Relating Protein Structure and Function Through a Bijection and Its Implications on Protein Structure Prediction

Ambriz-Rivas, Marco; Pastor, Nina; Río, Gabriel del

doi:10.5772/36472

Cited by 3 publications

(9 citation statements)

References 123 publications

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“…Because these two conformations are labile on the energy scale it is argued that in water both helical and random coil like structures co-exist in a dynamic state and moreover, this energy gap can be compensated by the choice of the solvent i.e., in protic or aprotic environment (55). It is worth mentioning here that these results are consistent with the NMR and CD spectroscopy results that have shown the occurrence of no regular geometry in Tat (48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) peptide. Solid-state NMR shows that Tat (48-60) is highly dynamic and adopts a random coil conformation (56).…”

Section: Simulationssupporting

confidence: 88%

“…Helical secondary structures have already been highlighted in the literature as being necessary for cell penetration properties (48,49) because such structures seem essential particularly within the lipid phase of the membrane for its cellular uptake and stabilization. Minimization studies (at a dielectric constant of 4) have revealed that Vim-TBS (58-81) adopts a secondary structure similar to that observed in Tat (48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60), and the same mechanism of cell penetration can be attributed to Vim-TBS (58-81) based on the structure-function and activity relationships (50,51).…”

Section: Tat (48-60) Peptidementioning

confidence: 95%

“…To have an in depth knowledge of its structural characteristics, structural properties of Tat (48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) and p10 peptides were also studied. Tat (48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) and p10 were particularly selected as the former is a well studied CPP and the later is a pro-apoptogenic fragment of p21/WAFI protein that is often used as a model cargo to evaluate cell penetrating properties. As structural versatility has been described as an important factor to be considered for deciphering cellular uptake properties by CPPs (26-28) the present work supports the hypothesis that structural plasticity could have a crucial role on its properties and functionality.…”

Section: Introductionmentioning

confidence: 99%

“…Conformation I, II, and III of Vim-TBS (58-81) correspond to the starting geometry with Φ, Ψ, ω values obtained after MM energy minimization studies. Similarly, final conformations obtained after MM studies for Tat (48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) and p10 peptides were the starting conformations for MD simulation studies.…”

Section: Simulationsmentioning

confidence: 99%

“…(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60) peptide Vim-TBS (58-81) peptide p10 peptide evaluation of coulomb interactions and Van der Waals interaction a cut off of 0.9 and 1.0 nm respectively was applied. Long range forces were updated every 10fs during generation of the neighbor list.…”

mentioning

confidence: 99%

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Insights on the structural characteristics of Vim-TBS (58-81) peptide for future applications as a cell penetrating peptide

et al. 2013

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Section: Simulationssupporting

confidence: 88%

Section: Tat (48-60) Peptidementioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Insights on the structural characteristics of Vim-TBS (58-81) peptide for future applications as a cell penetrating peptide

et al. 2013

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Challenges in the Computational Modeling of the Protein Structure—Activity Relationship

Río

2021

Computation

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Living organisms are composed of biopolymers (proteins, nucleic acids, carbohydrates and lipid polymers) that are used to keep or transmit information relevant to the state of these organisms at any given time. In these processes, proteins play a central role by displaying different activities required to keep or transmit this information. In this review, I present the current knowledge about the protein sequence–structure–activity relationship and the basis for modeling this relationship. Three representative predictors relevant to the modeling of this relationship are summarized to highlight areas that require further improvement and development. I will describe how a basic understanding of this relationship is fundamental in the development of new methods to design proteins, which represents an area of multiple applications in the areas of health and biotechnology.

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Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure

et al. 2017

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Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a proper analysis of the structure-function relationship of proteins: (i) the lack of a formal definition of what critical residues are and (ii) the lack of a systematic evaluation of methods and protein structure features. To address this problem, here we introduce an index to quantify the protein-function criticality of a residue based on experimental data and a strategy aimed to optimize both, descriptors of protein structure (physicochemical and centrality descriptors) and machine learning algorithms, to minimize the error in the classification of critical residues. We observed that both physicochemical and centrality descriptors of residues effectively relate protein structure and protein function, and that physicochemical descriptors better describe critical residues. We also show that critical residues are better classified when residue criticality is considered as a binary attribute (i.e., residues are considered critical or not critical). Using this binary annotation for critical residues 8 models rendered accurate and non-overlapping classification of critical residues, confirming the multi-factorial character of the structure-function relationship of proteins.

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Relating Protein Structure and Function Through a Bijection and Its Implications on Protein Structure Prediction

Cited by 3 publications

References 123 publications

Insights on the structural characteristics of Vim-TBS (58-81) peptide for future applications as a cell penetrating peptide

Insights on the structural characteristics of Vim-TBS (58-81) peptide for future applications as a cell penetrating peptide

Challenges in the Computational Modeling of the Protein Structure—Activity Relationship

Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure

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