Mihir Bafna

selected papers († = equal contribution)

1. biorxiv

   Promera: a unified model for biomolecular structure prediction, filtering, and design

   Bowen Jing,  Mihir Bafna, Daniel J Diaz, Adam R Klivans, and Bonnie Berger

   bioRxiv 2026

   Abs bioRXiv

   Generative models have become staple tools for modeling and designing biomolecular structures. However, although these tools have improved in structural prediction accuracy, their ability to filter designed binders—an essential use case—remains insufficient; whereas design methods have focused more on unconstrained binder generation rather than capabilities enabled by controllable design. We introduce Promera, a unified generative model that combines all-atom structure prediction with improved filtering and controllable design. We find that Promera’s confidence metrics are more accurate for filtering binders from non-binders for both miniproteins and nanobodies, while its co-folding performance surpasses popular open-source models (OpenFold3-p2, Boltz-2) on therapeutically relevant categories. As a design model, Promera generates binders by predicting masked protein sequences with optional epitope, paratope, and template constraints. Remarkably, our nanobody designs match the in silico success rates from backprop-based techniques (mBER) when evaluated under co-folding confidence filters. We further provide two in silico demonstrations of the versatile capabilities of our design method: epitope targeting of the Andes hantavirus glycoprotein with VHHs and active state stabilization of the β2 adrenergic GPCR. We conclude by proposing a scaling law for co-folding models, suggesting a path for further performance improvement.

2. ICML

   SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins

   Bowen Jing^†,  Mihir Bafna^†, Anisha Parsan, Heyuan Michael Ni, David Kwabi-Addo, Bryan Bryson, Adam Klivans, and Bonnie Berger

   International Conference on Machine Learning 2026

   Abs arXiv Bib

   Multistate mechanisms underlie many of the complex functions observed in natural proteins. The ability to rationally design multistate proteins would have transformative implications for many areas of biotechnology, yet lies beyond the capabilities of existing deep learning frameworks for protein design. To address this gap, we introduce SwitchCraft, a versatile and programmatic framework for designing state-switching proteins based on backpropagation through compositional design constraints parameterized by structure prediction models. In silico evaluations demonstrate success on a wide range of state-switching functional primitives, from allosteric regulation of motifs to discrimination of bound ligand identities. Using these primitives, we demonstrate an in silico strategy for de novo design of fluorescent biosensors to arbitrary small molecule analytes. These results position SwitchCraft at the inception of a powerful paradigm for higher-order functional protein design.

   @article{jing2026switchcraft,
     title = {SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins},
     author = {Jing, Bowen and Bafna, Mihir and Parsan, Anisha and Ni, Heyuan Michael and Kwabi-Addo, David and Bryson, Bryan and Klivans, Adam and Berger, Bonnie},
     equal = {Jing, Bowen; Bafna, Mihir},
     journal = {International Conference on Machine Learning},
     year = {2026},
     short = {ICML},
     shorttitle = {ICML}
   }

3. ICLR

   Learning residue level protein dynamics with multiscale Gaussians

   Mihir Bafna, Bowen Jing, and Bonnie Berger

   International Conference on Learning Representations 2026

   Abs arXiv Bib

   Many methods have been developed to predict static protein structures, however understanding the dynamics of protein structure is essential for elucidating biological function. While molecular dynamics (MD) simulations remain the in silico gold standard, its high computational cost limits scalability. We present DynaProt, a lightweight, SE(3)-invariant framework that predicts rich descriptors of protein dynamics directly from static structures. By casting the problem through the lens of multivariate Gaussians, DynaProt estimates dynamics at two complementary scales: (1) per-residue marginal anisotropy as 3×3 covariance matrices capturing local flexibility, and (2) joint scalar covariances encoding pairwise dynamic coupling across residues. From these dynamics outputs, DynaProt achieves high accuracy in predicting residue-level flexibility (RMSF) and, remarkably, enables reasonable reconstruction of the full covariance matrix for fast ensemble generation. Notably, it does so using orders of magnitude fewer parameters than prior methods. Our results highlight the potential of direct protein dynamics prediction as a scalable alternative to existing methods.

   @article{bafna2025learningresiduelevelprotein,
     title = {Learning residue level protein dynamics with multiscale Gaussians},
     author = {Bafna, Mihir and Jing, Bowen and Berger, Bonnie},
     year = {2026},
     eprint = {2509.01038},
     archiveprefix = {arXiv},
     primaryclass = {q-bio.BM},
     journal = {International Conference on Learning Representations},
     short = {ICLR},
     shorttitle = {ICLR},
   }

4. PNAS

   Sparse autoencoders uncover biologically interpretable features in protein language model representations

   Onkar Gujral,  Mihir Bafna, Eric Alm, and Bonnie Berger

   Proceedings of the National Academy of Sciences 2025

   Abs Paper Bib

   Foundation models in biology—particularly protein language models (PLMs)—have enabled ground-breaking predictions in protein structure, function, and beyond. However, the “black-box” nature of these representations limits transparency and explainability, posing challenges for human–AI collaboration and leaving open questions about their human-interpretable features. Here, we leverage sparse autoencoders (SAEs) and a variant, transcoders, from natural language processing to extract, in a completely unsupervised fashion, interpretable sparse features present in both protein-level and amino acid (AA)-level representations from ESM2, a popular PLM. Unlike other approaches such as training probes for features, the extraction of features by the SAE is performed without any supervision. We find that many sparse features extracted from SAEs trained on protein-level representations are tightly associated with Gene Ontology (GO) terms across all levels of the GO hierarchy. We also use Anthropic’s Claude to automate the interpretation of sparse features for both protein-level and AA-level representations and find that many of these features correspond to specific protein families and functions such as the NAD Kinase, IUNH, and the PTH family, as well as proteins involved in methyltransferase activity and in olfactory and gustatory sensory perception. We show that sparse features are more interpretable than ESM2 neurons across all our trained SAEs and transcoders. These findings demonstrate that SAEs offer a promising unsupervised approach for disentangling biologically relevant information present in PLM representations, thus aiding interpretability. This work opens the door to safety, trust, and explainability of PLMs and their applications, and paves the way to extracting meaningful biological insights across increasingly powerful models in the life sciences.

   @article{gujral2025sparse,
     author = {Gujral, Onkar and Bafna, Mihir and Alm, Eric and Berger, Bonnie},
     journal = {Proceedings of the National Academy of Sciences},
     volume = {122},
     number = {34},
     pages = {e2506316122},
     year = {2025},
     publisher = {National Academy of Sciences},
     short = {PNAS},
     shorttitle = {PNAS},
   }

5. Cell Syst.

   DANGO: Predicting Higher-Order Genetic Interactions

   Ruochi Zhang,  Mihir Bafna, Jianzhu Ma, and Jian Ma

   Cell Systems 2026

   Abs bioRXiv Paper Bib

   Higher-order genetic interactions, which have profound implications for understanding the molecular mechanisms of phenotypic variation, remain poorly characterized. Most studies to date have focused on pairwise interactions, as designing high-throughput experimental screenings for the vast combinatorial search space of higher-order molecular interactions is dauntingly challenging. Here, we develop DANGO, a computational method based on a self-attention hypergraph neural network, designed to effectively predict higher-order genetic interaction among groups of genes. As a proof-of-concept, we provide comprehensive predictions for over 400 million trigenic interactions in the yeast S. cerevisiae, significantly expanding the quantitative characterization of such interactions. Our results demonstrate that DANGO accurately predicts trigenic interactions, uncovering both known and novel biological functions related to cell growth. We further incorporate protein embeddings and model uncertainty scoring to enhance the biological relevance and interpretability of the predicted interactions. Moreover, the predicted interactions can serve as powerful genetic markers for growth response under diverse conditions. Together, DANGO enables a more complete map of complex genetic interactions that impinge upon phenotypic diversity.Competing Interest StatementThe authors have declared no competing interest.

   @article{Zhang2020.11.26.400739,
     title = {DANGO: Predicting Higher-Order Genetic Interactions},
     author = {Zhang, Ruochi and Bafna, Mihir and Ma, Jianzhu and Ma, Jian},
     year = {2026},
     journal = {Cell Systems},
     biorxiv = {10.1101/2020.11.26.400739v2},
     publisher = {Cell Systems},
     doi = {10.1101/2020.11.26.400739},
     elocation-id = {2020.11.26.400739},
     short = {Cell Syst.},
     shorttitle = {Cell Systems},
   }

6. ISMB

   CLARIFY: Cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics

   Mihir Bafna, Hechen Li, and Xiuwei Zhang

   ISMB and Bioinformatics 2023

   Abs Paper Code Press Bib

   Motivation: Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model. Results: We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputs refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.

   @article{Clarify,
     author = {Bafna, Mihir and Li, Hechen and Zhang, Xiuwei},
     title = {CLARIFY: Cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics},
     booktitle = {31st Conference on Intelligent Systems for Molecular Biology},
     short = {ISMB},
     year = {2023},
     journal = {Bioinformatics},
     volume = {39},
     number = {Supplement_1},
     pages = {i484-i493},
     press = {https://www.cc.gatech.edu/news/award-winning-computer-models-propel-research-cellular-differentiation},
     publisher = {Oxford University Press},
     appear = {true},
   }