Dmitry N. Ivankov

This study shows how sequence blockiness affects liquid-liquid phase separation in polypeptide-like polymers through coarse-grained simulations and mean-field theory.

The paper reanalyzes APOBEC3 mutagenesis in MPXV genomes, showing mutations are enriched at TC motifs in hairpin loops and downstream guanine-rich sequences, with structural and sequence context playing distinct roles at different genomic scales.

This study shows that sign epistasis may be absent in multi-peaked fitness landscapes when neutral mutations are included, challenging previous assumptions about reciprocal sign epistasis.

The paper applies Power Transform methods to a large GFP mutagenesis landscape to assess their effectiveness in reducing epistasis, finding mixed results depending on the scale and type of transform used.

This study benchmarks 11 protein-ligand interaction prediction tools, showing classical and deep learning methods perform similarly on drug-like ligands, while co-folding tools excel on complex ligands and all struggle with generalization to unseen pockets.

The paper analyzes peptide aggregation lag time as a function of concentration to determine critical nucleus size and improve models of amyloid formation.

The paper reanalyzes MPXV mutation data and finds no statistically significant enrichment of APOBEC-signature mutations in cruciform structures formed by inverted repeats.

The paper analyzes MPXV RNA-seq data to determine if APOBEC3 enzymes edit viral RNA, concluding that observed mutations result from DNA-level mutagenesis, not RNA editing.

The paper introduces MULAN, a multimodal protein language model that integrates sequence and structure information, showing improved performance on downstream tasks compared to sequence-only and structure-aware models.

The paper investigates chromatin looping in Dictyostelium discoideum, finding that convergent gene pairs act as directional barriers for chromatin extrusion during development.

The paper investigates the optimal number of Psi-BLAST iterations to maintain query sequence relevance in homolog detection, finding that query sequences are less frequently retained after 2 iterations in the 2021 nr database.

The paper uses computational simulations to reveal how uranyl ion disrupts the DNA-binding activity of PARP-1 by altering its zinc finger domains.

This study assesses AlphaFold's ability to predict the impact of single mutations on protein stability and function, finding weak or no correlation with experimental data.

The paper uses a deep neural network with a Light Attention mechanism to predict protein stability changes from mutations, achieving high accuracy on a new mega dataset.

The paper compares homology models and best templates for predicting mutation impact on protein stability, finding best templates significantly improve predictions over homology models.

The paper experimentally assays a fitness landscape on a macroevolutionary scale by analyzing the effects of amino acid substitutions in yeast His3 orthologs.