Biology subjects

Danurdoro, R.

Publications and source records attributed to Danurdoro, R..

2 recordsLinked to original sources

bioRxiv2026

Cyclome: Large-scale replica-exchange dynamics of 930 cyclic peptide reveal thermal stability and critical metal-binding behavior

Cyclic peptides are recognized as versatile scaffolds for therapeutic and functional applications due to their structural stability and resistance to degradation. Despite this promise, systematic analysis and prediction of their thermal stability remain limited by fragmented data resources, inadequate sequence comparison methods, and the lack of cyclicity-aware computational models. We provide a comprehensive, multi-scale computational framework to characterize cyclic peptides. First, we unified four fragmented public repositories of cyclic peptides into a single largest curated resource of 930 cyclic peptides, Cyclome930. This integrates cyclic topology, sequence, experimental structural coordinates, and source organism annotations into a consistently featurized dataset. Cyclome930 thus expands the dataset of annotated cyclic peptides by [~]3.4 fold (from 276 to 930). Second, we developed a novel cyclic sequence alignment algorithm that explicitly accounts for rotational symmetry and knot topology, enabling more accurate scoring of sequence similarity than conventional linear alignments. Third, we investigate the thermal stability of cyclic peptides using extensive all-atom replica-exchange molecular dynamics (100ns; REMD) simulations, allowing conformational sampling across 298 K - 400 K and track its stress tensors with increasing temperature. Finally, these simulation-derived thermo-stability metrics were used to train a machine learning model to predict cyclic peptide melting points from sequence and topology (STop2Melt). Crucially, the model introduces cyclicity-aware embeddings derived from ESMc representations coupled with cyclic offset vector, capturing the peptides knot topology. STop2Melt achieved strong predictive performance on held-out peptides and outperforms baseline methods that neglect cyclic structure. Finally, we scored Cyclome930 (cyclic ligands) for critical mineral metal binding using a multi-classifier model (CritiCL). To our knowledge, Cyclome930 represents the first effort in peptide literature to integrate physics-based temperature ramped simulations, cyclic sequence similarity scoring, machine learning for thermal stability prediction and scoring them for critical metal binding. Cyclicity-aware computational toolchains (cyclome930.studio/) provide a foundational resource for computational design of stable cyclic peptide prototype libraries thereby annotating and expanding genomic islands linked to critical mineral recovery.

bioinformatics↗

bioRxiv2025

Structurally Informed Fitness Landscapes for Surveillance of Emerging PRRSV Variants

Antibodies play a central role in neutralizing pathogens through direct interference with viral entry and recruitment of effector immune cells. However, viruses employ sophisticated escape mechanisms to evade these defenses, primarily through mutations in surface glycoproteins that reduce antibody binding affinity or alter critical functional domains. Antibody escape remains a formidable challenge in drug design efforts for Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), a pathogen notorious for its rapid evolution and structural plasticity. Here we present EscaPRRS (Escape scoring for PRRS virus), a Bayesian variational autoencoder trained on ESM-2 protein language model embeddings to predict the fitness landscape and escape propensities of 52,622 mutants (recorded over 10 years) of the immunodominant GP5 glycoprotein encoded by PRRSV ORF5 gene. Unlike conventional models that estimate escape propensity only from sequence information, EscaPRRS circumvents the need for extensive alignments, integrating contributions from surface accessibility and biochemical dissimilarity at the binding interface. Our escape propensity scores demonstrate reliable structural and biological fidelity, with EscaPRRS scores correlating with binding affinities on seven different porcine receptor proteins (Pearson r = 0.74). Notably, EscaPRRS captures seasonal trends in immune evasion, highlighting its applicability in forecasting and surveillance of emerging/re-emerging PRRSV variants. IMPORTANCEPorcine Reproductive and Respiratory Syndrome Virus (PRRSV) remains the most economically detrimental illness for swine products, causing more than $1.2 billion in annual production loss in the United States, with indirect implications to food security and human health. PRRSV infection is known to severely affect porcine alveolar macrophages (PAMs), causing respiratory difficulties, following blockage of inflammatory signals that aids easy viral reproduction in the host cell. Identifying critical amino acid mutations at the highly variant GP5 glycoprotein attached to the viral cell membrane provides information on structural features linked to antigenic diversity and antibody neutralization, that potentially lead to clinical outbreaks in sow farms. Our effort employs machine learning approaches to learn patterns from a large number of sequences to map these critical domains to three-dimensional structures to score them for antibody escape tendencies. This greatly enhances our understanding of receptor and antibody binding mechanisms in PRRSV GP5.

biochemistry↗