Using Sequence-Predicted Contacts to Guide Template-free Protein Structure Prediction

Zaman, Ahmed Bin; Parthasarathy, Prasanna; Shehu, Amarda

doi:10.1145/3307339.3342175

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Other works followed this approach of breaking the energy function into bonded and non-bonded energies [14], [15], [16], [17], [18]. Most works approaching the multi-objective PSP employed some evolutionary algorithm [14], [15], [16], [19], [20], [18], [21]. Regarding the protein structure representation, most works employed the full atomic structure [14], [15], [16], [22], [21], although some preferred the centroid representation [19], [20], [18].…”

Section: Related Workmentioning

confidence: 99%

“…Considering the extra information that can be added to complement the energy function, there are several possibilities. Some works employ structural information that energy functions may not capture accurately, such as solvent terms [15], [16], [22], compactness [19], and hydrogen bonding [19], [20]. Also, high-level information that can be predicted was explored, such as protein fragments [19], [20], secondary structures [14], [15], [16], [17], [19], [22], [18], [21], and contact maps [19], [20], [22].…”

Section: Related Workmentioning

confidence: 99%

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A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction

Marchi¹,

Parpinelli²

2022

Annals of Computer Science and Information Systems

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The protein structure prediction problem is one of the most important bioinformatics problems. Computational methods can be used to approach this problem and de novo methods are able to generate protein structures without the need of having known similar structures to the predicted protein. These methods transform the prediction problem into an optimization problem, using optimization models that combine different energy functions and high-level information. These models usually have only a single optimization objective. However, it is known that this single objective optimization approach may harm the optimization search due to the existence of conflicts between the different terms that compose the optimization objective. The proposed model has three objectives: energy function, secondary structure, and contact maps. A multi-objective Biased Random-Key Genetic Algorithm (BRKGA) with online parameter control, named MOBO, is proposed as the optimizer. The final predictor comprises two phases of the MOBO algorithm and selects a final structure using the MUFOLD-CL clustering method. Results obtained demonstrated that the proposed predictor generated highly competitive results with the literature.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction

Marchi¹,

Parpinelli²

2022

Annals of Computer Science and Information Systems

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“…Great progress has been made in template-free PSP. A detailed review is beyond the scope of this paper, but we note a key characteristic shared by popular methods and software platforms, such as Rosetta [15], Quark [32], and others [24,33]. These methods operate under the umbrella of stochastic optimization.…”

Section: Introductionmentioning

confidence: 99%

Variational Autoencoders for Protein Structure Prediction

Alam¹,

Shehu²

2020

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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“…Great algorithmic advances have been made in structure generation, most remarkably by Rosetta [10], Quark [11], and others [12][13][14][15][16]. Recent works have investigated incorporating complementary information like sequence-predicted contacts and constructing new energy functions based on predicted contacts or distances of pairs of amino acids for structure generation [17][18][19]. Algorithms utilizing deep neural networks are also becoming increasingly popular [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering

et al. 2020

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Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. In this paper, we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. Evaluations are related on both benchmark and CASP target proteins. Structure ensembles subjected to the proposed approach and the source code of the proposed approach are publicly-available at the links provided in Section 1.

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Using Sequence-Predicted Contacts to Guide Template-free Protein Structure Prediction

Cited by 7 publications

References 21 publications

A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction

A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction

Variational Autoencoders for Protein Structure Prediction

Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering

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