Complex Biomolecules Research Articles

GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins.

Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano-bio interface. This work introduces and validates a strategy to predict atom-scale interactions between water-soluble gold nanoclusters (AuNCs) and a set of blood proteins (albumin, apolipoprotein, immunoglobulin, and fibrinogen). Graph theory and neural networks are utilized to predict the strengths of interactions in AuNC-protein complexes on a coarse-grained level, which are then optimized in Monte Carlo-based structure search and refined to atomic-scale structures. The training data is based on extensive molecular dynamics (MD) simulations of AuNC-protein complexes, and the validating MD simulations show the robustness of the predictions. This strategy can be generalized to any complexes of inorganic nanostructures and biomolecules provided that one generates enough data about the interactions, and the bioactive parts of the nanostructure can be coarse-grainedrationally.

Mechanical Properties of a Solvated Biomolecule: RGD (1FUV) Peptide.

The mechanical properties of proteins/peptides play an essential role in their functionalities and implications, as well as their structure and dynamic properties. Understanding mechanical properties is pivotal to our knowledge of protein folding and the molecular basis of diverse cellular processes. Herein, we present a computational approach using ab initio quantum mechanical calculations to determine the mechanical properties-such as bulk modulus, shear modulus, Young's modulus, and Poisson's ratio-of a solvated Arg-Gly-Asp (RGD) peptide model. Since this peptide serves as the RGD-directed integrin recognition site and may participate in cellular adhesion, it is considered a promising small peptide for medicinal applications. This successful approach paves the way for investigating larger and more complex biomolecules.

Integrating, Validating, and Expanding Information Space in Single-Molecule Surface-Enhanced Raman Spectroscopy for Biomolecules.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is an ultrahigh-resolution spectroscopic method for directly obtaining the complex vibrational mode information on individual molecules. SM-SERS offers a wide range of submolecular information on the hidden heterogeneity in its functional groups and varying structures, dynamics of conformational changes, binding and reaction kinetics, and interactions with the neighboring molecule and environment. Despite the richness in information on individual molecules and potential of SM-SERS in various detection targets, including large and complex biomolecules, several issues and practical considerations remain to be addressed, such as the requirement of long integration time, challenges in forming reliable and controllable interfaces between nanostructures and biomolecules, difficulty in determining hotspot size and shape, and most importantly, insufficient signal reproducibility and stability. Moreover, utilizing and interpreting SERS spectra is challenging, mainly because of the complexity and dynamic nature of molecular fingerprint Raman spectra, and this leads to fragmentary analysis and incomplete understanding of the spectra. In this Perspective, we discuss the current challenges and future opportunities of SM-SERS in views of system approaches by integrating molecules of interest, Raman dyes, plasmonic nanostructures, and artificial intelligence, particularly for detecting and analyzing biomolecules to realize the validation and expansion of information space in SM-SERS.

Temporal witnesses of non-classicality in a macroscopic biological system

Exciton transfer along a bio-polymer is essential for many biological processes, for instance, light harvesting in photosynthetic biosystems. Here we apply a new witness of non-classicality to this phenomenon, to conclude that, if an exciton can mediate the coherent quantum evolution of a photon, then the exciton is non-classical. We then propose a general qubit model for the quantum transfer of an exciton along a bio-polymer chain, also discussing the effects of environmental decoherence. The generality of our results makes them ideal candidates to design new tests of quantum features in complex bio-molecules.

Characterization of a Monoclonal Antibody by Native and Denaturing Top-Down Mass Spectrometry.

Established in recent years as an important approach to unraveling the heterogeneity of intact monoclonal antibodies, native mass spectrometry has been rarely utilized for sequencing these complex biomolecules via tandem mass spectrometry. Typically, top-down mass spectrometry has been performed starting from highly charged precursor ions obtained via electrospray ionization under denaturing conditions (i.e., in the presence of organic solvents and acidic pH). Here we systematically benchmark four distinct ion dissociation methods─namely, higher-energy collisional dissociation, electron transfer dissociation, electron transfer dissociation/higher-energy collisional dissociation, and 213 nm ultraviolet photodissociation─in their capability to characterize a therapeutic monoclonal antibody, trastuzumab, starting from denatured and native-like precursor ions. Interestingly, native top-down mass spectrometry results in higher sequence coverage than the experiments carried out under denaturing conditions, with the exception of ultraviolet photodissociation. Globally, electron transfer dissociation followed by collision-based activation of product ions generates the largest number of backbone cleavages in disulfide protected regions, including the complementarity determining regions, regardless of electrospray ionization conditions. Overall, these findings suggest that native mass spectrometry can certainly be used for the gas-phase sequencing of whole monoclonal antibodies, although the dissociation of denatured precursor ions still returns a few backbone cleavages not identified in native experiments. Finally, a comparison of the fragmentation maps obtained under denaturing and native conditions strongly points toward disulfide bonds as the primary reason behind the largely overlapping dissociation patterns.

Advancing Nitrile-Aminothiol Strategy for Dual and Sequential Bioconjugation.

Nitrile-aminothiol conjugation (NATC) stands out as a promising biocompatible ligation technique due to its high chemo-selectivity. Herein we investigated the reactivity and substrate scope of NAT conjugation chemistry, thus developing a novel pH dependent orthogonal NATC as a valuable tool for chemical biology. The study of reaction kinetics elucidated that the combination of heteroaromatic nitrile and aminothiol groups led to the formation of an optimal bioorthogonal pairing, which is pH dependent. This pairing system was effectively utilized for sequential and dual conjugation. Subsequently, these rapid (≈1 h) and high yield (>90 %) conjugation strategies were successfully applied to a broad range of complex biomolecules, including oligonucleotides, chelates, small molecules and peptides. The effectiveness of this conjugation chemistry was demonstrated by synthesizing a fluorescently labelled antimicrobial peptide-oligonucleotide complex as a dual conjugate to imaging in live cells. This first-of-its-kind sequential NATC approach unveils unprecedented opportunities in modern chemical biology, showcasing exceptional adaptability in rapidly creating structurally complex bioconjugates. Furthermore, the results highlight its potential for versatile applications across fundamental and translational biomedical research.

Amesp: Atomic and molecular electronic structure program

The atomic and molecular electronic structure program (Amesp) is a general-purpose electronic structure program designed for the study of molecular electronic structures. It incorporates a series of modern electronic structure methods, including Hartree–Fock, density functional theory, Multiconfigurational self-consistent field, Møller–Plesset, configuration-interaction, coupled-cluster, semiempirical methods, and molecular force fields. Amesp strives to offer an efficient and user-friendly tool specifically designed for computing for molecules ranging from small to complex biomolecules. In this paper, we highlight the features of Amesp and offer an overview.

How advancements in molecular biology impact education and training.

Molecular biology, broadly defined as the investigation of complex biomolecules in the laboratory, is a rapidly advancing field and as such the technologies available to investigators are constantly evolving. This constant advancement has obvious advantages because it allows students and researchers to perform more complex experiments in shorter periods of time. One challenge with such a rapidly advancing field is that techniques that had been vital for students to learn how to perform are now not essential for a laboratory scientist. For example, while cloning a gene in the past could have led to a publication and form the bulk of a PhD thesis project, technology has now made this process only a step toward one of these larger goals and can, in many cases, be performed by a company or core facility. As teachers and mentors, it is imperative that we understand that the technologies we teach in the lab and classroom must also evolve to match these advancements. In this perspective, we discuss how the rapid advances in gene synthesis technologies are affecting curriculum and how our classrooms should evolve to ensure our lessons prepare students for the world in which they will do science.

Integrated chemoenzymatic synthesis of a comprehensive sulfated ganglioside glycan library to decipher functional sulfoglycomics and sialoglycomics.

Ganglioside glycans are ubiquitous and complex biomolecules that are involved in a wide range of biological functions and disease processes. Variations in sialylation and sulfation render the structural complexity and diversity of ganglioside glycans, and influence protein-carbohydrate interactions. Structural and functional insights into the biological roles of these glycans are impeded due to the limited accessibility of well-defined structures. Here we report an integrated chemoenzymatic strategy for expeditious and systematic synthesis of a comprehensive 65-membered ganglioside glycan library covering all possible patterns of sulfation and sialylation. This strategy relies on the streamlined modular assembly of three common sialylated precursors by highly stereoselective iterative sialylation, modular site-specific sulfation through flexible orthogonal protecting-group manipulations and enzymatic-catalysed diversification using three sialyltransferase modules and a galactosidase module. These diverse ganglioside glycans enable exploration into their structure-function relationships using high-throughput glycan microarray technology, which reveals that different patterns of sulfation and sialylation on these glycans mediate their unique binding specificities.

Evaluation of Essential Dynamics and Fixed-Length Coarse Graining for Multidomain Proteins.

For multiscale modeling of biomolecules, reliable coarse-grained (CG) models can offer great potential to simulate larger temporal and spatial scales than traditional all-atom (AA) models. In this study, we explore the essential dynamics coarse graining (EDCG) and fixed-length coarse graining (FLCG) approaches for constructing highly coarse-grained models for multidomain proteins (MDPs), with 1 to 10 amino acid residues per CG site. In the studies of 13 MDPs, our data indicate that both EDCG and FLCG can preserve the protein dynamics of MDPs. FLCG, which restricts an equal number of residues in each CG site, represents an excellent approximation to EDCG and a straightforward approach for coarse-graining MDPs. Furthermore, FLCG is tested with a class B G-protein-coupled receptor protein, and the agreement with prior experiments suggests its general application to various MDPs in different environments or conditions. Finally, we demonstrate another application of FLCG through progressive backmapping, showcasing the ability to recover from lower-resolution CG models (6 residues/CG site) to higher-resolution ones (1 residue/CG site). These promising outcomes underscore the broad applicability of FLCG to construct highly or ultra-coarse-grained models of complex biomolecules for multiscale simulations.

Exploring the relationship between GuaYi levels and microbial-metabolic dynamics in Daqu

Daqu is a crucial saccharifying and fermenting agent in the production of Baijiu, with GuaYi referring to the white fungal colonies on Daqu's surface, serving as a critical indicator for sensory evaluation. However, the correlation between distinct GuaYi and the consequent internal modifications in Daqu remains to be elucidated. Our investigation delineates that Heavy GuaYi Daqu (HGY), distinguished by an elevated prevalence of white microflora (notably Saccharomycopsis), sustains enhanced moisture levels internally. This environment is conducive to the proliferation of Aspergillus and Kroppenstedtia, resulting in significant increases of 50.95% and 40.75% in the concentrations of alcohols and pyrazines, respectively. Conversely, Light GuaYi Daqu (LGY) creates a comparatively arid environment, favoring Thermoascus and Staphylococcus growth, which is instrumental in the decomposition of complex biomolecules such as starch, reducing sugars, and proteins. These insights elucidate the nuanced interplay between GuaYi and Daqu's quality and stylistic attributes, providing an invaluable theoretical framework for the refinement of sensory evaluation standards for Daqu.

Developing a platform for secretion of biomolecules in Mycoplasma feriruminatoris

BackgroundHaving a simple and fast dividing organism capable of producing and exposing at its surface or secreting functional complex biomolecules with disulphide bridges is of great interest. The mycoplasma bacterial genus offers a set of relevant properties that make it an interesting chassis for such purposes, the main one being the absence of a cell wall. However, due to their slow growth, they have rarely been considered as a potential platform in this respect. This notion may be challenged with the recent discovery of Mycoplasma feriruminatoris, a species with a dividing time close to that of common microbial workhorses. So far, no tools for heterologous protein expression nor secretion have been described for it.ResultsThe work presented here develops the fast-dividing M. feriruminatoris as a tool for secreting functional biomolecules of therapeutic interest that could be used for screening functional mutants as well as potentially for protein-protein interactions. Based on RNAseq, quantitative proteomics and promoter sequence comparison we have rationally designed optimal promoter sequences. Then, using in silico analysis, we have identified putative secretion signals that we validated using a luminescent reporter. The potential of the resulting secretion cassette has been shown with set of active clinically relevant proteins (interleukins and nanobodies).ConclusionsWe have engineered Mycoplasma feriruminatoris for producing and secreting functional proteins of medical interest.

A Computational and Chemical Design Strategy for Manipulating Glycan-Protein Recognition.

Glycans are complex biomolecules that encode rich information and regulate various biological processes, such as fertilization, host-pathogen binding, and immune recognition, through interactions with glycan-binding proteins. A key driving force for glycan-protein recognition is the interaction between the π electron density of aromatic amino acid side chains and polarized C─H groups of the pyranose (termed the CH-π interaction). However, the relatively weak binding affinity between glycans and proteins has hindered the application of glycan detection and imaging. Here, computational modeling and molecular dynamics simulations are employed to design a chemical strategy that enhances the CH-π interaction between glycans and proteins by genetically incorporating electron-rich tryptophan derivatives into a lectin PhoSL, which specifically recognizes core fucosylated N-linked glycans. This significantly enhances the binding affinity of PhoSL with the core fucose ligand and enables sensitive detection and imaging of core fucosylated glycans in vitro and in xenograft tumors in mice. Further, the study showed that this strategy is applicable to improve the binding affinity of GafD lectin for N-acetylglucosamine-containing glycans. The approach thus provides a general and effective way to manipulate glycan-protein recognition for glycoscience applications.

Polar and photoelectronic properties of dileucine and diisoleucine nanotubes

The article presents computer modeling and quantum-chemical calculations of polar and photoelectronic properties of helix-like nanotubes based on dileucine (LL) and diisoleucine (II) dipeptides of L and D chiralities using HyperChem package. It has been shown that physical properties of LL-based peptide nanotubes (PNTs) are close to the properties of diphenylalanine (FF) nanotubes, whereas II-based PNTs demonstrate lower polarization and the absorption edge in the short-wavelength UV-C region. For all kinds of nanotubes, the chirality index calculated using scalar triple product of dipole moments has confirmed the chirality change rule established earlier for complex biomolecules.

Site-Selective Palladium-catalyzed Oxidation of Unprotected Aminoglycosides and Sugar Phosphates.

The site-selective modification of complex biomolecules by transition metal-catalysis is highly warranted, but often thwarted by the presence of Lewis basic functional groups. This study demonstrates that protonation of amines and phosphates in carbohydrates circumvents catalyst inhibition in palladium-catalyzed site-selective oxidation. Both aminoglycosides and sugar phosphates, compound classes that up till now largely escaped direct modification, are oxidized with good efficiency. Site-selective oxidation of kanamycin and amikacin was used to prepare a set of 3'-modified aminoglycoside derivatives of which two showed promising activity against antibiotic-resistant E. coli strains.

Multimodal pretraining for unsupervised protein representation learning

Abstract Proteins are complex biomolecules essential for numerous biological processes, making them crucial targets for advancements in molecular biology, medical research, and drug design. Understanding their intricate, hierarchical structures, and functions is vital for progress in these fields. To capture this complexity, we introduce Multimodal Protein Representation Learning (MPRL), a novel framework for symmetry-preserving multimodal pretraining that learns unified, unsupervised protein representations by integrating primary and tertiary structures. MPRL employs Evolutionary Scale Modeling (ESM-2) for sequence analysis, Variational Graph Auto-Encoders (VGAE) for residue-level graphs, and PointNet Autoencoder (PAE) for 3D point clouds of atoms, each designed to capture the spatial and evolutionary intricacies of proteins while preserving critical symmetries. By leveraging Auto-Fusion to synthesize joint representations from these pretrained models, MPRL ensures robust and comprehensive protein representations. Our extensive evaluation demonstrates that MPRL significantly enhances performance in various tasks such as protein–ligand binding affinity prediction, protein fold classification, enzyme activity identification, and mutation stability prediction. This framework advances the understanding of protein dynamics and facilitates future research in the field. Our source code is publicly available at https://github.com/HySonLab/Protein_Pretrain.

Computational toolbox for the analysis of protein-glycan interactions.

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

Targeted Analysis of Glycerophospholipids and Mono-, Di-, or Tri-Acylglycerides in Liver Cancer.

The metabolic rearrangements of hepatic metabolism associated with liver cancer are still incompletely understood. There is an ongoing need to identify novel and more efficient diagnostic biomarkers and therapeutic targets based on the metabolic mechanisms of these diseases. In comparison to traditional diagnostic biomarkers, metabolomics is a comprehensive technique for discovering chemical signatures for liver cancer screening, prediction, and earlier diagnosis. Lipids are a large and diverse group of complex biomolecules that are at the heart of liver physiology and play an important role in the development and progression of cancer. In this chapter, we described two detailed protocols for targeted lipids analysis: glycerophospholipids and mono, di, tri-acylglycerides, both by Flow Injection Analysis (FIA) HPLC coupled to a SelexIon/QTRAP 6500+ system. These approaches provide a targeted lipidomic metabolomic signature of dissimilar metabolic disorders affecting liver cancers.

Chemical Engineering of Artificial Transcription Factors by Orthogonal Palladium(II)‐Mediated S‐Arylation Reactions

AbstractSite‐selective functionalization strategies are in high demand to prepare well‐defined homogeneous proteins for basic research and biomedical applications. In this regard, cysteine‐based reactions have enabled a broad set of transformations to produce modified proteins for various applications. However, these approaches were mainly employed to modify a single reactive site with a specific transformation. Achieving site selectivity or multiple transformations, essential for preparing complex biomolecules, remains challenging. Herein we demonstrate the power of combining palladium(II)‐mediated C−S bond formation and C−S bond cleavage reactions to selectively edit desired cysteine sites in complex and uniquely modified proteins. We developed an orthogonal palladium(II) strategy for rapid and effective diversification of multiple cysteine sites (3–6 residues) with various transformations. Importantly, we employed our approach to prepare 10 complex analogues, including modified, stapled, and multimeric proteins on a milligram scale. Furthermore, we also synthesized a focused library of stabilized artificial transcription factors that displayed enhanced stability and potent DNA binding activity. Our approach enables rapid and effective protein editing and opens new avenues to engineer new biomolecules for fundamental research and therapeutic applications.

Chemical Engineering of Artificial Transcription Factors by Orthogonal Palladium(II)-Mediated S-Arylation Reactions.

Site-selective functionalization strategies are in high demand to prepare well-defined homogeneous proteins for basic research and biomedical applications. In this regard, cysteine-based reactions have enabled a broad set of transformations to produce modified proteins for various applications. However, these approaches were mainly employed to modify a single reactive site with a specific transformation. Achieving site selectivity or multiple transformations, essential for preparing complex biomolecules, remains challenging. Herein we demonstrate the power of combining palladium(II)-mediated C-S bond formation and C-S bond cleavage reactions to selectively edit desired cysteine sites in complex and uniquely modified proteins. We developed an orthogonal palladium(II) strategy for rapid and effective diversification of multiple cysteine sites (3-6 residues) with various transformations. Importantly, we employed our approach to prepare 10 complex analogues, including modified, stapled, and multimeric proteins on a milligram scale. Furthermore, we also synthesized a focused library of stabilized artificial transcription factors that displayed enhanced stability and potent DNA binding activity. Our approach enables rapid and effective protein editing and opens new avenues to engineer new biomolecules for fundamental research and therapeutic applications.