Enzyme structure dynamics of xylanase I from Trichoderma longibrachiatum

Artificial intelligence-based HDX (AI-HDX) prediction reveals fundamental characteristics to protein dynamics: Mechanisms on SARS-CoV-2 immune escape

Three-dimensional structure and dynamics are essential for protein function. Advancements in hydrogen-deuterium exchange (HDX) techniques enable probing protein dynamic information in physiologically relevant conditions. HDX-coupled mass spectrometry (HDX-MS) has been broadly applied in pharmaceutical industries. However, it is challenging to obtain dynamics information at the single amino acid resolution and time consuming to perform the experiments and process the data. Here, we demonstrate the first deep learning model, artificial intelligence-based HDX (AI-HDX), that predicts intrinsic protein dynamics based on the protein sequence. It uncovers the protein structural dynamics by combining deep learning, experimental HDX, sequence alignment, and protein structure prediction. AI-HDX can be broadly applied to drug discovery, protein engineering, and biomedical studies. As a demonstration, we elucidated receptor-binding domain structural dynamics as a potential mechanism of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody efficacy and immune escape. AI-HDX fundamentally differs from the current AI tools for protein analysis and may transform protein design for various applications.

Redesigning pH optimum of Geobacillus sp. TF16 endoxylanase through in silico designed DNA swapping strategy

Thermoalkaliphilic xylanases are highly desired and of great importance due to their vast potential in paper pulp and bleaching processes. Here, we report rapid, cost-effective, and result-oriented combinatorial potential of in silico DNA swapping strategy to engineer the pH optimum of industrially crucial enzymes, particularly engineering of Geobacillus sp. TF16 endoxylanase for alkaline environments. The 3D structures of Geobacillus sp. TF16 and donor Bacillus halodurans C-125 endoxylanases were firstly predicted, analyzed, and compared for their similarities before any in silico design of mutants. Reasonably, to improve its alkaline pH tolerance, the corresponding regions in Geobacillus sp.TF16 endoxylanase were further engineered by swapping with negatively-charged amino acid-rich regions from B. halodurans C-125 endoxylanase. Through only two of four in silico-designed mutants, the optimum pH of GeoTF16 endoxylanase was improved from 8.5 to 10.0. Moreover, as compared to GeoTF16 parental enzyme, both GeoInt3 and GeoInt4 mutants revealed (i) enhanced biobleaching performance, (ii) improved adaptability to alkaline conditions, and (iii) better activity for broader pH range. Unlike GeoTF16 losing activity at pH 11.0 completely, GeoInt4 retained 60% and 40% of its activity at pH 11.0 and 12.0, respectively. Thus, GeoInt4 stands out as a more competent biocatalyst that is suitable for alkaline environments of diverse industrial applications. The current study represents an efficient protein engineering strategy to adapt industrial catalysts to diverse processing conditions. Further comprehensive and fine-tuned research efforts may result in biotechnologically more promising outcomes.

Molecular approaches for ameliorating microbial xylanases

In industrial processes, chemical catalysis is being replaced by enzyme catalysis, since the latter is environmentally benign, non-persistent and cost effective. Microbial xylanases have significant applications in textile, baking, food and feed industries, and in paper and pulp industries for reducing the chlorine requirement. The hazardous chlorine required for bleaching can be reduced up to 25-30% by including an enzymatic step in the pulp bleaching process. The paper pulp bleaching requires xylanases that are active at alkaline pH and elevated temperatures. The enzymes from the cultured microbes do not perform optimally in the paper industry due to their inadequate stability under the process conditions of high temperature and alkaline pH. This review, therefore, deals with the rationale of molecular approaches such as protein engineering for designing xylanases with improved characteristics to suit the process conditions in industries, and prospects and problems.

HDX-analyzer: a novel package for statistical analysis of protein structure dynamics

Background

HDX mass spectrometry is a powerful platform to probe protein structure dynamics during ligand binding, protein folding, enzyme catalysis, and such. HDX mass spectrometry analysis derives the protein structure dynamics based on the mass increase of a protein of which the backbone protons exchanged with solvent deuterium. Coupled with enzyme digestion and MS/MS analysis, HDX mass spectrometry can be used to study the regional dynamics of protein based on the m/z value or percentage of deuterium incorporation for the digested peptides in the HDX experiments. Various software packages have been developed to analyze HDX mass spectrometry data. Despite the progresses, proper and explicit statistical treatment is still lacking in most of the current HDX mass spectrometry software. In order to address this issue, we have developed the HDXanalyzer for the statistical analysis of HDX mass spectrometry data using R, Python, and RPY2.

Implementation and results

HDXanalyzer package contains three major modules, the data processing module, the statistical analysis module, and the user interface. RPY2 is employed to enable the connection of these three components, where the data processing module is implemented using Python and the statistical analysis module is implemented with R. RPY2 creates a low-level interface for R and allows the effective integration of statistical module for data processing. The data processing module generates the centroid for the peptides in form of m/z value, and the differences of centroids between the peptides derived from apo and ligand-bound protein allow us to evaluate whether the regions have significant changes in structure dynamics or not. Another option of the software is to calculate the deuterium incorporation rate for the comparison. The two types of statistical analyses are Paired Student’s t-test and the linear combination of the intercept for multiple regression and ANCOVA model. The user interface is implemented with wxpython to facilitate the data visualization in graphs and the statistical analysis output presentation. In order to evaluate the software, a previously published xylanase HDX mass spectrometry analysis dataset is processed and presented. The results from the different statistical analysis methods are compared and shown to be similar. The statistical analysis results are overlaid with the three dimensional structure of the protein to highlight the regional structure dynamics changes in the xylanase enzyme.

Conclusion

Statistical analysis provides crucial evaluation of whether a protein region is significantly protected or unprotected during the HDX mass spectrometry studies. Although there are several other available software programs to process HDX experimental data, HDXanalyzer is the first software program to offer multiple statistical methods to evaluate the changes in protein structure dynamics based on HDX mass spectrometry analysis. Moreover, the statistical analysis can be carried out for both m/z value and deuterium incorporation rate. In addition, the software package can be used for the data generated from a wide range of mass spectrometry instruments.