P450-Catalyzed Intramolecular sp³ C–H Amination with Arylsulfonyl Azide Substrates

The direct amination of aliphatic C–H bonds represents a most valuable transformation in organic chemistry. While a number of transition-metal-based catalysts have been developed and investigated for this purpose, the possibility to execute this transformation with biological catalysts has remained largely unexplored. Here, we report that cytochrome P450 enzymes can serve as efficient catalysts for mediating intramolecular benzylic C–H amination reactions in a variety of arylsulfonyl azide compouds. Under optimized conditions, the P450 catalysts were found to support up to 390 total turnovers leading to the formation of the desired sultam products with excellent regioselectivity. In addition, the chiral environment provided by the enzyme active site allowed for the reaction to proceed in a stereo- and enantioselective manner. The C–H amination activity, substrate profile, and enantio/stereoselectivity of these catalysts could be modulated by utilizing enzyme variants with engineered active sites

Rhodium(II)-Catalyzed Undirected and Selective C(sp²)–H Amination en Route to Benzoxazolones

Rhodium­(II) can effectively promote the activation and cyclization of arylcarbamate substrates to yield benzoxazolones via an intramolecular nitrene C–H insertion reaction. Investigation of the substrate scope shows that the reaction undergoes selective aromatic C­(sp2)H amination over more labile o-C­(sp3)H bonds. Observation of inverse secondary KIE (PH/PD = 0.42 ± 0.03) indicates involvement of aromatic electrophilic substitution mechanism for this aryl C–H amidation transformation

Access to Sterically Hindered Thioethers (α-Thioamides) Under Mild Conditions Using α‑Halohydroxamates: Application toward 1,4-Benzothiazinones and 4,1-Benzothiazepinones

Herein, we report a new and highly efficient approach for synthesizing congested α-thioamides under mild reaction conditions (mild base, room temperature, and short duration) using α-halo hydroxamates as direct alkylating agents. The reaction works well with both (hetero)aryl and alkyl thiols, tolerating a broad functional group and diverse substrate scope, including benzeneselenol for selenoether construction. The strategy enables efficient synthesis of biologically relevant 1,4 benzothiazinone and 4,1-benzothiazepinone cores, along with various other functionalized sulfur-based scaffolds of biological importance

Enzymatic C(sp³)‑H Amination: P450-Catalyzed Conversion of Carbonazidates into Oxazolidinones

Cytochrome P450 enzymes can effectively promote the activation and cyclization of carbonazidate substrates to yield oxazolidinones via an intramolecular nitrene C–H insertion reaction. Investigation of the substrate scope shows that while benzylic/allylic C–H bonds are most readily aminated by these biocatalysts, stronger, secondary C–H bonds are also accessible to functionalization. Leveraging this “non-native” reactivity and assisted by fingerprint-based predictions, improved active-site variants of the bacterial P450 CYP102A1 could be identified to mediate the aminofunctionalization of two terpene natural products with high regio- and stereoselectivity. Mechanistic studies and KIE experiments show that the C–H activation step in these reactions is rate-limiting and proceeds in a stepwise manner, namely, via hydrogen atom abstraction followed by radical recombination. This study expands the reactivity scope of P450-based catalysts in the context of nitrene transfer transformations and provides first-time insights into the mechanism of P450-catalyzed C–H amination reactions

Metal- and Oxidant-Free Modular Approach To Access <i>N</i>‑Alkoxy Oxindoles via Aryne Annulation

An unprecedented metal- and oxidant-free (intermolecular) approach to access <i>N</i>-alkoxy oxindoles via [3 + 2] cycloadition of <i>in situ</i> generated electrophilic species <i>viz</i>. aryne and (putative) aza-oxyallyl cation is reported. This approach is amenable to both C3-unsubstituted as well as C3-substituted oxindoles. A one-pot manipulation further makes this reaction highly practical. The versatility of this approach was demonstrated through valuable synthetic transformations

Table_3_In-Silico Tool for Predicting, Scanning, and Designing Defensins.xlsx

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service “DefPred” to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.</p

Table_4_In-Silico Tool for Predicting, Scanning, and Designing Defensins.xlsx

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service “DefPred” to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.</p

Table_2_In-Silico Tool for Predicting, Scanning, and Designing Defensins.xlsx

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service “DefPred” to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.</p

Table_1_In-Silico Tool for Predicting, Scanning, and Designing Defensins.xlsx

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service “DefPred” to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.</p

Heteroarylation of Congested α‑Bromoamides with Imidazo-Heteroarenes and Indolizines via Aza-Oxyallyl Cations: Enroute to Dibenzoazepinone and Zolpidem Analogues

Herein, we report a highly efficient and unprecedented approach for heteroarylation of congested α-bromoamides via electrophilic aromatic substitution of imidazo-heteroarenes and indolizines under mild reaction conditions (room temperature, metal, and oxidant free). The participation of an in situ generated aza-oxyallyl cation as an alkylating agent is the hallmark of this transformation. The method was readily adapted to synthesize novel imidazo-heteroarene-fused dibenzoazepinone architectures of potential medicinal value