Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Machine learning predicts nucleosome binding modes of transcription factors.

ABSTRACT:

Background

Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding, dyad binding, groove binding, and gyre spanning. However, there are substantial experimental challenges in measuring nucleosome binding modes for thousands of TFs in different species.

Results

We present a computational prediction of the binding modes based on TF protein sequences. With a nested cross-validation procedure, our model outperforms several fine-tuned off-the-shelf machine learning (ML) methods in the multi-label classification task. Our binary classifier for the EB mode performs better than these ML methods with the area under precision-recall curve achieving 75%. The end preference of most TFs is consistent with low nucleosome occupancy around their binding site in GM12878 cells. The nucleosome occupancy data is used as an alternative dataset to confirm the superiority of our EB classifier.

Conclusions

We develop the first ML-based approach for efficient and comprehensive analysis of nucleosome binding modes of TFs.

SUBMITTER: Kishan KC

PROVIDER: S-EPMC8008688 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Structural Features of Transcription Factors Associating with Nucleosome Binding.

Project description:Fate-changing transcription factors (TFs) scan chromatin to initiate new genetic programs during cell differentiation and reprogramming. Yet the protein structure domains that allow TFs to target nucleosomal DNA remain unexplored. We screened diverse TFs for binding to nucleosomes containing motif-enriched sequences targeted by pioneer factors in vivo. FOXA1, OCT4, ASCL1/E12?, PU1, CEBP?, and ZELDA display a range of nucleosome binding affinities that correlate with their cell reprogramming potential. We further screened 593 full-length human TFs on protein microarrays against different nucleosome sequences, followed by confirmation in solution, to distinguish among factors that bound nucleosomes, such as the neuronal AP-2?/?/?, versus factors that only bound free DNA. Structural comparisons of DNA binding domains revealed that efficient nucleosome binders use short anchoring ? helices to bind DNA, whereas weak nucleosome binders use unstructured regions and/or ? sheets. Thus, specific modes of DNA interaction allow nucleosome scanning that confers pioneer activity to transcription factors.

| S-EPMC6731145 | biostudies-literature

Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets.

Project description:Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and binding modes. The understanding and analysis of these binding modes are expected to support the discovery of kinase-targeting drugs. The huge amounts of data made it possible to utilize computational techniques, including machine learning, to help in the discovery of kinase-targeting drugs. Machine learning gave reasonable predictions when applied to differentiate between the binding modes of kinase inhibitors, promoting a wider application in that domain. In this study, we applied machine learning supported by feature selection techniques to classify kinase inhibitors according to their binding modes. We represented inhibitors as a large number of molecular descriptors, as features, and systematically reduced these features in a multi-step manner while trying to attain high classification accuracy. Our predictive models could satisfy both goals by achieving high accuracy while utilizing at most 5% of the modeling features. The models could differentiate between binding mode types with MCC values between 0.67 and 0.92, and balanced accuracy values between 0.78 and 0.97 for independent test sets.

| S-EPMC7804204 | biostudies-literature

Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase.

Project description:TEM family of enzymes is one of the most commonly encountered β-lactamases groups with different catalytic capabilities against various antibiotics. Despite the studies investigating the catalytic mechanism of TEM β-lactamases, the binding modes of these enzymes against ligands in different functional catalytic states have been largely overlooked. But the binding modes may play a critical role in the function and even the evolution of these proteins. In this work, a newly developed machine learning analysis approach to the recognition of protein dynamics states was applied to compare the binding modes of TEM-1 β-lactamase with regard to penicillin in different catalytic states. While conventional analysis methods, including principal components analysis (PCA), could not differentiate TEM-1 in different binding modes, the application of a machine learning method led to excellent classification models differentiating these states. It was also revealed that both reactant/product states and apo/product states are more differentiable than the apo/reactant states. The feature importance generated by the training procedure of the machine learning model was utilized to evaluate the contribution from residues at active sites and in different secondary structures. Key active site residues, Ser70 and Ser130, play a critical role in differentiating reactant/product states, while other active site residues are more important for differentiating apo/product states. Overall, this study provides new insights into the different dynamical function states of TEM-1 and may open a new venue for β-lactamases functional and evolutional studies in general.

| S-EPMC6629954 | biostudies-literature

Nucleosome-mediated cooperativity between transcription factors.

Project description:Cooperative binding of transcription factors (TFs) to promoters and other regulatory regions is essential for precise gene expression. The classical model of cooperativity requires direct interactions between TFs, thus constraining the arrangement of TF sites in regulatory regions. Recent genomic and functional studies, however, demonstrate a great deal of flexibility in such arrangements with variable distances, numbers of sites, and identities of TF sites located in cis-regulatory regions. Such flexibility is inconsistent with cooperativity by direct interactions between TFs. Here, we demonstrate that strong cooperativity among noninteracting TFs can be achieved by their competition with nucleosomes. We find that the mechanism of nucleosome-mediated cooperativity is analogous to cooperativity in another multimolecular complex: hemoglobin. This surprising analogy provides deep insights, with parallels between the heterotropic regulation of hemoglobin (e.g., the Bohr effect) and the roles of nucleosome-positioning sequences and chromatin modifications in gene expression. Nucleosome-mediated cooperativity is consistent with several experimental studies, is equally applicable to repressors and activators, allows substantial flexibility in and modularity of regulatory regions, and provides a rationale for a broad range of genomic and evolutionary observations. Striking parallels between cooperativity in hemoglobin and in transcriptional regulation point to a general mechanism that can be used in various biological systems.

| S-EPMC3012490 | biostudies-literature

Simulated MRI Artifacts: Testing Machine Learning Failure Modes.

Project description:Objective. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that utilizes standard MRI and used to evaluate the performance of the model under duress. These simulated MRI artifacts consisted of motion, susceptibility induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures. Results. The artifact with the largest effect was the simplest, sequence mislabeling, though motion, field inhomogeneity, and sequence misalignment also caused significant performance decreases. The model was most susceptible to artifacts affecting the FLAIR (fluid attenuation inversion recovery) sequence. Conclusion. Overall, these simulated artifacts could be used to test other brain MRI models, but this approach could be used across medical imaging applications.

| S-EPMC10521705 | biostudies-literature

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules

Project description:This SuperSeries is composed of the SubSeries listed below.

2020-01-22 | GSE129144 | GEO

Nucleosome allostery in pioneer transcription factor binding.

Project description:While recent experiments revealed that some pioneer transcription factors (TFs) can bind to their target DNA sequences inside a nucleosome, the binding dynamics of their target recognitions are poorly understood. Here we used the latest coarse-grained models and molecular dynamics simulations to study the nucleosome-binding procedure of the two pioneer TFs, Sox2 and Oct4. In the simulations for a strongly positioning nucleosome, Sox2 selected its target DNA sequence only when the target was exposed. Otherwise, Sox2 entropically bound to the dyad region nonspecifically. In contrast, Oct4 plastically bound on the nucleosome mainly in two ways. First, the two POU domains of Oct4 separately bound to the two parallel gyres of the nucleosomal DNA, supporting the previous experimental results of the partial motif recognition. Second, the POUS domain of Oct4 favored binding on the acidic patch of histones. Then, simulating the TFs binding to a genomic nucleosome, the LIN28B nucleosome, we found that the recognition of a pseudo motif by Sox2 induced the local DNA bending and shifted the population of the rotational position of the nucleosomal DNA. The redistributed DNA phase, in turn, changed the accessibility of a distant TF binding site, which consequently affected the binding probability of a second Sox2 or Oct4. These results revealed a nucleosomal DNA-mediated allosteric mechanism, through which one TF binding event can change the global conformation, and effectively regulate the binding of another TF at distant sites. Our simulations provide insights into the binding mechanism of single and multiple TFs on the nucleosome.

| S-EPMC7456188 | biostudies-literature

Comparative analysis of prokaryotic and eukaryotic transcription factors using machine-learning techniques.

Project description:The DNA-protein interactions play vital roles in the central dogma of molecular biology. Proper interactions between DNA and protein would lead to the onset of various biological phenomena like transcription, translation, and replication. However, the mechanisms of these well-known processes vary between prokaryotic and eukaryotic organisms. The exact molecular mechanisms of these processes are unknown. Therefore, it is of interest to report the comparative estimate of the different properties of the DNA binding proteins from prokaryotic and eukaryotic organisms. We analyzed the different sequence-based features such as the frequency of amino acids and amino acid groups in the proteins of prokaryotes and eukaryotes by statistical measures. The general pattern of differences between the various DNA binding proteins for the development of a prediction system to discriminate between these proteins between prokaryotes and eukaryotes is documented.

| S-EPMC6137564 | biostudies-literature

Identifying transcription factor-DNA interactions using machine learning

Project description:We focused on building models that incorporated transcription factor (TF)-DNA interaction data for 12 members of the Auxin Response Factor (ARF) family from soybean as assessed by DNA Affinity Purification and sequencing (DAP-seq).

2023-06-01 | GSE193400 | GEO

Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy

Project description:Samples of primary tumors collected from 23 ovarian cancer patients

2018-04-07 | GSE112798 | GEO