David Prihoda

Building tools 🛠️ 🧬 Ovo, BioPhi, OASis, DeepBGC. Deep learning, protein design, bioinformatics & random Czech stuff

[David Prihoda @prihodad]

Our biggest "side project" so far. Ovo, an open-source ecosystem for de novo protein design, is released today 🧵👇

[David Prihoda @prihodad]

Created a free-for-all commentable google doc for the Anthropic binder design prompt @sokrypton @btnaughton @DdelAlamo @richardwshuai @MartinPacesa https://t.co/DK6FBsD75r

[David Prihoda @prihodad]

@itswillhua @CalvinMccarter Use promb if you want a pip installable oasis score https://t.co/msX4LkyGXI

[David Prihoda @prihodad]

@stephenzlu https://t.co/msX4LkyGXI

[David Prihoda @prihodad]

@stephenzlu Always happy to see OASis in the wild! Btw check out promb for a high throughput implementation if you haven’t already

[Zechen Zhang @ZechenZhang5]

4/ We propose the Agent-Native Research Artifact (ARA): a protocol that recasts the primary research object from a narrative document into an executable knowledge package, with four interlocking layers.
The paper, if you still want one, is a compiled view of the artifact, not the source.

[David Prihoda @prihodad]

@arjunrajlab I can recommend typst as alternative

[David Prihoda @prihodad]

Version 1.0.0 is out, you can now “pip install ovo”

[Biology+AI Daily @BiologyAIDaily]

Ovo, an Open-Source Ecosystem for De Novo Protein Design

1. Ovo is a novel open-source platform for de novo protein design, addressing the fragmented landscape of current tools. It integrates models, workflows, data management, and interactive visualization into a scalable ecosystem, making it easier for both experts and non-technical users to design proteins at scale.

2. The platform leverages Nextflow for workflow orchestration, ensuring modularity and scalability across different infrastructures, from local machines to cloud environments. This infrastructure-agnostic design allows for flexible deployment and execution of protein design pipelines.

3. Ovo introduces a novel ProteinQC module that computes comprehensive sequence and structure descriptors, contextualizing designs against reference sets. This feature helps users evaluate the quality and feasibility of their protein designs more effectively.

4. The ecosystem supports scaffold design, binder design, and diversification workflows, with interactive interfaces that simplify the process of choosing appropriate models and submitting jobs. It also includes advanced filtering capabilities to prioritize high-quality candidates for downstream validation.

5. Community-driven development is a core aspect of Ovo, allowing users to add new workflows and plugins. This extensibility ensures that the platform can rapidly adopt and integrate emerging methods, facilitating benchmarking and standardization in the field.

6. Ovo's data management layer ensures efficient organization and retrieval of designs and descriptors, supporting retrospective analysis and linking experimental success rates with computational scores. This robustness is crucial for reproducibility and scalability in industrial settings.

7. The platform's interactive visualization tools enable users to inspect and filter designs based on confidence scores and protein properties, making it easier to identify the most promising candidates for experimental testing.

📜Paper: https://t.co/IF4PNFexVW
#ProteinDesign #OpenSource #ComputationalBiology #Bioinformatics #Nextflow #DeNovoProteins

[David Prihoda @prihodad]

Generating proteins is now easier than ever 🐣

[Biology+AI Daily @BiologyAIDaily]

Ovo, an Open-Source Ecosystem for De Novo Protein Design

1. Ovo is a novel open-source platform for de novo protein design, addressing the fragmented landscape of current tools. It integrates models, workflows, data management, and interactive visualization into a scalable ecosystem, making it easier for both experts and non-technical users to design proteins at scale.

2. The platform leverages Nextflow for workflow orchestration, ensuring modularity and scalability across different infrastructures, from local machines to cloud environments. This infrastructure-agnostic design allows for flexible deployment and execution of protein design pipelines.

3. Ovo introduces a novel ProteinQC module that computes comprehensive sequence and structure descriptors, contextualizing designs against reference sets. This feature helps users evaluate the quality and feasibility of their protein designs more effectively.

4. The ecosystem supports scaffold design, binder design, and diversification workflows, with interactive interfaces that simplify the process of choosing appropriate models and submitting jobs. It also includes advanced filtering capabilities to prioritize high-quality candidates for downstream validation.

5. Community-driven development is a core aspect of Ovo, allowing users to add new workflows and plugins. This extensibility ensures that the platform can rapidly adopt and integrate emerging methods, facilitating benchmarking and standardization in the field.

6. Ovo's data management layer ensures efficient organization and retrieval of designs and descriptors, supporting retrospective analysis and linking experimental success rates with computational scores. This robustness is crucial for reproducibility and scalability in industrial settings.

7. The platform's interactive visualization tools enable users to inspect and filter designs based on confidence scores and protein properties, making it easier to identify the most promising candidates for experimental testing.

📜Paper: https://t.co/IF4PNFexVW
#ProteinDesign #OpenSource #ComputationalBiology #Bioinformatics #Nextflow #DeNovoProteins

[David Prihoda @prihodad]

Ovo was developed by our team, Applied Research and Innovation at MSD Czech Republic.

Pre-print: https://t.co/ZPhuv0TY4l
Code: https://t.co/ghVDJEdDNj

[David Prihoda @prihodad]

Our biggest "side project" so far. Ovo, an open-source ecosystem for de novo protein design, is released today 🧵👇

[David Prihoda @prihodad]

We are trying to establish an ecosystem where developers benefit from building on a shared tech stack: Nextflow, the standard for building bioinformatics pipelines, and Streamlit, the magic new way of building web apps in Python, on top of a single data model shared by all users

[David Prihoda @prihodad]

Also in case you want to number a large number of sequences quickly, check `Chain.batch()` that accepts a dictionary of sequences and returns a dictionary of Chain objects and a dictionary of errors. This is available since version 0.3.3.

[David Prihoda @prihodad]

If you are using abnumber to number your antibodies, it now supports ANARCII (the deep learning re-implementation of ANARCI). Just use `from abnumber.future import Chain`. This also means that you can `pip install abnumber` without conda. Feedback welcome.

[David Prihoda @prihodad]

Streamlit example: https://t.co/iEu6W83kc5

[David Prihoda @prihodad]

Looking to visualize protein structures in Jupyter, Colab, Streamlit, or anything that can embed an iframe? Check out the new molviewspec library:

[David Prihoda @prihodad]

Example notebook (works in Colab, Jupyter, and VS Code): https://t.co/2DCfnFS6OT