Fine-tuning tomato agronomic properties by computational genome redesign.
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ABSTRACT:
SUBMITTER: Carrera J
PROVIDER: S-EPMC3369923 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSearching thoroughly for plant cis-elements corresponding to transcription factors is worthwhile to reveal novel gene activation cascades. At the same time, a great deal of research is currently focused on epigenetic events in plants. A widely used method serving both purposes is chromatin immunoprecipitation, which was developed for Arabidopsis and other plants but is not yet operational for tomato (Solanum lycopersicum), a model plant species for a group of economically important crops.ResultsWe developed a chromatin immunoprecipitation protocol suitable for tomato by adjusting the parameters to optimise in vivo crosslinking, purification of nuclei, chromatin extraction, DNA shearing and precipitate analysis using real-time PCR. Results were obtained with two different antibodies, five control loci and two normalisation criteria.ConclusionHere we provide a chromatin immunoprecipitation procedure for tomato leaves that could be combined with high-throughput sequencing to generate a detailed map of epigenetic modifications or genome-wide nucleosome positioning data.
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Project description:We report a computational, structure-based redesign of the phenylalanine adenylation domain of the nonribosomal peptide synthetase enzyme gramicidin S synthetase A (GrsA-PheA) for a set of noncognate substrates for which the wild-type enzyme has little or virtually no specificity. Experimental validation of a set of top-ranked computationally predicted enzyme mutants shows significant improvement in the specificity for the target substrates. We further present enhancements to the methodology for computational enzyme redesign that are experimentally shown to result in significant additional improvements in the target substrate specificity. The mutant with the highest activity for a noncognate substrate exhibits 1/6 of the wild-type enzyme/wild-type substrate activity, further confirming the feasibility of our computational approach. Our results suggest that structure-based protein design can identify active mutants different from those selected by evolution.
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Project description:Rationale. The microvasculature of the central nervous system includes the blood-brain barrier (BBB), which regulates the permeability to nutrients and restricts the passage of toxic agents and inflammatory cells. Canonical Wnt/b-catenin signaling is responsible for the early phases of brain vascularization and blood-brain barrier differentiation. However, this signal declines after birth and other signaling pathways able to maintain barrier integrity at postnatal stage are still unknown. Objective. Sox17 constitutes a major downstream target of Wnt/b-catenin in endothelial cells and regulates arterial differentiation. In the present paper, we asked whether Sox17 may act downstream of Wnt/b-catenin in inducing BBB differentiation and maintenance. Methods and Results. Using reporter mice and nuclear staining of Sox17 and b-catenin, we report that while b-catenin signaling declines after birth, Sox17 activation increases and remains high in the adult. Endothelial-specific inactivation of Sox17 leads to increase of permeability of the brain microcirculation. The severity of this effect depends on the degree of BBB maturation: it is strong in the embryo, and progressively declines after birth. In search of Sox17 mechanism of action, RNA-Seq analysis of gene expression of brain endothelial cells has identified members of the Wnt/b-catenin signaling pathway as downstream targets of Sox17. Consistently, we found that Sox17 is a positive inducer of Wnt/b-catenin signaling and it acts in concert with this pathway to induce and maintain BBB properties. In vivo, inhibition of the b-catenin destruction complex or expression of a degradation-resistant b-catenin mutant, prevent the increase in permeability and retina vascular malformations observed in the absence of Sox17. Conclusions. Our data highlight a novel role for Sox17 in the induction and maintenance of the BBB and they underline the strict reciprocal tuning of this transcription factor and Wnt/b-catenin pathway. Modulation of Sox17 activity may be relevant to control BBB permeability in pathological conditions.
2019-02-12 | GSE122564 | GEO
Project description:Molecular dynamics was used to simulate the transition state for the first chemical reaction step (TS1) of cocaine hydrolysis catalyzed by human butyrylcholinesterase (BChE) and its mutants. The simulated results demonstrate that the overall hydrogen bonding between the carbonyl oxygen of (-)-cocaine benzoyl ester and the oxyanion hole of BChE in the TS1 structure for (-)-cocaine hydrolysis catalyzed by A199S/S287G/A328W/Y332G BChE should be significantly stronger than that in the TS1 structure for (-)-cocaine hydrolysis catalyzed by the WT BChE and other simulated BChE mutants. Thus, the transition-state simulations predict that A199S/S287G/A328W/Y332G mutant of BChE should have a significantly lower energy barrier for the reaction process and, therefore, a significantly higher catalytic efficiency for (-)-cocaine hydrolysis. The theoretical prediction has been confirmed by wet experimental tests showing an approximately (456 +/- 41)-fold improved catalytic efficiency of A199S/S287G/A328W/Y332G BChE against (-)-cocaine. This is a unique study to design an enzyme mutant based on transitionstate simulation. The designed BChE mutant has the highest catalytic efficiency against cocaine of all of the reported BChE mutants, demonstrating that the unique design approach based on transition-state simulation is promising for rational enzyme redesign and drug discovery.
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Project description:OptZyme is a new computational procedure for designing improved enzymatic activity (i.e., kcat or kcat/KM) with a novel substrate. The key concept is to use transition state analogue compounds, which are known for many reactions, as proxies for the typically unknown transition state structures. Mutations that minimize the interaction energy of the enzyme with its transition state analogue, rather than with its substrate, are identified that lower the transition state formation energy barrier. Using Escherichia coli ?-glucuronidase as a benchmark system, we confirm that KM correlates (R(2)?=?0.960) with the computed interaction energy between the enzyme and the para-nitrophenyl- ?, D-glucuronide substrate, kcat/KM correlates (R(2)?=?0.864) with the interaction energy of the transition state analogue, 1,5-glucarolactone, and kcat correlates (R(2)?=?0.854) with a weighted combination of interaction energies with the substrate and transition state analogue. OptZyme is subsequently used to identify mutants with improved KM, kcat, and kcat/KM for a new substrate, para-nitrophenyl- ?, D-galactoside. Differences between the three libraries reveal structural differences that underpin improving KM, kcat, or kcat/KM. Mutants predicted to enhance the activity for para-nitrophenyl- ?, D-galactoside directly or indirectly create hydrogen bonds with the altered sugar ring conformation or its substituents, namely H162S, L361G, W549R, and N550S.
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Project description:Productive replication of DNA viruses elicits host cell DNA damage responses, which cause both beneficial and detrimental effects on viral replication. In response to the viral productive replication, host cells attempt to attenuate the S-phase cyclin-dependent kinase (CDK) activities to inhibit viral replication. However, accumulating evidence regarding interactions between viral factors and cellular signaling molecules indicate that viruses utilize them and selectively block the downstream signaling pathways that lead to attenuation of the high S-phase CDK activities required for viral replication. In this review, we describe the sophisticated strategy of Epstein-Barr virus to cancel such "noisy" host defense signals in order to hijack the cellular environment.
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Project description:Cell proliferation and differentiation is a complex process involving many cellular mechanisms. One of the best-studied phenomena in cell differentiation is erythrocyte development during hematopoiesis in vertebrates. In recent years, a new class of small, endogenous, non-coding RNAs called microRNAs (miRNAs) emerged as important regulators of gene expression at the post-transcriptional level. Thousands of miRNAs have been identified in various organisms, including protozoa, fungi, bacteria and viruses, proving that the regulatory miRNA pathway is conserved in evolution. There are many examples of miRNA-mediated regulation of gene expression in the processes of cell proliferation, differentiation and apoptosis, and in cancer genesis. Many of the collected data clearly show the dependence of the proteome of a cell on the qualitative and quantitative composition of endogenous miRNAs. Numerous specific miRNAs are present in the hematopoietic erythroid line. This review attempts to summarize the state of knowledge on the role of miRNAs in the regulation of different stages of erythropoiesis. Original experimental data and results obtained with bioinformatics tools were combined to elucidate the currently known regulatory network of miRNAs that guide the process of differentiation of red blood cells.
| S-EPMC6276011 | biostudies-literature
Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits.
[Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018.
[Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022
[Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023
2024-02-05 | MSV000094014 | MassIVE
Project description:The reprogramming of DNA-binding specificity is an important challenge for computational protein design that tests current understanding of protein-DNA recognition, and has considerable practical relevance for biotechnology and medicine. Here we describe the computational redesign of the cleavage specificity of the intron-encoded homing endonuclease I-MsoI using a physically realistic atomic-level forcefield. Using an in silico screen, we identified single base-pair substitutions predicted to disrupt binding by the wild-type enzyme, and then optimized the identities and conformations of clusters of amino acids around each of these unfavourable substitutions using Monte Carlo sampling. A redesigned enzyme that was predicted to display altered target site specificity, while maintaining wild-type binding affinity, was experimentally characterized. The redesigned enzyme binds and cleaves the redesigned recognition site approximately 10,000 times more effectively than does the wild-type enzyme, with a level of target discrimination comparable to the original endonuclease. Determination of the structure of the redesigned nuclease-recognition site complex by X-ray crystallography confirms the accuracy of the computationally predicted interface. These results suggest that computational protein design methods can have an important role in the creation of novel highly specific endonucleases for gene therapy and other applications.
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