Protein Surface Interactions-Theoretical and Experimental Studies

The proteomic code: Novel amino acid residue pairing models "encode" protein folding and protein-protein interactions

Recent advances in protein 3D structure prediction using deep learning have focused on the importance of amino acid residue-residue connections (i.e., pairwise atomic contacts) for accuracy at the expense of mechanistic interpretability. Therefore, we decided to perform a series of analyses based on an alternative framework of residue-residue connections making primary use of the TOP2018 dataset. This framework of residue-residue connections is derived from amino acid residue pairing models both historic and new, all based on genetic principles complemented by relevant biophysical principles. Of these pairing models, three new models (named the GU, Transmuted and Shift pairing models) exhibit the highest observed-over-expected ratios and highest correlations in statistical analyses with various intra- and inter-chain datasets, in comparison to the remaining models. In addition, these new pairing models are universally frequent across different connection ranges, secondary structure connections, and protein sizes. Accordingly, following further statistical and other analyses described herein, we have come to a major conclusion that all three pairing models together could represent the basis of a universal proteomic code (second genetic code) sufficient, in and of itself, to "encode" for both protein folding mechanisms and protein-protein interactions.

Targeted Protein Degradation in Cancer Therapy via Hydrophobic Polymer-Tagged Nanoparticles

Targeted protein degradation (TPD) strategies offer a significant advantage over traditional small molecule inhibitors by selectively degrading disease-causing proteins. While small molecules can lead to recurrence and resistance due to compensatory pathway activation, TPD addresses this limitation by promoting protein degradation, thereby reducing the likelihood of recurrence and resistance over the long-term. Despite these benefits, bifunctional TPD molecules face challenges such as low solubility, poor bioavailability, and limited tumor specificity. In this study, we developed polymer-based nanoparticles that combine TPD strategies with nanotechnology through a hydrophobic tagging method. Hydrophobic polymer-tagged nanoparticles facilitate targeted protein degradation by incorporating hydrophobic polymers that mimic hydrophobic residues in misfolded proteins. This system combines degradation and delivery capabilities within a polymer-based platform, inducing protein degradation while improving solubility, stability, and tumor targeting. These nanoparticles consist of a block copolymer composed of an androgen receptor ligand (ARL)-conjugated hydrophobic polylactic acid (PLA) and a hydrophilic polyethylene glycol (PEG), connected by a GSH-cleavable disulfide bond. In aqueous solutions, this block copolymer (ARL-PLA-SS-PEG) forms micelles that degrade in reducible cellular environments. The micelles demonstrated significant in vitro degradation of the target androgen receptor (AR). Furthermore, they achieved substantial tumor accumulation and significantly inhibited tumor growth in a tumor-bearing mouse model. A mechanistic study revealed that the micelle-mediated TPD follows a dual pathway involving both proteasome and autophagosome. This approach has the potential to serve as a universal platform for protein degradation, eliminating the need to develop disease-specific TPD molecules.

Surface hydrophobic clusters modulate the folding stability and molecular recognition of the disintegrin jarastatin

Disintegrins are cysteine-rich proteins found in snake venoms. These proteins selectively bind to integrins, which play a key role in the regulation of many physiopathological processes. They are coreless proteins that display almost all hydrophobic residues on the protein surface. The exposed hydrophobic residues form surface clusters stabilized by the interaction with the hydrophilic residues in the vicinity and the hydration shell. In the present work, we aimed to determine the stability of surface hydrophobic clusters (SHCs) and their role in protein folding and biological activity. We used urea denaturation curves followed by 1H and 15N chemical shifts to determine the free energy of unfolding (ΔGF-U) and CLEANEX experiments to measure the water exchange rates of the surface amides (kex). The amides with higher local stability and protection from water exchange are those near or at the SHCs, which form a hydrophobic face. SHCs act as foldons, guiding oxidative folding and contributing to the formation of the disulfide bond framework, which is essential for establishing the concave shape and, ultimately, the binding cleft. On the opposite side of the protein are the residues with lower local stability and amides that exchange fast with water. This face coincides with the binding cleft of the protein to the αVβ3 integrin. Taken together, the present work established a correlation between protein hydration and the binding surface.

Conformational changes in 6 MeV electron beam irradiated aqueous bovine serum albumin

Understanding the folding and unfolding mechanism of the protein is not only crucial in applications like biomedical, pharmaceutical, tissue engineering but also to the food industry. In the present study, an electron beam with 6 MeV energy derived from the Microtron accelerator was utilized to irradiate the aqueous solution of bovine serum albumin (BSA) at fluences of 5 × 1014 and 10 × 1014 e-/cm2. The control and irradiated BSA solutions were analyzed using UV-visible and FTIR spectroscopy. UV-visible spectroscopy showed a hyperchromic red shift in 235 nm (π → π*) and a blue shift in 268 nm (n → π*) bands with increasing fluence. Changes in aromatic acid residues of the proteins tertiary structure were observed from the 2nd derivative of absorbance spectra. FTIR spectra revealed a decrease in peak area corresponding to β-turns (21.80 to 15.50 %), and random coil (41.30 to 28.80 %) and increase in peak area was observed for β-sheet (29.25 to 35.40 %). These findings reveal the conformal changes in the electron irradiated BSA. Further, a decrease in the interfacial tension at the air/water interface suggests increase in hydrophobicity of the aqueous solution with fluence.

Impact of pH-dependent dynamics of human serum proteins on dialysis membranes: Cryptographic structure assessment, synchrotron imaging of membrane-protein adsorption, and molecular docking studies

Proteins are fundamental to biochemical processes and critical in hemodialysis. This study investigates the impact of pH on human serum albumin (HSA), fibrinogen (FB), and transferrin (TRF) interactions with polyarylethersulfone (PAES) hemodialysis membranes. A multi-method approach was utilized, including protein crystallography for structural insights, hydration layer analysis to explore solvation and interaction potentials, molecular docking using AutoDock 4.0 for binding affinity simulations, and in-situ X-ray synchrotron SR-μCT imaging to observe protein deposition dynamics. Molecular docking revealed that PAES demonstrated superior binding energies and interaction patterns with FB and TRF compared to cellulose triacetate (CTA), facilitated by specific hydrogen bonding within a water shell. CTA displayed weaker, hydration-sensitive interactions varying with pH. Imaging studies indicated that FB showed higher adsorption at pH 6 than at pH 7.2, predominantly in the middle membrane regions. Similarly, HSA and TRF exhibited increased adsorption at pH 6, suggesting a stronger affinity under acidic conditions. Mixed protein solutions also indicated higher adsorption at pH 6, emphasizing an increased risk of membrane fouling. These findings highlight the crucial role of pH in modulating protein-membrane interactions and enhancing the efficacy of hemodialysis. A deeper understanding of hydration environments and their effects on protein binding affinities provides valuable insights for optimizing membrane design and performance. Clinically, this research suggests that fine-tuning pH during hemodialysis could mitigate protein fouling on membranes, thereby improving procedural efficiency and potentially leading to better patient outcomes through enhanced dialysis effectiveness.

Bioactive Deep Eutectic Solvent-Involved Sprayable Versatile Hydrogel for Monkeypox Virus Lesions Treatment

To address the issues of infectious virus, bacterial secondary infections, skin pigmentation, and scarring caused by monkeypox virus (MPXV), a sprayable hydrogel with versatile functions was developed with comprehensive properties. Based on current research, the bioactive deep eutectic solvent (DES) of rosmarinic acid-proanthocyanidin-glycol (RPG) was designed and synthesized as active agent, and molecular docking was applied to discover its binding to MPXV proteins through H-bonds and van der Waals interactions, and the docking results show the binding energies between RA, PC, Gly and MPXV proteins are -58.7188, -50.2311, and -18.4755 kcal/mol, respectively. Additionally, poly(vinyl alcohol) (PVA), borate, and xylitol (Xyl) were integrated with RPG to prepare the PB-RPG-Xyl hydrogel, which was characterized by popular ways. The pH-responsive properties of the hydrogel accelerated the release of RPG under acidic conditions, resulting in an increased cumulative release percentage of 84.83% at pH 5.5 at 210 min. Besides that, it was proved to have the expected sprayability, self-healing, adhesion, and shape-adaptability. The results of molecular dynamic simulation were meaningful to understanding its formation and self-healing mechanisms. Furthermore, the hydrogel shows ideal degradability, removability, and biocompatibility. Lastly, its multiple functions were systematically explored, including UV-blocking, blood clotting, cooling, antioxidant, antibacterial, and virus inhibition properties. The developed sprayable PB-RPG-Xyl hydrogel represents the first promising dressing based on natural bioactive DES for MPXV lesions management, which not only expands the application of green solvents in health care but also provides a convenient and effective treatment process for MPXV infection in the face of difficult skin lesions and complex treatment needs.

Understanding Cytochrome P450 Enzyme Substrate Inhibition and Prospects for Elimination Strategies

Cytochrome P450 (CYP450) enzymes, which are widely distributed and pivotal in various biochemical reactions, catalyze diverse processes such as hydroxylation, epoxidation, dehydrogenation, dealkylation, nitrification, and bond formation. These enzymes have been applied in drug metabolism, antibiotic production, bioremediation, and fine chemical synthesis. Recent research revealed that CYP450 catalytic kinetics deviated from the classic Michaelis-Menten model. A notable substrate inhibition phenomenon that affects the catalytic efficiency of CYP450 at high substrate concentrations was identified. However, the substrate inhibition of various reactions catalyzed by CYP450 enzymes have not been comprehensively reviewed. This review describes CYP450 substrate inhibition examples and atypical Michaelis-Menten kinetic models, and provides insight into mechanisms of these enzymes. We also reviewed 3D structure and dynamics of CYP450 with substrate binding. Outline methods for alleviating substrate inhibition in CYP450 and other enzymes, including traditional fermentation approaches and protein engineering modifications. The comprehensive analysis presented in this study lays the foundation for enhancing the catalytic efficiency of CYP450 by deregulating substrate inhibition.

Aurora kinase as a putative target to tick control

Aurora kinases (AURK) play a central role in controlling cell cycle in a wide range of organisms. They belong to the family of serine-threonine kinase proteins. Their role in the cell cycle includes, among others, the entry into mitosis, maturation of the centrosome and formation of the mitotic spindle. In mammals, 3 isoforms have been described: A, B and C, which are distinguished mainly by their function throughout the cell cycle. Two aurora kinase coding sequences have been identified in the transcriptome of the cattle tick Rhipicephalus microplus (Rm-AURKA and Rm-AURKB) containing the aurora kinase-specific domain. For both isoforms, the highest number of AURK coding transcripts is found in ovaries. Based on deduced amino acid sequences, it was possible to identify non-conserved threonine residues which are essential to AURK functions in vertebrates and which are not present in R. microplus sequences. A pan AURK inhibitor (CCT137690) caused cell viability decline in the BME26 tick embryonic cell line. In silico docking assay showed an interaction between Aurora kinase and CCT137690 with exclusive interaction sites in Rm-AURKA. The characterization of exclusive regions of the enzyme will enable new studies aimed at promoting species-specific enzymatic inhibition in ectoparasites.

Unraveling the role of physicochemical differences in predicting protein-protein interactions

The ability to accurately predict protein-protein interactions is critically important for understanding major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes the concept of physicochemical similarity to determine which interactions will most likely occur. In our approach, the physicochemical features of proteins are extracted using bioinformatics tools for different organisms. Then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It was found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid composition (the frequency of specific amino acid pairs in a protein sequence). While current approaches often overlook the specificity of protein-protein interactions with different organisms, our method yields context-specific features that determine protein-protein interactions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators, as well as to the barnase-barstar complex, demonstrating the method's versatility across different biological systems. Our approach can be applied to predict protein-protein interactions in any biological system, providing an important tool for investigating complex biological processes' mechanisms.

ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles

The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures poses a significant challenge for computational biology in leveraging structural information and accurate representation of protein surface properties. Recently, AlphaFold2 released the comprehensive proteomes of various species, and protein surface property representation plays a crucial role in protein-molecule interaction predictions, including those involving proteins, nucleic acids and compounds. Here, we proposed the first extensive database, namely ProNet DB, that integrates multiple protein surface representations and RNA-binding landscape for 326 175 protein structures. This collection encompasses the 16 model organism proteomes from the AlphaFold Protein Structure Database and experimentally validated structures from the Protein Data Bank. For each protein, ProNet DB provides access to the original protein structures along with the detailed surface property representations encompassing hydrophobicity, charge distribution and hydrogen bonding potential as well as interactive features such as the interacting face and RNA-binding sites and preferences. To facilitate an intuitive interpretation of these properties and the RNA-binding landscape, ProNet DB incorporates visualization tools like Mol* and an Online 3D Viewer, allowing for the direct observation and analysis of these representations on protein surfaces. The availability of pre-computed features enables instantaneous access for users, significantly advancing computational biology research in areas such as molecular mechanism elucidation, geometry-based drug discovery and the development of novel therapeutic approaches. Database URL:  https://proj.cse.cuhk.edu.hk/aihlab/pronet/.

The distribution, fate, and environmental impacts of food additive nanomaterials in soil and aquatic ecosystems

Nanomaterials in the food industry are used as food additives, and the main function of these food additives is to improve food qualities including texture, flavor, color, consistency, preservation, and nutrient bioavailability. This review aims to provide an overview of the distribution, fate, and environmental and health impacts of food additive nanomaterials in soil and aquatic ecosystems. Some of the major nanomaterials in food additives include titanium dioxide, silver, gold, silicon dioxide, iron oxide, and zinc oxide. Ingestion of food products containing food additive nanomaterials via dietary intake is considered to be one of the major pathways of human exposure to nanomaterials. Food additive nanomaterials reach the terrestrial and aquatic environments directly through the disposal of food wastes in landfills and the application of food waste-derived soil amendments. A significant amount of ingested food additive nanomaterials (> 90 %) is excreted, and these nanomaterials are not efficiently removed in the wastewater system, thereby reaching the environment indirectly through the disposal of recycled water and sewage sludge in agricultural land. Food additive nanomaterials undergo various transformation and reaction processes, such as adsorption, aggregation-sedimentation, desorption, degradation, dissolution, and bio-mediated reactions in the environment. These processes significantly impact the transport and bioavailability of nanomaterials as well as their behaviour and fate in the environment. These nanomaterials are toxic to soil and aquatic organisms, and reach the food chain through plant uptake and animal transfer. The environmental and health risks of food additive nanomaterials can be overcome by eliminating their emission through recycled water and sewage sludge.

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

The ability to accurately predict protein-protein interactions is critically important for our understanding of major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes a concept of physical-chemical similarity to determine which interactions will most probably occur. In our approach, the physical-chemical features of protein are extracted using bioinformatics tools for different organisms, and then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It is found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid compositions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators. Our theoretical approach provides a simple and robust method for quantifying the important details of complex mechanisms of biological processes.

Investigating Biomolecules in Deep Eutectic Solvents with Molecular Dynamics Simulations: Current State, Challenges and Future Perspectives

Deep eutectic solvents (DESs) have recently gained increased attention for their potential in biotechnological applications. DESs are binary mixtures often consisting of a hydrogen bond acceptor and a hydrogen bond donor, which allows for tailoring their properties for particular applications. If produced from sustainable resources, they can provide a greener alternative to many traditional organic solvents for usage in various applications (e.g., as reaction environment, crystallization agent, or storage medium). To navigate this large design space, it is crucial to comprehend the behavior of biomolecules (e.g., enzymes, proteins, cofactors, and DNA) in DESs and the impact of their individual components. Molecular dynamics (MD) simulations offer a powerful tool for understanding thermodynamic and transport processes at the atomic level and offer insights into their fundamental phenomena, which may not be accessible through experiments. While the experimental investigation of DESs for various biotechnological applications is well progressed, a thorough investigation of biomolecules in DESs via MD simulations has only gained popularity in recent years. Within this work, we aim to provide an overview of the current state of modeling biomolecules with MD simulations in DESs and discuss future directions with a focus for optimizing the molecular simulations and increasing our fundamental knowledge.

Fluorescence-Based Protein Stability Monitoring-A Review

Proteins are large biomolecules with a specific structure that is composed of one or more long amino acid chains. Correct protein structures are directly linked to their correct function, and many environmental factors can have either positive or negative effects on this structure. Thus, there is a clear need for methods enabling the study of proteins, their correct folding, and components affecting protein stability. There is a significant number of label-free methods to study protein stability. In this review, we provide a general overview of these methods, but the main focus is on fluorescence-based low-instrument and -expertise-demand techniques. Different aspects related to thermal shift assays (TSAs), also called differential scanning fluorimetry (DSF) or ThermoFluor, are introduced and compared to isothermal chemical denaturation (ICD). Finally, we discuss the challenges and comparative aspects related to these methods, as well as future opportunities and assay development directions.

Toward the mechanism of jarastatin (rJast) inhibition of the integrin αVβ3

Disintegrins are a family of cysteine-rich small proteins that were first identified in snake venom. The high divergence of disintegrins gave rise to a plethora of functions, all related to the interaction with integrins. Disintegrins evolved to interact selectively with different integrins, eliciting many physiological outcomes and being promising candidates for the therapy of many pathologies. We used NMR to determine the structure and dynamics of the recombinant disintegrin jarastatin (rJast) and its interaction with the cancer-related integrin αVβ3. rJast displayed the canonical fold of a medium-sized disintegrin and showed complex dynamic in multiple timescales. We used NMR experiments to map the interaction of rJast with αVβ3, and molecular docking followed by molecular dynamics (MD) simulation to describe the first structural model of a disintegrin/integrin complex. We showed that not only the RGD loop participates in the interaction, but also the N-terminal domain. rJast plasticity was essential for the interaction with αVβ3 and correlated with the main modes of motion depicted in the MD trajectories. In summary, our study provides novel structural insights that enhance our comprehension of the mechanisms underlying disintegrin functionality.

The nucleotide excision repair proteins through the lens of molecular dynamics simulations

Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.

Intramolecular covalent bonds in Gram-positive bacterial surface proteins

Gram-positive bacteria experience considerable mechanical perturbation when adhering to host surfaces during colonization and infection. They have evolved various adhesion proteins that are mechanically robust to ensure strong surface adhesion. Recently, it was discovered that these adhesion proteins contain rare, extra intramolecular covalent bonds that stabilize protein structures and participate in surface bonding. These intramolecular covalent bonds include isopeptides, thioesters, and ester bonds, which often form spontaneously without the need for additional enzymes. With the development of single-molecule force spectroscopy techniques, the detailed mechanical roles of these intramolecular covalent bonds have been revealed. In this review, we summarize the recent advances in this area of research, focusing on the link between the mechanical stability and function of these covalent bonds in Gram-positive bacterial surface proteins. We also highlight the potential impact of these discoveries on the development of novel antibiotics and chemical biology tools.

Structure-Function Relationship of the Disintegrin Family: Sequence Signature and Integrin Interaction

Disintegrins are small cysteine-rich proteins found in a variety of snake venom. These proteins selectively modulate integrin function, heterodimeric receptors involved in cell-cell and cell-matrix interaction that are widely studied as therapeutic targets. Snake venom disintegrins emerged from the snake venom metalloproteinase and are classified according to the sequence size and number of disulfide bonds. Evolutive structure and function diversification of disintegrin family involves a stepwise decrease in the polypeptide chain, loss of cysteine residues, and selectivity. Since the structure elucidation of echistatin, the description of the structural properties of disintegrins has allowed the investigation of the mechanisms involved in integrin-cell-extracellular matrix interaction. This review provides an analysis of the structures of all family groups enabling the description of an expanded classification of the disintegrin family in seven groups. Each group presents a particular disulfide pattern and sequence signatures, facilitating the identification of new disintegrins. The classification was based on the disintegrin-like domain of the human metalloproteinase (ADAM-10). We also present the sequence and structural signatures important for disintegrin-integrin interaction, unveiling the relationship between the structure and function of these proteins.