DeepConf: Machine Learning Conformer Reconstruction of Biomolecules from Scanning Tunneling Microscopy Images

TL;DR

This paper introduces a machine learning framework that rapidly reconstructs 3D biomolecular structures from STM images, enabling detailed analysis of biomolecules with high accuracy and potential for automation.

Contribution

It presents a novel ML-based method for generating 3D biomolecular structures and simulating STM images, addressing data scarcity and improving structural predictions from experimental images.

Findings

Achieves median atomic deviations below 2 Å for peptides

Achieves median atomic deviations below 4 Å for glycans

Demonstrates effective application to experimental STM data

Abstract

Improving the detailed understanding of the underlying properties and functions of biomolecules has recently attracted growing interest, enabled by the possibility of real-space imaging of single, intact macromolecules using Scanning Tunneling Microscopy (STM) in combination with electrospray ion beam deposition and soft landing. This combination provides key insights into biomolecular behavior, but it also imposes stringent requirements on rapid and reliable data analysis. A major limiting factor for applying machine learning to STM images is often the scarcity of training data, caused by the long acquisition times required for both experimental imaging and high-accuracy simulations. Here, we propose a framework for the rapid generation of three-dimensional structures of glycans, peptides, and glycopeptides and their corresponding STM-like image simulations, based on state-of-the-art, machine-learning-accelerated Density Functional Theory (DFT). We generate datasets for the polypeptide bradykinin and for a representative glycan molecule, and we train a conformer estimation model to predict a molecule's three-dimensional structure from an STM image. On synthetic data, our approach achieves high accuracy, with median atomic deviations below $2\,\AA{}$ for peptides and below $4\,\AA{}$ for glycans. Application to experimental data predominantly yields a precise, reliable, and visually convincing determination of the local positions of molecular subunits. The application to experimental data represents an important milestone towards a fully automated structural search pipeline for complex, biologically relevant systems imaged with STM.