AI-Enabled Ultra-large Virtual Screening Identifies Potential Inhibitors of Choline Acetyltransferase for Theranostic Purposes.

IF 4.1 3区 医学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
ACS Chemical Neuroscience Pub Date : 2024-11-20 Epub Date: 2024-10-31 DOI:10.1021/acschemneuro.4c00361
Anurag T K Baidya, Abhinav Kumar Goswami, Bhanuranjan Das, Taher Darreh-Shori, Rajnish Kumar
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Abstract

Alzheimer's disease (AD) and related dementias are among the primary neurological disorders and call for the urgent need for early-stage diagnosis to gain an upper edge in therapeutic intervention and increase the overall success rate. Choline acetyltransferase (ChAT) is the key acetylcholine (ACh) biosynthesizing enzyme and a legitimate target for the development of biomarkers for early-stage diagnosis and monitoring of therapeutic responses. It is also a theranostic target for tackling colon and lung cancers, where overexpression of non-neuronal ChAT leads to the production of acetylcholine, which acts as an autocrine growth factor for cancer cells. Theranostics is a hybrid of diagnostics and therapeutics that can be used to locate cancer cells using radiotracers and kill them without affecting other healthy tissues. Traditional virtual screening protocols have a lot of limitations; given the current rate of chemical database expansion exceeding billions, much faster screening protocols are required. Deep docking (DD) is one such platform that leverages the power of deep neural network (DNN)-based virtual screening, empowering researchers to dock billions of molecules in a speedy, yet explicit manner. Here, we have screened 1.3 billion compounds library from the ZINC20 database, identifying the best-performing hits. With each iteration run where the first iteration gave ∼116 million hits, the second iteration gave ∼3.7 million hits, and the final third iteration gave 168,447 hits from which further refinement gave us the top 5 compounds as potential ChAT inhibitors. The discovery of novel ChAT inhibitors will enable researchers to develop new probes that can be used as novel theranostic agents against cancer and as early-stage diagnostics for the onset of AD, for timely therapeutic intervention to halt the further progression of AD.

人工智能超大规模虚拟筛选确定了用于治疗的潜在胆碱乙酰转移酶抑制剂。
阿尔茨海默病(AD)和相关痴呆症是主要的神经系统疾病之一,迫切需要早期诊断,以便在治疗干预中占据优势并提高总体成功率。胆碱乙酰转移酶(ChAT)是乙酰胆碱(ACh)生物合成的关键酶,也是开发用于早期诊断和监测治疗反应的生物标记物的合法靶点。它也是治疗结肠癌和肺癌的靶点,因为非神经元 ChAT 的过度表达会导致乙酰胆碱的产生,而乙酰胆碱是癌细胞的自分泌生长因子。Theranostics 是诊断和治疗的混合体,可用于使用放射性示踪剂定位癌细胞,并在不影响其他健康组织的情况下杀死它们。传统的虚拟筛选方案有很多局限性;鉴于目前化学数据库的扩展速度已超过数十亿,因此需要更快的筛选方案。深度对接(DD)就是这样一个平台,它利用基于深度神经网络(DNN)的虚拟筛选功能,使研究人员能够以快速而明确的方式对接数十亿分子。在这里,我们对 ZINC20 数据库中的 13 亿个化合物库进行了筛选,找出了表现最好的化合物。每次迭代运行,第一次迭代有 1.16 亿次命中,第二次迭代有 370 万次命中,最后第三次迭代有 168,447 次命中,经过进一步细化,我们从中选出了前 5 个化合物作为潜在的 ChAT 抑制剂。新型 ChAT 抑制剂的发现将使研究人员能够开发出新的探针,这些探针可用作抗癌的新型治疗剂,也可用作注意力缺失症发病的早期诊断,以便及时进行治疗干预,阻止注意力缺失症的进一步发展。
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来源期刊
ACS Chemical Neuroscience
ACS Chemical Neuroscience BIOCHEMISTRY & MOLECULAR BIOLOGY-CHEMISTRY, MEDICINAL
CiteScore
9.20
自引率
4.00%
发文量
323
审稿时长
1 months
期刊介绍: ACS Chemical Neuroscience publishes high-quality research articles and reviews that showcase chemical, quantitative biological, biophysical and bioengineering approaches to the understanding of the nervous system and to the development of new treatments for neurological disorders. Research in the journal focuses on aspects of chemical neurobiology and bio-neurochemistry such as the following: Neurotransmitters and receptors Neuropharmaceuticals and therapeutics Neural development—Plasticity, and degeneration Chemical, physical, and computational methods in neuroscience Neuronal diseases—basis, detection, and treatment Mechanism of aging, learning, memory and behavior Pain and sensory processing Neurotoxins Neuroscience-inspired bioengineering Development of methods in chemical neurobiology Neuroimaging agents and technologies Animal models for central nervous system diseases Behavioral research
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