Modesto Orozco

This study identifies CTL1 as a phospholipid-binding protein that deforms membranes, revealing its structural basis and potential role in phospholipid homeostasis.

This study introduces OpenCGChromatin, a model that simulates chromatin structure and phase separation, revealing how linker-DNA length and histone modifications affect chromatin organization.

The paper reveals the structural basis of hDicer-tRNA complex formation and shows that m5C modification facilitates tRNA cleavage by hDicer.

This study uses simulations to analyze LNP-mediated siRNA delivery, revealing pH-dependent structural changes and a mechanism for endosomal escape involving RNA-lipid interactions.

This paper develops dually-decorated lipid nanoparticles for targeted siRNA delivery to HER2+ breast cancer cells, showing improved selectivity and antiproliferative activity.

This study investigates the mechanism of ClC-5 antiporter function and how a specific deletion causes Dent's disease, showing how simulations can aid in understanding rare diseases and personalized medicine.

The paper systematically studies hybrid triplex topology and stability, revealing a regulatory mechanism in the human genome and transcriptome through triplex formation.

This study investigates how xCas9 expands DNA targeting by recognizing alternative PAM sequences through increased flexibility in its PAM-interacting region.

This study identifies a conserved lysine in TM5 of BasC as critical for cytoplasmic gate dynamics, showing its role in gating transitions and modulation by substrates and mutations.

This study identifies novel SETD8 inhibitors and shows their toxicity correlates with ribosome biogenesis regulators, suggesting a biomarker for targeted cancer therapy.

The paper analyzes RNA backbone conformations using pseudo-torsional angles, showing that combining data from different sources provides a more complete picture and that protein interactions alter the {eta}-{theta} space.

DNAffinity predicts transcription factor DNA binding affinities using physics-based machine learning from molecular dynamics simulations, outperforming existing methods and extending to epigenetic variants and in vivo binding sites in yeast.

The paper investigates the structural and functional impact of the S:A222V mutation in SARS-CoV-2, showing increased RBD opening and ACE2 binding without reducing neutralization capacity.

The paper introduces deep neural networks to infer bioactivity signatures for uncharacterized compounds, improving activity prediction across various biological endpoints.

MuGVRE provides a cloud-based infrastructure for 3D/4D genomics analysis, supporting both high-computational and high-throughput applications.