AlphaFold Substrate Alignments#

References#

This appendix lists references relevant to protein‑folding inference systems, latent‑space modeling, substrate‑level interpretation, and validation frameworks. References are grouped by category for clarity. Citations are provided in a model‑agnostic, substrate‑neutral format consistent with the RSM canon.

1. Protein‑Folding Inference Systems#

• Jumper, J., Evans, R., Pritzel, A., et al.
  Highly accurate protein structure prediction with AlphaFold.
  Nature 596, 583–589 (2021).

• Senior, A. W., Evans, R., Jumper, J., et al.
  Improved protein structure prediction using potentials from deep learning.
  Nature 577, 706–710 (2020).

• Baek, M., DiMaio, F., Anishchenko, I., et al.
  Accurate prediction of protein structures and interactions using a three‑track neural network.
  Science 373, 871–876 (2021).

2. Latent‑Space and Representation Learning#

• Vaswani, A., Shazeer, N., Parmar, N., et al.
  Attention is All You Need.
  Advances in Neural Information Processing Systems (2017).

• Rives, A., Meier, J., Sercu, T., et al.
  Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences.
  PNAS 118, e2016239118 (2021).

• Rao, R., Liu, J., Verkuil, R., et al.
  MSA Transformer.
  bioRxiv (2021).

3. Structural Biology and Folding Pathways#

• Dill, K. A., & MacCallum, J. L.
  The protein‑folding problem, 50 years on.
  Science 338, 1042–1046 (2012).

• Onuchic, J. N., Luthey‑Schulten, Z., & Wolynes, P. G.
  Theory of protein folding: The energy landscape perspective.
  Annual Review of Physical Chemistry 48, 545–600 (1997).

• Bryngelson, J. D., & Wolynes, P. G.
  Spin glasses and the statistical mechanics of protein folding.
  PNAS 84, 7524–7528 (1987).

4. Validation, Stability, and Drift#

• Amershi, S., Begel, A., Bird, C., et al.
  Software Engineering for Machine Learning: A Case Study.
  ICSE‑SEIP (2019).

• Breck, E., Cai, S., Nielsen, E., et al.
  The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction.
  Google Research (2017).

• Sculley, D., Holt, G., Golovin, D., et al.
  Hidden Technical Debt in Machine Learning Systems.
  NIPS (2015).

5. Substrate‑Level and High‑Dimensional Modeling#

• Loswin, N.
  Resonance Substrate Model (RSM): Structural Foundations for High‑Dimensional Inference.
  TriadicFrameworks (2025).

• Loswin, N.
  Validation‑Space‑Time (vST): A Substrate‑Level Framework for Reproducibility and Drift Detection.
  TriadicFrameworks (2025).

• Loswin, N.
  Triadic Dimensional Cores: A 3D–9D Substrate for Structural and Inference‑Level Alignment.
  TriadicFrameworks (2025).

6. Additional Resources#

• UniProt Consortium.
  UniProt: A worldwide hub of protein knowledge.
  Nucleic Acids Research 47, D506–D515 (2019).

• Varadi, M., Anyango, S., Deshpande, M., et al.
  AlphaFold Protein Structure Database: massively expanding the structural coverage of protein‑sequence space.
  Nucleic Acids Research 50, D439–D444 (2022).