BMDExpress 2: enhanced transcriptomic dose-response analysis workflow.

SUMMARY: A new version (version 2) of the genomic dose-response analysis software, BMDExpress, has been created. The software addresses the increasing use of transcriptomic dose-response data in toxicology, drug design, risk assessment and translational research. In this new version, we have implemented additional statistical filtering options (e.g. Williams' trend test), curve fitting models, Linux and Macintosh compatibility and support for additional transcriptomic platforms with up-to-date gene annotations. Furthermore, we have implemented extensive data visualizations, on-the-fly data filtering, and a batch-wise analysis workflow. We have also significantly re-engineered the code base to reflect contemporary software engineering practices and streamline future development. The first version of BMDExpress was developed in 2007 to meet an unmet demand for easy-to-use transcriptomic dose-response analysis software. Since its original release, however, transcriptomic platforms, technologies, pathway annotations and quantitative methods for data analysis have undergone a large change necessitating a significant re-development of BMDExpress. To that end, as of 2016, the National Toxicology Program assumed stewardship of BMDExpress. The result is a modernized and updated BMDExpress 2 that addresses the needs of the growing toxicogenomics user community. AVAILABILITY AND IMPLEMENTATION: BMDExpress 2 is available at https://github.com/auerbachs/BMDExpress-2/releases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme.

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5'-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

Structure modeling of RNA using sparse NMR constraints.

RNAs fold into distinct molecular conformations that are often essential for their functions. Accurate structure modeling of complex RNA motifs, including ubiquitous non-canonical base pairs and pseudoknots, remains a challenge. Here, we present an NMR-guided all-atom discrete molecular dynamics (DMD) platform, iFoldNMR, for rapid and accurate structure modeling of complex RNAs. We show that sparse distance constraints from imino resonances, which can be readily obtained from routine NMR experiments and easier to compile than laborious assignments of non-solvent-exchangeable protons, are sufficient to direct a DMD search for low-energy RNA conformers. Benchmarking on a set of RNAs with complex folds spanning up to 56 nucleotides in length yields structural models that recapitulate experimentally determined structures with all-heavy-atom RMSDs ranging from 2.4 to 6.5 Å. This platform represents an efficient approach for high-throughput RNA structure modeling and will facilitate analysis of diverse, newly discovered functional RNAs.

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures.

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

Analysis of interactions between the epigenome and structural mutability of the genome using Genboree Workbench tools.

BACKGROUND: Interactions between the epigenome and structural genomic variation are potentially bi-directional. In one direction, structural variants may cause epigenomic changes in cis. In the other direction, specific local epigenomic states such as DNA hypomethylation associate with local genomic instability. METHODS: To study these interactions, we have developed several tools and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. One key tool is Breakout, an algorithm for fast and accurate detection of structural variants from mate pair sequencing data. RESULTS: By applying Breakout and other Genboree Workbench tools we map breakpoints in breast and prostate cancer cell lines and tumors, discriminate between polymorphic breakpoints of germline origin and those of somatic origin, and analyze both types of breakpoints in the context of the Human Epigenome Atlas, ENCODE databases, and other sources of epigenomic profiles. We confirm previous findings that genomic instability in human germline associates with hypomethylation of DNA, binding sites of Suz12, a key member of the PRC2 Polycomb complex, and with PRC2-associated histone marks H3K27me3 and H3K9me3. Breakpoints in germline and in breast cancer associate with distal regulatory of active gene transcription. Breast cancer cell lines and tumors show distinct patterns of structural mutability depending on their ER, PR, or HER2 status. CONCLUSIONS: The patterns of association that we detected suggest that cell-type specific epigenomes may determine cell-type specific patterns of selective structural mutability of the genome.

RNA tertiary structure analysis by 2'-hydroxyl molecular interference.

We introduce a melded chemical and computational approach for probing and modeling higher-order intramolecular tertiary interactions in RNA. 2'-Hydroxyl molecular interference (HMX) identifies nucleotides in highly packed regions of an RNA by exploiting the ability of bulky adducts at the 2'-hydroxyl position to disrupt overall RNA structure. HMX was found to be exceptionally selective for quantitative detection of higher-order and tertiary interactions. When incorporated as experimental constraints in discrete molecular dynamics simulations, HMX information yielded accurate three-dimensional models, emphasizing the power of molecular interference to guide RNA tertiary structure analysis and fold refinement. In the case of a large, multidomain RNA, the Tetrahymena group I intron, HMX identified multiple distinct sets of tertiary structure interaction groups in a single, concise experiment.

The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences.

BACKGROUND: Microbial metagenomic analyses rely on an increasing number of publicly available tools. Installation, integration, and maintenance of the tools poses significant burden on many researchers and creates a barrier to adoption of microbiome analysis, particularly in translational settings. METHODS: To address this need we have integrated a rich collection of microbiome analysis tools into the Genboree Microbiome Toolset and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. The Genboree Microbiome Toolset provides an interactive environment for users at all bioinformatic experience levels in which to conduct microbiome analysis. The Toolset drives hypothesis generation by providing a wide range of analyses including alpha diversity and beta diversity, phylogenetic profiling, supervised machine learning, and feature selection. RESULTS: We validate the Toolset in two studies of the gut microbiota, one involving obese and lean twins, and the other involving children suffering from the irritable bowel syndrome. CONCLUSIONS: By lowering the barrier to performing a comprehensive set of microbiome analyses, the Toolset empowers investigators to translate high-volume sequencing data into valuable biomedical discoveries.

Deep Learning Image Analysis of High-Throughput Toxicology Assay Images.

High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-consuming manual process, we have developed a method that uses deep learning to automatically classify digital assay images. We have trained a convolutional neural network (CNN) to perform binary and multi-class classification. The binary classifier binned assay images into healthy (comparable to untreated controls) and altered (not comparable to untreated-control) classes with >98% accuracy; the multi-class classifier assigned "Healthy," "Intermediate" and "Altered" labels to assay images with >95% accuracy. Our dataset comprised high-resolution assay images from primary human hepatocytes and undifferentiated (proliferating) and differentiated 2D cultures of HepaRG cells. In this study we have focused on testing and fine-tuning various CNN architectures, including ResNet 34, 50 and 101. To visualize regions in the images that the CNN model used for classification, we employed Class Activation Maps (CAM). This allowed us to better understand the inner workings of the neural network and led to additional optimizations of the algorithm. The results indicate a strong correspondence between dosage and classifier-predicted scores, suggesting that these scores might be useful in further characterizing benchmark dose. Together, these results clearly demonstrate that deep-learning based automated image classification of cell morphology changes upon chemical-induced stress can yield highly accurate and reproducible assessments of cytotoxicity across a variety of cell types.

Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map.

Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.

SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation.

BACKGROUND: In the screening phase of systematic review, researchers use detailed inclusion/exclusion criteria to decide whether each article in a set of candidate articles is relevant to the research question under consideration. A typical review may require screening thousands or tens of thousands of articles in and can utilize hundreds of person-hours of labor. METHODS: Here we introduce SWIFT-Active Screener, a web-based, collaborative systematic review software application, designed to reduce the overall screening burden required during this resource-intensive phase of the review process. To prioritize articles for review, SWIFT-Active Screener uses active learning, a type of machine learning that incorporates user feedback during screening. Meanwhile, a negative binomial model is employed to estimate the number of relevant articles remaining in the unscreened document list. Using a simulation involving 26 diverse systematic review datasets that were previously screened by reviewers, we evaluated both the document prioritization and recall estimation methods. RESULTS: On average, 95% of the relevant articles were identified after screening only 40% of the total reference list. In the 5 document sets with 5,000 or more references, 95% recall was achieved after screening only 34% of the available references, on average. Furthermore, the recall estimator we have proposed provides a useful, conservative estimate of the percentage of relevant documents identified during the screening process. CONCLUSION: SWIFT-Active Screener can result in significant time savings compared to traditional screening and the savings are increased for larger project sizes. Moreover, the integration of explicit recall estimation during screening solves an important challenge faced by all machine learning systems for document screening: when to stop screening a prioritized reference list. The software is currently available in the form of a multi-user, collaborative, online web application.

An integrated chemical environment with tools for chemical safety testing.

Moving toward species-relevant chemical safety assessments and away from animal testing requires access to reliable data to develop and build confidence in new approaches. The Integrated Chemical Environment (ICE) provides tools and curated data centered around chemical safety assessment. This article describes updates to ICE, including improved accessibility and interpretability of in vitro data via mechanistic target mapping and enhanced interactive tools for in vitro to in vivo extrapolation (IVIVE). Mapping of in vitro assay targets to toxicity endpoints of regulatory importance uses literature-based mode-of-action information and controlled terminology from existing knowledge organization systems to support data interoperability with external resources. The most recent ICE update includes Tox21 high-throughput screening data curated using analytical chemistry data and assay-specific parameters to eliminate potential artifacts or unreliable activity. Also included are physicochemical/ADME parameters for over 800,000 chemicals predicted by quantitative structure-activity relationship models. These parameters are used by the new ICE IVIVE tool in combination with the U.S. Environmental Protection Agency's httk R package to estimate in vivo exposures corresponding to in vitro bioactivity concentrations from stored or user-defined assay data. These new ICE features allow users to explore the applications of an expanded data space and facilitate building confidence in non-animal approaches.

A role for BRG1 in the regulation of genes required for development of the lymphatic system.

Lymphatic vasculature is an important part of the cardiovascular system with multiple functions, including regulation of the return of interstitial fluid (lymph) to the bloodstream, immune responses, and fat absorption. Consequently, lymphatic vasculature defects are involved in many pathological processes, including tumor metastasis and lymphedema. BRG1 is an important player in the developmental window when the lymphatic system is initiated. In the current study, we used tamoxifen inducible Rosa26CreERT2-BRG1floxed/floxed mice that allowed temporal analysis of the impact of BRG1 inactivation in the embryo. The BRG1floxed/floxed/Cre-TM embryos exhibited edema and hemorrhage at embryonic day-13 and began to die. BRG1 deficient embryos had abnormal lymphatic sac linings with fewer LYVE1 positive lymphatic endothelial cells. Indeed, loss of BRG1 attenuated expression of a subset of lymphatic genes in-vivo. Furthermore, BRG1 binds at the promoters of COUP-TFII and LYVE1, suggesting that BRG1 modulates expression of these genes in the developing embryos. Conversely, re-expression of BRG1 in cells lacking endogenous BRG1 resulted in induction of lymphatic gene expression in-vitro, suggesting that BRG1 was both required and sufficient for lymphatic gene expression. These studies provide important insights into intrinsic regulation of BRG1-mediated lymphatic-gene expression, and further an understanding of lymphatic gene dysregulation in lymphedema and other disease conditions.

A Structural Model for Vinculin Insertion into PIP2-Containing Membranes and the Effect of Insertion on Vinculin Activation and Localization.

Vinculin, a scaffolding protein that localizes to focal adhesions (FAs) and adherens junctions, links the actin cytoskeleton to the adhesive super-structure. While vinculin binds to a number of cytoskeletal proteins, it can also associate with phosphatidylinositol 4,5-bisphosphate (PIP2) to drive membrane association. To generate a structural model for PIP2-dependent interaction of vinculin with the lipid bilayer, we conducted lipid-association, nuclear magnetic resonance, and computational modeling experiments. We find that two basic patches on the vinculin tail drive membrane association: the basic collar specifically recognizes PIP2, while the basic ladder drives association with the lipid bilayer. Vinculin mutants with defects in PIP2-dependent liposome association were then expressed in vinculin knockout murine embryonic fibroblasts. Results from these analyses indicate that PIP2 binding is not required for localization of vinculin to FAs or FA strengthening, but is required for vinculin activation and turnover at FAs to promote its association with the force transduction FA nanodomain.

An Integrated Chemical Environment to Support 21st-Century Toxicology.

SUMMARY: Access to high-quality reference data is essential for the development, validation, and implementation of in vitro and in silico approaches that reduce and replace the use of animals in toxicity testing. Currently, these data must often be pooled from a variety of disparate sources to efficiently link a set of assay responses and model predictions to an outcome or hazard classification. To provide a central access point for these purposes, the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods developed the Integrated Chemical Environment (ICE) web resource. The ICE data integrator allows users to retrieve and combine data sets and to develop hypotheses through data exploration. Open-source computational workflows and models will be available for download and application to local data. ICE currently includes curated in vivo test data, reference chemical information, in vitro assay data (including Tox21TM/ToxCast™ high-throughput screening data), and in silico model predictions. Users can query these data collections focusing on end points of interest such as acute systemic toxicity, endocrine disruption, skin sensitization, and many others. ICE is publicly accessible at https://ice.ntp.niehs.nih.gov. https://doi.org/10.1289/EHP1759.

Brg1 Enables Rapid Growth of the Early Embryo by Suppressing Genes That Regulate Apoptosis and Cell Growth Arrest.

SWI/SNF (switching/sucrose nonfermenting)-dependent chromatin remodeling establishes coordinated gene expression programs during development, yet important functional details remain to be elucidated. We show that the Brg1 (Brahma-related gene 1; Smarca4) ATPase is globally expressed at high levels during postimplantation development and its conditional ablation, beginning at gastrulation, results in increased apoptosis, growth retardation, and, ultimately, embryonic death. Global gene expression analysis revealed that genes upregulated in Rosa26CreERT2; Brg1(flox/flox) embryos (here referred to as Brg1(d/d) embryos to describe embryos with deletion of the Brg1(flox/flox) alleles) negatively regulate cell cycle progression and cell growth. In addition, the p53 (Trp53) protein, which is virtually undetectable in early wild-type embryos, accumulated in the Brg1(d/d) embryos and activated the p53-dependent pathways. Using P19 cells, we show that Brg1 and CHD4 (chromodomain helicase DNA binding protein 4) coordinate to control target gene expression. Both proteins physically interact and show a substantial overlap of binding sites at chromatin-accessible regions adjacent to genes differentially expressed in the Brg1(d/d) embryos. Specifically, Brg1 deficiency results in reduced levels of the repressive histone H3 lysine K27 trimethylation (H3K27me3) histone mark and an increase in the amount of open chromatin at the regulatory region of the p53 and p21 (Cdkn1a) genes. These results provide insights into the mechanisms by which Brg1 functions, which is in part via the p53 program, to constrain gene expression and facilitate rapid embryonic growth.

SWIFT-Review: a text-mining workbench for systematic review.

BACKGROUND: There is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem formulation phase of systematic review can be challenging, especially for topics having a large literature base. Here, we assess the performance of the SWIFT-Review priority ranking algorithm for identifying studies relevant to a given research question. We also explore the use of SWIFT-Review during problem formulation to identify, categorize, and visualize research areas that are data rich/data poor within a large literature corpus. METHODS: Twenty case studies, including 15 public data sets, representing a range of complexity and size, were used to assess the priority ranking performance of SWIFT-Review. For each study, seed sets of manually annotated included and excluded titles and abstracts were used for machine training. The remaining references were then ranked for relevance using an algorithm that considers term frequency and latent Dirichlet allocation (LDA) topic modeling. This ranking was evaluated with respect to (1) the number of studies screened in order to identify 95 % of known relevant studies and (2) the "Work Saved over Sampling" (WSS) performance metric. To assess SWIFT-Review for use in problem formulation, PubMed literature search results for 171 chemicals implicated as EDCs were uploaded into SWIFT-Review (264,588 studies) and categorized based on evidence stream and health outcome. Patterns of search results were surveyed and visualized using a variety of interactive graphics. RESULTS: Compared with the reported performance of other tools using the same datasets, the SWIFT-Review ranking procedure obtained the highest scores on 11 out of 15 of the public datasets. Overall, these results suggest that using machine learning to triage documents for screening has the potential to save, on average, more than 50 % of the screening effort ordinarily required when using un-ordered document lists. In addition, the tagging and annotation capabilities of SWIFT-Review can be useful during the activities of scoping and problem formulation. CONCLUSIONS: Text-mining and machine learning software such as SWIFT-Review can be valuable tools to reduce the human screening burden and assist in problem formulation.

Computational approaches to understanding protein aggregation in neurodegeneration.

The generation of toxic non-native protein conformers has emerged as a unifying thread among disorders such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. Atomic-level detail regarding dynamical changes that facilitate protein aggregation, as well as the structural features of large-scale ordered aggregates and soluble non-native oligomers, would contribute significantly to current understanding of these complex phenomena and offer potential strategies for inhibiting formation of cytotoxic species. However, experimental limitations often preclude the acquisition of high-resolution structural and mechanistic information for aggregating systems. Computational methods, particularly those combine both all-atom and coarse-grained simulations to cover a wide range of time and length scales, have thus emerged as crucial tools for investigating protein aggregation. Here we review the current state of computational methodology for the study of protein self-assembly, with a focus on the application of these methods toward understanding of protein aggregates in human neurodegenerative disorders.

Deep sequencing identification of novel glucocorticoid-responsive miRNAs in apoptotic primary lymphocytes.

Apoptosis of lymphocytes governs the response of the immune system to environmental stress and toxic insult. Signaling through the ubiquitously expressed glucocorticoid receptor, stress-induced glucocorticoid hormones induce apoptosis via mechanisms requiring altered gene expression. Several reports have detailed the changes in gene expression mediating glucocorticoid-induced apoptosis of lymphocytes. However, few studies have examined the role of non-coding miRNAs in this essential physiological process. Previously, using hybridization-based gene expression analysis and deep sequencing of small RNAs, we described the prevalent post-transcriptional repression of annotated miRNAs during glucocorticoid-induced apoptosis of lymphocytes. Here, we describe the development of a customized bioinformatics pipeline that facilitates the deep sequencing-mediated discovery of novel glucocorticoid-responsive miRNAs in apoptotic primary lymphocytes. This analysis identifies the potential presence of over 200 novel glucocorticoid-responsive miRNAs. We have validated the expression of two novel glucocorticoid-responsive miRNAs using small RNA-specific qPCR. Furthermore, through the use of Ingenuity Pathways Analysis (IPA) we determined that the putative targets of these novel validated miRNAs are predicted to regulate cell death processes. These findings identify two and predict the presence of additional novel glucocorticoid-responsive miRNAs in the rat transcriptome, suggesting a potential role for both annotated and novel miRNAs in glucocorticoid-induced apoptosis of lymphocytes.

miRNA data analysis: next-gen sequencing.

MicroRNAs (miRNAs) are short, noncoding RNAs that have the capacity to bind, capture, and silence hundreds of genes within and across diverse signaling pathways 1(Bartel, Cell 136:215-33, 2009) Specific sets of miRNAs characterize specific cell lineages of normal organisms and an increasing number of diseases have been shown to be associated with the dysregulation of specific miRNAs. Deep sequencing platforms have revealed unexpected complexity in relation to miRNAs, including 5' and 3'-end-length heterogeneity and RNA editing. These insights not uncovered by previous microarray-based studies underscore the importance of data analysis tools that enable users to rapidly and easily analyze the unprecedented amounts of small RNA sequencing data that is emerging from next-generation sequencing platforms, such as Illumina/Solexa, SOLiD, and 454. In this chapter, we summarize the increasing number of analysis platforms that are available for miRNA discovery and profiling and the identification of functional miRNA-mRNA pairs in the context of biology and disease. We also discuss in greater detail our contributions to this effort.

Structural insights into the binding of MMP9 inhibitors.

Matrix Metalloproteinase are family of enzymes responsible for degradation of extracellular matrix. MMP9 (gelatinase B) is one of the common matrix metalloproteinase that is associated with tissue destruction in a number of disease states such as rheumatoid arthiritis, fibrotic lung disease, dilated cardiomyopathy, as well as cancer invasion and metastasis. Recent study demonstrates that increased expression of MMP9 results in augmentation of myopathy with increased inflammation and fibernecrosis. Previous studies do not provide any conclusive information related to structural specificity of MMP9 inhibitors towards its active site, but with the availability of experimental structures it is now possible to study the structural specificity of MMP9 inhibitors. In light of availability of this information, we have applied docking and molecular dynamics approach to study the binding of inhibitors to the active site of MMP9. Three categories of inhibitor consisting of sulfonamide hydroxamate, thioester, and carboxylic moieties as zinc binding groups (ZBG) were chosen in the present study. Our docking results demonstrate that thioester based zinc binding group gives favourable docking scores as compared to other two groups. Molecular Dynamics simulations further reveal that tight binding conformation for thioester group has high specificity for MMP9 active site. Our study provides valuable insights on inhibitor specificity of MMP9 which provides valuable hints for future design of potent inhibitors and drugs.