Machine Learning Prediction of Antimicrobial Peptides

Wang, Guangshun; Vaisman, Iosif I.; Hoek, Monique L. van

doi:10.1007/978-1-0716-1855-4_1

Cited by 55 publications

(46 citation statements)

References 161 publications

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“…It is predicted that DDIP1 is effective against a variety of mutated viral strains, since it may damage viral envelopes to prevent infection in a similar manner to disrupt bacterial membranes. Our discovery of novel antiviral peptides may be further accelerated by applying the machine learning/artificial intelligence algorithms [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ] that enable the prediction of both the antiviral activity and toxicity of peptides with the accumulation of experimental data of AMPs.…”

Section: Discussionmentioning

confidence: 99%

Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses

Verma

et al. 2022

Pharmaceuticals

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The rapid mutations of viruses such as SARS-CoV-2 require vaccine updates and the development of novel antiviral drugs. This article presents an improved database filtering technology for a more effective design of novel antiviral agents. Different from the previous approach, where the most probable parameters were obtained stepwise from the antimicrobial peptide database, we found it possible to accelerate the design process by deriving multiple parameters in a single step during the peptide amino acid analysis. The resulting peptide DFTavP1 displays the ability to inhibit Ebola virus. A deviation from the most probable peptide parameters reduces antiviral activity. The designed peptides appear to block viral entry. In addition, the amino acid signature provides a clue to peptide engineering to gain cell selectivity. Like human cathelicidin LL-37, our engineered peptide DDIP1 inhibits both Ebola and SARS-CoV-2 viruses. These peptides, with broad antiviral activity, may selectively disrupt viral envelopes and offer the lasting efficacy required to treat various RNA viruses, including their emerging mutants.

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Section: Discussionmentioning

confidence: 99%

Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses

Verma

et al. 2022

Pharmaceuticals

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“…Previous studies have shown that MLs are an important feature of databases, especially in the CAMP database, which contains all of the above MLs algorithms for the prediction and design of AMPs [ 77 ], whereas only parameter spaces and thresholds or cut-off discriminator algorithms are embedded in the APD and DBAASP databases, respectively [ 66 , 67 ]. Many specific AMP databases, such as those for linear cationic AMPs (LCAP), hemolytic and non-hemolytic AMPs, and anti-Gram-negative peptides (PHNX), combining ML algorithms have been established [ 85 , 86 , 87 , 88 , 89 , 90 ]. For example, the ML algorithms integrated with ANtiBP2, Hemdytik, and DASamp1 are called ANN and DNN [ 85 , 86 ].…”

Section: ML Methods Of the Four Amp Databasesmentioning

confidence: 99%

Thinking on the Construction of Antimicrobial Peptide Databases: Powerful Tools for the Molecular Design and Screening

Zhang

Teng

Mao

et al. 2023

IJMS

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With the accelerating growth of antimicrobial resistance (AMR), there is an urgent need for new antimicrobial agents with low or no AMR. Antimicrobial peptides (AMPs) have been extensively studied as alternatives to antibiotics (ATAs). Coupled with the new generation of high-throughput technology for AMP mining, the number of derivatives has increased dramatically, but manual running is time-consuming and laborious. Therefore, it is necessary to establish databases that combine computer algorithms to summarize, analyze, and design new AMPs. A number of AMP databases have already been established, such as the Antimicrobial Peptides Database (APD), the Collection of Antimicrobial Peptides (CAMP), the Database of Antimicrobial Activity and Structure of Peptides (DBAASP), and the Database of Antimicrobial Peptides (dbAMPs). These four AMP databases are comprehensive and are widely used. This review aims to cover the construction, evolution, characteristic function, prediction, and design of these four AMP databases. It also offers ideas for the improvement and application of these databases based on merging the various advantages of these four peptide libraries. This review promotes research and development into new AMPs and lays their foundation in the fields of druggability and clinical precision treatment.

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“…The biopeptides from toxins, ribosomally and post-translationally synthesized peptides, open new ventures for severe respiratory syndrome (corona) viruses ( Behsaz et al, 2021 ). New computational tools and databases are becoming very helpful in prediction and development of new best candidate peptide drugs, reducing costs and time before the in vitro experiments ( Ramazi et al, 2022 ; Wang G et al, 2022 ).…”

Section: Landscape Of Opportunities: Topical Insightsmentioning

confidence: 99%

Smart therapies against global pandemics: A potential of short peptides

et al. 2022

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BackgroundAs we entered the third year of this pandemic, since the World Health Organisation (WHO) declared in March 2020 the novel coronavirus severe acute respiratory syndrome (SARS-CoV2) outbreak as a global pandemic COVID-19 (COronaVIrus Disease 19), we are still fighting with newer and newer viral mutations. The pandemic has passed the grim milestone of over 6.4 million COVID19 deaths, from more than 550 million reported cases thus far. In fact, more than 15 million people can die by the end of this year. It is highly likely that this pandemic will become endemic, while the full evolutionary potential of coronaviruses has yet to be revealed. The next pandemic is coming. A microbe with features of SARS-Middle East respiratory syndrome (MERS) and SARS-CoV-2 could lead to significantly more catastrophic loss of life. The co-evolution with other viruses should not be neglected. According to the WHO, we should expect diverse zoonotic, outbreakprone microbes, including highly pathogenic strains of influenza, Nipah, Ebola, Zika, or hemorrhagic fever viruses. 'It's an evolutionary certainty that there will be another virus with the potential to be more transmittable and deadly than this one', said Tedros Adhanom Ghebreyesus, director-general of the WHO. On the other hand, in both poor countries and regions of armed conflict, where vaccination is hampered, historic diseases are re-emerging, with migration and displacement influencing transmission risk and limiting control, and raising potential for additional outbreaks. Furthermore, there are other looming terrible threats to humanity as damaging as the bubonic plagues, such as bioterrorism or antibiotic resistant microorganisms. In most cases, both effective prevention and treatment options are limited.

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Machine Learning Prediction of Antimicrobial Peptides

Cited by 55 publications

References 161 publications

Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses

Improved Database Filtering Technology Enables More Efficient Ab Initio Design of Potent Peptides against Ebola Viruses

Thinking on the Construction of Antimicrobial Peptide Databases: Powerful Tools for the Molecular Design and Screening

Smart therapies against global pandemics: A potential of short peptides

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