Protein folding: from the levinthal paradox to structure prediction

1999 ◽  
Vol 293 (2) ◽  
pp. 283-293 ◽  
Author(s):  
Barry Honig
2019 ◽  
Vol 15 (4) ◽  
Author(s):  
Tomasz Smolarczyk ◽  
Katarzyna Stapor ◽  
Irena Roterman-Konieczna

AbstractThree-dimensional protein structure prediction is an important task in science at the intersection of biology, chemistry, and informatics, and it is crucial for determining the protein function. In the two-stage protein folding model, based on an early- and late-stage intermediates, we propose to use state-of-the-art secondary structure prediction servers for backbone dihedral angles prediction and devise an early-stage structure. Early-stage structures are used as a starting point for protein folding simulations, and any errors in this stage affect the final predictions. We have shown that modern secondary structure prediction servers could increase the accuracy of early-stage predictions compared to previously reported models.


PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243331
Author(s):  
Andrew J. McGehee ◽  
Sutanu Bhattacharya ◽  
Rahmatullah Roche ◽  
Debswapna Bhattacharya

Recent advances in distance-based protein folding have led to a paradigm shift in protein structure prediction. Through sufficiently precise estimation of the inter-residue distance matrix for a protein sequence, it is now feasible to predict the correct folds for new proteins much more accurately than ever before. Despite the exciting progress, a dedicated visualization system that can dynamically capture the distance-based folding process is still lacking. Most molecular visualizers typically provide only a static view of a folded protein conformation, but do not capture the folding process. Even among the selected few graphical interfaces that do adopt a dynamic perspective, none of them are distance-based. Here we present PolyFold, an interactive visual simulator for dynamically capturing the distance-based protein folding process through real-time rendering of a distance matrix and its compatible spatial conformation as it folds in an intuitive and easy-to-use interface. PolyFold integrates highly convergent stochastic optimization algorithms with on-demand customizations and interactive manipulations to maximally satisfy the geometric constraints imposed by a distance matrix. PolyFold is capable of simulating the complex process of protein folding even on modest personal computers, thus making it accessible to the general public for fostering citizen science. Open source code of PolyFold is freely available for download at https://github.com/Bhattacharya-Lab/PolyFold. It is implemented in cross-platform Java and binary executables are available for macOS, Linux, and Windows.


2021 ◽  
Author(s):  
Marina A Pak ◽  
Karina A Markhieva ◽  
Mariia S Novikova ◽  
Dmitry S Petrov ◽  
Ilya S Vorobyev ◽  
...  

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the AlphaFold predictions on the impact of a single mutation on structure with a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold cannot be immediately applied to other problems or applications in protein folding.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Sebastian Bittrich ◽  
Michael Schroeder ◽  
Dirk Labudde

AbstractProtein folding and structure prediction are two sides of the same coin. Contact maps and the related techniques of constraint-based structure reconstruction can be considered as unifying aspects of both processes. We present the Structural Relevance (SR) score which quantifies the information content of individual contacts and residues in the context of the whole native structure. The physical process of protein folding is commonly characterized with spatial and temporal resolution: some residues are Early Folding while others are Highly Stable with respect to unfolding events. We employ the proposed SR score to demonstrate that folding initiation and structure stabilization are subprocesses realized by distinct sets of residues. The example of cytochrome c is used to demonstrate how StructureDistiller identifies the most important contacts needed for correct protein folding. This shows that entries of a contact map are not equally relevant for structural integrity. The proposed StructureDistiller algorithm identifies contacts with the highest information content; these entries convey unique constraints not captured by other contacts. Identification of the most informative contacts effectively doubles resilience toward contacts which are not observed in the native contact map. Furthermore, this knowledge increases reconstruction fidelity on sparse contact maps significantly by 0.4 Å.


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