Streamlit is an open-source Python coding framework for building web-applications or \"web-apps\" and is now being used by researchers to share large data sets from published studies and other resources. Here we present Stmol, an easy-to-use component for rendering interactive 3D molecular visualizations of protein and ligand structures within Streamlit web-apps. Stmol can render protein and ligand structures with just a few lines of Python code by utilizing popular visualization libraries, currently Py3DMol and Speck. On the user-end, Stmol does not require expertise to interactively navigate. On the developer-end, Stmol can be easily integrated within structural bioinformatic and cheminformatic pipelines to provide a simple means for user-end researchers to advance biological studies and drug discovery efforts. In this paper, we highlight a few examples of how Stmol has already been utilized by scientific communities to share interactive molecular visualizations of protein and ligand structures from known open databases. We hope Stmol will be used by researchers to build additional open-sourced web-apps to benefit current and future generations of scientists.

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Models from machine learning (ML) or artificial intelligence (AI) increasingly assist in guiding experimental design and decision making in molecular biology and medicine. Recently, Language Models (LMs) have been adapted from Natural Language Processing (NLP) to encode the implicit language written in protein sequences. Protein LMs show enormous potential in generating descriptive representations (embeddings) for proteins from just their sequences, in a fraction of the time with respect to previous approaches, yet with comparable or improved predictive ability. Researchers have trained a variety of protein LMs that are likely to illuminate different angles of the protein language. By leveraging the bio_embeddings pipeline and modules, simple and reproducible workflows can be laid out to generate protein embeddings and rich visualizations. Embeddings can then be leveraged as input features through machine learning libraries to develop methods predicting particular aspects of protein function and structure. Beyond the workflows included here, embeddings have been leveraged as proxies to traditional homology-based inference and even to align similar protein sequences. A wealth of possibilities remain for researchers to harness through the tools provided in the following protocols. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. The following protocols are included in this manuscript: Basic Protocol 1: Generic use of the bio_embeddings pipeline to plot protein sequences and annotations Basic Protocol 2: Generate embeddings from protein sequences using the bio_embeddings pipeline Basic Protocol 3: Overlay sequence annotations onto a protein space visualization Basic Protocol 4: Train a machine learning classifier on protein embeddings Alternate Protocol 1: Generate 3D instead of 2D visualizations Alternate Protocol 2: Visualize protein solubility instead of protein subcellular localization Support Protocol: Join embedding generation and sequence space visualization in a pipeline.

Post-translational modifications (PTMs) of protein amino acids are ubiquitous and important to protein function, localization, degradation, and more. In recent years, there has been an explosion in the discovery of PTMs as a result of improvements in PTM measurement techniques, including quantitative measurements of PTMs across multiple conditions. ProteomeScout is a repository for such discovery and quantitative experiments and provides tools for visualizing PTMs within proteins, including where they are relative to other PTMS, domains, mutations, and structure. ProteomeScout additionally provides analysis tools for identifying statistically significant relationships in experimental datasets. This unit describes four basic protocols for working with the ProteomeScout Web interface or programmatically with the database download. © 2017 by John Wiley & Sons, Inc.