Project description:X-ray crystallography provides the most accurate models of protein-ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein-ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein-ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein-ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein-ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein-ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein-ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.
Project description:There is a growing public concern about the lack of reproducibility of experimental data published in peer-reviewed scientific literature. Herein, we review the most recent alerts regarding experimental data quality and discuss initiatives taken thus far to address this problem, especially in the area of chemical genomics. Going beyond just acknowledging the issue, we propose a chemical and biological data curation workflow that relies on existing cheminformatics approaches to flag, and when appropriate, correct possibly erroneous entries in large chemogenomics data sets. We posit that the adherence to the best practices for data curation is important for both experimental scientists who generate primary data and deposit them in chemical genomics databases and computational researchers who rely on these data for model development.
Project description:BackgroundResistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.ResultsWe demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations.ConclusionsAs such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.
Project description:Mass spectrometry (MS)-based proteomics provides unprecedented opportunities for understanding the structure and function of proteins in complex biological systems; however, protein solubility and sample preparation before MS remain a bottleneck preventing high-throughput proteomics. Herein, we report a high-throughput bottom-up proteomic method enabled by a newly developed MS-compatible photocleavable surfactant, 4-hexylphenylazosulfonate (Azo) that facilitates robust protein extraction, rapid enzymatic digestion (30 min compared to overnight), and subsequent MS-analysis following UV degradation. Moreover, we developed an Azo-aided bottom-up method for analysis of integral membrane proteins, which are key drug targets and are generally underrepresented in global proteomic studies. Furthermore, we demonstrated the ability of Azo to serve as an "all-in-one" MS-compatible surfactant for both top-down and bottom-up proteomics, with streamlined workflows for high-throughput proteomics amenable to clinical applications.
Project description:Laser capture microdissection (LCM) has become an indispensable tool for mass spectrometry-based proteomic analysis of specific regions obtained from formalin-fixed paraffin-embedded (FFPE) tissue samples in both clinical and research settings. Low protein yields from LCM samples along with laborious sample processing steps present challenges for proteomic analysis without sacrificing protein and peptide recovery. Automation of sample preparation workflows is still under development, especially for samples such as laser-capture microdissected tissues. Here, we present a simplified and rapid workflow using adaptive focused acoustics (AFA) technology for sample processing for high-throughput FFPE-based proteomics. We evaluated three different workflows: standard extraction method followed by overnight trypsin digestion, AFA-assisted extraction and overnight trypsin digestion, and AFA-assisted extraction simultaneously performed with trypsin digestion. The use of AFA-based ultrasonication enables automated sample processing for high-throughput proteomic analysis of LCM-FFPE tissues in 96-well and 384-well formats. Further, accelerated trypsin digestion combined with AFA dramatically reduced the overall processing times. LC-MS/MS analysis revealed a slightly higher number of protein and peptide identifications in AFA accelerated workflows compared to standard and AFA overnight workflows. Further, we did not observe any difference in the proportion of peptides identified with missed cleavages or deamidated peptides across the three different workflows. Overall, our results demonstrate that the workflow described in this study enables rapid and high-throughput sample processing with greatly reduced sample handling, which is amenable to automation.
Project description:Phosphotyrosine (pY) enrichment is critical for expanding fundamental and clinical understanding of cellular signaling by mass spectrometry-based proteomics. However, current pY enrichment methods exhibit a high cost per sample and limited reproducibility due to expensive affinity reagents and manual processing. We present rapid-robotic phosphotyrosine proteomics (R2-pY), which uses a magnetic particle processor and pY superbinders or antibodies. R2-pY handles 96 samples in parallel, requires 2 days to go from cell lysate to mass spectrometry injections, and results in global proteomic, phosphoproteomic and tyrosine specific phosphoproteomic samples. We benchmark the method on HeLa cells stimulated with pervanadate and serum and report over 4000 unique pY sites from 1 mg of peptide input, strong reproducibility between replicates, and phosphopeptide enrichment efficiencies above 99%. R2-pY extends our previously reported R2-P2 proteomic and global phosphoproteomic sample preparation framework, opening the door to large-scale studies of pY signaling in concert with global proteome and phosphoproteome profiling.
Project description:Uncovering the relationships between peptide and protein sequences and binding properties is critical for successfully predicting, re-designing and inhibiting protein-protein interactions. Systematically collected data that link protein sequence to binding are valuable for elucidating determinants of protein interaction but are rare in the literature because such data are experimentally difficult to generate. Here we describe SORTCERY, a high-throughput method that we have used to rank hundreds of yeast-displayed peptides according to their affinities for a target interaction partner. The procedure involves fluorescence-activated cell sorting of a library, deep sequencing of sorted pools and downstream computational analysis. We have developed theoretical models and statistical tools that assist in planning these stages. We demonstrate SORTCERY's utility by ranking 1026 BH3 (Bcl-2 homology 3) peptides with respect to their affinities for the anti-apoptotic protein Bcl-xL. Our results are in striking agreement with measured affinities for 19 individual peptides with dissociation constants ranging from 0.1 to 60nM. High-resolution ranking can be used to improve our understanding of sequence-function relationships and to support the development of computational models for predicting and designing novel interactions.
Project description:There is ample evidence that polyphenols are important natural substances with pronounced antioxidative properties. This study aimed to develop a fast and reliable method to determine total polyphenol content (TPC) in foodstuffs and human samples. The microtitration format offers the advantage of low sample volumes in the microlitre range, facilitating high-throughput screening with 40 samples simultaneously. We accordingly adjusted the so-called Folin-Ciocalteu method to a microtitre format (polyphenols microtitre-PPm) with 90% reduction of reagents. The assay was standardized with gallic acid in the range between 0.1 and 3 mM, using a 20 µL sample volume. The intra-assay coefficient of variation (CV) was less than 5%, and inter-assay CV was in the range of 10%. Wavelength was measured at 766 nm after two hours of incubation. This micromethod correlates significantly with both the classical Folin-Ciocalteu method and High-Performance Thin-Layer Chromatography (HPTLC) (r2 = 0.9829). We further observed a significant correlation between PPm and total antioxidants (r2 = 0.918). The highest polyphenol concentrations were obtained for red, blue, and black fruits, vegetables, and juices. Extracts of red grapes could be harvested almost sugar free and might serve as a basis for polyphenol supplementation. Beer, flour, and bread contained polyphenol concentrations sufficient to meet the minimal daily requirement. We conclude that PPm is a sensitive and reliable method that detects polyphenols even in samples diluted 10-fold. The literature strongly recommends further investigations on the effects of polyphenol uptake on human and animal health.
Project description:Green fluorescent proteins (GFPs) are widely used in biological research. Although GFP can be visualized easily, its precise manipulation through binding partners is still burdensome because of the limited availability of high-affinity binding partners and related structural information. Here, we report the crystal structure of GFPuv in complex with the anti-GFP nanobody LaG16 at 1.67 Å resolution, revealing the details of the binding between GFPuv and LaG16. The LaG16 binding site was on the opposite side of the GFP β-barrel from the binding site of the GFP-enhancer, another anti-GFP nanobody, indicating that the GFP-enhancer and LaG16 can bind to GFP together. Thus, we further designed 3 linkers of different lengths to fuse LaG16 and GFP-enhancer together, and the GFP binding of the three constructs was further tested by ITC. The construct with the (GGGGS)4 linker had the highest affinity with a KD of 0.5 nM. The GFP-enhancer-(GGGGS)4-LaG16 chimeric nanobody was further covalently linked to NHS-activated agarose and then used in the purification of a GFP-tagged membrane protein, GFP-tagged zebrafish P2X4, resulting in higher yield than purification with the GFP-enhancer nanobody alone. This work provides a proof of concept for the design of ultra-high-affinity binders of target proteins through dimerized nanobody chimaeras, and this strategy may also be applied to link interesting target protein nanobodies without overlapping binding surfaces.
Project description:High-throughput drug screening based on a multi-component array can be used to identify a variety of interaction between cells and drugs for suitable purposes. The signaling of immune cells is affected by specific proteins, diverse drug combinations, and certain immunosuppressive drugs. The effect of a drug on an organism is usually complex and involves interactions at multiple levels. Herein, we developed a multilayer fabricating system through the high-throughput assembly of nanofilms with inkjet printing to investigate the effects of immunosuppressive drugs. Immunosuppressive drugs or agents occasionally cause side effects depending on drug combinations or a patient's condition. By incorporating various drug combinations for understanding interaction between drugs and immune cells, we were able to develop an immunological drug screening kit with immunosuppressive drugs. Moreover, the ability to control the combination of drugs, as well as their potential for high-throughput preparation should be of great benefit to the biomedical and bioanalytical field.