Mining the Protein Data Bank to inspire fragment library design

Imbernon, Julia Revillo; Chiesa, Luca; Kellenberger, Esther

doi:10.3389/fchem.2023.1089714

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…As an example of its pleiotropic mechanism of action, RBV was recently identified as a multiple bonding-mode fragment in a computational fragment drug design approach. [66] It is reasonable to hypothesize that all such nucleoside RBV analogs, including showdomycin and pyrazofurin, also act by pleiotropic mechanisms since they also revealed broad-spectrum activities. Regarding 1,2,3-triazolyl-N-ribosides, they show antiviral activities against several types of viruses [30,67] and have been extensively studied for biological properties.…”

Section: Biological Activitymentioning

confidence: 99%

Developments in the chemistry and biology of 1,2,3‐triazolyl‐C‐nucleosides

Nascimento,

Ferry,

Gallier

et al. 2023

Archiv der Pharmazie

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In the last 50 years, nucleoside analogs have been introduced to drug therapy as antivirals for different types of cancer due to their interference in cellular proliferation. Among the first line of nucleoside treatment drugs, ribavirin (RBV) is a synthetic N‐nucleoside with a 1,2,4‐triazole moiety that acts as a broad‐spectrum antiviral. It is on the World Health Organization (WHO) list of essential medicines. However, this important drug therapy causes several side effects due to its nonspecific mechanism of action. There is thus a need for a continuous study of its scaffold. A particular approach consists of connecting d‐ribose to the nitrogen‐containing base with a C–C bond. It provides more stability against enzymatic action and a better pharmacologic profile. The coronavirus disease (COVID) pandemic has increased the need for more solutions for the treatment of viral infections. Among these solutions, remdesivir, the first C‐nucleoside, has been approved by the Food and Drug Administration (FDA) for clinical use against severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). It drew attention to the study of the C‐nucleoside scaffold. Different C‐nucleoside patterns have been synthesized over the years. They show many important activities against viruses and cancer cell lines. 1,2,3‐Triazolyl‐C‐nucleoside derivatives are a prolific and efficient subclass of RBV analogs close to the already‐known RBV with a C–C bond modification. These compounds are often prepared by alkynylation of the d‐ribose ring followed by azide‐alkyne cycloaddition. They are reported to be active against the Crimean‐Congo hemorrhagic fever virus and several tumoral cell lines, showing promising biological potential. In this review, we explore such approaches to 1,2,3‐triazolyl‐C‐nucleosides and their evolution over the years.

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Section: Biological Activitymentioning

confidence: 99%

Developments in the chemistry and biology of 1,2,3‐triazolyl‐C‐nucleosides

Nascimento,

Ferry,

Gallier

et al. 2023

Archiv der Pharmazie

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A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions

Moiani,

Tainer

2024

Front. Mol. Biosci.

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While many researchers can design knockdown and knockout methodologies to remove a gene product, this is mainly untrue for new chemical inhibitor designs that empower multifunctional DNA Damage Response (DDR) networks. Here, we present a robust Goldilocks (GL) computational discovery protocol to efficiently innovate inhibitor tools and preclinical drug candidates for cellular and structural biologists without requiring extensive virtual screen (VS) and chemical synthesis expertise. By computationally targeting DDR replication and repair proteins, we exemplify the identification of DDR target sites and compounds to probe cancer biology. Our GL pipeline integrates experimental and predicted structures to efficiently discover leads, allowing early-structure and early-testing (ESET) experiments by many laboratories. By employing an efficient VS protocol to examine protein-protein interfaces (PPIs) and allosteric interactions, we identify ligand binding sites beyond active sites, leveraging in silico advances for molecular docking and modeling to screen PPIs and multiple targets. A diverse 3,174 compound ESET library combines Diamond Light Source DSI-poised, Protein Data Bank fragments, and FDA-approved drugs to span relevant chemotypes and facilitate downstream hit evaluation efficiency for academic laboratories. Two VS per library and multiple ranked ligand binding poses enable target testing for several DDR targets. This GL library and protocol can thus strategically probe multiple DDR network targets and identify readily available compounds for early structural and activity testing to overcome bottlenecks that can limit timely breakthrough drug discoveries. By testing accessible compounds to dissect multi-functional DDRs and suggesting inhibitor mechanisms from initial docking, the GL approach may enable more groups to help accelerate discovery, suggest new sites and compounds for challenging targets including emerging biothreats and advance cancer biology for future precision medicine clinical trials.

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Applications of reinforcement learning, machine learning, and virtual screening in SARS-CoV-2-related proteins

Matsuzaka,

Yashiro

2024

Comput. Artif. Intell.

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Similarly, to all coronaviruses, SARS-CoV-2 uses the S glycoprotein to enter host cells, which contains two functional domains: S1 and S2 receptor binding domain (RBD). Angiotensin-converting enzyme 2 (ACE2) is recognizable by the S proteins on the surface of the SARS-CoV-2 virus. The SARS-CoV-2 virus causes SARS, but some mutations in the RBD of the S protein markedly enhance their binding affinity to ACE2. Searching for new compounds in COVID-19 is an important initial step in drug discovery and materials design. Still, the problem is that this search requires trial-and-error experiments, which are costly and time-consuming. In the automatic molecular design method based on deep reinforcement learning, it is possible to design molecules with optimized physical properties by combining a newly devised coarse-grained representation of molecules with deep reinforcement learning. Also, structured-based virtual screening uses protein 3D structure information to evaluate the binding affinity between proteins and compounds based on physicochemical interactions such as van der Waals forces, Coulomb forces, and hydrogen bonds, and select drug candidate compounds. In addition, AlphaFold can predict 3D protein structures, given the amino acid sequence, and the protein building blocks. Ensemble docking, in which multiple protein structures are generated using the molecular dynamics method and docking calculations are performed for each, is often performed independently of docking calculations. In the future, the AlphaFold algorithm can be used to predict various protein structures related to COVID-19.

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Mining the Protein Data Bank to inspire fragment library design

Cited by 3 publications

References 48 publications

Developments in the chemistry and biology of 1,2,3‐triazolyl‐C‐nucleosides

Developments in the chemistry and biology of 1,2,3‐triazolyl‐C‐nucleosides

A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions

Applications of reinforcement learning, machine learning, and virtual screening in SARS-CoV-2-related proteins

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