A new Artificial Intelligence-based approach to Antibiotic Discovery

基于人工智能的抗生素发现新方法

• 批准号: 2747723
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2747723
• 关键词: new; Artificial; Intelligence; based; approach

Since the discovery of penicillin, antibiotics have become the foundation of modern medicine. However, the lack of production of new antibiotics in the private sector together with an uncontrolled increase in antibiotic resistance represents a major global public health issue. It is projected that deaths attributable to resistant infections will reach 10 million per year by 2050. Nowadays, a large proportion of biomedical research and the development of therapeutics focuses only on a small fraction of targets for which most lead drug compounds have shown relatively short-term effectiveness.Infections originating from foodborne pathogens are becoming more difficult to treat due to increasing levels of antimicrobial resistance. Rapid and intensive farming practices promote indiscriminate use of broad-spectrum antimicrobials, providing ideal selection pressure for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). The presence of antibiotic-resistant bacteria of animal origin in the human food chain is thus a major global public health issue, with several studies having reported food animals and products being colonized and/or infected and contaminated by antibiotic-resistant strains, such as methicillin-resistant Staphylococcus aureus (MRSA), antibiotic-resistant Campylobacter spp, and extended spectrum-beta-lactamase (ESBL) producing-Enterobacteriaceae (viz. Salmonella spp., Escherichia coli). Cholera is an acute diarrheal infection caused by ingestion of food or water contaminated with the bacterium Vibrio cholerae. Worldwide, 1.3 billion people are estimated to be at risk and approximately 1.3 to 4 million cases occur annually with 21,000 to 143,000 resulting in death. Also, for this bacterium, the indiscriminate use of wide-spectrum antibiotics creates an additional threat represented by the appearance and diffusion of antimicrobial resistance (AMR) profiles in the pathogen population.The aim of this project is to develop an Artificial Intelligence (AI)-based approach to broaden the possibility to discover: (i) a wider range of therapeutic targets and (ii) individual lead drug molecules showing high binding affinity to the identified targets in the resistant bacteria.Research Plan: WP 1: Next-generation sequencing (NGS) bioinformatics analysis, data mining, and statistical modelling powered by machine learning will be used to scan the genomes of different foodborne pathogens (V. cholerae, E. coli, Salmonella, S. aureus, Enterococcus, Campylobacter) to find new genes (new therapeutic targets) associated to AMR. The PhD project will rely and build on data collected on recently awarded (2) GCRF and (2) InnovateUK-China grants by Dottorini. For all these isolates conventional culture-based screening, antibiotic susceptibility tests (AST), and whole-genome DNA sequencing have been carried out so that machine learning can be applied to correlate the genotype (AMR genes) to the phenotype (AMR profiles). WP 2: The identified AMR-associated genes will be validated at the National Biofilms Innovation Centre (NBIC) labs in Nottingham using DNA recombinant techniques and microbiology. WP3: Genes with a clear AMR function will undergo 3D structure modelling and by using deep neural network and ChEMBL, DrugTargetCommons, DrugBank, Broad Institute Drug Repurposing Hub databanks we will expand our antibiotic arsenal by identifying new drugs. WP4. Drug efficiency together with the newly identified drugs will be validated at NBIC using bacteria inhibition growth assays.

自从青霉素被发现以来，抗生素就成为现代医学的基础。然而，私营部门缺乏新抗生素的生产，加上抗生素耐药性不受控制地增加，是一个重大的全球公共卫生问题。据预测，到2050年，每年死于耐药性感染的人数将达到1 000万。如今，大部分生物医学研究和治疗药物的开发仅集中在一小部分靶点上，大多数先导药物化合物已显示出相对短期的有效性。由于抗生素耐药性水平的增加，食源性病原体引起的感染变得越来越难以治疗。快速和集约化的农业实践促进了广谱抗菌剂的滥用，为抗生素抗性细菌（ARB）和抗生素抗性基因（ARG）提供了理想的选择压力。因此，在人类食物链中存在动物来源的耐药性细菌是一个主要的全球公共卫生问题，有几项研究报告了食用动物和产品被耐药性菌株定殖和/或感染和污染，如耐甲氧西林金黄色葡萄球菌（MRSA）、耐药性弯曲杆菌属，和产超广谱β-内酰胺酶（ESBL）的肠杆菌科（即沙门氏菌属，大肠杆菌）。霍乱是一种急性肠道传染病，由摄入被霍乱弧菌污染的食物或水引起。据估计，全世界有13亿人面临风险，每年约有130万至400万例病例，其中21 000至143 000例导致死亡。此外，对于这种细菌，广谱抗生素的滥用造成了额外的威胁，表现为病原体群体中抗菌素耐药性（AMR）谱的出现和扩散。该项目的目的是开发一种基于人工智能（AI）的方法，以扩大发现的可能性：（i）更广泛的治疗靶点和（ii）对耐药细菌中已鉴定的靶点显示出高结合亲和力的单个先导药物分子。下一代测序（NGS）生物信息学分析、数据挖掘和由机器学习驱动的统计建模将用于扫描不同食源性病原体的基因组（霍乱弧菌、大肠杆菌）。大肠杆菌、沙门氏菌、S.金黄色葡萄球菌，肠球菌，弯曲杆菌），以寻找与AMR相关的新基因（新的治疗靶点）。该博士项目将依赖并建立在Dottorini最近授予的（2）GCRF和（2）InnovateUK-China赠款中收集的数据。对于所有这些分离株，已经进行了常规的基于培养物的筛选、抗生素敏感性测试（AST）和全基因组DNA测序，使得机器学习可以应用于将基因型（AMR基因）与表型（AMR谱）相关联。WP 2：已鉴定的AMR相关基因将在诺丁汉的国家生物膜创新中心（NBIC）实验室使用DNA重组技术和微生物学进行验证。WP 3：具有明确AMR功能的基因将进行3D结构建模，通过使用深度神经网络和ChEMBL，DrugTargetCommons，DrugBank，Broad Institute Drug Repurposing Hub数据库，我们将通过识别新药来扩大我们的抗生素库。WP 4.药物效率以及新鉴定的药物将在NBIC使用细菌抑制生长试验进行验证。