Study of the Binding Energies between Unnatural Amino Acids and Engineered Orthogonal Tyrosyl-tRNA Synthetases

Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study

The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four (4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( - 9.19 kcal mol^-1) against the target protein NiV G, when compared to the control drug, Ribavirin ( - 6.95 kcal mol^-1). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-Poisson-Boltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( - 218.664 kJ mol^-1) could strongly bind with the target protein NiV G than the control drug Ribavirin ( - 83.812 kJ mol^-1).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03595-y.

Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking

Tea (Camellia sinensis L.) is considered as to be one of the most consumed beverages globally and a reservoir of phytochemicals with immense health benefits. Despite numerous advantages, tea compounds lack a robust multi-disease target study. In this work, we presented a unique in silico approach consisting of molecular docking, multivariate statistics, pharmacophore analysis, and network pharmacology approaches. Eight tea phytochemicals were identified through literature mining, namely gallic acid, catechin, epigallocatechin gallate, epicatechin, epicatechin gallate (ECG), quercetin, kaempferol, and ellagic acid, based on their richness in tea leaves. Further, exploration of databases revealed 30 target proteins related to the pharmacological properties of tea compounds and multiple associated diseases. Molecular docking experiment with eight tea compounds and all 30 proteins revealed that except gallic acid all other seven phytochemicals had potential inhibitory activities against these targets. The docking experiment was validated by comparing the binding affinities (Kcal mol^-1) of the compounds with known drug molecules for the respective proteins. Further, with the aid of the application of statistical tools (principal component analysis and clustering), we identified two major clusters of phytochemicals based on their chemical properties and docking scores (Kcal mol^-1). Pharmacophore analysis of these clusters revealed the functional descriptors of phytochemicals, related to the ligand-protein docking interactions. Tripartite network was constructed based on the docking scores, and it consisted of seven tea phytochemicals (gallic acid was excluded) targeting five proteins and ten associated diseases. Epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex was found to be highest in docking performance (10 kcal mol^-1). Finally, molecular dynamic simulation showed that ECG-1FYR could make a stable complex in the near-native physiological condition.

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine

The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify features which accurately capture these site-specific effects have been unsuccessful, we have developed a set of novel Rosetta Custom Score Functions and alternative Empirical Score Functions that accurately predict the effects of acridon-2-yl-alanine (Acd) incorporation on protein yield and solubility. Acd-containing mutants were simulated in PyRosetta, and machine learning (ML) was performed using either the decomposed values of the Rosetta energy function, or changes in residue contacts and bioinformatics. Using these feature sets, which represent Rosetta score function specific and bioinformatics-derived terms, ML models were trained to predict highly abstract experimental parameters such as mutant protein yield and solubility and displayed robust performance on well-balanced holdouts. Model feature importance analyses demonstrated that terms corresponding to hydrophobic interactions, desolvation, and amino acid angle preferences played a pivotal role in predicting tolerance of mutation to Acd. Overall, this work provides evidence that the application of ML to features extracted from simulated structural models allow for the accurate prediction of diverse and abstract biological phenomena, beyond the predictivity of traditional modeling and simulation approaches.

A general strategy to red-shift green fluorescent protein-based biosensors

Compared with green fluorescent protein-based biosensors, red fluorescent protein (RFP)-based biosensors are inherently advantageous because of reduced phototoxicity, decreased autofluorescence and enhanced tissue penetration. However, existing RFP-based biosensors often suffer from small dynamic ranges, mislocalization and undesired photoconversion. In addition, the choice of available RFP-based biosensors is limited, and development of each biosensor requires substantial effort. Herein, we describe a general and convenient method, which introduces a genetically encoded noncanonical amino acid, 3-aminotyrosine, to the chromophores of green fluorescent protein-like proteins and biosensors for spontaneous and efficient green-to-red conversion. We demonstrated that this method could be used to quickly expand the repertoire of RFP-based biosensors. With little optimization, the 3-aminotyrosine-modified biosensors preserved the molecular brightness, dynamic range and responsiveness of their green fluorescent predecessors. We further applied spectrally resolved biosensors for multiplexed imaging of metabolic dynamics in pancreatic β-cells.

C7-Prenylation of Tryptophan-Containing Cyclic Dipeptides by 7-Dimethylallyl Tryptophan Synthase Significantly Increases the Anticancer and Antimicrobial Activities

Prenylated natural products have interesting pharmacological properties and prenylation reactions play crucial roles in controlling the activities of biomolecules. They are difficult to synthesize chemically, but enzymatic synthesis production is a desirable pathway. Cyclic dipeptide prenyltransferase catalyzes the regioselective Friedel-Crafts alkylation of tryptophan-containing cyclic dipeptides. This class of enzymes, which belongs to the dimethylallyl tryptophan synthase superfamily, is known to be flexible to aromatic prenyl receptors, while mostly retaining its typical regioselectivity. In this study, seven tryptophan-containing cyclic dipeptides 1a-7a were converted to their C7-regularly prenylated derivatives 1b-7b in the presence of dimethylallyl diphosphate (DMAPP) by using the purified 7-dimethylallyl tryptophan synthase (7-DMATS) as catalyst. The HPLC analysis of the incubation mixture and the NMR analysis of the separated products showed that the stereochemical structure of the substrate had a great influence on their acceptance by 7-DMATS. Determination of the kinetic parameters proved that cyclo-l-Trp-Gly (1a) consisting of a tryptophanyl and glycine was accepted as the best substrate with a KM value of 169.7 μM and a turnover number of 0.1307 s-1. Furthermore, docking studies simulated the prenyl transfer reaction of 7-DMATS and it could be concluded that the highest affinity between 7-DMATS and 1a. Preliminary results have been clearly shown that prenylation at C7 led to a significant increase of the anticancer and antimicrobial activities of the prenylated derivatives 1b-7b in all the activity test experiment, especially the prenylated product 4b.

Overcoming Near-Cognate Suppression in a Release Factor 1-Deficient Host with an Improved Nitro-Tyrosine tRNA Synthetase

Genetic code expansion (GCE) technologies incorporate non-canonical amino acids (ncAAs) into proteins at amber stop codons. To avoid unwanted truncated protein and improve ncAA-protein yields, genomically recoded strains of Escherichia coli lacking Release Factor 1 (RF1) are becoming increasingly popular expression hosts for GCE applications. In the absence of RF1, however, endogenous near-cognate amber suppressing tRNAs can lead to contaminating protein forms with natural amino acids in place of the ncAA. Here, we show that a second-generation amino-acyl tRNA synthetase (aaRS)/tRNA_CUA pair for site-specific incorporation of 3-nitro-tyrosine could not outcompete near-cognate suppression in an RF1-deficient expression host and therefore could not produce homogenously nitrated protein. To resolve this, we used Rosetta to target positions in the nitroTyr aaRS active site for improved substrate binding, and then constructed of a small library of variants to subject to standard selection protocols. The top selected variant had an ~2-fold greater efficiency, and remarkably, this relatively small improvement enabled homogeneous incorporation of nitroTyr in an RF1-deficient expression host and thus eliminates truncation issues associated with typical RF1-containing expression hosts. Structural and biochemical data suggest the aaRS efficiency improvement is based on higher affinity substrate binding. Taken together, the modest improvement in aaRS efficiency provides a large practical impact and expands our ability to study the role protein nitration plays in disease development through producing homogenous, truncation-free nitroTyr-containing protein. This work establishes Rosetta-guided design and incremental aaRS improvement as a viable and accessible path to improve GCE systems challenged by truncation and/or near-cognate suppression issues.

Recent Developments and Applications of the MMPBSA Method

The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.

Identification of Phytochemicals Targeting c-Met Kinase Domain using Consensus Docking and Molecular Dynamics Simulation Studies

c-Met receptor tyrosine kinase is a proto-oncogene whose aberrant activation is attributed to a lower rate of survival in most cancers. Natural product-derived inhibitors known as “fourth generation inhibitors” constitute more than 60% of anticancer drugs. Furthermore, consensus docking approach has recently been introduced to augment docking accuracy and reduce false positives during a virtual screening. In order to obtain novel small-molecule Met inhibitors, consensus docking approach was performed using Autodock Vina and Autodock 4.2 to virtual screen Naturally Occurring Plant-based Anti-cancer Compound–Activity–Target database against active and inactive conformation of c-Met kinase domain structure. Two hit molecules that were in line with drug-likeness criteria, desired docking score, and binding pose were subjected to molecular dynamics simulations to elucidate intermolecular contacts in protein–ligand complexes. Analysis of molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area studies showed that ZINC08234189 is a plausible inhibitor for the active state of c-Met, whereas ZINC03871891 may be more effective toward active c-Met kinase domain compared to the inactive form due to higher binding energy. Our analysis showed that both the hit molecules formed hydrogen bonds with key residues of the hinge region (P1158, M1160) in the active form, which is a hallmark of kinase domain inhibitors. Considering the pivotal role of HGF/c-Met signaling in carcinogenesis, our results propose ZINC08234189 and ZINC03871891 as the therapeutic options to surmount Met-dependent cancers.

Stress Response and Adaptation Mediated by Amino Acid Misincorporation during Protein Synthesis

Translation of genetic information into functional proteins is critical for all cellular life. Accurate protein synthesis relies on proper aminoacylation of transfer RNAs (tRNAs) and decoding of mRNAs by the ribosome with the use of aminoacyl-tRNAs. Mistranslation can lead to pathologic consequences. All cells contain elaborate quality control mechanisms in translation, although translational fidelity may be regulated by various factors such as nutrient limitation or reactive oxygen species. Translation fidelity is maintained via the accuracy of tRNA aminoacylation by the aminoacyl-tRNA synthetases and matching of the mRNA codon with the tRNA anticodon by the ribosome. Stringent substrate discrimination and proofreading are critical in aminoacylating tRNAs with their cognate amino acid to maintain high accuracy of translation. Although the composition of the cellular proteome generally adheres to the genetic code, accumulating evidence indicates that cells can also deliberately mistranslate; they synthesize mutant proteins that deviate from the genetic code in response to stress or environmental changes. Mistranslation with tRNA charged with noncognate amino acids can expand the proteome to enhance stress response and help adaptation. Here, we review current knowledge on mistranslation through tRNA misacylation and describe advances in our understanding of translational control in the regulation of stress response and human diseases.

Regiospecific synthesis of prenylated flavonoids by a prenyltransferase cloned from Fusarium oxysporum

Due to their impressive pharmaceutical activities and safety, prenylated flavonoids have a high potent to be applied as medicines and nutraceuticals. Biocatalysis is an effective technique to synthesize prenylated flavonoids. The major concern of this technique is that the microbe-derived prenyltransferases usually have poor regiospecificity and generate multiple prenylated products. In this work, a highly regiospecific prenyltransferase (FoPT1) was found from Fusarium oxysporum. It could recognize apigenin, naringenin, genistein, dihydrogenistein, kampferol, luteolin and hesperetin as substrates, and only 6-C-prenylated flavonoids were detected as the products. The catalytic efficiency of FoPT1 on flavonoids was in a decreasing order with hesperetin >naringenin >apigenin >genistein >luteolin >dihydrogenistein >kaempferol. Chalcones, flavanols and stilbenes were not active when acting as the substrates. 5,7-Dihydroxy and 4-carbonyl groups of flavonid were required for the catalysis. 2,3-Alkenyl was beneficial to the catalysis whereas 3-hydroxy impaired the prenylation reaction. Docking studies simulated the prenyl transfer reaction of FoPT1. E186 was involved in the formation of prenyl carbonium ion. E98, F89, F182, Y197 and E246 positioned apigenin for catalysis.