Amino Acids, Peptides and Proteins

Schermann, J. P.

doi:10.1016/b978-044452708-0.50006-x

Cited by 4 publications

(3 citation statements)

References 263 publications

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“…Residues are formed when two or more amino acids are combined and elements of water are removed from the peptide chain. Residues can be classified as polar, hydrophobic and charged (Schermann, 2008). Residues are typically represented by their threeletter abbreviations.…”

Section: Resultsmentioning

confidence: 99%

Adsorption Behavior of Bone Morphogenetic Protein-2 on a Graphite Substrate for Biomedical Applications

Marquetti¹,

Desai²

2018

American Journal of Engineering and Applied Sciences

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The interaction of proteins with biomaterials plays an important role for several biomedical applications including implants and tissue engineering. Although different experimental approaches have been explored, the investigation of the molecular models for protein-substrate interaction is warranted to control of cellular functions. In this study, Molecular Dynamics (MD) simulations were used to study the adsorption behavior of Bone Morphogenetic Protein-2 (BMP-2) on a hydrophobic graphite substrate. The influence of four different orientations of the protein with the substrate was evaluated based on their adsorption behavior. Results indicate a strong influence of the initial configuration on the adsorption. Protein unfolding was observed for most orientations with preferential binding of residues within the vicinity of the substrate. Most of the secondary structure of the protein was preserved during the simulation with a minor reduction of β-sheet structures. This research provides a detailed understanding of the interaction of BMP-2 with biomaterial surfaces.

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Section: Resultsmentioning

confidence: 99%

Adsorption Behavior of Bone Morphogenetic Protein-2 on a Graphite Substrate for Biomedical Applications

Marquetti¹,

Desai²

2018

American Journal of Engineering and Applied Sciences

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“…Biological robustness describes a property of living systems whereby specific functions of the system are maintained despite external and internal perturbations. In proteomics, robustness is achieved in two ways: since protein structure is intimately related to function (Schermann 2008), proteins with similar structure can exhibit similar functions, and proteins can be synthesized through different pathways in the metabolic network. This means two proteins located upstream the network relative to a third causing disease will have redundant information, and so do two proteins whose structure is similar (this is depicted in figure 4).…”

Section: Cardiovascular Event Prediction With Proteomicsmentioning

confidence: 99%

Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance

Pereira¹,

Stroes²,

Zwinderman³

et al. 2021

Preprint

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Model transparency is a prerequisite in many domains and an increasingly popular area in machine learning research. In the medical domain, for instance, unveiling the mechanisms behind a disease often has higher priority than the diagnostic itself since it might dictate or guide potential treatments and research directions. One of the most popular approaches to explain model global predictions is the permutation importance where the performance on permuted data is benchmarked against the baseline. However, this method and other related approaches will undervalue the importance of a feature in the presence of covariates since these cover part of its provided information. To address this issue, we propose Covered Information Disentanglement (CID), a method that considers all feature information overlap to correct the values provided by permutation importance. We further show how to compute CID efficiently when coupled with Markov random fields. We demonstrate its efficacy in adjusting permutation importance first on a controlled toy dataset and discuss its effect on real-world medical data. 1 We make an implementation of CID publicly available at: https://github.com/JBPereira/CID.

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“…As it is mentioned before the amino acids are directed by the N terminal and C terminal, also the beta sheet is directed as always symbolized in the protein structure (carton method) as a wide arrow aiming to the C terminal as shown in figure 4. Beta sheets could be built up either in parallel method, in this case it is called parallel beta sheet or in antiparallel method and is called anti-parallel beta sheets [78][79][80][81][82] as shown in figure 5. In parallel beta sheets the backbones arranged in a manner that the amino groups direction is the same or are adjacent in both backbones, while in anti-parallel structure the backbones are arranged in a way that the amino groups are adjacent to the carboxyl group in the next backbone [83][84][85][86][87].…”

Section: Beta Sheet (β Sheet)mentioning

confidence: 99%

Protein structure from the essential amino acids to the 3D structure

2019

Biointerface Res Appl Chem

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Protein structure is a hot topic, not only to the specialist, but with others like the physicists. So this review is targeting those who are not biologists and have to deal with the protein in their research. In this review we travel with the protein structures from the amino acids and its classifications, and how the polypeptide chain is formed from these building blocks up to the final 3D structure. We introduced the secondary structure species like helices with its different types and how it is formed; also the beta sheet formation and types are explained briefly. Finally the tertiary and quaternary structures are presented. The approaches of molecular modeling as well as other important computational methods present significant contribution to studying proteins.

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Amino Acids, Peptides and Proteins

Cited by 4 publications

References 263 publications

Adsorption Behavior of Bone Morphogenetic Protein-2 on a Graphite Substrate for Biomedical Applications

Adsorption Behavior of Bone Morphogenetic Protein-2 on a Graphite Substrate for Biomedical Applications

Covered Information Disentanglement: Model Transparency via Unbiased Permutation Importance

Protein structure from the essential amino acids to the 3D structure

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