Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins

Raimondi, Daniele; Orlando, Gabriele; Pancsa, Rita; Khan, Taushif; Vranken, Wim

doi:10.1038/s41598-017-08366-3

Cited by 43 publications

(88 citation statements)

References 58 publications

(78 reference statements)

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“…The ROC curves of Figure 3 compare our random forest model to Vranken’s SVM early folding model [6]. We created a data set with 326 early folding residues and 326 non-exchanged residues, all from the same set of proteins.…”

Section: Resultsmentioning

confidence: 99%

“…In general, existing computational studies focus on individual proteins and utilize complex, time-consuming methods such as MD simulations. Recently, Vranken utilized a support-vector machine (SVM) algorithm to predict the early-folding residues from the S tart 2F old database [6], [7]. They achieved an accuracy of 0.741 on a test data set of 30 proteins, with a precision of 0.361 [6].…”

Section: Introductionmentioning

confidence: 99%

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A general method for predicting amino acid residues experiencing hydrogen exchange

Wang

Perez-Rathke

et al. 2018

2018 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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Information on protein hydrogen exchange can help delineate key regions involved in protein-protein interactions and provides important insight towards determining functional roles of genetic variants and their possible mechanisms in disease processes. Previous studies have shown that the degree of hydrogen exchange is affected by hydrogen bond formations, solvent accessibility, proximity to other residues, and experimental conditions. However, a general predictive method for identifying residues capable of hydrogen exchange transferable to a broad set of proteins is lacking. We have developed a machine learning method based on random forest that can predict whether a residue experiences hydrogen exchange. Using data from the Start2Fold database, which contains information on 13,306 residues (3,790 of which experience hydrogen exchange and 9,516 which do not exchange), our method achieves good performance. Specifically, we achieve an overall out-of-bag (OOB) error, an unbiased estimate of the test set error, of 20.3 percent. Using a randomly selected test data set consisting of 500 residues experiencing hydrogen exchange and 500 which do not, our method achieves an accuracy of 0.79, a recall of 0.74, a precision of 0.82, and an F1 score of 0.78.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A general method for predicting amino acid residues experiencing hydrogen exchange

Wang

Perez-Rathke

et al. 2018

2018 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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“…61 Subsequently, it was shown that EFR are likely buried according their relative accessible 62 surface area (RASA) and proposed that they are also the residues which form the 63 greatest number of contacts in a structure [39]. EFoldMine [10] is a classifier that 64 predicts EFR from sequence. Due to the nature of the trained models [10,38], it is still 65 unclear what the relation between sequence and structure is and if EFR cause their 66 surroundings to fold first or vice versa [23].…”

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confidence: 99%

“…EFoldMine [10] is a classifier that 64 predicts EFR from sequence. Due to the nature of the trained models [10,38], it is still 65 unclear what the relation between sequence and structure is and if EFR cause their 66 surroundings to fold first or vice versa [23]. 67 Representing proteins by Energy Profiling and graphs 68 A protein's native structure exhibits minimal free energy [14].…”

mentioning

confidence: 99%

“…Functional 94 residues also were shown to exhibit distinct topological features [45]. A division of labor 95 between fold and function increases robustness and evolvability of protein 96 sequences [40,49,54,55,58] because functional residues can be changed without any The Start2Fold database [39] constitute a dataset of EFR [10,14,17,23]. Previous It is unknown what sequence features causes particular residues to fold early and 106 how these residues contribute to the formation of the native structure ( Fig 1A).…”

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confidence: 99%

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Functional and Early Folding Residues are separated in proteins to increase evolvability and robustness

Bittrich

Schroeder

Labudde

2018

Preprint

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The three-dimensional structure of proteins captures evolutionary ancestry, and serves as starting point to understand the origin of diseases. Proteins adopt their structure autonomously by the process of protein folding. Over the last decades, the folding process of several proteins has been studied with temporal and spatial resolution which allowed the identification of so-called Early Folding Residues (EFR) in the folding process. These structurally relevant residues become affected early in the folding process and initiate the formation of secondary structure elements and guide their assembly.Using a dataset of 30 proteins and 3,337 residues provided by the Start2Fold database, discriminative features of EFR were identified by a systematical characterization. Therefore, proteins were represented as graphs in order to analyze topological descriptors of EFR. They constitute crucial connectors of protein regions which are distant at sequence level. Especially, these residues exhibit a high number of non-covalent contacts such as hydrogen bonds and hydrophobic interactions. This tendency also manifest as energetically stable local regions in a knowledge-based potential. Conclusively, these features are not only characteristic for EFR but also differ significantly with respect to functional residues. This unveils a split between structurally and functionally relevant residues in proteins which can drastically improve their evolvability and robustness.The characteristics of EFR cannot be attributed to trivial features such as the accessible surface area. Thus, the presented features are novel descriptors for EFR of the folding process. Potentially, these features can be used to design classifiers to predict EFR from structure or to implement structure quality assessment programs. The shown division of labor between functional and EFR has implications for the prediction of mutation effects as well as protein design and can provide insights into the evolution of proteins. Finally, EFR allow to further the understanding of the protein folding process due to their pivotal role. Author summaryProteins are chains of amino acids which adopt a three-dimensional structure and are then able to catalyze chemical reactions or propagate signals in organisms. Without external influence, most proteins fold into their correct structure, and a small number of Early Folding Residues (EFR) have been shown to become affected at the very start of 1/18 the process. We demonstrated that these residues are located in energetically stable local conformations. EFR are in contact to many other residues of a protein and act as hubs between sequentially distant regions of a proteins. These distinct characteristics can give insights into what causes certain residues to initiate and guide the folding process. Furthermore, it can help our understanding regarding diseases such as Alzheimer's or amyotrophic lateral sclerosis which are the result of protein folding gone wrong. We further found that the structurally relevant EFR are almost exclusivel...

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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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Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins

Cited by 43 publications

References 58 publications

A general method for predicting amino acid residues experiencing hydrogen exchange

A general method for predicting amino acid residues experiencing hydrogen exchange

Functional and Early Folding Residues are separated in proteins to increase evolvability and robustness

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

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