Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling

HaloTag labeling technology has introduced unrivaled potential in protein chemistry, molecular and cellular biology. A wide variety of ligands have been developed to meet the specific needs of diverse applications, but only a single protein tag, DhaAHT, is routinely used for their incorporation. Following a systematic kinetic and computational analysis of different reporters, tetramethylrhodamine and three 4-stilbazolium-based fluorescent ligands, we showed that the mechanism of incorporating different ligands depends both on the binding step and the efficiency of the chemical reaction. By studying the different haloalkane dehalogenases DhaA, LinB, and DmmA, we found that the architecture of the access tunnels is critical for the kinetics of both steps and the ligand specificity. We show that highly efficient labelling with specific ligands is achievable with natural dehalogenases. We propose a simple protocol for selecting the optimal protein tag for a specific ligand from a wide pool of available enzymes with diverse access tunnel architectures. The application of this protocol eliminates a need for expensive and laborious protein engineering.

Traceless enzymatic protein synthesis without ligation sites constraint

Protein synthesis and semisynthesis offer immense promise for life science and have impacted pharmaceutical innovation. Nevertheless, the absence of a generally applicable method for traceless peptide conjugation with a flexible choice of junction sites remains a bottleneck for accessing many important synthetic targets. Here we introduce the protein activation and ligation with multiple enzymes (PALME) platform designed for the sequence-unconstrained synthesis and modification of biomacromolecules. The upstream activating modules accept and process easily accessible synthetic peptides and recombinant proteins, avoiding the challenges associated with the preparation and manipulation of activated peptide substrates. Cooperatively, the downstream coupling module provides comprehensive solutions for sequential peptide condensation, cyclization, and protein N/C-terminal or internal functionalization. This methodology's practical utility was demonstrated by synthesizing a series of bioactive targets ranging from pharmaceutical ingredients to synthetically challenging proteins. Together, the modular PALME platform exhibits unprecedented broad accessibility for the traceless protein synthesis and functionalization and holds enormous potential to extend the scope of protein chemistry and synthetic biology.

Protein structure prediction by AlphaFold2: are attention and symmetries all you need?

The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a protein from its primary sequence has long been a grand challenge in bioinformatics, intimately connected with understanding protein chemistry and dynamics. Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is complementary to longstanding physics-based approaches. The outstanding performance of AlphaFold2 in the recent Critical Assessment of protein Structure Prediction (CASP14) experiment demonstrates the remarkable power of deep learning in structure prediction. In this perspective, we focus on the key features of AlphaFold2, including its use of (i) attention mechanisms and Transformers to capture long-range dependencies, (ii) symmetry principles to facilitate reasoning over protein structures in three dimensions and (iii) end-to-end differentiability as a unifying framework for learning from protein data. The rules of protein folding are ultimately encoded in the physical principles that underpin it; to conclude, the implications of having a powerful computational model for structure prediction that does not explicitly rely on those principles are discussed.

Applying Peptide and Protein Synthesis to Study Post-translational Modifications in Epigenetics and Beyond

Epigenetics research focuses on the study of heritable gene regulatory mechanisms that do not involve changes of the DNA sequence. Such mechanisms include post-translational modifications of histone proteins that organize the genome in the nucleus into a nucleoprotein complex called chromatin, and which are of key importance in development and disease. Chemical biology tools as developed by my group, in particular synthetic peptide and protein chemistry, have been critical to elucidate epigenetic signaling mechanisms. As outlined below, they allow the reconstitution of chromatin carrying defined modifications and thus the elucidation of detailed molecular mechanisms.

High Potential for Biomass-Degrading Enzymes Revealed by Hot Spring Metagenomics

Enzyme stability and activity at elevated temperatures are important aspects in biotechnological industries, such as the conversion of plant biomass into biofuels. In order to reduce the costs and increase the efficiency of biomass conversion, better enzymatic processing must be developed. Hot springs represent a treasure trove of underexplored microbiological and protein chemistry diversity. Herein, we conduct an exploratory study into the diversity of hot spring biomass-degrading potential. We describe the taxonomic diversity and carbohydrate active enzyme (CAZyme) coding potential in 71 publicly available metagenomic datasets from 58 globally distributed terrestrial geothermal features. Through taxonomic profiling, we detected a wide diversity of microbes unique to varying temperature and pH ranges. Biomass-degrading enzyme potential included all five classes of CAZymes and we described the presence or absence of genes encoding 19 glycosyl hydrolases hypothesized to be involved with cellulose, hemicellulose, and oligosaccharide degradation. Our results highlight hot springs as a promising system for the further discovery and development of thermo-stable biomass-degrading enzymes that can be applied toward generation of renewable biofuels. This study lays a foundation for future research to further investigate the functional diversity of hot spring biomass-degrading enzymes and their potential utility in biotechnological processing.

The Interaction of Human Glutathione Transferase GSTA1-1 with Reactive Dyes

Human glutathione transferase A1-1 (hGSTA1-1) contributes to developing resistance to anticancer drugs and, therefore, is promising in terms of drug-design targets for coping with this phenomenon. In the present study, the interaction of anthraquinone and diazo dichlorotriazine dyes (DCTD) with hGSTA1-1 was investigated. The anthraquinone dye Procion blue MX-R (PBMX-R) appeared to interact with higher affinity and was selected for further study. The enzyme was specifically and irreversibly inactivated by PBMX-R, following a biphasic pseudo-first-order saturation kinetics, with approximately 1 mol of inhibitor per mol of the dimeric enzyme being incorporated. Molecular modeling and protein chemistry data suggested that the modified residue is the Cys112, which is located at the entrance of the solvent channel at the subunits interface. The results suggest that negative cooperativity exists upon PBMX-R binding, indicating a structural communication between the two subunits. Kinetic inhibition analysis showed that the dye is a competitive inhibitor towards glutathione (GSH) and mixed-type inhibitor towards 1-chloro-2,4-dinitrobenzene (CDNB). The present study results suggest that PBMX-R is a useful probe suitable for assessing by kinetic means the drugability of the enzyme in future drug-design efforts.

PPI-GA: A Novel Clustering Algorithm to Identify Protein Complexes within Protein-Protein Interaction Networks Using Genetic Algorithm

Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational methods for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks (PPI). To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms.