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1. Fuzzy supertertiary interactions within PSD-95 enable ligand binding

   https://doi.org/10.7554/eLife.77242  

   Hamilton, George L; Saikia, Nabanita; Basak, Sujit; Welcome, Franceine S; Wu, Fang; Kubiak, Jakub; Zhang, Changcheng; Hao, Yan; Seidel, Claus AM; Ding, Feng; et al (September 2022, eLife)

   The scaffold protein PSD-95 links postsynaptic receptors to sites of presynaptic neurotransmitter release. Flexible linkers between folded domains in PSD-95 enable a dynamic supertertiary structure. Interdomain interactions within the PSG supramodule, formed by P DZ3, S H3, and G uanylate Kinase domains, regulate PSD-95 activity. Here we combined discrete molecular dynamics and single molecule Förster resonance energy transfer (FRET) to characterize the PSG supramodule, with time resolution spanning picoseconds to seconds. We used a FRET network to measure distances in full-length PSD-95 and model the conformational ensemble. We found that PDZ3 samples two conformational basins, which we confirmed with disulfide mapping. To understand effects on activity, we measured binding of the synaptic adhesion protein neuroligin. We found that PSD-95 bound neuroligin well at physiological pH while truncated PDZ3 bound poorly. Our hybrid structural models reveal how the supertertiary context of PDZ3 enables recognition of this critical synaptic ligand. 

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2. Robust and Sample Optimal Algorithms for PSD Low Rank Approximation

   https://doi.org/10.1109/FOCS46700.2020.00054  

   Bakshi, Ainesh; Chepurko, Nadiia; Woodruff, David P. (November 2020, FOCS)

   null (Ed.)
3. Riemannian optimization using three different metrics for Hermitian PSD fixed-rank constraints

   https://doi.org/10.1007/s10589-025-00687-8  

   Zheng, Shixin; Huang, Wen; Vandereycken, Bart; Zhang, Xiangxiong (May 2025, Computational Optimization and Applications)
4. Pin1 Binding to Phosphorylated PSD-95 Regulates the Number of Functional Excitatory Synapses

   https://doi.org/10.3389/fnmol.2020.00010  

   Delgado, Jary Y.; Nall, Duncan; Selvin, Paul R. (March 2020, Frontiers in Molecular Neuroscience)
5. Applicability of Hyperdimensional Computing for Seizure Prediction Using LBP and PSD Features from iEEG

   https://doi.org/10.1109/MWSCAS57524.2023.10405889  

   Ge, Lulu; Parhi, Keshab K. (August 2023, Proc. of 2023 Midwest Symposium on Circuits and Systems)

   Hyperdimensional computing (HDC) has been assumed to be attractive for time-series classification. These classifiers are ideal for one or few-shot learning and require fewer resources. These classifiers have been demonstrated to be useful in seizure detection. This paper investigates subject-specific seizure prediction using HDC from intracranial elec-troencephalogram (iEEG) from the publicly available Kaggle dataset. In comparison to seizure detection (interictal vs. ictal), seizure prediction (interictal vs. preictal) is a more challenging problem. Two HDC-based encoding strategies are explored: local binary pattern (LBP) and power spectral density (PSD). The average performance of HDC classifiers using the two encoding approaches is computed using the leave-one-seizure-out cross-validation method. Experimental results show that the PSD method using a small number of features selected by the minimum redundancy maximum relevance (mRMR) achieves better seizure prediction performance than the LBP method on the trairring and validation data. 

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