Pub Date : 2024-07-06DOI: 10.1007/s41061-024-00467-8
Kevin R. Venrooij, Lucienne de Bondt, Kimberly M. Bonger
Bioorthogonal click chemistry has played a transformative role in many research fields, including chemistry, biology, and medicine. Click reactions are crucial to produce increasingly complex bioconjugates, to visualize and manipulate biomolecules in living systems and for various applications in bioengineering and drug delivery. As biological (model) systems grow more complex, researchers have an increasing need for using multiple orthogonal click reactions simultaneously. In this review, we will introduce the most common bioorthogonal reactions and discuss their orthogonal use on the basis of their mechanism and electronic or steric tuning. We provide an overview of strategies to create reaction orthogonality and show recent examples of mutual orthogonal chemistry used for simultaneous biomolecule labeling. We end by discussing some considerations for the type of chemistry needed for labeling biomolecules in a system of choice.
{"title":"Mutually Orthogonal Bioorthogonal Reactions: Selective Chemistries for Labeling Multiple Biomolecules Simultaneously","authors":"Kevin R. Venrooij, Lucienne de Bondt, Kimberly M. Bonger","doi":"10.1007/s41061-024-00467-8","DOIUrl":"10.1007/s41061-024-00467-8","url":null,"abstract":"<div><p>Bioorthogonal click chemistry has played a transformative role in many research fields, including chemistry, biology, and medicine. Click reactions are crucial to produce increasingly complex bioconjugates, to visualize and manipulate biomolecules in living systems and for various applications in bioengineering and drug delivery. As biological (model) systems grow more complex, researchers have an increasing need for using multiple orthogonal click reactions simultaneously. In this review, we will introduce the most common bioorthogonal reactions and discuss their orthogonal use on the basis of their mechanism and electronic or steric tuning. We provide an overview of strategies to create reaction orthogonality and show recent examples of mutual orthogonal chemistry used for simultaneous biomolecule labeling. We end by discussing some considerations for the type of chemistry needed for labeling biomolecules in a system of choice.</p></div>","PeriodicalId":802,"journal":{"name":"Topics in Current Chemistry","volume":"382 3","pages":""},"PeriodicalIF":8.6,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11227474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141545551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.
{"title":"The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction","authors":"Saber Saharkhiz, Mehrnaz Mostafavi, Amin Birashk, Shiva Karimian, Shayan Khalilollah, Sohrab Jaferian, Yalda Yazdani, Iraj Alipourfard, Yun Suk Huh, Marzieh Ramezani Farani, Reza Akhavan-Sigari","doi":"10.1007/s41061-024-00469-6","DOIUrl":"10.1007/s41061-024-00469-6","url":null,"abstract":"<div><p>In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.</p></div>","PeriodicalId":802,"journal":{"name":"Topics in Current Chemistry","volume":"382 3","pages":""},"PeriodicalIF":8.6,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11224075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141535958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-27DOI: 10.1007/s41061-024-00468-7
Rishav Mazumder, Ichudaule, Ashmita Ghosh, Subrata Deb, Rajat Ghosh
Chalcone is a simple naturally occurring α,β-unsaturated ketone with biological importance, which can also be easily synthesized in laboratories by reaction between two aromatic scaffolds. In plants, chalcones occur as polyphenolic compounds of different frameworks which are bioactive molecules that have been in traditional medicinal practice for many years. Chalcone-based lead molecules have been developed, possessing varied potentials such as antimicrobial, antiviral, anti-inflammatory, anticancer, anti-oxidant, antidiabetic, antihyperurecemic, and anti-ulcer effects. Chalcones contribute considerable fragments to give important heterocyclic molecules with therapeutic utilities targeting various diseases. These characteristic features have made chalcone a topic of interest among researchers and have attracted investigations into this widely applicable structure. This review highlights the extensive exploration carried out on the synthesis, biotransformations, chemical reactions, hybridization, and pharmacological potentials of chalcones, and aims to provide an extensive, thorough, and critical review of their importance, with emphasis on their properties, chemistry, and biomedical applications to boost future investigations into this potential scaffold in medicinal chemistry.