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

2016 ◽  
Vol 24 (4) ◽  
pp. 577-607 ◽  
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.

Prediction of Structural and Functional Aspects of Protein

2014 ◽  
pp. 317-333
Author(s):  
Arun G. Ingale
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Predict Protein Structure ◽  
Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

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

2020 ◽  
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.

scholarly journals Prediction of Structural and Functional Aspects of Protein

2017 ◽  
pp. 551-568
Author(s):  
Arun G. Ingale
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Predict Protein Structure ◽  
Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

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

2018 ◽  
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.

scholarly journals Building a Better Fragment Library for De Novo Protein Structure Prediction

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123998 ◽  
Author(s):  
Saulo H. P. de Oliveira ◽  
Jiye Shi ◽  
Charlotte M. Deane
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Fragment Library

scholarly journals Sampling Bottlenecks in De novo Protein Structure Prediction

2009 ◽  
Vol 393 (1) ◽  
pp. 249-260 ◽  
Author(s):  
David E. Kim ◽  
Ben Blum ◽  
Philip Bradley ◽  
David Baker
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo

APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction

2015 ◽  
Vol 59 ◽  
pp. 142-157 ◽  
Author(s):  
Bruno Borguesan ◽  
Mariel Barbachan e Silva ◽  
Bruno Grisci ◽  
Mario Inostroza-Ponta ◽  
Márcio Dorn
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Knowledge Based

scholarly journals Protein structure prediction and design in a biologically-realistic implicit membrane

2019 ◽  
Author(s):  
Rebecca F. Alford ◽  
Patrick J. Fleming ◽  
Karen G. Fleming ◽  
Jeffrey J. Gray
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Membrane Proteins ◽  
Membrane Protein ◽  
Protein Structure Prediction ◽  
Protein Design ◽  
Structure Prediction ◽  
De Novo ◽  
Computational Design ◽  
Amino Acid Distribution

ABSTRACTProtein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the lipid bilayer. In this work, we developed an implicit approach that captures the anisotropic structure, shape of water-filled pores, and nanoscale dimensions of membranes with different lipid compositions. The model improves performance in computational bench-marks against experimental targets including prediction of protein orientations in the bilayer, ΔΔG calculations, native structure dis-crimination, and native sequence recovery. When applied to de novo protein design, this approach designs sequences with an amino acid distribution near the native amino acid distribution in membrane proteins, overcoming a critical flaw in previous membrane models that were prone to generating leucine-rich designs. Further, the proteins designed in the new membrane model exhibit native-like features including interfacial aromatic side chains, hydrophobic lengths compatible with bilayer thickness, and polar pores. Our method advances high-resolution membrane protein structure prediction and design toward tackling key biological questions and engineering challenges.Significance StatementMembrane proteins participate in many life processes including transport, signaling, and catalysis. They constitute over 30% of all proteins and are targets for over 60% of pharmaceuticals. Computational design tools for membrane proteins will transform the interrogation of basic science questions such as membrane protein thermodynamics and the pipeline for engineering new therapeutics and nanotechnologies. Existing tools are either too expensive to compute or rely on manual design strategies. In this work, we developed a fast and accurate method for membrane protein design. The tool is available to the public and will accelerate the experimental design pipeline for membrane proteins.

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