Computational Innovation: Streamlining NMR Prediction via Clustering and Dimensionality Reduction

Project Goal: Designing Tractable Conformational Ensembles for IDPs

The principal objective of this research is to tackle the computational challenge inherent in characterizing Intrinsically Disordered Proteins (IDPs) via NMR Chemical Shift (CS) predictions. Accurately modeling these flexible systems at the quantum level requires large conformational ensembles, a process that rapidly becomes intractable.

We developed and validated a novel computational protocol that integrates Dimensionality Reduction (DR) and Clustering Algorithms (CA) to produce small, highly representative Clustered Ensembles (CEs). This technique effectively bridges the gap between computationally demanding quantum calculations and the vast conformational space of IDPs.

Key Methodological Innovations

1. Multiscale Dimensionality Reduction and Clustering:
   • The Problem: Traditional clustering methods, like GROMOS (which uses RMSD), perform poorly on IDPs because they fail to account for the local interactions and high-dimensional nature of disordered systems.
   • The Solution: We implemented a multiscale approach that uses DR (specifically tICA and tSNE) before clustering to project the data into a lower-dimensional latent space, mitigating multicollinearity and computational complexity.
   • Validated Performance: Our CEs consistently demonstrated superior performance over traditional Sequential Ensembles (SEs) of similar size, outperforming them in approximately 85.7% of all ensemble sizes evaluated.
2. Novel Fingerprint Selection:
   • New Feature: We introduced Solvent-Accessible Surface Area (SASA) as a key structural fingerprint for DR, alongside traditional features like $\phi/\psi$ angles and $\alpha$-carbon distances.
   • SASA Advantage: Using SASA-based tSNE yielded CEs that were better aligned with experimental NMR CSs (improving the $r^2$ value by $0.17$ to $0.36$), as it offers a more comprehensive view of the protein’s external interactions and transient structures.
3. Integrated Ensemble Selection:
   • To overcome the complex parametrization of nonlinear DR techniques (like tSNE), we developed an integrated silhouette score scanning protocol (SS$_{INT}$). This metric rigorously selects the optimal combination of parameters (perplexity and cluster size) to ensure the resulting ensemble accurately represents both the local and global dynamics of the original trajectory.

Evaluation and Impact on Advanced Prediction

This work also critically assessed rapid CS predictors for IDPs and directly addressed the challenge of Post-Translational Modifications (PTMs):

• Phosphorylation Deficiency: We demonstrated that mainstream neural network-based tools like Sparta+ and ShiftX2 fail to accurately incorporate the influence of phosphorylation, as they were not trained on PTM data sets.
• Need for Quantum Methods: This deficiency highlights the necessity of implementing higher-level quantum calculations (DFT) to accurately capture PTM effects. Our clustering methodology makes these resource-intensive DFT calculations tractable by drastically reducing the size of the structural ensemble required for convergence.

Project Collaborators

Dr. Jana Přecechtělová Pavlíková is the principal developer of our multi-scale computational framework, integrating Molecular Dynamics, Dimensionality Reduction, and Clustering to model dynamic systems. She applies tICA/tSNE and machine learning to generate compact Clustered Ensembles for accurate NMR Chemical Shift predictions in IDPs. Her work is crucial for ensuring rigorous ensemble selection and robustly coupling trajectory data with DFT for high-accuracy predictions.