Deterministic Global Optimization and Ab Initio Approaches for the Structure Prediction of Polypeptides, Dynamics of Protein Folding, and Protein-Protein Interactions

2002 ◽  
pp. 265-457 ◽  
Author(s):  
John L. Klepeis ◽  
Heather D. Schafroth ◽  
Karl M. Westerberg ◽  
Christodoulos A. Floudas
Keyword(s):  
Protein Folding ◽  
Global Optimization ◽  
Ab Initio ◽  
Protein Interactions ◽  
Structure Prediction ◽  
Deterministic Global Optimization ◽  
Protein Protein Interactions

Neuronal SNARE complex: A protein folding system with intricate protein-protein interactions, and its common neuropathological hallmark, SNAP25

2019 ◽  
Vol 122 ◽  
pp. 196-207 ◽  
Author(s):  
Srijeeb Karmakar ◽  
Laipubam Gayatri Sharma ◽  
Abhishek Roy ◽  
Anjali Patel ◽  
Lalit Mohan Pandey
Keyword(s):  
Protein Folding ◽  
Protein Interactions ◽  
Snare Complex ◽  
Protein Protein Interactions

Assessing the performance of the MM/PBSA and MM/GBSA methods. 10. Impacts of enhanced sampling and variable dielectric model on protein–protein Interactions

2019 ◽  
Vol 21 (35) ◽  
pp. 18958-18969 ◽  
Author(s):  
Ercheng Wang ◽  
Gaoqi Weng ◽  
Huiyong Sun ◽  
Hongyan Du ◽  
Feng Zhu ◽  
...  
Keyword(s):  
Protein Folding ◽  
Protein Interactions ◽  
Conformational Transitions ◽  
Protein Recognition ◽  
Enhanced Sampling ◽  
Protein Protein Interactions ◽  
Dielectric Model

Enhanced sampling has been extensively used to capture the conformational transitions in protein folding, but it attracts much less attention in the studies of protein–protein recognition.

Divide-and-Link Peptide Docking: A Fragment-Based Peptide Docking Protocol

2021 ◽  
Author(s):  
Lu Sun ◽  
Tingting Fu ◽  
Dan Zhao ◽  
Hongjun Fan ◽  
Shijun Zhong
Keyword(s):  
Protein Folding ◽  
Drug Design ◽  
Protein Interactions ◽  
Structural Data ◽  
Peptide Drug ◽  
Protein Protein Interactions ◽  
Peptide Docking ◽  
Docking Protocol ◽  
Peptide Interaction

Protein-peptide interaction is crucial for various important cellular regulations, and also a basis for understanding protein-protein interactions, protein folding and peptide drug design. Due to the limited structural data obtained...

scholarly journals Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins

2020 ◽  
Author(s):  
Rahmatullah Roche ◽  
Sutanu Bhattacharya ◽  
Debswapna Bhattacharya
Keyword(s):  
Protein Folding ◽  
Protein Structure ◽  
Membrane Proteins ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Hierarchical Structure ◽  
Structure Determination ◽  
Structure Prediction ◽  
Structure Modeling ◽  
Contact Maps

AbstractCrystallography and NMR system (CNS) is currently the de facto standard 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 difficult free modeling targets from the 12th and 13th rounds of the Critical Assessment of techniques for protein Structure Prediction (CASP) experiments compared to several popular CNS- and fragment-based approaches, 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.Author summaryPredicting the folded and functional 3-dimensional structure of a protein molecule from its amino acid sequence is of central importance to structural biology. Recently, promising advances have been made in ab initio protein folding due to the reasonably accurate estimation of inter-residue interaction maps at increasingly higher resolutions that range from binary contacts to finer-grained distances. Despite the progress in predicting the interaction maps, approaches for turning the residue-residue interactions projected in these maps into their precise spatial positioning heavily rely on a decade-old experimental structure determination protocol that is not suitable for predictive modeling. This paper presents a new hierarchical structure modeling method, DConStruct, which can better exploit the information encoded in the interaction maps at multiple granularities, from binary contact maps to distance-based hybrid maps at tri-level thresholding, for improved ab initio folding. Multiple large-scale benchmarking experiments show that our proposed method can substantially improve the folding accuracy for both soluble and membrane proteins compared to state-of-the-art approaches. DConStruct is licensed under the GNU General Public License v3 and freely available at https://github.com/Bhattacharya-Lab/DConStruct.

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