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1. FastConformation: A Standalone ML-Based Toolkit for Modeling and Analyzing Protein Conformational Ensembles at Scale

   https://doi.org/10.1101/2025.05.09.653048  

   Galeazzi, Flavia Maria; Monteiro_da_Silva, Gabriel; Arantes, Pablo; Varghese, Iz; Shukla, Ananya; Rubenstein, Brenda M (May 2025, bioRxiv)

   Abstract Deep learning approaches like AlphaFold 2 (AF2) have revolutionized structural biology by accurately predicting the ground state structures of proteins. Recently, clustering and subsampling techniques that manipulate multiple sequence alignment (MSA) inputs into AlphaFold to generate conformational ensembles of proteins have also been proposed. Although many of these techniques have been made open source, they often require integrating multiple packages and can be challenging for researchers who have a limited programming background to employ. This is especially true when researchers are interested in subsampling to produce predictions of protein conformational ensembles, which require multiple computational steps. This manuscript introduces FastConformation, a Python-based application that integrates MSA generation, structure prediction via AF2, and interactive analysis of protein conformations and their distributions, all in one place. FastConformation is accessible through a user-friendly GUI suitable for non-programmers, allowing users to iteratively refine subsampling parameters based on their analyses to achieve diverse conformational ensembles. Starting from an amino acid sequence, users can make protein conformation predictions and analyze results in just a few hours on their local machines, which is significantly faster than traditional molecular dynamics (MD) simulations. Uniquely, by leveraging the subsampling of MSAs, our tool enables the generation of alternative protein conformations. We demonstrate the utility of FastConformation on proteins including the Abl1 kinase, LAT1 transporter, and CCR5 receptor, showcasing its ability to predict and analyze the protein conformational ensembles and effects of mutations on a variety of proteins. This tool enables a wide range of high-throughput applications in protein biochemistry, drug discovery, and protein engineering. 

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2. Computational microscopy: Revealing molecular mechanisms in plants using molecular dynamics simulations

   https://doi.org/10.1105/tpc.119.tt1219  

   Feng, J.; Chen, J.; Selvam, B.; Shukla, D. (December 2019, The Plant Cell)

   Structural biology has provided valuable insights and high-resolution views of the biophysical processes in plants, such as photosynthesis, hormone signaling, nutrient transport, and toxin efflux. However, structural biology only provides a few “snapshots” of protein structure, whereas in vivo, protein function involves complex dynamical processes such as ligand binding and conformational changes that structures alone are unable to capture in full detail. Here, we present all-atom molecular dynamics (MD) simulations as a “computational microscope” that can be used to capture detailed structural and dynamical information about the molecular machinery in plants and gain high-resolution insights into plant growth and function. In addition to the background information provided here, we have prepared a set of tutorials that allow students to run and explore MD simulations of plant proteins. 

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3. Compliant mechanical response of the ultrafast folding protein EnHD under force

   https://doi.org/10.1038/s42005-022-01125-5  

   Reifs, Antonio; Ruiz Ortiz, Irene; Saa, Amaia Ochandorena; Schönfelder, Jörg; De Sancho, David; Muñoz, Victor; Perez-Jimenez, Raul (December 2023, Communications Physics)

   Abstract Ultrafast folding proteins have become an important paradigm in the study of protein folding dynamics. Due to their low energetic barriers and fast kinetics, they are amenable for study by both experiment and simulation. However, single molecule force spectroscopy experiments on these systems are challenging as these proteins do not provide the mechanical fingerprints characteristic of more mechanically stable proteins, which makes it difficult to extract information about the folding dynamics of the molecule. Here, we investigate the unfolding of the ultrafast protein Engrailed Homeodomain (EnHD) by single-molecule atomic force microscopy experiments. Constant speed experiments on EnHD result in featureless transitions typical of compliant proteins. However, in the force-ramp mode we recover sigmoidal curves that we interpret as a very compliant protein that folds and unfolds many times over a marginal barrier. This is supported by a simple theoretical model and coarse-grained molecular simulations. Our results show the ability of force to modulate the unfolding dynamics of ultrafast folding proteins. 

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4. High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

   https://doi.org/10.1038/s41467-024-46715-9  

   Monteiro da Silva, Gabriel; Cui, Jennifer Y.; Dalgarno, David C.; Lisi, George P.; Rubenstein, Brenda M. (March 2024, Nature Communications)

   Abstract This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins’ ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution. 

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5. Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction

   https://doi.org/10.3390/biom12070985  

   Stevens, Amy O.; He, Yi (July 2022, Biomolecules)

   The inhibition of protein–protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein–protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2’s prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict α-helices and β-strands in proteins. 

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