{"title":"新生成的针对COVID-19靶点的肽","authors":"Allison M. Rossetto, Wenjin Zhou","doi":"10.1145/3388440.3414919","DOIUrl":null,"url":null,"abstract":"With the world in the midst of a global pandemic, it is important to be able to quickly generate new drug-like compounds for drug research purposes. While some successful work has been done [3, 6] there is still much work to be done, especially as viruses like Coronavirus are notoriously hard to treat. Since peptide drugs are generally better at blocking protein-protein interactions than small molecule drugs [5], something important in anti-viral work, we will use our GANDALF methodology to generate new peptides to interact with targets of interest. Here we are working with two important COVID-19 targets: the SARS-CoV-2 main protease (M[Pro]) and the andangiotensin-converting enzyme 2 (ACE2). Covid-19 is able to enter human cells via interaction between its spike protein and ACE2 and, once in the cell, MPro breaks down polyproteins to create more of the virus [1]. We have generated peptides for each of our targets using our GANDALF (Generative Adversarial Network Drug-tArget Ligand Fructifier) methodology [4]. We compare our generated peptides with a previously discovered novel ACE2 inhibitor [2]. We also compare our results for MPro with a recently publish small molecule based on α-ketoamide inhibitors recently developed as a drug lead [7]. Our best generated peptide for ACE2 is a small, six residue peptide [SSNATV]. This peptide has a binding affinity of --29.880. The novel, peptide inhibitor previously designed has a binding affinity of -19.843. Our generated peptide has a lower binding affinity, which is generally more desirable and indicates more stable binding. However, the novel inhibitor is larger at 26 peptides and may be more suitable for use without the need for too many additional modifications. Our peptide though is a good starting place for further improvements and optimization. The binding affinity for our best generated peptide of MPro is --41.038. This peptide has a size of eleven residues [WWTWTPFHLLV]. Our peptide has a similar binding affinity to that of the small molecule, α-ketoamide based inhibitor is --5.501. Not only does our peptide have a better binding affinity, but as a peptide, it has the added advantage of being better able to disrupt the activity of the MPro than the small molecule inhibitor. It is also encouraging that our binding affinity for our best MPro generated peptide is comparable to the best available compounds. Peptide based drugs are an important part of viral treatment. Our work here provides reasonable starting peptides for further drug research and development.","PeriodicalId":411338,"journal":{"name":"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"120 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Novel Generated Peptides for COVID-19 Targets\",\"authors\":\"Allison M. Rossetto, Wenjin Zhou\",\"doi\":\"10.1145/3388440.3414919\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the world in the midst of a global pandemic, it is important to be able to quickly generate new drug-like compounds for drug research purposes. While some successful work has been done [3, 6] there is still much work to be done, especially as viruses like Coronavirus are notoriously hard to treat. Since peptide drugs are generally better at blocking protein-protein interactions than small molecule drugs [5], something important in anti-viral work, we will use our GANDALF methodology to generate new peptides to interact with targets of interest. Here we are working with two important COVID-19 targets: the SARS-CoV-2 main protease (M[Pro]) and the andangiotensin-converting enzyme 2 (ACE2). Covid-19 is able to enter human cells via interaction between its spike protein and ACE2 and, once in the cell, MPro breaks down polyproteins to create more of the virus [1]. We have generated peptides for each of our targets using our GANDALF (Generative Adversarial Network Drug-tArget Ligand Fructifier) methodology [4]. We compare our generated peptides with a previously discovered novel ACE2 inhibitor [2]. We also compare our results for MPro with a recently publish small molecule based on α-ketoamide inhibitors recently developed as a drug lead [7]. Our best generated peptide for ACE2 is a small, six residue peptide [SSNATV]. This peptide has a binding affinity of --29.880. The novel, peptide inhibitor previously designed has a binding affinity of -19.843. Our generated peptide has a lower binding affinity, which is generally more desirable and indicates more stable binding. However, the novel inhibitor is larger at 26 peptides and may be more suitable for use without the need for too many additional modifications. Our peptide though is a good starting place for further improvements and optimization. The binding affinity for our best generated peptide of MPro is --41.038. This peptide has a size of eleven residues [WWTWTPFHLLV]. Our peptide has a similar binding affinity to that of the small molecule, α-ketoamide based inhibitor is --5.501. Not only does our peptide have a better binding affinity, but as a peptide, it has the added advantage of being better able to disrupt the activity of the MPro than the small molecule inhibitor. It is also encouraging that our binding affinity for our best MPro generated peptide is comparable to the best available compounds. Peptide based drugs are an important part of viral treatment. Our work here provides reasonable starting peptides for further drug research and development.\",\"PeriodicalId\":411338,\"journal\":{\"name\":\"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics\",\"volume\":\"120 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3388440.3414919\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3388440.3414919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With the world in the midst of a global pandemic, it is important to be able to quickly generate new drug-like compounds for drug research purposes. While some successful work has been done [3, 6] there is still much work to be done, especially as viruses like Coronavirus are notoriously hard to treat. Since peptide drugs are generally better at blocking protein-protein interactions than small molecule drugs [5], something important in anti-viral work, we will use our GANDALF methodology to generate new peptides to interact with targets of interest. Here we are working with two important COVID-19 targets: the SARS-CoV-2 main protease (M[Pro]) and the andangiotensin-converting enzyme 2 (ACE2). Covid-19 is able to enter human cells via interaction between its spike protein and ACE2 and, once in the cell, MPro breaks down polyproteins to create more of the virus [1]. We have generated peptides for each of our targets using our GANDALF (Generative Adversarial Network Drug-tArget Ligand Fructifier) methodology [4]. We compare our generated peptides with a previously discovered novel ACE2 inhibitor [2]. We also compare our results for MPro with a recently publish small molecule based on α-ketoamide inhibitors recently developed as a drug lead [7]. Our best generated peptide for ACE2 is a small, six residue peptide [SSNATV]. This peptide has a binding affinity of --29.880. The novel, peptide inhibitor previously designed has a binding affinity of -19.843. Our generated peptide has a lower binding affinity, which is generally more desirable and indicates more stable binding. However, the novel inhibitor is larger at 26 peptides and may be more suitable for use without the need for too many additional modifications. Our peptide though is a good starting place for further improvements and optimization. The binding affinity for our best generated peptide of MPro is --41.038. This peptide has a size of eleven residues [WWTWTPFHLLV]. Our peptide has a similar binding affinity to that of the small molecule, α-ketoamide based inhibitor is --5.501. Not only does our peptide have a better binding affinity, but as a peptide, it has the added advantage of being better able to disrupt the activity of the MPro than the small molecule inhibitor. It is also encouraging that our binding affinity for our best MPro generated peptide is comparable to the best available compounds. Peptide based drugs are an important part of viral treatment. Our work here provides reasonable starting peptides for further drug research and development.