3 citations · 14 across the 18 of their papers we have counts for
6 papers · 1 filter
SF-Cluster: Frustration-Guided MSA Subsampling for Alternative Protein Conformation Recovery
Hanqun Cao, Zijun Gao, Chunbin Gu +3
Deep-learning structure predictors are sensitive to their multiple sequence alignment (MSA) input, making MSA subsampling a practical route to recovering alternative conformations.…
AlloGen: Conformation-Selective Binder Generation with Differential State Scoring
Hanqun Cao, Zachary Quinn, Aastha Pal +4
Protein binder design has largely optimized for affinity alone, leaving conformational selectivity unaddressed: for allosteric targets such as kinases, nuclear receptors, and GPCRs…
mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties
Sawan Patel, Sophia Tang, Yesol Kim +8
Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regions (UTRs), and their coupling…
TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation
Hanqun Cao, Aastha Pal, Sophia Tang +4
Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This…
Minimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design
Shrey Goel, Pranam Chatterjee
Generative modeling of peptide sequences requires navigating a discrete and highly constrained space in which many intermediate states are chemically implausible or unstable. Exist…
Token-Level Guided Discrete Diffusion for Membrane Protein Design
Shrey Goel, Peregrine M. Schray, Yinuo Zhang +3
Reparameterized diffusion models (RDMs) have recently matched autoregressive methods in protein generation, motivating their use for challenging tasks such as designing membrane pr…