Construction and characterization of a bacterial artificial chromosome library of the causal agent of Black Sigatoka fungal leaf spot disease of banana and plantain, <Emphasis Type="Italic">Mycosphaerella fijiensis</Emphasis>

A bacterial artificial chromosome library of the causal agent of the Black Sigatoka leaf spot disease of banana and plantain, Mycosphaerella fijiensis, has been constructed using a non-sphaeroplasting technique and characterized using both homologous and heterologous probes. After first and a second size selection of PFGE-fractionated DNA, a ligation was obtained using a 1:4 molar ratio (insert:vector). One hundred random clones were analyzed, and the mean insert size was estimated to be 90 kb. The range of the insert sizes was between 40 and 160 kb. The highest percentage of inserts belonged to the range between 80 and 100 kb; 32% of the inserts had 2 or 3 internal NotI sites. This library consists of 1920 clones, if the genomic size is at least 35 Mb, then this represents 4.9× genome equivalents, which was supported by hybridization results with homologous and heterologous probes. Blondy Canto-Canché and Diana Karina Guillén-Maldonado contributed equally to this work and should be regarded as co-first authors.     

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.     

MarkerML – Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning

Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis testing and black-box machine learnt models which are conventionally used for identification of these features are generally not exhaustive, especially because they generally do-not provide any quantifiable relevance (context) of/between the identified features. We present MarkerML web-server, that seeks to leverage the emergence of interpretable machine learning for facilitating the contextual discovery of metagenomic features of interest. It does so through a comprehensive and automated application of the concept of Shapley Additive Explanations in companionship to the compositionality accounted hypothesis testing for the multi-variate microbiome datasets. MarkerML not only helps in identification of marker-features, but also enables insights into the role and inter-dependence of the identified features in driving the decision making of the supervised machine learnt model. Generation of high quality and intuitive visualizations spanning prediction effect plots, model performance reports, feature dependency plots, Shapley and abundance informed cladograms (Sungrams), hypothesis tested violin plots along-with necessary provisions for excluding the participant bias and ensuring reproducibility of results, further seek to make the platform a useful asset for the scientists in the field of microbiome (and even beyond). The MarkerML web-server is freely available for the academic community at https://microbiome.igib.res.in/markerml/.     

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.