Skip to content
Log in

Author

Suyong Re

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Review Open access Jul 2026

Intrinsically disordered regions as drivers of protein aggregation: mechanisms, phase separation, and emerging predictive frameworks

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) lack stable tertiary structures yet perform essential roles in cellular signaling, molecular recognition, transcriptional regulation, and biomolecular assembly. Their conformational flexibility enables functional adaptability but also increases susceptibility to aberrant intermolecular interactions and protein aggregation. Unlike folded proteins, aggregation in IDPs arises from transient conformational ensembles that expose cryptic aggregation-prone regions (APRs), facilitating oligomerization and fibril formation under specific cellular and environmental conditions. Several studies have further established a mechanistic relationship between intrinsic disorder, liquid–liquid phase separation (LLPS), and pathological aggregation, where dynamic condensates can undergo maturation into irreversible amyloid-like assemblies. These transitions are strongly influenced by sequence grammar, charge distribution, aromatic residue patterning, post-translational modifications, molecular crowding, and proteostasis regulation. This mini-review summarizes the molecular principles governing aggregation in disordered systems, with emphasis on conformational ensemble dynamics, disorder-to-order transitions, and the interplay between LLPS and fibrillization. The review further discusses computational approaches used to predict aggregation propensity in IDRs, including classical physicochemical predictors, ensemble-aware simulations, molecular dynamics frameworks, and emerging protein language model-based methods. Further, integration of artificial intelligence, structural biophysics, and multiscale modeling have substantially improved understanding of disorder-driven aggregation pathways. Collectively, these findings support a unified framework in which sequence composition, conformational heterogeneity, and cellular environment cooperatively regulate functional assembly and pathological aggregation in intrinsically disordered proteins.

Rahul Kaushik, Suyong Re · 1 citation
Review Aug 2026

Computational navigation of constrained multidimensional protein fitness landscapes.

Protein engineering relies heavily on computational characterization of constrained protein fitness landscapes, in which only a limited fraction of sequence space corresponds to stable and functional biomolecules. Advances in structural biology and machine learning are progressively shifting protein design strategies from empirical optimization toward multidimensional evaluation of sequence-structure-function relationships. This review examines current computational strategies for exploring these landscapes, including sequence-derived evolutionary descriptors, structural fitness assessment, energetic evaluation, and integrated multi-parameter scoring. Recent developments in protein language models, deep-learning-based structure prediction, generative protein design, and consensus scoring approaches support large-scale exploration of biologically accessible sequence space. Negative-design constraints, including aggregation propensity, intrinsic disorder, and developability are important in prioritizing experimentally tractable protein candidates. Finally, the integration of computational prediction with iterative experimental validation is discussed as a central framework for rational protein engineering. By framing structure prediction, sequence representation learning, and generative design as complementary strategies for navigating a single constrained fitness landscape, this review highlights integrated, multidimensional scoring and negative-design filtering as the critical link between computational candidate generation and experimentally tractable protein design.

Rahul Kaushik, Firoozeh Piroozmand, Suyong Re · 0 citations