The Structure/Function Relationship in Antimicrobial Peptides: What Can we Obtain From Structural Data?

Cardoso, Marlon Henrique; Oshiro, Karen G. N.; Rezende, Samilla B.; Cândido, Elizabete de Souza; Franco, Octávio L.

doi:10.1016/bs.apcsb.2018.01.008

Cited by 23 publications

(15 citation statements)

References 79 publications

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“…Antimicrobial peptides feature diverse structural conformations to display antimicrobial activities (Cardoso et al, 2018c). Previous works have reported the clustering of AMPs according to backbone torsion angles, revealing that this class of antimicrobial presents many different folds that could be used to classify them (Fjell et al, 2012).…”

Section: Structure Profile In Computer-made Ampsmentioning

confidence: 99%

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

et al. 2020

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Computer-Aided Design of Antimicrobial Peptides http://crdd.osdd.net/raghava/antibp2/ ClassAMP Uses RF and SVM to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity http://www.bicnirrh.res.in/classamp/ AMPA Web tool for assessing the antimicrobial domains of proteins, with a focus on the design on new antimicrobial drugs http://tcoffee.crg.cat/apps/ampa/do DBAASP Provides users with information on detailed structure (chemical, 3D) and activity for those peptides, for which antimicrobial activity against particular target species have been evaluated experimentally https://dbaasp.org/home Joker An algorithm to design antimicrobial peptides using their language https://github.com/williamfp7/Joker

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Section: Structure Profile In Computer-made Ampsmentioning

confidence: 99%

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

et al. 2020

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“…For example, the cryo-EM structure of ribosome in complex with apidaecin, a proline rich peptide that targets the ribosome, suggests that the peptide terminates translation by arresting the ribosome at the stop codon [ 251 ]. Unfortunately, due to the rigidness of the contrast mechanisms, cryo-EM lacks the flexibility offered by other approaches such as solution NMR or X-ray crystallography [ 252 ].…”

Section: Live Imagingmentioning

confidence: 99%

Membrane Active Peptides and Their Biophysical Characterization

2018

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In the last 20 years, an increasing number of studies have been reported on membrane active peptides. These peptides exert their biological activity by interacting with the cell membrane, either to disrupt it and lead to cell lysis or to translocate through it to deliver cargos into the cell and reach their target. Membrane active peptides are attractive alternatives to currently used pharmaceuticals and the number of antimicrobial peptides (AMPs) and peptides designed for drug and gene delivery in the drug pipeline is increasing. Here, we focus on two most prominent classes of membrane active peptides; AMPs and cell-penetrating peptides (CPPs). Antimicrobial peptides are a group of membrane active peptides that disrupt the membrane integrity or inhibit the cellular functions of bacteria, virus, and fungi. Cell penetrating peptides are another group of membrane active peptides that mainly function as cargo-carriers even though they may also show antimicrobial activity. Biophysical techniques shed light on peptide–membrane interactions at higher resolution due to the advances in optics, image processing, and computational resources. Structural investigation of membrane active peptides in the presence of the membrane provides important clues on the effect of the membrane environment on peptide conformations. Live imaging techniques allow examination of peptide action at a single cell or single molecule level. In addition to these experimental biophysical techniques, molecular dynamics simulations provide clues on the peptide–lipid interactions and dynamics of the cell entry process at atomic detail. In this review, we summarize the recent advances in experimental and computational investigation of membrane active peptides with particular emphasis on two amphipathic membrane active peptides, the AMP melittin and the CPP pVEC.

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“…Albeit this encoding has been mainly employed in the context of computational HIV research, it might work as well for antimicrobial peptides, owing to very good classification results of several studies [95]. To sum up, structural encodings are an appropriate extension to sequence-based encodings since antimicrobial activity is determined by the three-dimensional composition of the residues [96] and in addition, the combination of sequence- and structure-based encodings increases discriminating power [97].…”

Section: Encodingsmentioning

confidence: 99%

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

Spänig

Heider

2019

BioData Mining

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Antimicrobial peptides (AMPs) are part of the inherent immune system. In fact, they occur in almost all organisms including, e.g., plants, animals, and humans. Remarkably, they show effectivity also against multi-resistant pathogens with a high selectivity. This is especially crucial in times, where society is faced with the major threat of an ever-increasing amount of antibiotic resistant microbes. In addition, AMPs can also exhibit antitumor and antiviral effects, thus a variety of scientific studies dealt with the prediction of active peptides in recent years. Due to their potential, even the pharmaceutical industry is keen on discovering and developing novel AMPs. However, AMPs are difficult to verify in vitro, hence researchers conduct sequence similarity experiments against known, active peptides. Unfortunately, this approach is very time-consuming and limits potential candidates to sequences with a high similarity to known AMPs. Machine learning methods offer the opportunity to explore the huge space of sequence variations in a timely manner. These algorithms have, in principal, paved the way for an automated discovery of AMPs. However, machine learning models require a numerical input, thus an informative encoding is very important. Unfortunately, developing an appropriate encoding is a major challenge, which has not been entirely solved so far. For this reason, the development of novel amino acid encodings is established as a stand-alone research branch. The present review introduces state-of-the-art encodings of amino acids as well as their properties in sequence and structure based aggregation. Moreover, albeit a wellchosen encoding is essential, performant classifiers are required, which is reflected by a tendency towards specifically designed models in the literature. Furthermore, we introduce these models with a particular focus on encodings derived from support vector machines and deep learning approaches. Albeit a strong focus has been set on AMP predictions, not all of the mentioned encodings have been elaborated as part of antimicrobial research studies, but rather as general protein or peptide representations.

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The Structure/Function Relationship in Antimicrobial Peptides: What Can we Obtain From Structural Data?

Cited by 23 publications

References 79 publications

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Membrane Active Peptides and Their Biophysical Characterization

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

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