Use BindFilter
Official Neurosnap webserver for accessing BindFilter online.
Overview
A composite pipeline that folds target+binder candidates and computes a comprehensive set of structure- and sequence-based developability/quality metrics across multiple backends.
Neurosnap Overview
The BindFilter online webserver allows anybody with a Neurosnap account to run and access BindFilter, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Folds each binder candidate independently using the selected folding backend.
- Runs multiple downstream tools and merges scalar results into output.csv.
- Infers peptide runs from the input binders (short, single-chain binders) to enable peptide-only tools.
- Outputs one PDB per binder plus dedicated JSON files for detailed mutation-level tables.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access BindFilter using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using BindFilter in publications or research outputs.
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Amani, Keaun, and Danial Gharaie Amirabadi. 2024. Neurosnap SDK Package. Software. https://github.com/NeurosnapInc/neurosnap. |
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Passaro, S., Corso, G., Wohlwend, J., Reveiz, M., Thaler, S., Somnath, V. R., Getz, N., Portnoi, T., Roy, J., Stark, H., Kwabi-Addo, D., Beaini, D., Jaakkola, T., & Barzilay, R. (2025). Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. |
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Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2022). ColabFold: making protein folding accessible to all. Nature Methods. |
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Chai Discovery, Jacques Boitreaud, Jack Dent, Matthew McPartlon, Joshua Meier, Vinicius Reis, Alex Rogozhnikov, Kevin Wu bioRxiv 2024.10.10.615955; doi: https://doi.org/10.1101/2024.10.10.615955 |
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Vangone, A. and Bonvin, A. M. J. J. (2017). PRODIGY: A Contact-based Predictor of Binding Affinity in Protein-protein Complexes. Bio-protocol 7(3): e2124. DOI: 10.21769/BioProtoc.2124. |
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Vangone, A., and Bonvin, A. M. J. J. (2015). Contacts-based prediction of binding affinity in protein-protein complexes. eLife 4: 291. |
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Huang X, Pearce R, Zhang Y. EvoEF2: accurate and fast energy function for computational protein design. Bioinformatics. 2020 Feb 15;36(4):1135-42. |
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Amani, K. Prot2Prop: structure-aware fine-tuning of protein language models for joint prediction of multiple developability properties from protein inputs. bioRxiv (2026). https://www.biorxiv.org/content/10.64898/2026.06.28.735009v1 |
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Thumuluri, V., Martiny, H.M., Almagro Armenteros, J., Salomon, J., Nielsen, H., & Johansen, A. (2021). NetSolP: predicting protein solubility in Escherichia coli using language models. Bioinformatics, 38(4), 941-946. |
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Aleksander Kuriata, Valentin Iglesias, Jordi Pujols, Mateusz Kurcinski, Sebastian Kmiecik, Salvador Ventura, Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility, Nucleic Acids Research, Volume 47, Issue W1, 02 July 2019, Pages W300–W307, https://doi.org/10.1093/nar/gkz321 |
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Pudžiuvelytė, I. et al. "TemStaPro: protein thermostability prediction using sequence representations from protein language models", https://academic.oup.com/, 04 April 2024, https://academic.oup.com/bioinformatics/article/40/4/btae157/7632735. |
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Gajendra, P.S. R. et al., ToxinPred 3.0: An improved method for predicting the toxicity of peptides, https://www.biorxiv.org, 14 August 2023, https://www.biorxiv.org/content/10.1101/2023.08.11.552911v1. |
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Article Source: OpenMM 7: Rapid development of high performance algorithms for molecular dynamics Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, et al. (2017) OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLOS Computational Biology 13(7): e1005659. https://doi.org/10.1371/journal.pcbi.1005659 |
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ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB James A. Maier, Carmenza Martinez, Koushik Kasavajhala, Lauren Wickstrom, Kevin E. Hauser, and Carlos Simmerling Journal of Chemical Theory and Computation 2015 11 (8), 3696-3713 DOI: 10.1021/acs.jctc.5b00255 |
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Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79(2), 926–935. https://doi.org/10.1063/1.445869 |
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McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018. |
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Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 226–231. Portland, Oregon: AAAI Press. |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |
Job Note
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Inputs & Configuration
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