ReviewThe Journal of chemical physics2020
Hybrid methods for combined experimental and computational determination of protein structure.
Review in The Journal of chemical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- TINA: a geometry-aware post-hoc scoring framework for protein-protein interaction.Acta pharmacologica Sinica · 2026Article
- Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data.Mass spectrometry reviews · 2026Review
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.Journal of chemical theory and computation · 2026Article
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.bioRxiv : the preprint server for biology · 2026Article
- HDXRank: A Deep Learning Framework for Ranking Protein Complex Predictions with Hydrogen-Deuterium Exchange Data.Journal of chemical theory and computation · 2025Article
- Current perspectives in drug targeting intrinsically disordered proteins and biomolecular condensates.BMC biology · 2025Review
- RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data.Journal of structural biology · 2025Article
- Post-processing enhances protein secondary structure prediction with second order deep learning and embeddings.Computational and structural biotechnology journal · 2025Article
- Navigating the unstructured by evaluating alphafold's efficacy in predicting missing residues and structural disorder in proteins.PloS one · 2025Article
- Integrating Computational Design and Experimental Approaches for Next-Generation Biologics.Biomolecules · 2024Review
- Multimodal Mass Spectrometry Identifies a Conserved Protective Epitope inAnalytical chemistry · 2024Article
- Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics.Molecules (Basel, Switzerland) · 2023Review
- SARS-CoV-2 proteins structural studies using synchrotron radiation.Biophysical reviews · 2023Review
- Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver.Briefings in bioinformatics · 2023Article
- Computational insight into stability-enhanced systems of anthocyanin with protein/peptide.Food chemistry. Molecular sciences · 2023Article
- Big versus small: The impact of aggregate size in disease.Protein science : a publication of the Protein Society · 2023Review
- Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data.Scientific data · 2023Article
- Structure Determination of Challenging Protein-Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2023Article
- Nuclear Magnetic Resonance-Guided Structural Analysis of Moderate-Affinity Protein Complexes with Intrinsically Disordered Polypeptides.Methods in molecular biology (Clifton, N.J.) · 2023Article
- An automated pipeline integrating AlphaFold 2 and MODELLER for protein structure prediction.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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2 authors.
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Abstract
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.
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