State-of-the-art web services for de novo protein structure prediction

Briefings in Bioinformatics ◽

10.1093/bib/bbaa139 ◽

2020 ◽

Cited By ~ 1

Author(s):

Luciano A Abriata ◽

Matteo Dal Peraro

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Tertiary Structure ◽

De Novo ◽

State Of The Art ◽

Data Bank ◽

End Users ◽

Model Quality ◽

Uncharacterized Protein

Abstract Residue coevolution estimations coupled to machine learning methods are revolutionizing the ability of protein structure prediction approaches to model proteins that lack clear homologous templates in the Protein Data Bank (PDB). This has been patent in the last round of the Critical Assessment of Structure Prediction (CASP), which presented several very good models for the hardest targets. Unfortunately, literature reporting on these advances often lacks digests tailored to lay end users; moreover, some of the top-ranking predictors do not provide webservers that can be used by nonexperts. How can then end users benefit from these advances and correctly interpret the predicted models? Here we review the web resources that biologists can use today to take advantage of these state-of-the-art methods in their research, including not only the best de novo modeling servers but also datasets of models precomputed by experts for structurally uncharacterized protein families. We highlight their features, advantages and pitfalls for predicting structures of proteins without clear templates. We present a broad number of applications that span from driving forward biochemical investigations that lack experimental structures to actually assisting experimental structure determination in X-ray diffraction, cryo-EM and other forms of integrative modeling. We also discuss issues that must be considered by users yet still require further developments, such as global and residue-wise model quality estimates and sources of residue coevolution other than monomeric tertiary structure.

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LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.724947 ◽

2021 ◽

Vol 8 ◽

Author(s):

Charles Christoffer ◽

Vijay Bharadwaj ◽

Ryan Luu ◽

Daisuke Kihara

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

De Novo ◽

Protein Complexes ◽

Protein Sequences ◽

Data Bank ◽

Protein Docking ◽

Functional Mechanisms ◽

Established Technique

Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.

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Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13

10.1101/597724 ◽

2019 ◽

Author(s):

Georg Kuenze ◽

Jens Meiler

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

De Novo ◽

Dipolar Coupling ◽

Nuclear Overhauser Effect ◽

Residual Dipolar Coupling ◽

Comparative Modeling ◽

Model Quality ◽

Blind Test

AbstractComputational methods that produce accurate protein structure models from limited experimental data, e.g. from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predictedde novousing a two-stage protocol. First, low-resolution models were generated with the Rosettade novomethod guided by non-ambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and non-ambiguous NOE contacts and RDCs. Nine out of 16 of the Rosettade novomodels had the correct fold (GDT-TS score >45) and in three cases high-resolution models were achieved (RMSD <3.5 Å). We also show that a meta-approach applying iterative Rosetta+NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.

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De novo protein structure prediction by incremental inter-residue geometries prediction and model quality assessment using deep learning

10.1101/2022.01.11.475831 ◽

2022 ◽

Author(s):

Jun Liu ◽

Guangxing He ◽

Kailong Zhao ◽

Guijun Zhang

Keyword(s):

Protein Structure ◽

Quality Assessment ◽

Protein Structure Prediction ◽

Structure Prediction ◽

De Novo ◽

Feedback Mechanism ◽

Geometric Constraints ◽

Model Quality ◽

Model Quality Assessment ◽

Loop Feedback

Motivation: The successful application of deep learning has promoted progress in protein model quality assessment. How to use model quality assessment to further improve the accuracy of protein structure prediction, especially not reliant on the existing templates, is helpful for unraveling the folding mechanism. Here, we investigate whether model quality assessment can be introduced into structure prediction to form a closed-loop feedback, and iteratively improve the accuracy of de novo protein structure prediction. Results: In this study, we propose a de novo protein structure prediction method called RocketX. In RocketX, a feedback mechanism is constructed through the geometric constraint prediction network GeomNet, the structural simulation module, and the model quality evaluation network EmaNet. In GeomNet, the co-evolutionary features extracted from MSA that search from the sequence databases are sent to an improved residual neural network to predict the inter-residue geometric constraints. The structure model is folded based on the predicted geometric constraints. In EmaNet, the 1D and 2D features are extracted from the folded model and sent to the deep residual neural network to estimate the inter-residue distance deviation and per-residue lDDT of the model, which will be fed back to GeomNet as dynamic features to correct the geometries prediction and progressively improve model accuracy. RocketX is tested on 483 benchmark proteins and 20 FM targets of CASP14. Experimental results show that the closed-loop feedback mechanism significantly contributes to the performance of RocketX, and the prediction accuracy of RocketX outperforms that of the state-of-the-art methods trRosetta (without templates) and RaptorX. In addition, the blind test results on CAMEO show that although no template is used, the prediction accuracy of RocketX on medium and hard targets is comparable to the advanced methods that integrate templates.

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Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction

Evolutionary Computation ◽

10.1162/evco_a_00176 ◽

2016 ◽

Vol 24 (4) ◽

pp. 577-607 ◽

Cited By ~ 23

Author(s):

Mario Garza-Fabre ◽

Shaun M. Kandathil ◽

Julia Handl ◽

Joshua Knowles ◽

Simon C. Lovell

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Tertiary Structure ◽

De Novo ◽

Memetic Algorithm ◽

Scale Up ◽

Three Dimensional ◽

Limiting Factors ◽

Genetic Operators

Computational approaches to de novo protein tertiary structure prediction, including those based on the preeminent “fragment-assembly” technique, have failed to scale up fully to larger proteins (on the order of 100 residues and above). A number of limiting factors are thought to contribute to the scaling problem over and above the simple combinatorial explosion, but the key ones relate to the lack of exploration of properly diverse protein folds, and to an acute form of “deception” in the energy function, whereby low-energy conformations do not reliably equate with native structures. In this article, solutions to both of these problems are investigated through a multistage memetic algorithm incorporating the successful Rosetta method as a local search routine. We found that specialised genetic operators significantly add to structural diversity and that this translates well to reaching low energies. The use of a generalised stochastic ranking procedure for selection enables the memetic algorithm to handle and traverse deep energy wells that can be considered deceptive, which further adds to the ability of the algorithm to obtain a much-improved diversity of folds. The results should translate to a tangible improvement in the performance of protein structure prediction algorithms in blind experiments such as CASP, and potentially to a further step towards the more challenging problem of predicting the three-dimensional shape of large proteins.

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Conceptual Framework for Invariant Protein Fragment Library

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1034.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 240-247

Keyword(s):

Amino Acids ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

De Novo ◽

Data Bank ◽

Computational Techniques ◽

Excess Kurtosis ◽

T Score ◽

Fragment Library

Proteins are essential and are present in all life forms and determining its structure is cumbersome, laborious and time consuming. Hence, over 3-4 decades, researchers have been using computational techniques such as template and template free based protein structure prediction from its sequence. This research focuses on developing a conceptual basis for establishing an invariant fragment library which can be used for protein structure prediction. Based on 20 amino acids, fragments can be classified into lengths of 3 to 41 size. Further, they can be classified based on the identical number of amino acids present in the fragment. This encompasses theoretically the number of fragments that can exist and in no way represent the actual possible fragments that can exist in nature. Invariant fragments are ones which are rigid in structure 3-dimensionally and do not change. A formula was arrived at to determine all possible permutations that can exist for length 3 to 41 based on the 20 amino acids. 100 proteins from the Protein Data Bank were downloaded, broken into fragments of 3 to 41 resulting in a total of 6102,102 fragments using Asynchronous Distributed Processing. Then identical fragments in sequence were superimposed and Root Mean Square Deviation (RMSD) values were obtained resulting in roughly 3.2% of the original framgnets.. t-score and z-scores were obtained from which Skewness, Kurtosis and Excess Kurtosis were determined. For invariance, skewness cutoff was set at + 0.1 and using the excess kurtosis, fragments whose distribution were either leptokurtic or platykurtic and were within + 1 standard deviation of the mean value were considered as invariant i.e., if there were no outliers in the distribution and if most of the t-score or z-score values were centered around its average value. Using these cutoff values, fragments were classified and deposited into an invariant fragment library. Roughly 3,81,799 invariant fragments were obtained which is roughly 6.3% of the total number of initial fragments. This would be way less than the number of fragments that one has to either use in homology or de-novo modelling thereby reducing the design space. Further work is underway to set up the entire invariant fragment library which can then be used to predict protein structure by template-based approach.

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De novo protein structure prediction using ultra-fast molecular dynamics simulation

10.1101/262188 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ngaam J. Cheung ◽

Wookyung Yu

Keyword(s):

Molecular Dynamics ◽

Protein Structure ◽

Molecular Dynamics Simulation ◽

Protein Structure Prediction ◽

Structure Prediction ◽

High Efficiency ◽

Tertiary Structure ◽

De Novo ◽

Protein Structures ◽

Dynamics Simulation

ABSTRACTModern genomics sequencing techniques have provided a massive amount of protein sequences, but experimental endeavor in determining protein structures is largely lagging far behind the vast and unexplored sequences. Apparently, computational biology is playing a more important role in protein structure prediction than ever. Here, we present a system of de novo predictor, termed NiDelta, building on a deep convolutional neural network and statistical potential enabling molecular dynamics simulation for modeling protein tertiary structure. Combining with evolutionary-based residue-contacts, the presented predictor can predict the tertiary structures of a number of target proteins with remarkable accuracy. The proposed approach is demonstrated by calculations on a set of eighteen large proteins from different fold classes. The results show that the ultra-fast molecular dynamics simulation could dramatically reduce the gap between the sequence and its structure at atom level, and it could also present high efficiency in protein structure determination if sparse experimental data is available.

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