bims-lances 2024-07-07 papers

Nat Commun. 2024 Jul 02. 15(1): 5538

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

Xintao Song, Lei Bao, Chenjie Feng, Qiang Huang, Fa Zhang, Xin Gao, Renmin Han.

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

DOI: https://doi.org/10.1038/s41467-024-49858-x

J Chem Inf Model. 2024 Jul 03.

Are Protein Conformational Ensembles in Agreement with Experimental Data? A Geometrical Interpretation of the Problem.

Letizia Fiorucci, Marco Schiavina, Isabella C Felli, Roberta Pierattelli, Enrico Ravera.

The conformational variability of biological macromolecules can play an important role in their biological function. Therefore, understanding conformational variability is expected to be key for predicting the behavior of a particular molecule in the context of organism-wide studies. Several experimental methods have been developed and deployed for accessing this information, and computational methods are continuously updated for the profitable integration of different experimental sources. The outcome of this endeavor is conformational ensembles, which may vary significantly in properties and composition when different ensemble reconstruction methods are used, and this raises the issue of comparing the predicted ensembles against experimental data. In this article, we discuss a geometrical formulation to provide a framework for understanding the agreement of an ensemble prediction to the experimental observations.

DOI: https://doi.org/10.1021/acs.jcim.4c00582

IUCrJ. 2024 Jul 01. 11(Pt 4): 494-501

Comprehensive encoding of conformational and compositional protein structural ensembles through the mmCIF data structure.

Stephanie A Wankowicz, James S Fraser.

In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to the achievements of AlphaFold with single-structure prediction.

Keywords: biomolecules; cryoEM; ensemble–function predictions; macromolecular ensembles; mmCIF

DOI: https://doi.org/10.1107/S2052252524005098

J Phys Chem B. 2024 Jul 05.

Optimizing Force Fields with Experimental Data Using Ensemble Reweighting and Potential Contrasting.

Xinqiang Ding.

Despite force field improvements over the past decades, we still encounter situations where simulation results disagree with experiments due to force field inaccuracies. Such situations provide opportunities to improve force fields. In this study, we introduce a novel framework for optimizing force fields using experimental data. The unique feature of this framework is that it aims to optimize force fields to match experiments while minimizing the perturbation made to the original force field. To achieve this, we combine ensemble reweighting techniques with the potential contrasting method. Ensemble reweighting is used to reweight an ensemble of conformations generated using an existing force field to match experimental data while minimizing the perturbation to the original ensemble. Potential contrasting is then utilized to optimize force field parameters to reproduce the reweighted ensemble. We demonstrate the framework's effectiveness by optimizing a coarse-grained force field for intrinsically disordered proteins using experimental radius of gyration data.

DOI: https://doi.org/10.1021/acs.jpcb.4c02147