WaterFlow: Prediction of Ordered Water Molecule Positions on Protein Structures

Bridging Discrete and Continuous Protein Folding through Hydrophobicity and Molecular Dynamics

It is concluded that molecular dynamics has an important place in improving the physicality of existing protein structure prediction paradigms, leading to the development of the Subspace Relaxation Operator (SRO).

Does Surface Conservation Yield? Application to Data-Driven Docking

The interface prediction program WHISCY is presented, which combines surface conservation and structural information to predict protein–protein interfaces and demonstrates the potential of using interface predictions to drive protein–protein docking.

PreFold-dG: estimating binding affinity of protein–protein interaction from intermediate representations of protein folding model

Abstract Motivation Binding affinity governs how proteins interact and underlies essential biological processes. Computational approaches have been developed to simulate and predict protein binding, but the scarcity of high-quality data has imposed significant constraints. One consequence is that most methods focus on predicting mutational changes in binding affinity (ΔΔG), rather than binding affinity (ΔG) itself. This practice risks overfitting to skewed data distributions, limiting the generalizability of predictions. Recent advances in protein structure prediction have enabled computational modeling of protein conformations in mass, providing rich structural information from which binding interactions can be largely explained. However, leveraging these advances for effective prediction of binding affinity has yet to translate into reliable predictions. Results We present PreFold-dG, a model that estimates binding affinities of protein complexes utilizing intermediate embeddings from Boltz-2, an open-source foundation model for protein structure prediction. Our approach aggregates residue-level information weighted by interresidue distance, and predicts ΔG directly rather than its derivative, ΔΔG. PreFold-dG achieved state-of-the-art performance on well-established binding affinity prediction benchmarks and demonstrated robustness on independent test sets. Ablation studies suggest that all intermediate embeddings are utilized in the prediction, whereas their contributions to modeling ΔΔG and ΔG vary. We further validated our model through case studies on real-world broadly neutralizing antibody data with evolutionary relevance. Availability https://github.com/LGAI-Research/PreFold-dG.

Conserved water molecules shape the pathogenicity of missense variants in human proteins.

Conserved water molecules (CWMs) are tightly bound solvent molecules that occupy well-defined, recurrent positions in protein structures. Although they are known to influence protein stability, function, and ligand binding, their role in shaping the effects of human missense variants remains largely unexplored. Here, we demonstrate that CWMs are a previously underappreciated determinant of missense variant pathogenicity. By predicting ligand-binding and CWM sites across human PDB structures and mapping missense variants to these sites and the remaining protein surface, we found that pathogenic variants were significantly enriched at CWM sites, whether overlapping or outside other ligand-binding regions. This enrichment exceeded that observed for binding sites as a whole, indicating a broader role for water-mediated interactions in modulating variant effects. To explore a mechanistic basis for this association, we performed molecular dynamics simulations of human lysosomal acid glucosylceramidase (GCase), encoded by GBA1 and implicated in Gaucher disease and Parkinson's disease risk. Selective destabilization of a CWM site in wild-type GCase produced structural and dynamical changes resembling those observed in the pathogenic L444P variant, whereas stabilization of this site in L444P shifted several measures toward wild-type behavior. These results suggest that disruption of a single CWM can contribute to long-range structural remodeling observed in a disease-associated variant. Together, our findings identify CWMs as a novel structural constraint shaping the distribution and effects of pathogenic missense variants. Incorporating water-mediated interactions into structural models provides a generalizable framework for interpreting human genetic variation and its contribution to disease.

Predictive all-atom simulations of disordered proteins and biomolecular condensates through osmometry-guided force-field optimization

Solvent Interaction Analysis: A New Lens for Protein Structure and Diagnostics

Aqueous two-phase systems (ATPSs) provide a versatile, fully aqueous platform for probing solute–water interactions and protein structure. This review first surveys the diversity and phase behavior of biphasic aqueous systems formed by polymers and salts. We describe how phase diagrams characterize ATPS formation and composition and how both polymer chemistry and salt identity, rather than molecular size alone, govern phase separation by modulating the solvent properties of water. Building on a modified binodal model, we show that phase separation and solute partitioning can be understood in terms of changes in aqueous solvent dipolarity/polarizability, hydrogen-bond donor/acceptor properties, hydrophobicity, and electrostatics, quantified via solvatochromic probes and homologous solute series. These measurements underpin solvent interaction analysis (SIA), in which the partition coefficients of small molecules and proteins across panels of ATPSs are used to generate “structural signatures” that sensitively report on amino acid substitutions, conformational changes, aggregation, ligand binding, osmolyte effects, and post-translational modifications, independent of protein size. We discuss how SIA can be implemented in vial-, plate-, and microfluidic formats and combined with diverse analytical readouts (HPLC, MS, colorimetric assays, and immunoassays), and we contrast this structure-focused approach with conventional concentration-only proteomic and biomarker strategies. Particular emphasis is placed on structure-based biomarker discovery, where disease-relevant shifts in proteoform distributions—especially glycosylation changes—are often more informative than bulk protein levels and where SIA can complement or simplify complex glycomics and top-down proteomics workflows. As a case study, we describe the recently FDA-approved IsoPSA assay, which applies SIA principles to prostate-specific antigen by measuring cancer-associated structural alterations in circulating PSA via its partition behavior in a proprietary ATPS. IsoPSA generates a single index that discriminates between high-grade prostate cancer and benign and low-grade conditions. Prospective, longitudinal, and MRI-integrated clinical studies demonstrate that IsoPSA improves pre-biopsy risk stratification, reduces unnecessary biopsies, and provides robust negative and positive predictive values within the PSA “gray zone.” Collectively, the data support aqueous solvent interaction analysis as a broadly applicable, mechanistically grounded technology for protein characterization, drug–protein interaction studies, and structure-centric biomarker development, exemplified by the clinical translation of IsoPSA.