After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

The study of molecular recognition has followed the second and third precepts of Descartes [1], also known as the reductionism approach. This approach assumes that understanding the three-dimensional (3D) structures of biological macromolecules and complexes is a prerequisite for comprehending their assembly and subsequent function. Over the past 30 years, high-resolution molecular structures obtained through x-ray diffraction or nuclear magnetic resonance (NMR) techniques have provided a detailed understanding of the molecular mechanisms of these macromolecules [2]. However, these techniques have mostly been successful in studying small, globular, and soluble molecules. Despite the successes achieved in the study of some large assemblies, such as nucleosomes, ribosomes, or viruses, significant challenges remain when attempting to investigate membrane proteins, large multivalent machineries, or large highly flexible