SUMMARY
The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library.


AVAILABILITY
SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.


SUPPLEMENTARY INFORMATION
Available at https://github.com/structuralbioinformatics/SBILib. doi:10.5281/zenodo.8402071.

SBILib: a handle for protein modeling and engineering

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ~25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. Here, we introduce ModCRE, a web server implementing a structure homology-modelling approach to predict TF motifs and automatically model higher-order TF regulatory complexes. Starting from a TF sequence or structure, ModCRE predicts a set of motifs for that TF. The predicted motifs are then used to scan the DNA for occurrences of each of them, and the best matches are either profiled with a modeling into a higher-order regulatory complex with DNA, as well as other TFs and co-factors. Moreover, we demonstrate that incorporating high-throughput TF binding data, such as from protein binding microarrays, addresses the protein-DNA structure scarcity problem for deriving statistical potentials. In turn, these statistical potentials are proven to be capable predictors of TF motifs. We also show the conditional advantage of using ModCRE over a nearest-neighbor approach for predicting TF binding sites as well as an improvement in prediction accuracy when using a rank-enrichment selection system. Finally, as case examples, we apply ModCRE to model the interferon beta enhanceosome and the complex of SOX2 and 11 with a nucleosome.

ModCRE: a structure homology-modeling approach to predict TF binding in cis -regulatory elements

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ~25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. Here, we introduce a structure-based learning approach to predict the binding preferences of TFs and a web server to automatically model higher-order TF regulatory complexes (ModCRE). Our approach uses high-throughput TF binding data, such as from protein binding microarrays, to address the protein-DNA structure scarcity problem for learning the binding preferences of TFs. We show the approach over the state-of-art nearest-neighbor method for predicting TF binding sites. We improve prediction accuracy when using an enrichment selection system that uses many neighbors or structure-models. Starting from a TF sequence or structure, ModCRE predicts its binding preferences in the form of motifs. The predicted motifs are then used to scan a DNA sequence for occurrences, and the best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: i) the co-localization of TFs; and ii) the structural modeling of protein-protein interactions between TFs and with co-factors. As case examples, we apply our approach to automatically model the interferon- b enhanceosome and the pioneering complex of OCT4, SOX2 and SOX11 with a nucleosome, which are compared with the experimentally known structures.

Automated structure-based learning to model co-operativity and protein-DNA interactions in cis -regulatory modules

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Molina-Fernández

Author Tools

Chat about Author

Papers

ModCRE: a structure homology-modeling approach to predict TF binding in cis -regulatory elements

Automated structure-based learning to model co-operativity and protein-DNA interactions in cis -regulatory modules