SilicoGenesis

Can you download your protein structures in parallel from a public repository such as the Protein Data Bank, store them as a single efficient file and visualise the protein structures, all in less than 20 lines of code? With Protkit you can! Check out this Google Colab notebook with a more detailed description at: https://lnkd.in/duzhfM5B Alternatively, you can check out the notebook examples on Protkit's Github respository: https://lnkd.in/dKGYHxZb

Did you know that Protkit provides an easy-to-use and modern file format to store structural data of proteins? It improves on the PDB file format both in terms of capability and file size. With Prot files, you can store additional calculated attributes as part of the protein, chains, residue or atoms, which is not possible with PDB. The file structure has an inherent hierarchical structure, making it easy to isolate the biological entities you are interested in.  You can even store multiple proteins in a single file, making it a convenient file format to store an entire database of proteins! It is based on compressed JSON, making it easy to develop tools in various computer languages or use it as a serialization format for APIs. Check out the File I/O section in Protkit’s Quick Start Guide (https://lnkd.in/djV-S3cx) for extensive documentation and examples. Try Protkit today: Project Homepage: https://lnkd.in/dQcYTRM4 Github Repository: https://lnkd.in/dKGYHxZb PyPI Installation Instructions: https://lnkd.in/di9ic4nh #protkit #computationalbiology #bioinformatics #proteins #opensource

Did you know that Protkit allows you to download protein structures and sequences from a variety of trusted sources, such as RCSB PDB, Uniprot and SAbDab? With just a few lines of code, you can programmatically access thousands of proteins for your next project or research. Protkit allows you to download various files in parallel, optimising the available network bandwidth and CPU utilisation. Want to try it out? Check out the Downloading Files section in the Quick Start Guide (https://lnkd.in/geEXrTf6) for extensive documentation and examples. Give Protkit a try today and simplify your computational biology workflows! Project Homepage: https://lnkd.in/dQcYTRM4 Github Repository: https://lnkd.in/dKGYHxZb PyPI Installation Instructions: https://lnkd.in/di9ic4nh #protkit #computationalbiology #bioinformatics #proteins #opensource

Are you tired of juggling multiple computational biology tools? Protkit streamlines your workflow by integrating commonly used tools into a single, easy-to-use Python library. With just a few lines of code, you can access a suite of functionalities for tasks ranging from structural bioinformatics to protein engineering and machine learning. Say goodbye to the hassle of installing and managing multiple tools – with Protkit, it's as simple as 'pip install protkit'. Gain access to a wealth of tools and resources with a single installation. Give Protkit a try today and simplify your computational biology projects! Project Homepage:  https://lnkd.in/dQcYTRM4 Github Repository:  https://lnkd.in/dKGYHxZb PyPI Installation Instructions:  https://lnkd.in/di9ic4nh Quick Start Guide:  https://lnkd.in/dgZbF3PC #protkit #computationalbiology #bioinformatics #proteins #opensource

PSST. Protkit v0.3 has just been released! This version of Protkit allows you to export your protein structures and sequences to Pandas DataFrames, for use in a variety of machine learning and data science applications. You can create rich datasets with just a few lines of code! It’s as easy as: 1) Load a set of proteins, 2) Add properties using our property featurizers and 3) Export as a Pandas DataFrame. It has never been so easy! Protkit is an open source Python library that can be used for a variety of tasks in computational biology and bioinformatics, focusing on structural bioinformatics, protein engineering and machine learning. Have you tried Protkit yet? If not, now’s the perfect time to get started. Check out the links below: Project Homepage: https://lnkd.in/dQcYTRM4 Github Repository: https://lnkd.in/dKGYHxZb PyPI installation instructions: https://lnkd.in/di9ic4nh Project documentation: https://lnkd.in/dAdfQe9U #protkit #computationalbiology #bioinformatics #proteins #opensource

Are you working in computational biology, bioinformatics, or machine learning? Protkit is your one-stop solution for everything from data preprocessing to protein structure analysis. With features like easy data extraction from popular formats (PDB, FASTA), anomaly detection and cleanup, unified data representation, and seamless interoperability, Protkit is designed to streamline your workflows. Whether you're dealing with complex protein folding, docking, or machine learning-based predictions, Protkit's modular and extensible design has you covered. And because it's open-source, you can customize it to suit your needs and collaborate with a growing community of researchers. Check out Protkit today and join our community! Project Homepage: https://lnkd.in/dQcYTRM4 Github Repository: https://lnkd.in/dKGYHxZb PyPI Installation Instructions: https://lnkd.in/di9ic4nh Quick Start Guide: https://lnkd.in/dgZbF3PC #ComputationalBiology #Bioinformatics #MachineLearning #ProteinEngineering #OpenSource #Protkit

At SilicoGenesis, we've developed cutting-edge AI and machine learning models to accelerate the discovery of antibodies at an unprecedented speed and scale. Curious about how we can help drive your drug discovery projects forward? Or perhaps you're interested in Protkit, our open-source Python library designed for a wide range of computational biology tasks? Connect with us today, and let's explore how we can collaborate. #ComputationalBiology #MachineLearning #DrugDiscovery #ProteinEngineering #OpenSource

Protkit offers a solution to the issues researchers face when working with many different computational biology tools. Protkit is Open Source - check it out!

Version 0.2.4 of Protkit is out! This version of Protkit adds support for Propka, a popular tool for predicting pKa values of ionizable groups in proteins and protein-ligand complexes based on the 3D structure of the protein. Protkit supports a growing number of tools, including Propka, Reduce, PDB2PQR, FreeSASA, etc. which is fully integrated with our Protein representations. You can get the results from these tools within a few lines of code in Protkit. Protkit is an open source Python library that can be used for a variety of tasks in computational biology and bioinformatics, focusing on structural bioinformatics, protein engineering and machine learning. Get started with Protkit today: Project Homepage:  https://lnkd.in/dQcYTRM4 Github Repository:  https://lnkd.in/dKGYHxZb PyPI Installation Instructions:  https://lnkd.in/di9ic4nh Quick Start Guide:  https://lnkd.in/dgZbF3PC #protkit #bioinformatics #proteinengineering #opensource #computationalbiology #propka

Integration of algorithms is key to advancing computational biology. Protkit's modular design and interoperable APIs facilitate seamless integration of existing and new computational biology tools. Protkit is an open source Python library that can be used for a variety of tasks in computational biology and bioinformatics, focusing on structural bioinformatics, protein engineering and machine learning. Have you tried Protkit yet? Visit the links below to get started: Project Homepage: https://lnkd.in/dQcYTRM4 Github Repository: https://lnkd.in/dKGYHxZb PyPI installation instructions: https://lnkd.in/di9ic4nh Project documentation: https://lnkd.in/dAdfQe9U #protkit #computationalbiology #bioinformatics #proteins #opensource