Mutant SLC35F2 modeling

https://doi.org/10.5281/zenodo.18895865

This repository archives modeling data for the following manuscript:

Guiding conformational state preferences of Solute Carrier Protein 35F2
to discover differential state-specific functions with AlphaFold

G.V.T. Swapna¹, Anindita Sarangi¹, Roland Felkner¹,
Gaetano T. Montelione², Monica J. Roth¹**

¹Department of Pharmacology, Robert Wood Johnson Medical School,
Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA

²Department of Chemistry and Chemical Biology, Center for Biotechnology
and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA

Submitted to Proceedings of the National Academy of Sciences (PNAS)

Corresponding authors
Monica J. Roth – roth@rwjms.rutgers.edu
Gaetano T. Montelione – monteg3@rpi.edu

📁 Available Data

This repository contains AlphaFold modeling data used to analyze alternative conformational states of the SLC35F2 transporter.

Figure Modeling Data

SLC35F2-Fig2-AFsample2

AlphaFold sampling models used in the structural analysis presented in
Figure 4 of the manuscript and associated analysis from Supplementary Table S1.

These models illustrate alternative predicted conformational states of SLC35F2 and were used to guide identification of mutation clusters that bias conformational state preferences.

Supplemental Tables

SLC35F2-Supp-Table

Supplementary Table S1: Mutations predicted to favor inward-facing conformations based on regions “a”, “b”, or “c” defined in Figure 1A of the manuscript.

🧾 Abstract

Deep learning methods often successfully model alternative conformational states of integral membrane proteins but are challenged in predicting how mutations impact conformational distributions.

Solute carrier proteins (SLCs) represent one of the largest classes of integral membrane proteins, with more than 400 members in the human genome.

The SLC35F2 protein serves as a transporter of queuine and anticancer therapeutic drugs such as YM155, and also functions as a receptor for gammaretroviral isolates.

Using AlphaFold-based modeling, clusters of mutations were designed to bias structures toward either outward-facing or inward-facing conformations.

Experimental studies demonstrated that these mutant proteins maintain the same subcellular localization as wild-type SLC35F2 but lose the ability to transport YM155. Remarkably, viral entry by gammaretroviruses bearing the FeLV Env A5 isolate was limited to mutants biased toward the outward-facing conformation.

These results demonstrate how AlphaFold modeling can guide the design of mutational clusters that bias conformational state preferences and reveal distinct state-specific functions of SLC-family transporter proteins.

⚙️ Software Versions

Structural models and analysis used the following tools:

AlphaFold2: v2.3.0 / AlphaFold sampling
ESMFold: as implemented via Hugging Face / Colab
Python: v3.8+
Other tools: Biopython, TM-score, PyMOL

📚 Associated Manuscript

Swapna GVT, Sarangi A, Felkner R, Montelione GT, Roth MJ.

Guiding conformational state preferences of Solute Carrier Protein SLC35F2 to discover differential state-specific functions with AlphaFold.

Submitted to Proceedings of the National Academy of Sciences (PNAS).

📄 License

This repository is provided for academic research use.

📖 Citation

If you use these models or data, please cite the associated publication once available.

License

RPIBioinformatics/SLC35F2-modeling

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