 respectively. 50–54 years were most affected. Left breast cancer is mainly common and the time between first symptoms and consultation is more than 48 months. Most (69%) of the samples analysed were lumpectomy. The most common morphology of breast cancer was invasive ductal carcinoma (64.9%). Scarff Bloom Richardson III was the main grade in both common pathological types, but their proportion showed no significant increase along with time (χ2 = 7.06, p = 0.54). Invasion of regional lymph node differed significantly among the pathological type of breast cancer (χ2 = 24.6, p = 0.02). Surgery and chemotherapy were appropriate treatment yet 84.5% of the cases died.ConclusionThe findings of this study showed that breast cancer is common and mostly affected women. Epidemiological trends are more or less common to those of developing countries with a predominance of invasive ductal carcinoma. However, most of the women studied live in an urban area and developed the disease in advanced stage. The establishment of an appropriate framework will effectively contribute to promoting the early detection and reducing the incidence of this disease in the population.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-3863-6) contains supplementary material, which is available to authorized users.

Pseudomonas syringae, a major hemibiotrophic bacterial pathogen, causes many devastating plant diseases. However, the transcriptional regulation of plant defense responses to P. syringae remains largely unknown. Here, we found that gain-offunction of BTB AND TAZ DOMAIN PROTEIN 4 (BT4) enhanced the resistance of Arabidopsis (Arabidopsis thaliana) to Pst DC3000 (Pseudomonas syringae pv. tomato DC3000). Disruption of BT4 also weakened the salicylic acid (SA)-induced defense response to Pst DC3000 in bt4 mutants. Further investigation indicated that, under Pst infection, transcription of BT4 is modulated by components of both the SA and ethylene (ET) signaling pathways. Intriguingly, the specific binding elements of ETHYLENE RESPONSE FACTOR (ERF) proteins, including dehydration responsive/C-repeat elements and the GCC box, were found in the putative promoter of BT4. Based on publicly available microarray data and transcriptional confirmation, we determined that ERF11 is inducible by salicylic acid and Pst DC3000 and is modulated by the SA and ET signaling pathways. Consistent with the function of BT4, loss-of-function of ERF11 weakened Arabidopsis resistance to Pst DC3000 and the SAinduced defense response. Biochemical and molecular assays revealed that ERF11 binds specifically to the GCC box of the BT4 promoter to activate its transcription. Genetic studies further revealed that the BT4-regulated Arabidopsis defense response to Pst DC3000 functions directly downstream of ERF11. Our findings indicate that transcriptional activation of BT4 by ERF11 is a key step in SA/ET-regulated plant resistance against Pst DC3000, enhancing our understanding of plant defense responses to hemibiotrophic bacterial pathogens.