Biology subjects

Ikeguchi, M.

Publications and source records attributed to Ikeguchi, M..

14 recordsLinked to original sources

bioRxiv2026

Development of force-field corrections for the RNA A-bulge motif

Many functional RNA motifs adopt structures that deviate from the canonical A-form helix and are emerging targets for RNA-directed therapeutics. The microtubule-associated protein tau (MAPT) A-bulge motif (5'-GCAGU/5'-ACGU) is one such motif. Because its structure is stabilized by a delicate balance of local interactions, its accurate modeling remains a major challenge for molecular dynamics (MD) simulations. The experimentally determined nuclear magnetic resonance (NMR) structure of the MAPT A-bulge motif provides a stringent test of whether RNA force fields can accurately reproduce the experimentally observed conformation. Most current AMBER-family RNA force-field models have incorrectly favored a non-native base-triple state of the MAPT A-bulge motif over the experimentally observed stacked state. Structural comparison of the stacked and base-triple conformations revealed that overly favorable NH-N hydrogen bonds between the bulged adenosine and an adjacent Watson-Crick base pair were the primary source of this imbalance. We developed gHBfix-18Ab, an 18-component hydrogen-bond correction that distinguishes NH and NH2; donors. gHBfix-18Ab was combined with the previously developed OL3CP and NBfix0BPh corrections to generate the composite model gHBfix-18Ab*. This model restored the experimentally observed stacked state as the global minimum in the calculated free-energy profile and improved agreement with NMR-derived distance data for the A-bulge region. Importantly, gHBfix-18Ab* did not produce marked structural destabilization of the cUUCGg tetraloop, a widely used benchmark for RNA force-field validation, suggesting that the refinement preserves the stability of the unrelated RNA motif. These results demonstrate that targeted refinement of hydrogen-bond interactions provides a practical strategy for systematic improvement of RNA force fields toward more accurate modeling of noncanonical RNA motifs.

biophysics↗

bioRxiv2026

Resolution-standardized evaluation of ligand atomic coordinates in crystallographic structures using machine learning

Accurate assessment of ligand coordinate-density consistency across different resolutions remains challenging in macromolecular crystallography. We introduce the atomic Box Correlation Coefficient (aBCC), an atom-level metric for evaluating the consistency between ligand atomic coordinates and electron density in a resolution-standardized framework. To predict aBCC values from electron-density maps, we developed QAEmap, a machine-learning model based on three-dimensional convolutional neural networks (3D-CNNs). The model was trained using Fourier-truncated electron-density maps and corresponding ligand coordinates generated from high-resolution structures in the Protein Data Bank. It was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures. was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures.The prediction accuracy gradually decreased with decreasing resolution, but remained reliable up to [~]3.5 [A]. These results demonstrate that aBCC enables resolution-standardized atom-wise evaluation of coordinate-density consistency across different resolutions and provide a foundation for further development and refinement of machine learning-based coordinate validation. SynopsisWe introduce the atomic box correlation coefficient (aBCC), a machine learning-based metric for the resolution-standardized atom-level evaluation of ligand coordinate-density consistency in crystallographic structures. aBCC provides a common framework for assessing and communicating the local coordinate reliability between structural biologists and researchers in structure-based drug discovery.

molecular biology↗

bioRxiv2026

Co-folding of Membrane Proteins and Lipid Molecules Improves Membrane-Protein Structure Prediction Accuracy

Recent advances in deep-learning-based structure prediction have greatly improved the accuracy of protein structure modeling and enabled prediction of biomolecular complexes. However, the surrounding molecular environments are typically not represented explicitly, and their effects are therefore captured only implicitly. This limitation is particularly relevant for membrane proteins, whose structures and interactions are strongly influenced by the membrane environment. Here we introduce co-folding of membrane proteins and lipid molecules (CoMPLip), a method that imposes an explicit membrane context by co-folding proteins with lipid molecules during AlphaFold 3 (AF3) predictions. In CoMPLip, lipid molecules arrange into bilayer-like configurations around transmembrane regions, providing a membrane-like environment during structure prediction. Across benchmark datasets of ligand-bound membrane proteins, full-length single-pass membrane proteins, and dynamic transporters, CoMPLip improves ligand-pose prediction, promotes correct extracellular-intracellular domain separation, and enables sampling of multiple conformational states. CoMPLip is training-free and is compatible with the existing AF3 workflows.

bioinformatics↗

bioRxiv2026

Identification of molecular determinants governing KIF4 cargo recognition of NTCP and their role in HBV/HDV infection

The surface availability of the sodium taurocholate co-transporting polypeptide (NTCP), which serves as entry receptor for Hepatitis B virus (HBV) and Hepatitis D virus (HDV), is a key determinant of HBV/HDV infectivity. We previously identified the Kinesin motor protein KIF4 as a key regulator of NTCP trafficking to the plasma membrane. However, the molecular mechanism by which NTCP is recognized as a KIF4 transport cargo has remained undefined. Here, we identified a leucine-rich motif, LLLI (aa 298-301), within the C-terminal transmembrane domain of NTCP as an essential determinant for KIF4 transport. Co-immunoprecipitation analyses revealed its importance for NTCP interaction with KIF4. This motif is conserved in another established KIF4 cargo, integrin {beta}1 (ITGB1), and mutation of the corresponding residues similarly disrupted ITGB1-KIF4 interaction, indicating a shared cargo recognition mechanism employed by KIF4. Functionally, disruption of the NTCP leucine-rich motif markedly impaired NTCP surface localization without affecting total protein expression, resulting in severe attenuation of HBV and HDV infection. Structural modeling and computational protein-protein interaction analysis indicated that the mutation does not alter NTCP structure or NTCP/preS1 binding affinity, supporting a trafficking-specific defect. Substitution mutations identified L298 and L299 as the minimal residues required for interaction with KIF4. Our findings define a recognition motif that regulates KIF4-dependent cargo identification and surface trafficking. It also underscores the potential of targeting KIF4-mediated NTCP recognition and transport to inhibit HBV and HDV entry. ImportanceHepatitis B and D viruses require the hepatocyte surface receptor NTCP for entry. We identified a conserved leucine-rich motif (LLLI, aa 298-301) in NTCP that is necessary for its recognition by the kinesin motor KIF4; disrupting this motif selectively impairs NTCP surface trafficking and markedly attenuates HBV/HDV infection without reducing total NTCP or intrinsic receptor function. Importantly, we identified a similar motif in integrin {beta}1 (ITGB1; aa 747-750) that is required for interaction with KIF4, revealing a similar cargo selection mechanism for different proteins. Scientifically, these findings define a motif-derived cargo-selection determinant implied by KIF4, advancing a general principle for kinesin-dependent cargo recognition. More broadly, this work highlights a possible host-directed antiviral approach that targets cargo-selection interfaces and provides a conceptual framework for manipulating kinesin-mediated trafficking of clinically relevant membrane proteins beyond NTCP, with implications for receptor regulation and membrane protein therapeutics.

microbiology↗

bioRxiv2025

intDesc-AbMut: Describing and understanding how antibody mutations impact their environmental interactions

Previously, we proposed a double-point mutation (DPM) strategy involving the simultaneous substitution of two amino acids to optimize antibodies. By selecting mutants based on the criterion that favorable interactions between mutated residues and their local environments are preserved or enhanced, improvements in antibody affinity were achieved. Nonetheless, manual extraction of these interactions proved to be time-consuming and labor-intensive. Thus, to streamline this process, we developed intDesc-AbMut, an automated software tool designed to extract interactions between designated mutant residues and their surroundings from three-dimensional antigen-antibody complex structures. intDesc-AbMut identifies and classifies 36 distinct types of interactions, enabling visualization of changes before and after mutation. Additionally, the effects of mutations can be represented by interaction descriptors generated from the extracted interactions. As a practical application, we constructed a machine learning model using these descriptors combined with side-chain rotamer frequencies to determine whether the environment around mutated residues in antigen-antibody complexes is crystal-like, achieving a Matthews correlation coefficient of 0.505 and an accuracy of 0.855. The intDesc-AbMut software is available at: https://github.com/riken-yokohama-AI-drug/intDesc/tree/intDesc-AbMut.

bioengineering↗

bioRxiv2024

Molecular dynamics unveils multiple-site binding of inhibitors with reduced activity on the surface of dihydrofolate reductase

The sensitivity to protein inhibitors is altered by modifications or protein mutations, as represented by drug resistance. The mode of stable drug binding to the protein pocket has been experimentally clarified. However, the nature of the binding of inhibitors with reduced sensitivity remains unclear at the atomic level. In this study, we analyzed the thermodynamics and kinetics of inhibitor binding to the surface of wild-type and mutant dihydrofolate reductase (DHFR) using molecular dynamics simulations combined with Markov state modeling. A strong inhibitor of methotrexate (MTX) showed a preference for the active site of wild-type DHFR with minimal binding to unrelated (secondary) sites. Deletion of a side-chain fragment in MTX largely destabilized the active site-bound state, with clear evidence of binding to secondary sites. Similarly, the F31V mutation in DHFR diminished the specificity of MTX binding to the active site. These results reveal the presence of multiple-bound states whose stabilities are comparable to or higher than those of the unbound state, suggesting that a reduction in the binding affinity for the active site significantly elevates the fractions of these states. This study sheds light on the specific drug recognition by proteins and the selectivity of drug binding sites on protein surfaces. (199 words)

biophysics↗

bioRxiv2022

Binding and unbinding pathways of peptide substrate on SARS-CoV-2 3CL protease

Based on many crystal structures of ligand complexes, much study has been devoted to understanding the molecular recognition of SARS-CoV-2 3C-like protease (3CLpro), a potent drug target for COVID-19. In this research, to extend this present static view, we examined the kinetic process of binding/unbinding of an eight-residue substrate peptide to/from 3CLpro by evaluating the path ensemble with the weighted ensemble simulation. The path ensemble showed the mechanism of how a highly flexible peptide folded into the bound form. At the early stage, the dominant motion was the diffusion on the protein surface showing a broad distribution, whose center was led into the cleft of the Chymotrypsin fold. We observed a definite sequential formation of the hydrogen bonds at the later stage occurring in the cleft, initiated between Glu166 (3CLpro) and P3_Val (peptide), followed by binding to the oxyanion hole and completed by the sequencespecific recognition at P1_Gln.

biophysics↗

bioRxiv2022

gr Predictor: a Deep-Learning Model for Predicting the Hydration Structures around Proteins

Among the factors affecting biological processes such as protein folding and ligand binding, hydration, which is represented by a three-dimensional water-site-distribution-function around the protein, is crucial. The typical methods for computing the distribution functions, including molecular dynamics simulations and the three-dimensional reference interaction site model (3D-RISM) theory, require a long computation time from hours to tens of hours. Here, we propose a deep-learning model rapidly estimating the distribution functions around proteins obtained by the 3D-RISM theory from the protein 3D structure. The distribution functions predicted using our deep-learning model are in good agreement with those obtained by the 3D-RISM theory. Particularly, the coefficient of determination between the distribution function obtained by the deep-learning model and that obtained using the 3D-RISM theory is approximately 0.98. Furthermore, using a graphics processing unit (GPU), the calculation by the deep learning model is completed in less than one minute, more than 2 orders of magnitude faster than the calculation time of 3D-RISM theory. Therefore, our deep learning model provides a practical and efficient way to calculate the three-dimensional water-site-distribution-functions. The program called "gr Predictor" is available under the GNU General Public License from https://github.com/YoshidomeGroup-Hydration/gr-predictor. Table of Contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=55 SRC="FIGDIR/small/488616v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@108b3c7org.highwire.dtl.DTLVardef@52ec18org.highwire.dtl.DTLVardef@a388borg.highwire.dtl.DTLVardef@10654ad_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Allosteric regulation of 3CL protease of SARS-CoV-2 and SARS-CoV observed in the crystal structure ensemble

The 3C-like protease (3CLpro) of SARS-CoV-2 is a potential therapeutic target for COVID-19. Importantly, it has an abundance of structural information solved as a complex with various drug candidate compounds. Collecting these crystal structures (83 Protein Data Bank (PDB) entries) together with those of the highly homologous 3CLpro of SARS-CoV (101 PDB entries), we constructed the crystal structure ensemble of 3CLpro to analyze the dynamic regulation of its catalytic function. The structural dynamics of the 3CLpro dimer observed in the ensemble were characterized by the motions of four separate loops (the C-loop, E-loop, H-loop, and Linker) and the C-terminal domain III on the rigid core of the chymotrypsin fold. Among the four moving loops, the C-loop (also known as the oxyanion binding loop) causes the order (active)-disorder (collapsed) transition, which is regulated cooperatively by five hydrogen bonds made with the surrounding residues. The C-loop, E-loop, and Linker constitute the major ligand binding sites, which consist of a limited variety of binding residues including the substrate binding subsites. Ligand binding causes a ligand size dependent conformational change to the E-loop and Linker, which further stabilize the C-loop via the hydrogen bond between the C-loop and E-loop. The T285A mutation from SARS-CoV 3CLpro to SARS-CoV-2 3CLpro significantly closes the interface of the domain III dimer and allosterically stabilizes the active conformation of the C-loop via hydrogen bonds with Ser1 and Gly2; thus, SARS-CoV-2 3CLpro seems to have increased activity relative to that of SARS-CoV 3CLpro.

biophysics↗

bioRxiv2021

The molecular basis of ubiquitin-specific protease 8 autoinhibition by the WW-like domain

Ubiquitin-specific protease 8 (USP8) is a deubiquitinating enzyme involved in multiple membrane trafficking pathways. The enzyme activity is inhibited by binding to 14-3-3 proteins, and mutations of the 14-3-3 binding motif in USP8 are related to Cushings disease. However, the molecular basis of USP8 enzyme activity regulation remains unclear. Here, we identified amino acids 645-684 of USP8 as an autoinhibitory region, which our pull-down and single-molecule FRET assay results suggested interacts with the catalytic USP domain. In silico modelling indicated that the region forms a WW-like domain structure, plugs the catalytic cleft, and narrows the entrance to the ubiquitin-binding pocket. Furthermore, 14-3-3 was found to inhibit USP8 enzyme activity partly by enhancing the interaction between the WW-like and USP domains. These findings provide the molecular basis of USP8 autoinhibition via the WW-like domain. Moreover, they suggest that the release of autoinhibition may underlie Cushings disease caused by USP8 mutations.

biochemistry↗

bioRxiv2021

Novel cancer subtyping method based on patient-specific gene regulatory network

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the classification processes. In this study, we present a novel method to classify cancer subtypes based on patient-specific molecular systems. Our method quantifies patient-specific gene networks, which are estimated from their transcriptome data. By clustering their quantified networks, our method allows for cancer subtyping, taking into consideration the differences in the molecular systems of patients. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings show that the proposed method, based on a simple classification using the patient-specific molecular systems, can identify cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

bioinformatics↗

bioRxiv2020

Moving toward generalizable NZ-1 labeling for 3D structure determination with optimized epitope tag insertion

Antibody labeling has been extensively conducted for structure determination in both x-ray crystallography and EM analysis. However, establishing target-specific antibodies is a prerequisite for applying antibody-assisted structural analysis. To expand the applicability of this strategy, we have developed an alternative method to prepare an antibody-complex by inserting an exogenous epitope into the target. We have already demonstrated that the Fab of monoclonal antibody NZ-1 could form a stable complex with the target containing a PA12 tag as an inserted epitope. Nevertheless, we also found that the complex formation through the inserted PA12 tag inevitably caused structural change around the insertion site of the target. Hence, we here attempted to improve the insertion method and consequently discovered that utilization of a PA14 tag significantly reduced the structural change in the target. By adopting a closed ring-like conformation inside the antigen-binding pocket, the inserted PA14 tag had less impact on the folding of the target. Due to this structural property, the PA14 tag could also be inserted into the sterically hindered loop for labeling. Molecular dynamics simulations also indicated that the folding of the target was rigid regardless of the PA14 insertion and the complex formation with the NZ-1 Fab. Using the improved labeling technique, we performed negative-stain EM on a bacterial site-2 protease, which enabled us to approximate the domain arrangement based on the docking mode of the NZ-1 Fab.

biophysics↗

bioRxiv2019

Real-time tracking reveals the catalytic process of Rad51-driven DNA strand exchange

During homologous recombination, Rad51 forms a nucleoprotein filament on single-stranded DNA to promote DNA strand exchange. This filament binds to double-stranded DNA (dsDNA), searches for homology, and promotes transfer of the complementary strand, producing a new heteroduplex. Strand exchange proceeds via two distinct three-strand intermediates, C1 and C2. C1 contains the intact donor dsDNA whereas C2 contains newly formed heteroduplex DNA. Here, we show that conserved DNA binding motifs, loop 1 (L1) and loop 2 (L2) in site I of Rad51, play distinct roles in this process. L1 is involved in formation of the C1 complex whereas L2 mediates the C1-C2 transition, producing the heteroduplex. Another DNA binding motif, site II, serves as the DNA entry position for initial Rad51 filament formation, as well as for second donor dsDNA incorporation. Our study provides a comprehensive molecular model for the catalytic process of strand exchange mediated by eukaryotic RecA family recombinases.

molecular biology↗

bioRxiv2017

Energetics and Conformational Pathways of Functional Rotation in the Multidrug Transporter AcrB

The multidrug transporter AcrB transports a broad range of drugs out of the cell by means of the proton-motive force. The asymmetric crystal structure of trimeric AcrB suggests a functionally rotating mechanism for drug transport. Despite various supportive evidences from biochemical and simulation studies for this mechanism, the link between the functional rotation and proton translocation across the membrane remains elusive. Here, calculating the minimum free energy pathway of the functional rotation for the complete AcrB trimer, we describe the structural and energetic basis behind the coupling between the functional rotation and the proton translocation at atomic-level. Free energy calculations show that protonation of Asp408 in the transmembrane portion of the drug-bound protomer drives the functional rotation. The conformational pathway identifies vertical shear motions among several transmembrane helices, which regulates alternate access of water in the transmembrane as well as peristaltic motions pumping drugs in the periplasm.

biophysics↗