AI4AMP: an Antimicrobial Peptide Predictor Using Physicochemical Property-Based Encoding Method and Deep Learning

Lin, Tzu-Tang; Yang, Li-Yen; Lu, I-Hsuan; Cheng, Wen-Chih; Hsu, Zhe-Ren; Chen, Shu-Hwa; Lin, Chung-Yen

doi:10.1128/msystems.00299-21

Cited by 50 publications

(41 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The early study by Fjell et al [ 114 ] used QSAR descriptors and fed them to an artificial neural network that predicted peptide activity against P. aeruginosa , and achieved 94% accuracy in identifying highly active peptides. Since then, many DL methods have been proposed for predicting AMps [ 80 , 89 , 100 , 114 , 115 , 116 , 117 , 118 , 119 ].…”

Section: Amp Prediction By Deep Learningmentioning

confidence: 99%

“…Lin et al [ 119 ] proposed AI4AMP trained on sequences encoded with a combined physicochemical property matrix called PC6. The properties, namely hydrophobicity, volume of side chains, polarity, pH at the isoelectric point, pKa, and the net charge index of side chains, were selected from six clusters of properties based on the result of hierarchical clustering.…”

Section: Amp Prediction By Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

et al. 2022

View full text Add to dashboard Cite

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

show abstract

Section: Amp Prediction By Deep Learningmentioning

confidence: 99%

Section: Amp Prediction By Deep Learningmentioning

confidence: 99%

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The authors concluded that an ideal encoding DL-based model should consider protein features, such as the physicochemical properties of peptides. Good analysis and choice of parameters lead to a better performance of the prediction software, thus reducing the loss of information when encoding peptides for further processing [ 21 ].…”

Section: Presentmentioning

confidence: 99%

Deep Learning and Antibiotic Resistance

et al. 2022

View full text Add to dashboard Cite

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.

show abstract

“…According to the results, authors ultimately found that the dose of 4.5×10 6 CFU/mice of E. coli infection. Finally, the bacteria were administered to mice at 4.5×10 6 CFU/mice in the experiment.…”

Section: Antimicrobial Peptide Synthesis Mpx (H-inwk-giaamakkll-nh2mentioning

confidence: 99%

“…Antimicrobial peptides are molecules made up of amino acids produced by the body, they are an important part of the body's immune system (Vimberg et al, 2022). Peptides have low molecular weight, broad antibacterial spectrum, and endurance to resistance (Lin et al, 2021). Peptide molecules consist of 12 to 100 amino acids (Sudadech et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Effect of Oral Administration of the Antibacterial Peptide MPX on Intestinal Inflammation of Mice in Experimental Infection with Escherichia Coli Strain O157: H7

Zhao

Fotina

Фотіна

et al. 2022

Scientific Horizons

View full text Add to dashboard Cite

Escherichia coli is a gram-negative bacterium, an intestinal pathogen that can cause intestinal inflammation. Antimicrobial peptides are a class of small molecule peptides, which has good antibacterial activity against a variety of gram-positive and negative bacteria. In this regard, the authors aimed to study the effect of the antimicrobial peptide MPX, which was administered orally, on the intestinal wall of mice infected with the intestinal barrier function, which were infected with E. coli. Synthesis and purification of the antimicrobial peptide MPX (H-INWKGIAAMAKKLL-NH2) was performed by Jier Sheng Hua (Shanghai, China). Mass spectrometry and liquid chromatography (HPLC) were used for this purpose. Escherichia Coli (O157: H7 ATCC43889) was isolated from human faeces in which haemolytic uremic syndrome was reported. Solid LB agar (Solarbio, China) was used to isolate enterohemorrhagic E. coli. The culture was obtained from the Chinese Institute of Veterinary Drug Control (Beijing, China). The results of necropsy found that using of an orally administered MPX could alleviate the damage of E. coli to the liver, spleen, and the lungs were less affected. According to H&E results in case of an orally administered MPX group considerably relieved duodenum and organs on day 7 and day 28. qRT-PCR results showed orally administered MPX could reduce the inflammation-related factors in the mRNA expression of IL-2 and IL-6 and TNF-α on day 7 and day 28. In addition, orally administered MPX could significantly increase them RNA expression of tight junction proteins Occludin and Zo-1 on day 7 and day 28. The results of immune histochemistry further showed that an orally administered MPX could increase the mRNA expression of MUC2 in jejunum. The above results showed that orally administered MPX could alleviate the attack of E. coli on the intestinal tract of mice, relieve intestinal inflammation, and improve the intestinal barrier function. This study lays a theoretical foundation for adding antimicrobial peptides to food. In orally administered MPX authors can see reducing the mRNA expression of inflammation-related factors, thereby alleviating the intestinal inflammation caused by E. coli infection in mice. Authors can add that orally administered MPX shows an increase in mRNA expression of tight junction protein in intestines and improves the intestinal barrier function. This study lays the foundation for adding antimicrobial peptides to food to relieve inflammation and improve barrier function in clinical practices

show abstract

AI4AMP: an Antimicrobial Peptide Predictor Using Physicochemical Property-Based Encoding Method and Deep Learning

Cited by 50 publications

References 25 publications

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Deep Learning and Antibiotic Resistance

The Effect of Oral Administration of the Antibacterial Peptide MPX on Intestinal Inflammation of Mice in Experimental Infection with Escherichia Coli Strain O157: H7

Contact Info

Product

Resources

About