Challenges in structure prediction of oligomeric proteins at the united-residue level: Searching the multiple-chain energy landscape with CSA and CFMC

ABSTRACTMotivationThe Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. When predictions are made for proteins of which we do not know the native structure, we run into an issue to tell how good a tertiary structure prediction is, especially the protein binding regions, which are useful for drug discovery. Currently, most methods only evaluate the overall quality of a protein decoy, and few can work on residue level and protein complex. Here we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure / complex prediction at residue level. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius r of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grades their placement within the protein as a whole. Moreover, ZoomQA can evaluate the quality of protein complex, which is unique.ResultsWe benchmark ZoomQA on CASP14, it outperforms other state of the art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features, and shows our method is able to match the performance of other state-of-the-art methods without the use of homology searching against database or PSSM matrix.Availabilityhttp://[email protected]

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A Predictive Energy Landscape Model of Metamorphic Protein Conformational Specificity

10.1101/2021.11.16.468851 ◽

2021 ◽

Author(s):

James O. Wrabl ◽

Keila Voortman-Sheetz ◽

Vincent J. Hilser

Keyword(s):

Amino Acid ◽

Structure Prediction ◽

Large Scale ◽

Energy Landscape ◽

Thermodynamic Approach ◽

High Identity ◽

Denatured State ◽

Metamorphic Protein ◽

The Stability ◽

The Impact

'Metamorphic' proteins challenge state-of-the-art structure prediction methods reliant on amino acid similarity. Unfortunately, this obviates a more effective thermodynamic approach necessary to properly evaluate the impact of amino acid changes on the stability of two different folds. A vital capability of such a thermodynamic approach would be the quantification of the free energy differences between 1) the energy landscape minima of each native fold, and 2) each fold and the denatured state. Here we develop an energetic framework for conformational specificity, based on an ensemble description of protein thermodynamics. This energetic framework was able to successfully recapitulate the structures of high-identity engineered sequences experimentally shown to adopt either Streptococcus protein GA or GB folds, demonstrating that this approach indeed reflected the energetic determinants of fold. Residue-level decomposition of the conformational specificity suggested several testable hypotheses, notably among them that fold-switching could be affected by local de-stabilization of the populated fold at positions sensitive to equilibrium perturbation. Since this ensemble-based compatibility framework is applicable to any structure and any sequence, it may be practically useful for the future targeted design, or large-scale proteomic detection, of novel metamorphic proteins.

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Improving the Efficiency of Protein Conformation Prediction With Energy Landscape Modification

Volume 7: 33rd Mechanisms and Robotics Conference, Parts A and B ◽

10.1115/detc2009-86863 ◽

2009 ◽

Author(s):

Temsiri Sapsaman ◽

Harvey Lipkin

Keyword(s):

Protein Conformation ◽

Structure Prediction ◽

Global Minimum ◽

Energy Landscape ◽

Minimum Energy ◽

Prediction Algorithm ◽

Average Score ◽

Landscape Modification ◽

Protein Prediction ◽

Optimization Search

Since the native conformation or the natural shape of a protein largely determines its function, a prediction of protein conformation can shorten the process of drug discovery. This prediction is an optimization search to locate a configuration associated with the global minimum energy for the molecule. Due to the complexity of the multidimensional energy landscape, the prediction process can be extensive, which leads to very long simulation run times. For example, a high-resolution structure prediction algorithm [1] refining 20,000 to 30,000 models of several 49 to 88 residue long molecules takes about 150 CPU days per molecule. This paper presents the method of modified energy landscape (MEL) that improves the efficiency of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method by 12.8% on average, and more than 30% in some cases for a representative sample of cases. Since the efficiency improvement allows the probabilistic search to cover more areas of the energy landscape, locating the global minimum is more likely. Also, in a practical protein prediction running coarse refinements on more decoys is more preferable than comprehensively refining few decoys because of the low accuracy of energy functions. Therefore, the MEL can significantly improve the protein prediction simulation even though it yields less average score improvement. The MEL is implemented in a refinement protocol in Rosetta [2].

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Decoy selection for protein structure prediction via extreme gradient boosting and ranking

BMC Bioinformatics ◽

10.1186/s12859-020-3523-9 ◽

2020 ◽

Vol 21 (S1) ◽

Author(s):

Nasrin Akhter ◽

Gopinath Chennupati ◽

Hristo Djidjev ◽

Amarda Shehu

Keyword(s):

Machine Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Energy Landscape ◽

Biologically Active ◽

Test Cases ◽

Consensus Methods ◽

Extreme Gradient Boosting ◽

Template Free

Abstract Background Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. Results We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. Conclusions ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

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Experimental accuracy in protein structure refinement via molecular dynamics simulations

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1811364115 ◽

2018 ◽

Vol 115 (52) ◽

pp. 13276-13281 ◽

Cited By ~ 23

Author(s):

Lim Heo ◽

Michael Feig

Keyword(s):

Molecular Dynamics ◽

Protein Structure ◽

Structure Prediction ◽

Structure Refinement ◽

Energy Landscape ◽

Md Simulations ◽

Homology Model ◽

Experimental Accuracy ◽

Markov State Modeling ◽

Homology Models

Refinement is the last step in protein structure prediction pipelines to convert approximate homology models to experimental accuracy. Protocols based on molecular dynamics (MD) simulations have shown promise, but current methods are limited to moderate levels of consistent refinement. To explore the energy landscape between homology models and native structures and analyze the challenges of MD-based refinement, eight test cases were studied via extensive simulations followed by Markov state modeling. In all cases, native states were found very close to the experimental structures and at the lowest free energies, but refinement was hindered by a rough energy landscape. Transitions from the homology model to the native states require the crossing of significant kinetic barriers on at least microsecond time scales. A significant energetic driving force toward the native state was lacking until its immediate vicinity, and there was significant sampling of off-pathway states competing for productive refinement. The role of recent force field improvements is discussed and transition paths are analyzed in detail to inform which key transitions have to be overcome to achieve successful refinement.

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Identification of AtHsp90.6 involved in early embryogenesis and its structure prediction by molecular dynamics simulations

Royal Society Open Science ◽

10.1098/rsos.190219 ◽

2019 ◽

Vol 6 (5) ◽

pp. 190219 ◽

Cited By ~ 2

Author(s):

An Luo ◽

Xinbo Li ◽

Xuecheng Zhang ◽

Huadong Zhan ◽

Hewei Du ◽

...

Keyword(s):

Conformational Changes ◽

Molecular Chaperone ◽

Structure Prediction ◽

Protein Quality ◽

Energy Landscape ◽

Conformational Dynamics ◽

Early Embryogenesis ◽

Potential Analysis ◽

Up States ◽

Dynamics Simulations

Heat-shock protein of 90 kDa (Hsp90) is a key molecular chaperone involved in folding the synthesized protein and controlling protein quality. Conformational dynamics coupled to ATPase activity in N-terminal domain is essential for Hsp90's function. However, the relevant process is still largely unknown in plant Hsp90s, especially those required for plant embryogenesis which is inextricably tied up with human survival. Here, AtHsp90.6, a member of Hsp90 family in Arabidopsis , was firstly identified as a protein essential for embryogenesis. Thus we modelled AtHsp90.6 in its functionally closed ‘lid-down’ and open ‘lid-up’ states, exploring the nucleotide binding mechanism in these two states. Free energy landscape and electrostatic potential analysis revealed the switching mechanism between these two states. Collectively, this study quantitatively analysed the conformational changes of AtHsp90.6 bound to ATP or ADP. This result may help us understand the mechanism of action of AtHsp90.6 in future.

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Energy landscapes and solved protein–folding problems

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2004.1502 ◽

2004 ◽

Vol 363 (1827) ◽

pp. 453-467 ◽

Cited By ~ 126

Author(s):

Peter G. Wolynes

Keyword(s):

Protein Folding ◽

Protein Design ◽

Structure Prediction ◽

Energy Landscape ◽

Protein Structures ◽

Energy Landscapes ◽

Folding Kinetics ◽

Energy Functions ◽

Energy Landscape Theory ◽

Novel Proteins

Energy–landscape theory has led to much progress in protein folding kinetics, protein structure prediction and protein design. Funnel landscapes describe protein folding and binding and explain how protein topology determines kinetics. Landscape–optimized energy functions based on bioinformatic input have been used to correctly predict low–resolution protein structures and also to design novel proteins automatically.

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A de novo protein structure prediction by iterative partition sampling, topology adjustment, and residue-level distance deviation optimization

Bioinformatics ◽

10.1093/bioinformatics/btab620 ◽

2021 ◽

Author(s):

Jun Liu ◽

Kai-Long Zhao ◽

Guang-Xing He ◽

Liu-Jing Wang ◽

Xiao-Gen Zhou ◽

...

Keyword(s):

Protein Structure ◽

Dihedral Angle ◽

Structure Prediction ◽

Prediction Accuracy ◽

Closed Loop ◽

De Novo ◽

Residue Level ◽

Fragment Assembly ◽

Assembly Method ◽

Distance Deviation

Abstract Motivation With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achieved. Designing an effective closed-loop continuous dihedral angle optimization strategy that complements the discrete fragment assembly is crucial to improve the performance of the distance-assisted fragment assembly method. Results In this article, we proposed a de novo protein structure prediction method called IPTDFold based on closed-loop iterative partition sampling, topology adjustment and residue-level distance deviation optimization. First, local dihedral angle crossover and mutation operators are designed to explore the conformational space extensively and achieve information exchange between the conformations in the population. Then, the dihedral angle rotation model of loop region with partial inter-residue distance constraints is constructed, and the rotation angle satisfying the constraints is obtained by differential evolution algorithm, so as to adjust the spatial position relationship between the secondary structures. Lastly, the residue distance deviation is evaluated according to the difference between the conformation and the predicted distance, and the dihedral angle of the residue is optimized with biased probability. The final model is generated by iterating the above three steps. IPTDFold is tested on 462 benchmark proteins, 24 FM targets of CASP13, and 20 FM targets of CASP14. Results show that IPTDFold is significantly superior to the distance-assisted fragment assembly method Rosetta_D (Rosetta with distance). In particular, the prediction accuracy of IPTDFold does not decrease as the length of the protein increases. When using the same FastRelax protocol, the prediction accuracy of IPTDFold is significantly superior to that of trRosetta without orientation constraints, and is equivalent to that of the full version of trRosetta. Availability The source code and executable are freely available at https://github.com/iobio-zjut/IPTDFold. Supplementary information Supplementary data are available at Bioinformatics online.

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Exploring biomolecular energy landscapes

Chemical Communications ◽

10.1039/c7cc02413d ◽

2017 ◽

Vol 53 (52) ◽

pp. 6974-6988 ◽

Cited By ~ 32

Author(s):

Jerelle A. Joseph ◽

Konstantin Röder ◽

Debayan Chakraborty ◽

Rosemary G. Mantell ◽

David J. Wales

Keyword(s):

Potential Energy ◽

Structure Prediction ◽

Energy Landscape ◽

Energy Landscapes ◽

Computational Framework ◽

Feature Article ◽

Thermodynamics And Kinetics ◽

Potential Energy Landscape ◽

Kinetics Of

This feature article presents the potential energy landscape perspective, which provides both a conceptual and computational framework for structure prediction, and decoding the global thermodynamics and kinetics of biomolecules.

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QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks

Bioinformatics ◽

10.1093/bioinformatics/btaa455 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i285-i291 ◽

Cited By ~ 1

Author(s):

Md Hossain Shuvo ◽

Sutanu Bhattacharya ◽

Debswapna Bhattacharya

Keyword(s):

Neural Networks ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Residue Level ◽

Supplementary Information ◽

Model Quality ◽

Quality Estimation ◽

Distance Information ◽

Protein Model

Abstract Motivation Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction. Results We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep. Availability and implementation https://github.com/Bhattacharya-Lab/QDeep. Supplementary information Supplementary data are available at Bioinformatics online.

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