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The Rohs Lab

Department of Quantitative and Computational Biology

University of Southern California

About

Welcome to the Rohs Lab

About our Computational Biology and Bioinformatics research group

Main Focus

The main focus of the Rohs lab is the understanding of molecular interactions based on three-dimensional structure." to "The main focus of the Rohs lab is the understanding of molecular interactions and their role in biology and chemistry.

Who Can Join

Professor Rohs currently only accepts students from the academic programs of the QCB Department, with a preference for students from the Ph.D. Program in Computational Biology and Bioinformatics.

Primary Goal

We currently work on three research topics: (1) protein-DNA binding specificity, (2) structure-based drug design, and (3) RNA structure. The primarily goal is to use three-dimensional structure and sequence to reveal readout mechanisms across multiple scales." to "We currently work on four research topics: (1) protein-DNA binding specificity, (2) structure-based drug design, (3) DNA and (4) RNA structure. The primarily goal is to integrate sequence and structure to reveal readout mechanisms across multiple scales.

Front of large, brick building. The front reads: Michelson Hall

We use a combination of different approaches to understand readout and binding mechanisms.

This includes artificial intelligence, machine learning, statistics, biophysics, molecular simulations, and wet-lab experiments such as crystallography and high-throughput sequencing."

- Remo Rohs, Ph.D. 

Selected Publications

Y. Jiang et al.:
Readout of intrinsic and induced DNA shape by homeodomain transcription factor complexes.
Biophys. J. (2026)

Y. Wang et al.:
Sequence-based modeling of low-affinity transcription factor-DNA binding through deep learning.
NAR Genom. Bioinform. 8, lqag027 (2026)

G.L. Wang et al.:
Novel fold and wing structure of Forkhead transcription factor facilitate DNA binding.
Nucleic Acids Res. 53, gkaf946 (2025)

R. Mitra et al.:
RNAproDB: a webserver and interactive database for analyzing protein-RNA interactions.
J. Mol. Biol. 437, 169012 (2025)

R. Mitra, et al.:

Geometric deep learning of protein-DNA binding specificity. 

Nat. Methods 21, 1674-1683 (2024)

J.A. Weller, et al.:

Structure-based drug design with a deep hierarchical generative model.

J. Chem. Inf. Model. 64, 6450-6463 (2024)

J.M. Sagendorf, et al.:

Structure-based prediction of protein-nucleic acid binding using graph neural networks.

Biophys. Rev. 16, 297-314 (2024)

J. Li, et al.:
Predicting DNA structure using a deep learning method
Nat. Commun. 15, 1243 (2024)

T.P. Chiu, et al.:
Physicochemical models of protein-DNA binding with standard and modified base pairs.

Proc. Natl. Acad. Sci. USA 120, e2205796120 (2023)

Front of large, brick building

Latest News

By The Numbers

12

Current Lab Members

17

Graduated Ph.D. Students

96

Published Peer-Reviewed Papers

2010

Est. at University of Southern California

6

Academic Department Affiliations

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