MEDUSA: Prediction of Protein Flexibility from Sequence

Meersche, Yann Vander; Cretin, Gabriel; Brevern, Alexandre G. de; Gelly, Jean‐Christophe; Galochkina, Tatiana

doi:10.1016/j.jmb.2021.166882

Cited by 34 publications

(29 citation statements)

References 33 publications

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“… The three-class prediction of secondary structure by PYTHIA [26] . The five-class flexibility prediction provided by MEDUSA (0 = rigid, 4 = flexible) [25] . The two-class prediction of intrinsically disordered regions provided by MobiDB-lite3.0 (S = structured, D = disordered) [27] .…”

Section: Resultsmentioning

confidence: 99%

“…DeepREx-WS integrates DeepREx predictions with external resources. We include predictions obtained with MEDUSA [25] , estimating residue flexibility of the proteins across five classes (0 = rigid, 4 = flexible). MEDUSA is based on a deep convolutional neural network architecture processing an input comprising evolutionary information, derived from MSAs and residue physicochemical properties [25] .…”

Section: Methodsmentioning

confidence: 99%

“…The server DeepREx-WS, for each position, supplements the exposure prediction of DeepREx with the Kyte-Doolittle hydrophobicity and residue conservation obtained from a multiple sequence alignment. Furthermore, three external resources, MEDUSA [25] , PYTHIA [26] and MobiDB-Lite3.0 [27] , are present to estimate, for each residue position, protein flexibility, protein secondary structure and the presence of intrinsically disordered regions, respectively.…”

Section: Introductionmentioning

confidence: 99%

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DeepREx-WS: A web server for characterising protein–solvent interaction starting from sequence

Manfredi

Savojardo

Martelli

et al. 2021

Computational and Structural Biotechnology Journal

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Protein–solvent interaction provides important features for protein surface engineering when the structure is absent or partially solved. Presently, we can integrate the notion of solvent exposed/buried residues with that of their flexibility and intrinsic disorder to highlight regions where mutations may increase or decrease protein stability in order to modify proteins for biotechnological reasons, while preserving their functional integrity. Here we describe a web server, which provides the unique possibility of integrating knowledge of solvent and non-solvent exposure with that of residue conservation, flexibility and disorder of a protein sequence, for a better understanding of which regions are relevant for protein integrity. The core of the webserver is DeepREx, a novel deep learning-based tool that classifies each residue in the sequence as buried or exposed. DeepREx is trained on a high-quality, non-redundant dataset derived from the Protein Data Bank comprising 2332 monomeric protein chains and benchmarked on a blind test set including 200 protein sequences unrelated with the training set. Results show that DeepREx performs at the state-of-the-art in the field. In turn, the Web Server, DeepREx-WS, supplements the predictions of DeepREx with features that allow a better characterisation of exposed and buried regions: i) residue conservation derived from multiple sequence alignment; ii) local sequence hydrophobicity; iii) residue flexibility computed with MEDUSA; iv) a predictor of secondary structure; v) the presence of disordered regions as derived from MobiDB-Lite3.0. The web server allows browsing, selecting and intersecting the different features. We demonstrate a possible application of the DeepREx-WS for assisting the identification of residues to be variated in protein surface engineering processes.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DeepREx-WS: A web server for characterising protein–solvent interaction starting from sequence

Manfredi

Savojardo

Martelli

et al. 2021

Computational and Structural Biotechnology Journal

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“…The observed gain derives from several factors, which include (i) the quantity of the available data on protein structures; (ii) the efficient protein sequence encoding; and (iii) the implementation and tuning of a deep learning model. In our method, each of these factors were chosen in accordance with the results reported for the similar problems of structural bioinformatics, such as secondary structure prediction [29][30][31][32] and flexibility prediction [8]. At the same time, the final network architecture as well as the combination of descriptors chosen for the sequence encoding are original and demonstrate the best results during model tuning.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, structural alphabets give users the ability to investigate and analyze protein properties such as protein dynamic and flexibility. Thus, Protein Blocks have been widely used to predict protein flexibility [8,9], backbone deformability [10,11], allosteric effects [12], protein disorders [7], and molecular dynamics [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction

Cretin

Galochkina

Brevern

et al. 2021

IJMS

Self Cite

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Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a deep residual inception-inside-inception neural network with convolutional block attention modules, predicting 1 of 16 PB classes from evolutionary information combined to physicochemical properties of individual amino acids. PYTHIA clearly outperforms the LOCUSTRA reference method for all PB classes and demonstrates great performance for PB prediction on particularly challenging proteins from the CASP14 free modelling category.

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PredictingDNA‐binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

Wang

Liu

et al. 2022

Proteins

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The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA‐binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA‐binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA‐binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA‐binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°–120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.

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MEDUSA: Prediction of Protein Flexibility from Sequence

Cited by 34 publications

References 33 publications

DeepREx-WS: A web server for characterising protein–solvent interaction starting from sequence

DeepREx-WS: A web server for characterising protein–solvent interaction starting from sequence

PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction

PredictingDNA‐binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

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