I am a first-year Ph.D. student in EECS at MIT advised by Bonnie Berger, interested in deep learning for structural biology and drug discovery. I am also a Machine Learning Scientist at Liquid AI, where I work on PLMs.
Previously, I completed my B.S. in CS from Georgia Tech specializing in the machine learning and theory threads. There, I worked on graph representation learning for spatial transcriptomics and was incredibly fortunate to have been advised by Xiuwei Zhang (GT), Jian Ma (CMU), and Ruochi Zhang (Broad).
Generating proteins with the full diversity and complexity of functions found in nature is a grand challenge in protein design. Here, we present ProDiT, a multimodal diffusion model that unifies sequence and structure modeling paradigms to enable the design of functional proteins at scale. Trained on sequences, 3D structures, and annotations for 214M proteins across the evolutionary landscape, ProDiT generates diverse, novel proteins that preserve known active and binding site motifs and can be successfully conditioned on a wide range of molecular functions, spanning 465 Gene Ontology terms. We introduce a diffusion sampling protocol to design proteins with multiple functional states, and demonstrate this protocol by scaffolding active sites from enzymes such as carbonic anhydrase and lysozyme to be allosterically deactivated by a calcium effector. Our results showcase ProDiT9s unique capacity to satisfy design specifications inaccessible to existing generative models, thereby expanding the protein design toolkit.
@article{jing2025generating,
title = {Generating functional and multistate proteins with a multimodal diffusion transformer},
author = {Jing, Bowen and Sappington, Anna and Bafna, Mihir and Shah, Ravi and Tang, Adrina and Krishna, Rohith and Klivans, Adam and Diaz, Daniel J and Berger, Bonnie},
journal = {bioRxiv},
year = {2025},
month = sep,
publisher = {Cold Spring Harbor Laboratory},
short = {biorxiv},
shorttitle = {biorxiv},
biorxiv = {10.1101/2025.09.03.672144v2}
}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},
month = sep,
year = {2025},
eprint = {2509.01038},
archiveprefix = {arXiv},
primaryclass = {q-bio.BM},
}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},
}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},
month = jan,
year = {2025},
journal = {biorxiv},
biorxiv = {10.1101/2020.11.26.400739v2},
publisher = {bioRxiv},
doi = {10.1101/2020.11.26.400739},
elocation-id = {2020.11.26.400739},
short = {biorxiv},
shorttitle = {biorxiv},
}Abstract RNA molecules provide an exciting frontier for novel therapeutics. Accurate determination of RNA structure could accelerate development of therapeutics through an improved understanding of function. However, the extremely large conformation space has kept the RNA 3D structure space largely unresolved. Using recent advances in generative modeling, we propose DiffRNAFold, a latent space diffusion model for RNA tertiary structure design. Our preliminary results suggest that DiffRNAFold generated molecules are similar in 3D space to true RNA molecules, providing an important first step towards accurate structure and function prediction in vivo.
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},
month = jun,
press = {https://www.cc.gatech.edu/news/award-winning-computer-models-propel-research-cellular-differentiation},
publisher = {Oxford University Press},
appear = {true},
}