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1. Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins

   https://doi.org/10.1371/journal.pcbi.1008753  

   Roche, Rahmatullah; Bhattacharya, Sutanu; Bhattacharya, Debswapna (February 2021, PLOS Computational Biology)

   Kolodny, Rachel (Ed.)

   Crystallography and NMR system (CNS) is currently a widely used method for fragment-free ab initio protein folding from inter-residue distance or contact maps. Despite its widespread use in protein structure prediction, CNS is a decade-old macromolecular structure determination system that was originally developed for solving macromolecular geometry from experimental restraints as opposed to predictive modeling driven by interaction map data. As such, the adaptation of the CNS experimental structure determination protocol for ab initio protein folding is intrinsically anomalous that may undermine the folding accuracy of computational protein structure prediction. In this paper, we propose a new CNS-free hierarchical structure modeling method called DConStruct for folding both soluble and membrane proteins driven by distance and contact information. Rigorous experimental validation shows that DConStruct attains much better reconstruction accuracy than CNS when tested with the same input contact map at varying contact thresholds. The hierarchical modeling with iterative self-correction employed in DConStruct scales at a much higher degree of folding accuracy than CNS with the increase in contact thresholds, ultimately approaching near-optimal reconstruction accuracy at higher-thresholded contact maps. The folding accuracy of DConStruct can be further improved by exploiting distance-based hybrid interaction maps at tri-level thresholding, as demonstrated by the better performance of our method in folding free modeling targets from the 12th and 13th rounds of the Critical Assessment of techniques for protein Structure Prediction (CASP) experiments compared to popular CNS- and fragment-based approaches and energy-minimization protocols, some of which even using much finer-grained distance maps than ours. Additional large-scale benchmarking shows that DConStruct can significantly improve the folding accuracy of membrane proteins compared to a CNS-based approach. These results collectively demonstrate the feasibility of greatly improving the accuracy of ab initio protein folding by optimally exploiting the information encoded in inter-residue interaction maps beyond what is possible by CNS. 

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2. Atomistic Peptide Folding Simulations Reveal Interplay of Entropy and Long-Range Interactions in Folding Cooperativity

   https://doi.org/10.1038/s41598-018-32028-7  

   Chen, Jianlin; Liu, Xiaorong; Chen, Jianhan (September 2018, Scientific Reports)

   Abstract Understanding how proteins fold has remained a problem of great interest in biophysical research. Atomistic computer simulations using physics-based force fields can provide important insights on the interplay of different interactions and energetics and their roles in governing the folding thermodynamics and mechanism. In particular, generalized Born (GB)-based implicit solvent force fields can be optimized to provide an appropriate balance between solvation and intramolecular interactions and successfully recapitulate experimental conformational equilibria for a set of helical and β-hairpin peptides. Here, we further demonstrate that key thermodynamic properties and their temperature dependence obtained from replica exchange molecular dynamics simulations of these peptides are in quantitative agreement with experimental results. Useful lessons can be learned on how the interplay of entropy and sequentially long-range interactions governs the mechanism and cooperativity of folding. These results highlight the great potential of high-quality implicit solvent force fields for studying protein folding and large-scale conformational transitions. 

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3. SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome

   https://doi.org/10.1038/s41557-021-00707-0  

   Zimmerman, Maxwell I.; Porter, Justin R.; Ward, Michael D.; Singh, Sukrit; Vithani, Neha; Meller, Artur; Mallimadugula, Upasana L.; Kuhn, Catherine E.; Borowsky, Jonathan H.; Wiewiora, Rafal P.; et al (January 2021, Nature Chemistry)

   null (Ed.)

   SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression and replication that depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate 0.1 seconds of the viral proteome. Our adaptive sampling simulations predict dramatic opening of the apo spike complex, far beyond that seen experimentally, explaining and predicting the existence of ‘cryptic’ epitopes. Different spike variants modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also discover dramatic conformational changes across the proteome, which reveal over 50 ‘cryptic’ pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas. 

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4. Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

   https://doi.org/10.1073/pnas.2410662121  

   Guan, Xingyue; Tang, Qian-Yuan; Ren, Weitong; Chen, Mingchen; Wang, Wei; Wolynes, Peter G; Li, Wenfei (August 2024, Proceedings of the National Academy of Sciences)

   Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins. 

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5. Characteristics of Protein Fold Space Exhibits Close Dependence on Domain Usage

   https://doi.org/10.1007/978-3-030-17938-0_32  

   Zimmermann, Micharl T.; Towfic, Fadi; Jernigan, Robert L.; Kloczkowski, A. (January 2019, Lecture notes in bioinformatics)

   With the growth of the PDB and simultaneous slowing of the discovery of new protein folds, we may be able to answer the question of how discrete protein fold space is. Studies by Skolnick et al. (PNAS, 106, 15690, 2009) have concluded that it is in fact continuous. In the present work we extend our initial observation (PNAS, 106(51) E137, 2009) that this conclusion depends upon the resolution with which structures are considered, making the determination of what resolution is most useful of importance. We utilize graph theoretical approaches to investigate the connectedness of the protein structure universe, showing that the modularity of protein domain architecture is of fundamental importance for future improvements in structure matching, impacting our understanding of protein domain evolution and modification. We show that state-of-the-art structure superimposition algorithms are unable to distinguish between conformational and topological variation. This work is not only important for our understanding of the discreteness of protein fold space, but informs the more critical question of what precisely should be spatially aligned in structure superimposition. The metric-dependence is also investigated leading to the conclusion that fold usage in homology reduced datasets is very similar to usage across all of PDB and should not be ignored in large scale studies of protein structure similarity. 

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