Year: 2025
Type: article
Abstract: Deep learning has revolutionised de novo protein design, with new models achieving unprecedented success in creating novel proteins with specific functions, including artificial protein binders. However, current workflows remain computationally demanding and challenging to operate without dedicated infrastructure and expertise. To overcome these limitations, we present BinderFlow, an open, structured, and parallelised pipeline that automates end-to-end protein binder design. Its batch-based architecture enables live monitoring of design campaigns, seamless coexistence with other GPU-intensive processes, and minimal user intervention. BinderFlow's modular design facilitates the integration of new tools, allowing rapid adaptation to emerging methods. We demonstrate its utility by running automated design campaigns that rapidly generate diverse, high-confidence candidates suitable for experimental validation. To complement the pipeline, we developed BFmonitor, a web-based dashboard for real-time campaign monitoring, design evaluation, and hit selection. Together, BinderFlow and BFmonitor make generative protein design more accessible, scalable, and reproducible, streamlining both exploratory and production-level research. The software is freely available at https://github.com/cryoEM-CNIO/BinderFlow under the GNU LGPL v3.0 license.
Source: PLoS Computational Biology
Institution: Spanish National Cancer Research Centre
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: G1175115578, G1188944981, G1710482639, G210041235, G2118149051, G2212489884, G2262748287, G24120701, G248502196, G2601397997, G2672435700, G294895050, G300979063, G34346518, G3480869486, G3523405818, G3681454997, G4042783231, G410571670, G425668148, G451917667, G464105488, G507957923, G5080475149, G5195188938, G523076202, G5539308778, G5967599077, G6874468849, G7084143925, G8696694383, G8911147183, G929308097
OpenAlex ID: W4416513504