Biology subjects

Scherlo, M.

Publications and source records attributed to Scherlo, M..

3 recordsLinked to original sources

bioRxiv2026

kontakteUR: transforming coordinates to chemical intuition to focus on essential interactions in biomolecular systems

Molecular interactions govern cellular function, making them essential to discover biomolecular mechanisms by unravelling structure-function relationships. The rapid growth of AI-based prediction, experimental determination, and molecular dynamics simulations generates structural data at an unprecedented scale. However, structural information is typically represented as Cartesian coordinates, leaving chemical interactions and conformational relationships largely implicit. We introduce a high-throughput framework transforming structural geometry into a standardized, compact contact space. Moving beyond simple distance cutoffs, it provides a chemically and geometrically informed representation of various residue-residue interactions, their temporal changes, and conformations at residue-level resolution. Our contact-space representation enables systematic comparison and classification even for large-scale analysis. Case studies spanning structure comparison or studies of protein-protein, protein-ligand, protein-RNA, and antibody-antigen complexes, demonstrate how contact-space analysis reveals interaction patterns, identifies key mutation sites, and links structural features to experimental observations. With these and further applications, kontakteUR elucidates biomolecular function and assists targeted protein design, with results suited for further processing by artificial intelligence algorithms.

biochemistry↗

bioRxiv2025

IR spectroscopy: from experimental spectra to high-resolution structural analysis by integrating simulations and machine learning

Understanding biomolecular function at the atomic scale requires detailed insight into the structural changes underlying dynamic processes. Vibrational infrared (IR) spectroscopy--when paired with biomolecular simulations and quantum-chemical calculations--determines bond length variations on the order of 0.01 [A], providing insights into these structutral changes. Here, we address the forward problem in IR spectroscopy: predicting high-accuracy vibrational spectra from known molecular structures identified by biomolecular simulations. Solving this problem lays the groundwork for the inverse problem: inferring structural ensembles directly from experimental IR spectra. We evaluate two computational approaches, normal mode analysis and Fourier-transformed dipole autocorrelation, against experimental IR spectra of N-Methylacetamide, a prototypical model for peptide bond vibrations. Spectra are derived from simulation models at multiple levels of theory, including hybrid quantum mechanics/molecular mechanics, machine-learned and classical molecular mechanics approaches. Our results highlight the capabilities and limitations of current theoretical biophysical approaches to decode structural information from experimental vibrational spectroscopy data. These insights underscore the potential of future artificial intelligence (AI)-enhanced models to enable direct IR-based structure determination. For example, resolving the so far experimentally inaccessible structures of toxic oligomers involved in neurodegenerative diseases, enabling improved disease diagnostics and targeted therapies. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/665767v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@6603d3org.highwire.dtl.DTLVardef@1852c85org.highwire.dtl.DTLVardef@2ddaccorg.highwire.dtl.DTLVardef@77b554_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

Replacement of a single residue in an antibody completely abolishes cognate antigen binding, as predicted by theoretical methods

Structural insights into the interaction between antibodies and antigens at the atomic level are pivotal for understanding the molecular mechanisms of antigen binding. Despite the availability of structural models generated by recent artificial intelligence advancements, computational predictions require experimental validation to confirm their accuracy. Here, we demonstrate a novel approach that combines computational protein modeling with spectroscopic experiments to validate antibody-antigen interactions. As a case example we use solanezumab, a monoclonal antibody that targets amyloid-beta (A{beta}), whose misfolding is the main factor responsible for Alzheimers disease. For this antibody we predicted a single mutation, G95AHC, within the paratope of the heavy chain to disrupt antigen binding. This mutation, referred to as a "dead mutant", was experimentally validated using an immuno-infrared biosensor (iRS). Our results confirmed that the mutation abolished antigen binding without affecting the native structure of the antibody. The use of dead mutants enables precise differentiation between specific and nonspecific binding, which is particularly important in medical diagnostics. We applied this approach to analyze the binding of solanezumab to synthetically produced A{beta} variants and A{beta} catched by the iRS functionalized surface from cerebrospinal fluid, showcasing its utility in Alzheimers disease diagnostics. These findings highlight the value of computational modeling and experimental validation in understanding antigen-antibody interactions, with significant implications for diagnostic and therapeutic applications.

biophysics↗