Efficient conformational ensemble generation of protein-bound peptides

Yan, Yumeng; Zhang, Di; Huang, Sheng‐You

doi:10.1186/s13321-017-0246-7

Cited by 65 publications

(68 citation statements)

References 49 publications

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“…A classical way to measure the quality of conformational sampling is to compute how often the ensemble of selected dockable candidates contains a native-like conformation. However, the optimal size of that ensemble, one that affords the best trade-off between native coverage and computational cost of the subsequent docking, may vary with peptide length and secondary structure [57]. Hence, a fair evaluation of conformational search under our optimized weights requires consideration of a spectrum of sampling options and selection thresholds, and so it is better deferred to a dedicated study.…”

Section: Discussionmentioning

confidence: 99%

Receptor-free Discrimination of Peptide Native Bound Conformations Suggests Limited Transferability of the “cen_std+score4l” Rosetta Energy Function From Proteins to Peptides

Bazzoli¹,

Contini²

2020

Preprint

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One of the strategies of peptide–protein docking is to pregenerate an ensemble of peptide conformations in the absence of the receptor, and then dock them as rigid bodies onto its surface. Success of this strategy requires that the scoring function that drives the pregeneration step be able to discriminate in favor of conformations that resemble the native bound conformation. Here we present a study on the discrimination of peptide native bound conformations as achieved without receptor by the “cen_std+score4L” Rosetta energy function, a low-resolution scoring function equivalent to one chosen for other tasks where the modeling of solvent effects is of special importance. The cen_std+score4L function was able to assign, on average, lower energies to native-like than to non-native decoy conformations for only 3 of our 18 test peptides; it also ranked one or more native-like decoys in the top 1% for only 2 peptides. However, by optimizing the weights of the energy terms that define the cen_std+score4L function, native discrimination improved substantially: Native-like decoys were assigned lower energies than non-native decoys for 16 peptides, with a discrimination signal larger than noise for 9 peptides, that is, 50% of the test set. And for 9 peptides, too, native-like decoys ranked in the top 1%. An ensuing energetic analysis of native-like versus non-native decoys suggests that native peptide conformations have solvation and non-local electrostatics that poorly recapitulate those of native protein conformations. Native peptide conformations are also characterized by few backbone–backbone H-bonds and by lack of compactness, presumably to optimize interaction with the receptor. Overall, this study lays groundwork for pregenerating dockable peptide conformations with Rosetta, whether the subsequent docking will be performed by Rosetta or some other software.

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Section: Discussionmentioning

confidence: 99%

Receptor-free Discrimination of Peptide Native Bound Conformations Suggests Limited Transferability of the “cen_std+score4l” Rosetta Energy Function From Proteins to Peptides

Bazzoli¹,

Contini²

2020

Preprint

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“…HPEPDOCK is a newly released (in the year 2018) web‐based software used to blindly dock peptides into proteins using a hierarchical algorithm . MODPEP is utilized by the software to generate ensemble conformations of the peptide and perform docking using each conformation . For the top 10 conformations, HPEPDOCK gets a success rate of 72.6% compared to the well‐established HADDOCK (success rate 45.2% for peptide‐protein docking protocol) .…”

Section: Methodsmentioning

confidence: 99%

“…33 MODPEP is utilized by the software to generate ensemble conformations of the peptide and perform docking using each conformation. [34][35][36][37] For the top 10 conformations, HPEPDOCK gets a success rate of 72.6% compared to the well-established HAD-DOCK (success rate 45.2% for peptide-protein docking protocol). 33,38 So, HPEPDOCK is more accurate and less computationally expensive.…”

Section: Molecular Dockingmentioning

confidence: 99%

Recognition of gluconeogenic enzymes; Icl1, Fbp1, and Mdh2 by Gid4 ligase: A molecular docking study

Elfiky

Ismail

Elshemey

2019

J of Molecular Recognition

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The pro/N‐degron pathway is an evolved protein degradation pathway through the ubiquitin‐proteasome system. It is a vital pathway to attain protein homeostasis inside the liver cells with varying glucose levels. N‐terminal proline exists in more than 300 proteins in Saccharomyces cerevisiae, but only three of them are the gluconeogenic enzymes; isocitrate lyase (Icl1), fructose‐1,6‐bisphosphatase (Fbp1), and malate dehydrogenase (Mdh2). The present in silico study aims to structurally illustrate the binding of Icl1 enzyme to Gid4 ligase concerning its peers; Fbp1 and Mdh2. Based on the molecular docking scores and interactions, one can attribute the binding stability of Gid4 with degrons, to peptides of length six up to eight from the N‐terminal. Moreover, the percent change in the docking score provides a rationale for the unique Gid4‐Icl11‐4 interaction. The present study provides insights on the binding attitude of Gid4 ligase to degrons of different lengths, so one will consider in designing peptidomimetics to target Gid4 ligase.

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“…In our research, virtual screening was applied to validating the prediction results of our models. HPEPDOCK Server was selected to carry out our virtual screening task due to its outstanding performance and accurate result [45][46][47][48][49][50][51][52][53][54]. The targets of the verification experiment were the 3 group peptides with differentiable possibility of anti-hypertensive peptide (the verified dataset was obtained from the above part of prediction).…”

Section: Prediction Model and Peptide-protein Docking Verificationmentioning

confidence: 99%

A Novel Machine Learning Strategy for Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

Wang

Niu

Wang

et al. 2020

Preprint

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Strategies to screen antihypertensive peptides with high throughput and rapid speed will be doubtlessly contributed to the treatment of hypertension. The food-derived antihypertensive peptides can reduce blood pressure without side effects. In present study, a novel model based on Extreme Gradient Boosting (XGBoost) algorithm was developed using the primary structural features of the food-derived peptides, and its performance in the prediction of antihypertensive peptides was compared with the dominating machine learning models. To further reflect the reliability of the method in real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of k-mer peptides cutting from 6 key proteins in bovine milk and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance with the accuracy of 0.9841 and the area under the receiver operating characteristic curve of 0.9428, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using XGBoost algorithm as a novel auxiliary tool is feasible for screening antihypertensive peptide derived from food with high throughput and high efficiency.

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Efficient conformational ensemble generation of protein-bound peptides

Cited by 65 publications

References 49 publications

Receptor-free Discrimination of Peptide Native Bound Conformations Suggests Limited Transferability of the “cen_std+score4l” Rosetta Energy Function From Proteins to Peptides

Receptor-free Discrimination of Peptide Native Bound Conformations Suggests Limited Transferability of the “cen_std+score4l” Rosetta Energy Function From Proteins to Peptides

Recognition of gluconeogenic enzymes; Icl1, Fbp1, and Mdh2 by Gid4 ligase: A molecular docking study

A Novel Machine Learning Strategy for Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

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