Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey

Siqueira, Lucas; Venske, Sandra M.

doi:10.22456/2175-2745.111993

Cited by 7 publications

(5 citation statements)

References 50 publications

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“…The advent of artificial intelligence (AI) allowed the development of new methods and tools to predict the structure or the function of proteins and peptides [34,35]. Furthermore, Evolutionary Algorithms (EA) are widely used in the computational design of proteins and peptides [36,37].…”

Section: Computer-aided Design Of Peptidesmentioning

confidence: 99%

Computational peptide discovery with a genetic programming approach

Scalzitti,

Miralavy,

Korenchan

et al. 2023

Preprint

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Background The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search space. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and facilitating the discovery of new peptides. Results This study presents the development and use of a variant of the initial POET algorithm, called POETRegex, which is based on genetic programming, where individuals are represented by a list of regular expressions. The program was trained on a small curated dataset and employed to predict new peptides that can improve the problem of sensitivity in detecting peptides through magnetic resonance imaging using chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET variant and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. Conclusions By combining the power of genetic programming with the flexibility of regular expressions, new potential peptide targets were identified to improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.

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Section: Computer-aided Design Of Peptidesmentioning

confidence: 99%

Computational peptide discovery with a genetic programming approach

Scalzitti,

Miralavy,

Korenchan

et al. 2023

Preprint

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“…Many research groups have shown that evolutionary approaches can be valuable in solving the protein structure and function prediction challenge. Siqueira & Venske (2021) defines the Protein Structure Problem (PSP) and introduces different classes of evolutionary algorithms and quality metrics to solve the problem. In 1997, Khimasia & Coveney (1997) defined this challenge as an NP-Hard optimization problem and, aside from performance evaluation, showed how a Simple Genetic Algorithm (SGA) can be applied to evolve simple lattice-based structure-predicting models.…”

Section: Related Workmentioning

confidence: 99%

Using genetic programming to predict and optimize protein function

Miralavy

Bricco

Gilad

et al. 2022

PeerJ Physical Chemistry

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Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this article, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept, we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modeling tool like POET can help to find peptides with 400% better functionality than used before.

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“…Many research groups have shown that evolutionary approaches can be valuable in solving the protein structure and function prediction challenge [21]. In 1997, Khimasia [22] defined this challenge as an NP-Hard optimization problem and, aside from performance evaluation, showed how a Simple Genetic Algorithm (SGA) can be applied to evolve simple lattice-based structure-predicting models.…”

Section: Related Workmentioning

confidence: 99%

Using Genetic Programming to Predict and Optimize Protein Function

Miralavy¹,

Bricco²,

Gilad³

et al. 2022

Preprint

View full text Add to dashboard Cite

Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this paper, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modelling tool like POET can help to find peptides with 400% better functionality than used before.

show abstract

Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey

Cited by 7 publications

References 50 publications

Computational peptide discovery with a genetic programming approach

Computational peptide discovery with a genetic programming approach

Using genetic programming to predict and optimize protein function

Using Genetic Programming to Predict and Optimize Protein Function

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