Accurate multiple sequence alignment (MSA) is central to understanding protein evolution, structure, and function. We present BioMatics 1.0, a novel MSA algorithm that applies optimal transport principles through the Wasserstein first-order distance function to align amino acid distributions across positions, enabling refined detection of structural and evolutionary patterns. Unlike conventional score-based methods, BioMatics 1.0 constructs profile-to-profile alignments using Earth Mover's Distance over per-position frequency vectors, guided by BLOSUM62 log-odds similarity. This is complemented by entropy-adaptive gap penalties that dynamically modulate alignment behavior in variable or weakly conserved regions. Benchmark evaluations across curated datasets spanning conserved domains, structural motifs, and heterogeneous families demonstrate that BioMatics 1.0 outperforms widely used tools in column score (CS) accuracy and achieves competitive or comparable sum-of-pairs score (SPS) results. Its architecture prioritizes residue-level alignment precision, yielding results that are particularly informative for downstream tasks such as phylogenetic reconstruction and structure-informed modeling.
Orkid Coskuner-Weber, Yusuf Emre Ari, Yildiray Efe Berberoglu et al.· Proteins: Structure, Functio...· 0 citations
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.
B. Zaslavsky, M. Stovsky, V. Uversky· International Journal of Mol...· 0 citations
