New targets for an old drug

Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

Biosynthesis-a process utilizing biological systems to synthesize chemical compounds-has emerged as a revolutionary solution to 21st-century challenges due to its environmental sustainability, scalability, and high stereoselectivity and regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, and optimization of enzymatic reactions and biological systems. We first introduce the molecular retrosynthesis route planning in biochemical pathway design, including single-step retrosynthesis algorithms and AI-based chemical retrosynthesis route design tools. We highlight the advantages and challenges of large language models in addressing the sparsity of chemical data. Furthermore, we review enzyme discovery methods based on sequence and structure alignment techniques. Breakthroughs in AI-based structural prediction methods are expected to significantly improve the accuracy of enzyme discovery. We also summarize methods for de novo enzyme generation for nonnatural or orphan reactions, focusing on AI-based enzyme functional annotation and enzyme discovery techniques based on reaction or small molecule similarity. Turning to enzyme engineering, we discuss strategies to improve enzyme thermostability, solubility, and activity, as well as the applications of AI in these fields. The shift from traditional experiment-driven models to data-driven and computationally driven intelligent models is already underway. Finally, we present potential challenges and provide a perspective on future research directions. We envision expanded applications of biocatalysis in drug development, green chemistry, and complex molecule synthesis.

Pharmacogenetic studies of thiopurine methyltransferase genotype-phenotype concordance and effect of methotrexate on thiopurine metabolism

The discovery and implementation of thiopurine methyltransferase (TPMT) pharmacogenetics has been a success story and has reduced the suffering from serious adverse reactions during thiopurine treatment of childhood leukaemia and inflammatory bowel disease. This MiniReview summarizes four studies included in Dr Zimdahl Kahlin's doctoral thesis as well as the current knowledge on this field of research. The genotype‐phenotype concordance of TPMT in a cohort of 12 663 individuals with clinically analysed TPMT status is described. Notwithstanding the high concordance, the benefits of combined genotyping and phenotyping for TPMT status determination are discussed. The results from the large cohort also demonstrate that the factors of gender and age affect TPMT enzyme activity. In addition, characterization of four previously undescribed TPMT alleles (TPMT*41, TPMT*42, TPMT*43 and TPMT*44) shows that a defective TPMT enzyme could be caused by several different mechanisms. Moreover, the folate analogue methotrexate (MTX), used in combination with thiopurines during maintenance therapy of childhood leukaemia, affects the metabolism of thiopurines and interacts with TPMT, not only by binding and inhibiting the enzyme activity but also by regulation of its gene expression.

Docking-based inverse virtual screening: methods, applications, and challenges

Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug-target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering.