Stepwise prediction of conformational discontinuous B-cell epitopes using the Mapitope algorithm

Mapping the epitope of an antibody is of great interest, since it contributes much to our understanding of the mechanisms of molecular recognition and provides the basis for rational vaccine design. Here we present Mapitope, a computer algorithm for epitope mapping. The algorithm input is a set of affinity isolated peptides obtained by screening phage display peptide-libraries with the antibody of interest. The output is usually 1-3 epitope candidates on the surface of the atomic structure of the antigen. We have systematically tested the performance of Mapitope by assessing the effect of the algorithm parameters on the final prediction. Thus, we have examined the effect of the statistical threshold (ST) parameter, relating to the frequency distribution and enrichment of amino acid pairs from the isolated peptides and the D (distance) and E (exposure) parameters which relate to the physical parameters of the antigen. Two model systems were analyzed in which the antibody of interest had previously been co-crystallized with the antigen and thus the epitope is a given. The Mapitope algorithm successfully predicted the epitopes in both models. Accordingly, we formulated a stepwise paradigm for the prediction of discontinuous conformational epitopes using peptides obtained from screening phage display libraries. We applied this paradigm to successfully predict the epitope of the Trastuzumab antibody on the surface of the Her-2/neu receptor in a third model system.     

Isolation of a gene that is involved in Campylobacter jejuni 81116 cytotoxin activation

Cytotoxin fractions were isolated from Campylobacter jejuni 81116 and semi-purified by size-exclusion liquid chromatography. The fraction showing the strongest toxicity was injected into mice to produce antiserum. The antiserum was used to screen a C. jejuni 81116 cosmid library. Nine genes were identified in overlapping cosmid inserts that induced reactivity with the antiserum. One of these genes showed high similarity to a periplasmic protein of unknown function and its isogenic mutant showed decreased toxicity compared to the C. jejuni 81116 wild type. This gene contains a Gram-negative bacterial RTX toxin-activating protein C signature, which suggests it may play a role in C. jejuni 81116 cytotoxin activation.     

A novel method to screen genomic libraries that combines genomic immunization with the prime-boost strategy

We employed a prime-boost regimen in combination with the expression library immunization protocol to improve the protective effectiveness of a genomic library used as immunogen. To demonstrate the feasibility of this novel strategy, we used as a prime a serogroup B Neisseria meningitidis random genomic library constructed in a eukaryotic expression vector. Mice immunized with different fractions of this library and boosted with a single dose of meningococcal outer membrane vesicles elicited higher bactericidal antibody titers compared with mice primed with the empty vector. After the boost, passive administration of sera from mice primed with two of these fractions significantly reduced the number of viable bacteria in the blood of infant rats challenged with live N. meningitidis. The method proposed could be applied to the identification of subimmunogenic antigens during vaccine candidate screening by employing expression library immunization.     

QSAR of multiple mutated antibodies

The aim of this study was to develop predictive quantitative structure-activity relationship (QSAR) modeling for antibody-peptide interactions. A small single chain antibody library was designed and manufactured around the murine anti-p24 (HIV-1) monoclonal antibody CB4-1 by use of statistical molecular design (SMD) principles and site directed mutagenesis, and its affinity for a p24 derived antigen was determined by fluorescence polarization. A satisfactory QSAR model (Q(2) = 0.74, R(2) = 0.88) was derived by correlating the affinity data to physicochemical property scales of the amino acids varied in the library. The model explains most of the antibody-antigen interactions of the studied set, and provides insights into the molecular mechanism involved in antigen binding.     

Predicting mutant outcome by combining deep mutational scanning and machine learning

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted “targets”: (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment—the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.     

Specific GFP-binding artificial proteins (αRep): a new tool for in?vitro to live cell applications

A family of artificial proteins, named αRep, based on a natural family of helical repeat was previously designed. αRep members are efficiently expressed, folded and extremely stable proteins. A large αRep library was constructed creating proteins with a randomized interaction surface. In the present study, we show that the αRep library is an efficient source of tailor-made specific proteins with direct applications in biochemistry and cell biology. From this library, we selected by phage display αRep binders with nanomolar dissociation constants against the GFP. The structures of two independent αRep binders in complex with the GFP target were solved by X-ray crystallography revealing two totally different binding modes. The affinity of the selected αReps for GFP proved sufficient for practically useful applications such as pull-down experiments. αReps are disulfide free proteins and are efficiently and functionally expressed in eukaryotic cells: GFP-specific αReps are clearly sequestrated by their cognate target protein addressed to various cell compartments. These results suggest that αRep proteins with tailor-made specificity can be selected and used in living cells to track, modulate or interfere with intracellular processes.     

水葫芦根际细菌群落结构多样性分析

【目的】了解水葫芦根际细菌群落结构。【方法】运用末端限制性片段长度多态性(Terminal restriction fragment length polymorphism,T-RFLP)技术分析富营养化水体中水葫芦根际和水葫芦近、远水样的细菌群落特征及多样性,结合克隆文库技术和培养法分析根际的细菌种群类型。【结果】同一时期水葫芦根际细菌多样性(Shannon-Weiner指数H′或Simpson指数D)更高,水葫芦近水样次之,远水样最小。10月份的细菌多样性高于5月份的。通过水葫芦根际细菌的克隆文库可知变形杆菌门(Proteobacteria)是水葫芦根际细菌的主要类群,占总群体的65.1%,包括噬菌弧菌(Bacteriovorax sp.)、Dechloromonas sp.、Leptothrix sp.、红螺菌科(Rhodospirillaceae)、Rhodoferax sp.和红环菌科(Rhodocyclaceae)等。T-RFLP图谱显示159 bp为最大优势菌,247 bp为第二大优势菌,对照克隆文库及培养结果分析247 bp属于γ-Proteobacteria,159 bp为不动杆菌(Acinetobacter sp.)。【结论】水葫芦根际细菌的群落结构丰富,不同时段水葫芦根际细菌的丰度略有变化,主要类群为变形杆菌门。     

青岛市秋季空气微生物群落多样性

采用KC-6120空气综合采样器采集空气微生物样品,通过构建16S/18SrDNA克隆文库方法分析青岛市市区街道秋季空气微生物群落结构特征.结果表明： 空气细菌分布在6大类,分别为变形菌门(78.8%)、厚壁菌门(14.6%)、放线菌门(4.0%)、浮霉菌门(1.3%)、蓝藻门(0.7%)和栖热菌门(0.6%),优势菌属为不动杆菌属(39.7%)、葡萄球菌属(11.3%)、鞘脂单胞菌属(8.6%)和副球菌属(6.0%).空气真菌分布在子囊菌门(97.5%)和担子菌门(2.5%),优势菌属为核腔菌属(76.5%)、炭角菌属(13.6%)和外瓶霉属(2.5%).空气微生物中存在不动杆菌属、鞘脂单胞菌、葡萄球菌等致病菌或条件致病菌,以及引发多种农作物枯萎死亡的麦类核腔菌、团炭角菌和角状平脐疣孢等真菌.