scholarly journals AMPing Up the Search: A Structural and Functional Repository of Antimicrobial Peptides for Biofilm Studies, and a Case Study of Its Application to Corynebacterium striatum, an Emerging Pathogen

2021 ◽  
Vol 11 ◽  
Author(s):  
Shreeya Mhade ◽  
Stutee Panse ◽  
Gandhar Tendulkar ◽  
Rohit Awate ◽  
Yatindrapravanan Narasimhan ◽  
...  
Keyword(s):  
Antimicrobial Peptides ◽  
In Silico ◽  
Emerging Pathogen ◽  
Preference Scale ◽  
Modeling Tools ◽  
Interaction Models ◽  
Corynebacterium Striatum

Antimicrobial peptides (AMPs) have been recognized for their ability to target processes important for biofilm formation. Given the vast array of AMPs, identifying potential anti-biofilm candidates remains a significant challenge, and prompts the need for preliminary in silico investigations prior to extensive in vitro and in vivo studies. We have developed Biofilm-AMP (B-AMP), a curated 3D structural and functional repository of AMPs relevant to biofilm studies. In its current version, B-AMP contains predicted 3D structural models of 5544 AMPs (from the DRAMP database) developed using a suite of molecular modeling tools. The repository supports a user-friendly search, using source, name, DRAMP ID, and PepID (unique to B-AMP). Further, AMPs are annotated to existing biofilm literature, consisting of a vast library of over 10,000 articles, enhancing the functional capabilities of B-AMP. To provide an example of the usability of B-AMP, we use the sortase C biofilm target of the emerging pathogen Corynebacterium striatum as a case study. For this, 100 structural AMP models from B-AMP were subject to in silico protein-peptide molecular docking against the catalytic site residues of the C. striatum sortase C protein. Based on docking scores and interacting residues, we suggest a preference scale using which candidate AMPs could be taken up for further in silico, in vitro and in vivo testing. The 3D protein-peptide interaction models and preference scale are available in B-AMP. B-AMP is a comprehensive structural and functional repository of AMPs, and will serve as a starting point for future studies exploring AMPs for biofilm studies. B-AMP is freely available to the community at https://b-amp.karishmakaushiklab.com and will be regularly updated with AMP structures, interaction models with potential biofilm targets, and annotations to biofilm literature.

ABSORPTION AND METABOLISM OF ASTRAGALI RADIX DECOCTION: IN SILICO, IN VITRO, AND A CASE STUDY IN VIVO

2006 ◽  
Vol 34 (6) ◽  
pp. 913-924 ◽  
Author(s):  
Feng Xu ◽  
Yue Zhang ◽  
Shengyuan Xiao ◽  
Xiaowei Lu ◽  
Donghui Yang ◽  
...  
Keyword(s):  
In Silico ◽  
Astragali Radix

scholarly journals Applying Bioinformatic Platforms, In Vitro, and In Vivo Functional Assays in the Characterization of Genetic Variants in the GH/IGF Pathway Affecting Growth and Development

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 2063
Author(s):  
Sabina Domené ◽  
Paula A. Scaglia ◽  
Mariana L. Gutiérrez ◽  
Horacio M. Domené
Keyword(s):  
In Silico ◽  
Growth And Development ◽  
Genetic Variant ◽  
Modeling Tools ◽  
In Vivo Models ◽  
Bioinformatic Tools ◽  
Functional Assays

Heritability accounts for over 80% of adult human height, indicating that genetic variability is the main determinant of stature. The rapid technological development of Next-Generation Sequencing (NGS), particularly Whole Exome Sequencing (WES), has resulted in the characterization of several genetic conditions affecting growth and development. The greatest challenge of NGS remains the high number of candidate variants identified. In silico bioinformatic tools represent the first approach for classifying these variants. However, solving the complicated problem of variant interpretation requires the use of experimental approaches such as in vitro and, when needed, in vivo functional assays. In this review, we will discuss a rational approach to apply to the gene variants identified in children with growth and developmental defects including: (i) bioinformatic tools; (ii) in silico modeling tools; (iii) in vitro functional assays; and (iv) the development of in vivo models. While bioinformatic tools are useful for a preliminary selection of potentially pathogenic variants, in vitro—and sometimes also in vivo—functional assays are further required to unequivocally determine the pathogenicity of a novel genetic variant. This long, time-consuming, and expensive process is the only scientifically proven method to determine causality between a genetic variant and a human genetic disease.

scholarly journals AMPing up the search: An in silico approach to identifying Antimicrobial Peptides (AMPs) with potential anti-biofilm activity

2021 ◽  
Author(s):  
Shreeya Mhade ◽  
Stutee Panse ◽  
Gandhar Tendulkar ◽  
Rohit Awate ◽  
Snehal Kadam ◽  
...  
Keyword(s):  
Antimicrobial Peptides ◽  
In Silico ◽  
Biofilm Formation ◽  
Multidrug Resistant ◽  
Resistant Bacteria ◽  
C Protein ◽  
Pilus Biogenesis ◽  
Natural And Synthetic

AbstractAntibiotic resistance is a public health threat, and the rise of multidrug-resistant bacteria, including those that form protective biofilms, further compounds this challenge. Antimicrobial peptides (AMPs) have been recognized for their anti-infective properties, including their ability to target processes important for biofilm formation. However, given the vast array of natural and synthetic AMPs, determining potential candidates for anti-biofilm testing is a significant challenge. In this study, we present an in silico approach, based on open-source tools, to identify AMPs with potential anti-biofilm activity. This approach is developed using the sortase-pilin machinery, important for adhesion and biofilm formation, of the multidrug-resistant, biofilm-forming pathogen C. striatum as the target. Using homology modeling, we modeled the structure of the C. striatum sortase C protein, resembling the semi-open lid conformation adopted during pilus biogenesis. Next, we developed a structural library of 5544 natural and synthetic AMPs from sequences in the DRAMP database. From this library, AMPs with known anti-Gram positive activity were filtered, and 100 select AMPs were evaluated for their ability to interact with the sortase C protein using in-silico molecular docking. Based on interacting residues and docking scores, we built a preference scale to categorize candidate AMPs in order of priority for future in vitro and in vivo biofilm studies. The considerations and challenges of our approach, and the resources developed, which includes a search-enabled repository of predicted AMP structures and protein-peptide interaction models relevant to biofilm studies (B-AMP), can be leveraged for similar investigations across other biofilm targets and biofilm-forming pathogens.

scholarly journals Cnicin as an Anti-SARS-CoV-2: An Integrated In Silico and In Vitro Approach for the Rapid Identification of Potential COVID-19 Therapeutics

Antibiotics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 542
Author(s):  
Hani A. Alhadrami ◽  
Ahmed M. Sayed ◽  
Hossam M. Hassan ◽  
Khayrya A. Youssif ◽  
Yasser Gaber ◽  
...  
Keyword(s):  
Natural Products ◽  
In Silico ◽  
Structural Information ◽  
Chemical Constituents ◽  
Rapid Identification ◽  
Dependent Manner ◽  
Modeling Tools ◽  
Clinical Experiments

Since the emergence of the SARS-CoV-2 pandemic in 2019, it has remained a significant global threat, especially with the newly evolved variants. Despite the presence of different COVID-19 vaccines, the discovery of proper antiviral therapeutics is an urgent necessity. Nature is considered as a historical trove for drug discovery, especially in global crises. During our efforts to discover potential anti-SARS CoV-2 natural therapeutics, screening our in-house natural products and plant crude extracts library led to the identification of C. benedictus extract as a promising candidate. To find out the main chemical constituents responsible for the extract’s antiviral activity, we utilized recently reported SARS CoV-2 structural information in comprehensive in silico investigations (e.g., ensemble docking and physics-based molecular modeling). As a result, we constructed protein–protein and protein–compound interaction networks that suggest cnicin as the most promising anti-SARS CoV-2 hit that might inhibit viral multi-targets. The subsequent in vitro validation confirmed that cnicin could impede the viral replication of SARS CoV-2 in a dose-dependent manner, with an IC50 value of 1.18 µg/mL. Furthermore, drug-like property calculations strongly recommended cnicin for further in vivo and clinical experiments. The present investigation highlighted natural products as crucial and readily available sources for developing antiviral therapeutics. Additionally, it revealed the key contributions of bioinformatics and computer-aided modeling tools in accelerating the discovery rate of potential therapeutics, particularly in emergency times like the current COVID-19 pandemic.

scholarly journals In silicoidentification of novel peptides with antibacterial activity against multidrug resistantStaphylococcus aureus

2019 ◽  
Author(s):  
Linda B Oyama ◽  
Hamza Olleik ◽  
Ana Carolina Nery Teixeira ◽  
Matheus M Guidini ◽  
James A Pickup ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Antimicrobial Peptides ◽  
In Silico ◽  
Multidrug Resistant ◽  
Biofilm Inhibition ◽  
Methicillin Resistant ◽  
Drug Candidates ◽  
Wide Range

AbstractHerein we report the identification and characterisation of two linear antimicrobial peptides (AMPs), HG2 and HG4, with activity against a wide range of multidrug resistant (MDR) bacteria, especially methicillin resistantStaphylococcus aureus(MRSA) strains, a highly problematic group of Gram-positive bacteria in the hospital and community environment. To identify the novel AMPs presented here, we employed the classifier model design, a feature extraction method using molecular descriptors for amino acids for the analysis, visualization, and interpretation of AMP activities from a rumen metagenomic dataset. This allowed for thein silicodiscrimination of active and inactive peptides in order to define a small number of promising novel lead AMP test candidates for chemical synthesis and experimental evaluation.In vitrodata suggest that the chosen AMPs are fast acting, show strong biofilm inhibition and dispersal activity and are efficacious in anin vivomodel of MRSA USA300 infection, whilst showing little toxicity to human erythrocytes and human primary cell linesex vivo. Observations from biophysical AMP-lipid-interactions and electron microscopy suggest that the newly identified peptides interact with the cell membrane and may be involved in the inhibition of other cellular processes. Amphiphilic conformations associated with membrane disruption are also observed in 3D molecular modelling of the peptides. HG2 and HG4 both preferentially bind to MRSA total lipids rather than with human cell lipids indicating that HG4 may form superior templates for safer therapeutic candidates for MDR bacterial infections.Author SummaryWe are losing our ability to treat multidrug resistant (MDR) bacteria, otherwise known as superbugs. This poses a serious global threat to human health as bacteria are increasingly acquiring resistance to antibiotics. There is therefore urgent need to intensify our efforts to develop new safer alternative drug candidates. We emphasise the usefulness of complementing wet-lab andin silicotechniques for the rapid identification of new drug candidates from environmental samples, especially antimicrobial peptides (AMPs). HG2 and HG4, the AMPs identified in our study show promise as effective therapies for the treatment of methicillin resistantStaphylococcus aureusinfections bothin vitroandin vivowhilst having little cytotoxicity against human primary cells, a step forward in the fight against MDR infections.

scholarly journals Selection of safe artemisinin derivatives using a machine learning-based cardiotoxicity platform and in vitro and in vivo validation

2021 ◽  
Author(s):  
Onat Kadioglu ◽  
Sabine M. Klauck ◽  
Edmond Fleischer ◽  
Letian Shan ◽  
Thomas Efferth
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Artemisia Annua ◽  
Reproductive Toxicity ◽  
Toxicity Prediction ◽  
Artemisinin Derivatives ◽  
Drug Candidates

AbstractThe majority of drug candidates fails the approval phase due to unwanted toxicities and side effects. Establishment of an effective toxicity prediction platform is of utmost importance, to increase the efficiency of the drug discovery process. For this purpose, we developed a toxicity prediction platform with machine-learning strategies. Cardiotoxicity prediction was performed by establishing a model with five parameters (arrhythmia, cardiac failure, heart block, hypertension, myocardial infarction) and additional toxicity predictions such as hepatotoxicity, reproductive toxicity, mutagenicity, and tumorigenicity are performed by using Data Warrior and Pro-Tox-II software. As a case study, we selected artemisinin derivatives to evaluate the platform and to provide a list of safe artemisinin derivatives. Artemisinin from Artemisia annua was described first as an anti-malarial compound and later its anticancer properties were discovered. Here, random forest feature selection algorithm was used for the establishment of cardiotoxicity models. High AUC scores above 0.830 were achieved for all five cardiotoxicity indications. Using a chemical library of 374 artemisinin derivatives as a case study, 7 compounds (deoxydihydro-artemisinin, 3-hydroxy-deoxy-dihydroartemisinin, 3-desoxy-dihydroartemisinin, dihydroartemisinin-furano acetate-d3, deoxyartemisinin, artemisinin G, artemisinin B) passed the toxicity filtering process for hepatotoxicity, mutagenicity, tumorigenicity, and reproductive toxicity in addition to cardiotoxicity. Experimental validation with the cardiomyocyte cell line AC16 supported the findings from the in silico cardiotoxicity model predictions. Transcriptomic profiling of AC16 cells upon artemisinin B treatment revealed a similar gene expression profile as that of the control compound, dexrazoxane. In vivo experiments with a Zebrafish model further substantiated the in silico and in vitro data, as only slight cardiotoxicity in picomolar range was observed. In conclusion, our machine-learning approach combined with in vitro and in vivo experimentation represents a suitable method to predict cardiotoxicity of drug candidates.

Antimicrobial peptides for the treatment of pulmonary tuberculosis, allies or foes?

2018 ◽  
Vol 24 (10) ◽  
pp. 1138-1147
Author(s):  
Bruno Rivas-Santiago ◽  
Flor Torres-Juarez
Keyword(s):  
Pulmonary Tuberculosis ◽  
Antimicrobial Peptides ◽  
Experimental Models ◽  
New Drugs ◽  
World Health ◽  
Public Health Issue ◽  
Health Organization ◽  
Drug Resistant Strains

Tuberculosis is an ancient disease that has become a serious public health issue in recent years, although increasing incidence has been controlled, deaths caused by Mycobacterium tuberculosis have been accentuated due to the emerging of multi-drug resistant strains and the comorbidity with diabetes mellitus and HIV. This situation is threatening the goals of World Health Organization (WHO) to eradicate tuberculosis in 2035. WHO has called for the creation of new drugs as an alternative for the treatment of pulmonary tuberculosis, among the plausible molecules that can be used are the Antimicrobial Peptides (AMPs). These peptides have demonstrated remarkable efficacy to kill mycobacteria in vitro and in vivo in experimental models, nevertheless, these peptides not only have antimicrobial activity but also have a wide variety of functions such as angiogenesis, wound healing, immunomodulation and other well-described roles into the human physiology. Therapeutic strategies for tuberculosis using AMPs must be well thought prior to their clinical use; evaluating comorbidities, family history and risk factors to other diseases, since the wide function of AMPs, they could lead to collateral undesirable effects.

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