Biology subjects

Seabra, G.

Publications and source records attributed to Seabra, G..

5 recordsLinked to original sources

bioRxiv2026

SMARTFlexDB: a paired apo-holo RNA structure database and analysis platform for RNA conformational flexibility and small-molecule recognition

RNA structures can undergo substantial conformational changes upon small-molecule binding, yet existing structural resources provide only static RNA-ligand snapshots without connecting ligand-bound (holo) RNA structures to corresponding ligand-free (apo) conformations. SMARTFlexDB is a curated database and analysis platform that uses paired apo-holo RNA structures to characterize conformational differences and RNA-small-molecule recognition. Candidate pairs were identified from the Protein Data Bank by matching RNA chains with at least 90% sequence identity and coverage, and fully conserved binding pocket nucleotides. The current release contains 434 binding pockets from 266 holo structures, linked to 186 apo structures to yield 1,187 pocket-level apo-holo pairs organized into 734 database entries, spanning 150 small molecules and 23 RNA functional classes. Each entry integrates RNA sequence and secondary-structure annotations, apo and holo three-dimensional structures, RNA-ligand atomic interactions, and quantitative descriptors of global and local conformational changes. An interactive web interface enables users to explore sequence alignments and structural overlays, compare secondary structures and RNA motifs, and upload paired apo-holo structures for customized analysis. By linking ligand-bound and ligand-free RNA structures, SMARTFlexDB provides a robust foundation for investigating RNA structural flexibility and supporting RNA-targeted small-molecule drug discovery. The database and analysis platform are available at https://aidd.rc.ufl.edu/app/smartflexdb/.

bioinformatics↗

bioRxiv2026

Pep2Mol: 3D Molecule Generation Targeting Protein-Protein Interfaces with Diffusion Models

Protein-protein interactions (PPIs) are central to biological processes. Designing small molecules that modulate dysregulated PPIs holds strong promise for targeting undruggable proteins. However, existing structure-based drug design approaches focus on well-defined small-molecule binding pockets and struggle to generalize to large, shallow, and chemically complex PPI interfaces. Here, we introduce Pep2Mol, a diffusion-based generative model for 3D molecule design that targets orthosteric PPI sites by explicitly incorporating binding peptides or proteins as structural guidance, moving beyond conventional pocket-conditioned generation. To enable model development and benchmarking, we curate a large-scale, high-quality dataset of 10,956 experimentally resolved protein complex structure pairs, each pairing an orthosteric competitive ligand with a protein binder at overlapping receptor interfaces. Pep2Mol integrates two SE(3)-equivariant graph neural networks that encode protein-ligand and protein-peptide interactions respectively, and fuses these representations via attention-based conditioning to jointly guide the diffusion trajectory. Extensive evaluations demonstrate that Pep2Mol generates chemically valid ligands with state-of-the-art binding affinities, providing a strong foundation for small-molecule inhibitor design against challenging PPI interfaces.

bioinformatics↗

bioRxiv2025

GatorAffinity: Boosting Protein-Ligand Binding Affinity Prediction with Large-Scale Synthetic Structural Data

Protein-ligand binding affinity prediction is a fundamental task in computational drug discovery. Although substantial efforts have been made to enhance prediction accuracy using data-driven approaches, progress remains limited by persistent data scarcity. The widely used PDBbind dataset, for example, contains fewer than 20, 000 experimental structures with annotated binding affinities, while a vast number of affinity measurements remain underutilized due to missing structural data. Here, we investigate this untapped potential by curating more than 450, 000 synthetic protein-ligand complexes annotated with Kd and Ki values using the Boltz-1 structure prediction model. Building on this unprecedented scale of synthetic data, further augmented with over 1 million synthetic complexes from the recently released SAIR database annotated with IC50 values, we develop GatorAffinity, a geometric deep learning-based scoring function pretrained on large-scale synthetic data and fine-tuned using high-quality experimental structures from PDBbind. Extensive evaluation on a leak-proof benchmark demonstrates that GatorAffinity significantly outperforms state-of-the-art affinity prediction methods, offering superior accuracy and generalizability. Our findings show that augmenting available experimental data with synthetic complexes can effectively address the data scarcity challenge while maintaining strong predictive reliability. By releasing the pretrained GatorAffinity model and the large-scale synthetic dataset GatorAffinity-DB, we provide a scalable and reproducible foundation for affinity prediction, virtual screening, and broader structure-based drug design applications (https://github.com/AIDD-LiLab/GatorAffinity).

bioinformatics↗

bioRxiv2025

MGMG: Cell Morphology-Guided Molecule Generation for Drug Discovery

Designing novel molecules with desired bioactivity remains a fundamental challenge in drug discovery. Most molecular design methods follow target-based drug discovery paradigms that rely on well-defined drug targets, thereby limiting their applicability to diseases lacking known targets or reference compounds. Here we introduce Morphology-Guided Molecule Generation (MGMG), a phenotypic drug discovery-oriented approach that integrates cellular morphological profiles from compound treatments with molecular textual descriptions without requiring molecular target information. Cell morphology offers the guidance on desired bioactivity-relevant phenotypic effects, while textual descriptions provide direct and interpretable cues about molecular structure. Leveraging complementary structural and bioactivity context, MGMG significantly enhances molecule generation performance, especially in scenarios where textual descriptions are under-informative or morphological signals are weak. MGMG can also be applied to genetic perturbations, enabling activator design from gene overexpression morphology without requiring knowledge of reference compound structure. In addition, in silico docking demonstrates that MGMG-generated molecules, despite lacking target information, exhibit binding affinities comparable to reference compounds, preserving key interactions while introducing structural diversity. Overall, MGMG jointly utilizes morphological and textual description inputs to guide molecule generation, enabling diverse, bioactivity-aware compound design in a target-agnostic fashion.

bioinformatics↗

bioRxiv2020

Ultrasensitive small molecule fluorogenic probe for human heparanase

Heparanase is a critical enzyme involved in the remodeling of the extracellular matrix (ECM), and its elevated expression has been linked with diseases such as cancer and inflammation. The detection of heparanase enzymatic activity holds tremendous value in the study of the cellular microenvironment, and search of molecular therapeutics targeting heparanase, however, assays developed for this enzyme so far have suffered prohibitive drawbacks. Here we present an ultrasensitive fluorogenic small-molecule probe for heparanase enzymatic activity. The probe exhibits a 756-fold fluorescence turn-on response in the presence of human heparanase, allowing one-step detection of heparanase activity in real-time with a picomolar detection limit. The high sensitivity and robustness of the probe are exemplified in a high-throughput screening assay for heparanase inhibitors.

biochemistry↗