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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
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<meta name="title" content="ForceFold: An open-source AI platform for predicting single-protein mechanics and designing targeted mechanotherapies - Yiyuan Zhang, Murti Salapaka">
<meta name="description" content="ForceFold is an open-source AI framework integrating polymer physics with machine learning to automate single-molecule data extraction and inversely design targeted mechanotherapeutics.">
<meta name="keywords" content="ForceFold, Single-Molecule Force Spectroscopy, SMFS, Mechanobiology, Protein Design, Generative AI, Diffusion Models, Dystrophin">
<meta name="author" content="Salapaka Lab, University of Minnesota">
<meta property="og:site_name" content="Salapaka Lab, University of Minnesota">
<meta property="og:title" content="ForceFold: An open-source AI platform for predicting single-protein mechanics">
<meta property="og:description" content="An open-source AI framework integrating polymer physics with machine learning to revolutionize force-bearing protein analysis and design.">
<title>ForceFold: Predicting Single-Protein Mechanics | Salapaka Lab</title>
<meta name="language" content="English">
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<meta property="og:type" content="article">
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<meta property="F" content="PAPER_TITLE">
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<title>PAPER_TITLE - AUTHOR_NAMES | Academic Research</title>
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</button>
<!-- More Works Dropdown -->
<div class="more-works-container">
<button class="more-works-btn" onclick="toggleMoreWorks()" title="View More Works from Our Lab">
<i class="fas fa-flask"></i>
More Works
<i class="fas fa-chevron-down dropdown-arrow"></i>
</button>
<div class="more-works-dropdown" id="moreWorksDropdown">
<div class="dropdown-header">
<h4>More Works from Our Lab</h4>
<button class="close-btn" onclick="toggleMoreWorks()">
<i class="fas fa-times"></i>
</button>
</div>
<div class="works-list">
<!-- TODO: Replace with your lab's related works -->
<a href="https://doi.org/10.1038/s41598-019-41569-4" class="work-item" target="_blank">
<div class="work-info">
<h5>Distinct mechanical properties in homologous spectrin-like repeats of utrophin</h5>
<p>This paper reports the first mechanical characterization of utrophin using AFM. The data indicates that
the mechanical properties of utrophin's spectrin-like repeats are more similar to the stiff repeats of
titin than those of spectrin or dystrophin, suggesting utrophin functions as a stiff elastic element at
the myotendinous junction.</p>
<span class="work-venue">Scientific Reports 2019</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="https://icml.cc/virtual/2025/poster/45169" class="work-item" target="_blank">
<div class="work-info">
<!-- TODO: Replace with actual paper title -->
<h5>A Physics-Augmented Deep Learning Framework for Classifying Single Molecule Force Spectroscopy Data</h5>
<!-- TODO: Replace with brief description -->
<p>An automation tool to filter data that results from a single molecule.</p>
<!-- TODO: Replace with venue and year -->
<span class="work-venue">ICML 2025</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="https://doi.org/10.1073/pnas.2511722123" class="work-item" target="_blank">
<div class="work-info">
<h5>Multiple modes of AFM reveal distinct mechanical properties for dystrophin and utrophin not manifest by
small fragments</h5>
<p>Using two modes of atomic force microscopy (AFM) and Monte Carlo simulations, this study reveals that
full-length dystrophin exhibits brittle unfolding behavior, while full-length utrophin demonstrates a
complex stiffening spring behavior. These findings provide critical insights into the potential efficacy
of utrophin upregulation as a therapy for Duchenne muscular dystrophy.</p>
<span class="work-venue">PNAS 2026</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="static/pdfs/ICML_GenUnfold.pdf" class="work-item" target="_blank">
<div class="work-info">
<h5>GenUnfold: Rapidly Predict Protein Mechanical Unfolding Trajectory via a Physics-Guided Diffusion
Model</h5>
<p>This work introduces GenUnfold, the first scalable generative diffusion framework for predicting full
protein unfolding trajectories. By combining global coevolutionary context with local structural
stiffness, the model achieves state-of-the-art performance in predicting physically consistent mechanical
properties.</p>
<span class="work-venue">ICML 2026</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
</div>
</div>
</div>
<main id="main-content">
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<!-- TODO: Replace with your paper title -->
<h1 class="title is-1 publication-title">ForceFold: An open-source AI platform for predicting single-protein mechanics and designing targeted mechanotherapies</h1>
<div class="is-size-5 publication-authors">
<!-- TODO: Replace with your paper authors and their personal links -->
<span class="Salapaka Lab">
<a href="http://nanodynamics.ece.umn.edu/" target="_blank">Salapaka Lab</a><sup>1</sup>,</span>
<span class="Ervasti Lab">
<a href="https://cbs.umn.edu/directory/james-ervasti" target="_blank">Ervasti Lab</a><sup>2</sup>,</span>
<span class="Muretta Lab">
<a href="https://cbs.umn.edu/directory/joseph-muretta-0" target="_blank">Muretta Lab</a><sup>2</sup></span>
</div>
<div class="is-size-5 publication-authors">
<!-- TODO: Replace with your institution and conference/journal info -->
<span class="author-block">University of Minnesota<br><sup>1</sup>Department of Electrical and Computer Engineering, <sup>2</sup>Department of Biochemistry, Molecular Biology and Biophysics</span>
<!-- TODO: Remove this line if no equal contribution -->
</div>
<span class="link-block">
<a href="static/pdfs/salapakaMurtiVita.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-alt"></i>
</span>
<span>CV (Salapaka)</span>
</a>
</span>
<span class="link-block">
<a href="static/pdfs/Ervasti Bio Sketch 4-25-2024.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-alt"></i>
</span>
<span>CV (Ervasti)</span>
</a>
</span>
<span class="link-block">
<a href="static/pdfs/Muretta_NIHBiosketch_SalapakaGoogle.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-alt"></i>
</span>
<span>CV (Muretta)</span>
</a>
</span>
<!-- TODO: Replace with your GitHub repository URL -->
<span class="link-block">
<a href="https://github.com/SalapakaLab-SIMBioSys" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- TODO: Update with your arXiv paper ID -->
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<!-- TODO: Replace with your paper abstract -->
<p>
Analysis of single-molecule data is vital to studying the force-bearing molecules driving diseases like muscular dystrophy and cancer. However, discovering targeted mechanotherapies remains severely bottlenecked because current experimental methods struggle with confounding multi-molecule noise and dauntingly slow manual analysis. Furthermore, traditional molecular dynamics simulations are computationally prohibitive for high-throughput screening. To overcome these critical barriers, we propose ForceFold: an open-source AI framework integrating polymer physics with machine learning to revolutionize force-bearing protein analysis and design. Our closed-loop pipeline automates single-molecule data extraction via a physics-augmented neural network (PemNN) and utilizes a novel physics-aware deep clustering architecture (Latent Unfold) to automatically decipher complex, heterogeneous protein domains. Furthermore, we deploy a physics-guided generative diffusion model (GenUnfold) to predict mechanical fingerprints directly from sequences and solve the inverse problem, engineering novel therapeutics for mechanobiological diseases. By open-sourcing our AI tools and massive single-molecule datasets, ForceFold aims to accelerate targeted therapeutic discovery for academic institutions and industry leaders worldwide. </p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Paper poster -->
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<div class="hero-body">
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<h2 class="title">Poster</h2>
<!-- TODO: Replace with your poster PDF -->
<iframe src="static/pdfs/forceFold.pdf" width="100%" height="550">
</iframe>
</div>
</div>
</section>
<!--End paper poster -->
<section class="section">
<div class="container is-max-desktop">
<h2 class="title is-3">Publications</h2>
<div class="content">
<a href="https://doi.org/10.1073/pnas.2511722123" class="work-item" target="_blank">
<div class="work-info">
<h5>Multiple modes of AFM reveal distinct mechanical properties for dystrophin and utrophin not manifest by small fragments</h5>
<p>Using two modes of atomic force microscopy (AFM) and Monte Carlo simulations, this study reveals that full-length dystrophin exhibits brittle unfolding behavior, while full-length utrophin demonstrates a complex stiffening spring behavior. These findings provide critical insights into the potential efficacy of utrophin upregulation as a therapy for Duchenne muscular dystrophy.</p>
<span class="work-venue">PNAS 2026</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="static/pdfs/ICML_GenUnfold.pdf" class="work-item" target="_blank">
<div class="work-info">
<h5>GenUnfold: Rapidly Predict Protein Mechanical Unfolding Trajectory via a Physics-Guided Diffusion Model</h5>
<p>This work introduces GenUnfold, the first scalable generative diffusion framework for predicting full protein unfolding trajectories. By combining global coevolutionary context with local structural stiffness, the model achieves state-of-the-art performance in predicting physically consistent mechanical properties.</p>
<span class="work-venue">ICML 2026</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="#" class="work-item" target="_blank">
<div class="work-info">
<h5>Resolving Heterogeneous Mechanical Domains via Physics-Aware Deep Clustering of Single-Molecule Force Spectroscopy Data</h5>
<p>This work introduces Latent Unfold, the first automated framework to identify heterogeneous folding domains in SMFS data. By utilizing a physics-aware deep clustering architecture with dual autoencoders, the model resolves domain-level behavior in complex proteins like dystrophin and utrophin without prior knowledge.</p>
<span class="work-venue">Submitted to ACS Nano</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="https://icml.cc/virtual/2025/poster/45169" class="work-item" target="_blank">
<div class="work-info">
<h5>A Physics-Augmented Deep Learning Framework for Classifying Single Molecule Force Spectroscopy Data</h5>
<p>A physics-augmented deep learning framework designed to automatically classify and filter single-molecule force spectroscopy data, providing an automation tool to ensure analysis is based on validated single-molecule events.</p>
<span class="work-venue">ICML 2025</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
<a href="https://doi.org/10.1038/s41598-019-41569-4" class="work-item" target="_blank">
<div class="work-info">
<h5>Distinct mechanical properties in homologous spectrin-like repeats of utrophin</h5>
<p>This paper reports the first mechanical characterization of utrophin using AFM. The data indicates that the mechanical properties of utrophin's spectrin-like repeats are more similar to the stiff repeats of titin than those of spectrin or dystrophin, suggesting utrophin functions as a stiff elastic element at the myotendinous junction.</p>
<span class="work-venue">Scientific Reports 2019</span>
</div>
<i class="fas fa-external-link-alt"></i>
</a>
</div>
</div>
</section>
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