R. Gonzalo Parra

The paper introduces a PyTorch-based tool for fast proteome-scale local frustration analysis, achieving 70-13,000x speedup over the original FrustratometeR.

The paper investigates whether functional frustration in proteins is an evolutionary adaptation or a byproduct of fold architecture, finding that frustration hotspots are inherent physical constraints co-opted for function.

A fine-tuned ProtT5 protein language model predicts per-residue energetic frustration from sequence (Pearson r 0.86 on unseen CATH topologies) and profiles the human proteome in ~17 minutes on one GPU.

FrustraMPNN rapidly predicts single-residue frustration in proteins at proteome scale with high accuracy, enabling new insights into protein function and dynamics.

FrustraEvo calculates and quantifies the conservation of local energetic frustration in protein families to identify evolutionary stability and functional constraints.

FrustraPocket identifies protein-ligand binding sites from unbound protein structures by detecting regions of local energetic frustration.

FrustratometeR computes local energetic frustration in protein structures, point mutants, and MD simulations for structural analysis.

MERLoT reconstructs complex lineage trees from scRNA-seq data and imputes temporal gene expression profiles using simulated and real datasets.

PROSSTT simulates scRNA-seq data for complex differentiation processes with customizable lineage trees and noise, providing tools to evaluate lineage tree reconstruction.