arXiv Machine Learning
Aug 27

A meta-algorithm for ab initio reconstruction of complex mixtures in cryo-EM

The article presents a systematic meta-algorithm for spawning and aggregating multi-class cryo-EM reconstruction jobs, formalizing iterative classification and filtering strategies used by practitioners. It claims to be the first method capable of ab initio reconstruction on datasets with dozens of distinct species, achieving 97% accuracy on a 45-class subset of Tomotwin-100 and 75% on the full dataset, and successfully recovering ribosomal assembly states from an unfiltered experimental cryo-EM dataset. The approach scales with compute resources and aims to underpin automated cryo-EM workflows in contemporary experimental settings.

By Alkin Kaz, Arda Kaz, Ellen D. Zhong
arXiv Machine Learning
Jul 28

Chamaileon: Cross-Context Binder Design with Contextualized Modeling and Mixed Sampling

arXiv:2607. 23518v1 Announce Type: new Abstract: The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination.

By Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang, Yunhong Lu, Chenxi Cai, Yan Zhang, Min Zhang