Protein Design: From the Aspect of Water Solubility and Stability

Linguistics-based formalization of the antibody language as a basis for antibody language models

Apparent parallels between natural language and antibody sequences have led to a surge in deep language models applied to antibody sequences for predicting cognate antigen recognition. However, a linguistic formal definition of antibody language does not exist, and insight into how antibody language models capture antibody-specific binding features remains largely uninterpretable. Here we describe how a linguistic formalization of the antibody language, by characterizing its tokens and grammar, could address current challenges in antibody language model rule mining.

Design of a water-soluble transmembrane receptor kinase with intact molecular function by QTY code

Membrane proteins are critical to biological processes and central to life sciences and modern medicine. However, membrane proteins are notoriously challenging to study, mainly owing to difficulties dictated by their highly hydrophobic nature. Previously, we reported QTY code, which is a simple method for designing water-soluble membrane proteins. Here, we apply QTY code to a transmembrane receptor, histidine kinase CpxA, to render it completely water-soluble. The designed CpxAQTY exhibits expected biophysical properties and highly preserved native molecular function, including the activities of (i) autokinase, (ii) phosphotransferase, (iii) phosphatase, and (iv) signaling receptor, involving a water-solubilized transmembrane domain. We probe the principles underlying the balance of structural stability and activity in the water-solubilized transmembrane domain. Computational approaches suggest that an extensive and dynamic hydrogen-bond network introduced by QTY code and its flexibility may play an important role. Our successful functional preservation further substantiates the robustness and comprehensiveness of QTY code.

Druggability properties of a L309K mutation in the antibody CH2 domain

In the early stages of antibody drug development, it is imperative to conduct a comprehensive assessment and enhancement of the druggability attributes of potential molecules by considering their fundamental physicochemical properties. This study specifically concentrates on the surface-exposed hydrophobic region of the candidate antibody aPDL1-WT and explores the effectiveness of the L309K mutation strategy. The resulting aPDL1-LK variant demonstrates a notable enhancement over the original antibody in addressing the issue of aggregation and formation of large molecular impurities under accelerated high-temperature conditions. The mutated molecule, aPDL1-LK, exhibits excellent physicochemical properties such as hydrophilicity, conformational stability, charge variant stability, post-translational modifications, and serum stability. In terms of biological function, aPDL1-LK maintains the same glycosylation pattern as the original antibody and shows no significant difference in affinity for antigen hPDL1 protein, CD16a-F158, CD64, CD32a-H131, and complement C1q, compared to aPDL1-WT. The L309K mutation results in an approximately twofold reduction in its affinity for CD16a-V158 and CD32a-R131. In vitro biological assays, including antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and complement-dependent cytotoxicity (CDC), reveal that the L309K mutation may decrease CD16a-V158-mediated ADCC activity due to the mutation-induced decrease in ligand affinity, while not affect CD32a-R131-mediated ADCP activity. In conclusion, the L309K mutation offers a promising strategy to enhance the druggability properties of candidate antibodies.

Hydrophobic core evolution of major histocompatibility complex class I chain-related protein A for dramatic enhancing binding affinity

Interface residues at sites of protein-protein interaction (PPI) are the focus for affinity optimisation. However, protein hydrophobic cores (HCs) play critical roles and shape the protein surface. We hypothesise that manipulating protein HCs can enhance PPI interaction affinities. A cell stress molecule, major histocompatibility complex class I chain-related protein A (MICA), binds to the natural killer group 2D (NKG2D) homodimer to form three molecule interactions. MICA was used as a study subject to support our hypothesis. We redesigned MICA HCs by directed mutagenesis and isolated high-affinity variants through a newly designed partial-denature panning (PDP) method. A few mutations in MICA HCs increased the NKG2D-MICA interaction affinity by 325-5613-fold. Crystal structures of the NKG2D-MICA variant complexes indicated that mutagenesis of MICA HCs stabilised helical elements for decreasing intermolecular interactive free energy (ΔG) of the NKG2D-MICA heterotrimer. The repacking of MICA HC mutants maintained overall surface residues and the authentic binding specificity of MICA. In conclusion, this study provides a new method for MICA redesign and affinity optimisation through HC manipulation without mutating PPI interface residues. Our study introduces a novel approach to protein manipulation, potentially expanding the toolkit for protein affinity optimisation.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling

BACKGROUND

Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.

RESULTS

In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.

CONCLUSIONS

GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

Structure-Based De Novo Design for the Discovery of Miniprotein Inhibitors Targeting Oncogenic Mutant BRAF

Being a component of the Ras/Raf/MEK/ERK signaling pathway crucial for cellular responses, the VRAF murine sarcoma viral oncogene homologue B1 (BRAF) kinase has emerged as a promising target for anticancer drug discovery due to oncogenic mutations that lead to pathway hyperactivation. Despite the discovery of several small-molecule BRAF kinase inhibitors targeting oncogenic mutants, their clinical utility has been limited by challenges such as off-target effects and suboptimal pharmacological properties. This study focuses on identifying miniprotein inhibitors for the oncogenic V600E mutant BRAF, leveraging their potential as versatile drug candidates. Using a structure-based de novo design approach based on binding affinity to V600E mutant BRAF and hydration energy, 39 candidate miniprotein inhibitors comprising three helices and 69 amino acids were generated from the substructure of the endogenous ligand protein (14-3-3). Through in vitro binding and kinase inhibition assays, two miniproteins (63 and 76) were discovered as novel inhibitors of V600E mutant BRAF with low-micromolar activity, with miniprotein 76 demonstrating a specific impediment to MEK1 phosphorylation in mammalian cells. These findings highlight miniprotein 76 as a potential lead compound for developing new cancer therapeutics, and the structural features contributing to its biochemical potency against V600E mutant BRAF are discussed in detail.

Enhancing Protein Solubility via Glycosylation: From Chemical Synthesis to Machine Learning Predictions

Glycosylation is a valuable tool for modulating protein solubility; however, the lack of reliable research strategies has impeded efficient progress in understanding and applying this modification. This study aimed to bridge this gap by investigating the solubility of a model glycoprotein molecule, the carbohydrate-binding module (CBM), through a two-stage process. In the first stage, an approach involving chemical synthesis, comparative analysis, and molecular dynamics simulations of a library of glycoforms was employed to elucidate the effect of different glycosylation patterns on solubility and the key factors responsible for the effect. In the second stage, a predictive mathematical formula, innovatively harnessing machine learning algorithms, was derived to relate solubility to the identified key factors and accurately predict the solubility of the newly designed glycoforms. Demonstrating feasibility and effectiveness, this two-stage approach offers a valuable strategy for advancing glycosylation research, especially for the discovery of glycoforms with increased solubility.

DeepKa Web Server: High-Throughput Protein pKa Prediction

DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.

Pathways of amyloid fibril formation and protein aggregation

The main cause of many neurodegenerative diseases and systemic amyloidoses is protein and peptide aggregation and the formation of amyloid fibrils. The study of aggregation mechanisms, the discovery and description of aggregate structures, and a comprehensive understanding of the molecular mechanisms of amyloid formation are of great importance for the diagnostic processes at the molecular level and for the development of therapeutic strategies to counter aggregation-associated disorders. Given that understanding protein misfolding phenomena is directly related to the protein folding process, we will briefly explain the protein folding mechanism and then discuss the important factors involved in protein aggregation. In the following, we review different mechanisms of amyloid formation and finally represent the current knowledge on how amyloid fibrils are formed based on kinetic and thermodynamic factors.

Changing Cinnamaldehyde Skeleton Achieves Antibacterial Nanoswitch

Changeable substituent groups of organic molecules can provide an opportunity to clarify the antibacterial mechanism of organic molecules by tuning the electron cloud density of their skeleton. However, understanding the antibacterial mechanism of organic molecules is challenging. Herein, we reported a molecular view strategy for clarifying the antibacterial switch mechanism by tuning electron cloud density of cinnamaldehyde molecule skeleton. The cinnamaldehyde and its derivatives were self-assembled into nanosheets with excellent water solubility, respectively. The experimental results show that α-bromocinnamaldehyde (BCA) nanosheets exhibits unprecedented antibacterial activity, but there is no antibacterial activity for α-methylcinnamaldehyde nanosheets. Therefore, the BCA nanosheets and α-methylcinnamaldehyde nanosheets achieve an antibacterial switch. Theoretical calculations further confirmed that the electron-withdrawing substituent of the bromine atom leads to a lower electron cloud density of the aldehyde group than that of the electron-donor substituent of the methyl group at the α-position of the cinnamaldehyde skeleton, which is a key point in elucidating the antimicrobial switch mechanism. The excellent biocompatibility of BCA nanosheets was confirmed by CCK-8. The mouse wound infection model, H&E staining, and the crawling ability of drosophila larvae show that as-prepared BCA nanosheets are safe and promising for wound healing. This study provides a new strategy for the synthesis of low-cost organic nanomaterials with good biocompatibility. It is expected to expand the application of natural organic small molecule materials in antimicrobial agents.

Structural bioinformatics studies of glutamate transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L->Q, I->T, F->Y and Q->L, T->I and Y->F

Glutamate transporters play key roles in nervous physiology by modulating excitatory neurotransmitter levels, when malfunctioning, involving in a wide range of neurological and physiological disorders. However, integral transmembrane proteins including the glutamate transporters remain notoriously difficult to study, due to their localization within the cell membrane. Here we present the structural bioinformatics studies of glutamate transporters and their water-soluble variants generated through QTY-code, a protein design strategy based on systematic amino acid substitutions. These include 2 structures determined by X-ray crystallography, cryo-EM, and 6 predicted by AlphaFold2, and their predicted water-soluble QTY variants. In the native structures of glutamate transporters, transmembrane helices contain hydrophobic amino acids such as leucine (L), isoleucine (I), and phenylalanine (F). To design water-soluble variants, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, namely glutamine (Q), threonine (T) and tyrosine (Y). The QTY variants exhibited water-solubility, with four having identical isoelectric focusing points (pI) and the other four having very similar pI. We present the superposed structures of the native glutamate transporters and their water-soluble QTY variants. The superposed structures displayed remarkable similarity with RMSD 0.528Å-2.456Å, despite significant protein transmembrane sequence differences (41.1%->53.8%). Additionally, we examined the differences of hydrophobicity patches between the native glutamate transporters and their QTY variants. Upon closer inspection, we discovered multiple natural variations of L->Q, I->T, F->Y and Q->L, T->I, Y->F in these transporters. Some of these natural variations were benign and the remaining were reported in specific neurological disorders. We further investigated the characteristics of hydrophobic to hydrophilic substitutions in glutamate transporters, utilizing variant analysis and evolutionary profiling. Our structural bioinformatics studies not only provided insight into the differences between the hydrophobic helices and hydrophilic helices in the glutamate transporters, but they are also expected to stimulate further study of other water-soluble transmembrane proteins.

Transmucosal Delivery of Peptides and Proteins Through Nanofibers: Current Status and Emerging Developments

Advancements in recombinant DNA technology have made proteins and peptides available for diagnostic and therapeutic applications, but their effectiveness when taken orally leads to poor patient compliance, requiring clinical administration. Among the alternative routes, transmucosal delivery has the advantage of being noninvasive and bypassing hepato-gastrointestinal clearance. Various mucosal routes-buccal, nasal, pulmonary, rectal, and vaginal-have been explored for delivering these macromolecules. Nanofibers, due to their unique properties like high surface-area-to-volume ratio, mechanical strength, and improved encapsulation efficiency, serve as promising carriers for proteins and peptides. These nanofibers can be tailored for quick dissolution, controlled release, enhanced encapsulation, targeted delivery, and improved bioavailability, offering superior pharmaceutical and pharmacokinetic performance compared to conventional methods. This leads to reduced dosages, fewer side effects, and enhanced patient compliance. Hence, nanofibers hold tremendous potential for protein/peptide delivery, especially through mucosal routes. This review focuses on the therapeutic application of proteins and peptides, challenges faced in their conventional delivery, techniques for fabricating different types of nanofibers and, various nanofiber-based dosage forms, and factors influencing nanofiber generation. Insights pertaining to the precise selection of materials used for fabricating nanofibers and regulatory aspects have been covered. Case studies wherein the use of specific protein/peptide-loaded nanofibers and delivered via oral/vaginal/nasal mucosa for diagnostic/therapeutic use and related preclinical and clinical studies conducted have been included in this review.

Synthesis of bioengineered heparin chemically and biologically similar to porcine-derived products and convertible to low MW heparin

Heparins have been invaluable therapeutic anticoagulant polysaccharides for over a century, whether used as unfractionated heparin or as low molecular weight heparin (LMWH) derivatives. However, heparin production by extraction from animal tissues presents multiple challenges, including the risk of adulteration, contamination, prion and viral impurities, limited supply, insecure supply chain, and significant batch-to-batch variability. The use of animal-derived heparin also raises ethical and religious concerns, as well as carries the risk of transmitting zoonotic diseases. Chemoenzymatic synthesis of animal-free heparin products would offer several advantages, including reliable and scalable production processes, improved purity and consistency, and the ability to produce heparin polysaccharides with molecular weight, structural, and functional properties equivalent to those of the United States Pharmacopeia (USP) heparin, currently only sourced from porcine intestinal mucosa. We report a scalable process for the production of bioengineered heparin that is biologically and compositionally similar to USP heparin. This process relies on enzymes from the heparin biosynthetic pathway, immobilized on an inert support and requires a tailored N-sulfoheparosan with N-sulfo levels similar to those of porcine heparins. We also report the conversion of our bioengineered heparin into a LMWH that is biologically and compositionally similar to USP enoxaparin. Ultimately, we demonstrate major advances to a process to provide a potential clinical and sustainable alternative to porcine-derived heparin products.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Enhancing bezlotoxumab binding to C. difficile toxin B2: insights from computational simulations and mutational analyses for antibody design

Clostridioides difficile infection (CDI) is a significant concern caused by widespread antibiotic use, resulting in diarrhea and inflammation from the gram-positive anaerobic bacterium C. difficile. Although bezlotoxumab (Bez), a monoclonal antibody (mAb), was developed to address CDI recurrences, the recurrence rate remains high, partly due to reduced neutralization efficiency against toxin B2. In this study, we aimed to enhance the binding of Bez to C. difficile toxin B2 by combining computational simulations and mutational analyses. We identified specific mutations in Bez, including S28R, S31W/K, Y32R, S56W and G103D/S in the heavy chain (Hc), and S32F/H/R/W/Y in the light chain (Lc), which significantly improved binding to toxin B2 and formed critical protein-protein interactions. Through molecular dynamics simulations, several single mutations, such as HcS28R, LcS32H, LcS32R, LcS32W and LcS32Y, exhibited superior binding affinities to toxin B2 compared to Bez wild-type (WT), primarily attributed to Coulombic interactions. Combining the HcS28R mutation with four different mutations at residue LcS32 led to even greater binding affinities in double mutants (MTs), particularly HcS28R/LcS32H, HcS28R/LcS32R and HcS28R/LcS32Y, reinforcing protein-protein binding. Analysis of per-residue decomposition free energy highlighted key residues contributing significantly to enhanced binding interactions, emphasizing the role of electrostatic interactions. These findings offer insights into rational Bez MT design for improved toxin B2 binding, providing a foundation for developing more effective antibodies to neutralize toxin B2 and combat-related infections.Communicated by Ramaswamy H. Sarma.

Computational design of soluble functional analogues of integral membrane proteins

De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.

Lipid vesicle-based molecular robots

A molecular robot, which is a system comprised of one or more molecular machines and computers, can execute sophisticated tasks in many fields that span from nanomedicine to green nanotechnology. The core parts of molecular robots are fairly consistent from system to system and always include (i) a body to encapsulate molecular machines, (ii) sensors to capture signals, (iii) computers to make decisions, and (iv) actuators to perform tasks. This review aims to provide an overview of approaches and considerations to develop molecular robots. We first introduce the basic technologies required for constructing the core parts of molecular robots, describe the recent progress towards achieving higher functionality, and subsequently discuss the current challenges and outlook. We also highlight the applications of molecular robots in sensing biomarkers, signal communications with living cells, and conversion of energy. Although molecular robots are still in their infancy, they will unquestionably initiate massive change in biomedical and environmental technology in the not too distant future.

Extracellular vesicles for developing targeted hearing loss therapy

Substantial efforts have been made for local administration of small molecules or biologics in treating hearing loss diseases caused by either trauma, genetic mutations, or drug ototoxicity. Recently, extracellular vesicles (EVs) naturally secreted from cells have drawn increasing attention on attenuating hearing impairment from both preclinical studies and clinical studies. Highly emerging field utilizing diverse bioengineering technologies for developing EVs as the bioderived therapeutic materials, along with artificial intelligence (AI)-based targeting toolkits, shed the light on the unique properties of EVs specific to inner ear delivery. This review will illuminate such exciting research field from fundamentals of hearing protective functions of EVs to biotechnology advancement and potential clinical translation of functionalized EVs. Specifically, the advancements in assessing targeting ligands using AI algorithms are systematically discussed. The overall translational potential of EVs is reviewed in the context of auditory sensing system for developing next generation gene therapy.

QTY code designed antibodies for aggregation prevention: A structural bioinformatic and computational study

Therapeutic monoclonal antibodies are the most rapidly growing class of molecular medicine, and they are beneficial to the treatment of a broad spectrum of human diseases. However, the aggregation of antibodies during the process of manufacture, distribution, and storage poses significant challenges, potentially compromising efficacy and inducing adverse immune responses. We previously conceived a QTY (glutamine, threonine, tyrosine) code, a simple tool for enhancing protein water-solubility by systematically pairwise replacing hydrophobic residues L (leucine), V (valine)/I (isoleucine), and F (phenylalanine). The QTY code offers a promising alternative to traditional methods of controlling aggregation in integral transmembrane proteins. In this study, we designed variants of four antibodies applying the QTY code, changing only the β-sheets. Through the structure-based aggregation analysis, we found that these QTY antibody variants demonstrated significantly decreased aggregation propensity compared to their wild-type counter parts. Our results of molecular dynamics simulations showed that the design by QTY code is capable of maintaining the antigen-binding affinity and structural stability. Our structural informatic and computational study suggests that the QTY code offers a significant potential in mitigating antibody aggregation.

Artificial intelligence in Immuno-genetics

Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

Structural bioinformatics studies of six human ABC transporters and their AlphaFold2-predicted water-soluble QTY variants

Human ATP-binding cassette (ABC) transporters are one of the largest families of membrane proteins and perform diverse functions. Many of them are associated with multidrug resistance that often results in cancer treatment with poor outcomes. Here, we present the structural bioinformatics study of six human ABC membrane transporters with experimentally determined cryo-electron microscopy (CryoEM) structures including ABCB7, ABCC8, ABCD1, ABCD4, ABCG1, ABCG5, and their AlphaFold2-predicted water-soluble QTY variants. In the native structures, there are hydrophobic amino acids such as leucine (L), isoleucine (I), valine (V), and phenylalanine (F) in the transmembrane alpha helices. These hydrophobic amino acids are systematically replaced by hydrophilic amino acids glutamine (Q), threonine (T), and tyrosine (Y). Therefore, these QTY variants become water soluble. We also present the superposed structures of native ABC transporters and their water-soluble QTY variants. The superposed structures show remarkable similarity with root mean square deviations between 1.064 and 3.413 Å despite significant (41.90-54.33%) changes to the protein sequence of the transmembrane domains. We also show the differences in hydrophobicity patches between the native ABC transporters and their QTY variants. We explain the rationale behind why the QTY membrane protein variants become water soluble. Our structural bioinformatics studies provide insight into the differences between the hydrophobic helices and hydrophilic helices and will likely further stimulate designs of water-soluble multispan transmembrane proteins and other aggregated proteins. The water-soluble ABC transporters may be useful as soluble antigens to generate therapeutic monoclonal antibodies for combating multidrug resistance in clinics.

RNA-dependent proteome solubility maintenance in Escherichia coli lysates analysed by quantitative mass spectrometry: Proteomic characterization in terms of isoelectric point, structural disorder, functional hub, and chaperone network

Protein aggregation, a consequence of misfolding and impaired proteostasis, can lead to cellular malfunctions such as various proteinopathies. The mechanisms protecting proteins from aggregation in complex cellular environments have long been investigated, often from a protein-centric viewpoint. However, our study provides insights into a crucial, yet overlooked actor: RNA. We found that depleting RNAs from Escherichia coli lysates induces global protein aggregation. Our quantitative mass spectrometry analysis identified over 900 statistically significant proteins from the Escherichia coli proteome whose solubility depends on RNAs. Proteome-wide characterization showed that the RNA dependency is particularly enriched among acidic proteins, intrinsically disordered proteins, and structural hub proteins. Moreover, we observed distinct differences in RNA-binding mode and Gene Ontology categories between RNA-dependent acidic and basic proteins. Notably, the solubility of key molecular chaperones [Trigger factor, DnaJ, and GroES] is largely dependent on RNAs, suggesting a yet-to-be-explored hierarchical relationship between RNA-based chaperone (termed as chaperna) and protein-based chaperones, both of which constitute the whole chaperone network. These findings provide new insights into the RNA-centric role in maintaining healthy proteome solubility in vivo, where proteins associate with a variety of RNAs, either stably or transiently.

Coevolution-based computational approach to detect resistance mechanism of epidermal growth factor receptor

Tyrosine kinase epidermal growth factor receptor (EGFR) correlates the neoplastic cell metastasis, angiogenesis, neoplastic incursion, and apoptosis. Due to the involvement of EGFR in these biological processes, it becomes a most potent target for treating non-small cell lung cancer (NSCLC). The tyrosine kinase inhibitors (TKI) have endorsed high efficacy and anticipation to patients but unfortunately, within a year of treatment, drug targets develop resistance due to mutations. The present study detected the compensatory mutations in EGFR to know the evolutionary mechanism of drug resistance. The results of this study demonstrate that compensatory mutations enlarge the drug-binding pocket which may lead to the altered orientation of the ligand (gefitinib and erlotinib) causing drug resistance. This indicates that coevolutionary forces play a significant role in fine-tuning the structure of EGFR protein against the drugs. The analysis provides insight into the evolution-induced structural aspects of drug resistance changes in EGFR which in turn be useful in designing drugs with better efficacy.

Assessing the effect of Maillard reaction products on the functionality and antioxidant properties of Amaranth-red seaweed blends

Plant-based proteins, represented by amaranth in our study, embrace a potential as an ingredient for the functional-food formulation. However, their efficacy is hindered by inherent limitations in solubility, emulsification, and antioxidant traits. The Maillard reaction, a complex chemical-process resulting in a diverse array of products, including Maillard conjugates and Maillard reaction products (MRPs), can employ variable effects on these specific attributes. To elucidate the influence of this reaction and the MRPs on the aforementioned properties, we used a complex blend of dehydrated seaweed Gracilaria and amaranth protein to create a conjugate-MRP blend. Our investigations revealed that the resultant incorporation enhanced solubility, emulsification, and antioxidant properties, while the intermediates formed did not progress to advanced glycation stages. This change is likely attributed to the dual effect of conjugates that altered the secondary protein structure, while the generation and/or preservation of MRPs post ultrasonication and spray drying enhanced its antioxidant potential.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

Shape-Anisotropic Assembly of Protein Nanocages with Identical Building Blocks by Designed Intermolecular π-π Interactions

Protein lattices that shift the structure and shape anisotropy in response to environmental cues are closely coupled to potential functionality. However, to design and construct shape-anisotropic protein arrays from the same building blocks in response to different external stimuli remains challenging. Here, by a combination of the multiple, symmetric interaction sites on the outer surface of protein nanocages and the tunable features of phenylalanine-phenylalanine interactions, a protein engineering approach is reported to construct a variety of superstructures with shape anisotropy, including 3D cubic, 2D hexagonal layered, and 1D rod-like crystalline protein nanocage arrays by using one single protein building block. Notably, the assembly of these crystalline protein arrays is reversible, which can be tuned by external stimuli (pH and ionic strength). The anisotropic morphologies of the fabricated macroscopic crystals can be correlated with the Å-to-nm scale protein arrangement details by crystallographic elucidation. These results enhance the understanding of the freedom offered by an object's symmetry and inter-object π-π stacking interactions for protein building blocks to assemble into direction- and shape-anisotropic biomaterials.

Dynamic Dimerization of Chemokine Receptors and Potential Inhibitory Role of Their Truncated Isoforms Revealed through Combinatorial Prediction

Chemokine receptors play crucial roles in fundamental biological processes. Their malfunction may result in many diseases, including cancer, autoimmune diseases, and HIV. The oligomerization of chemokine receptors holds significant functional implications that directly affect their signaling patterns and pharmacological responses. However, the oligomerization patterns of many chemokine receptors remain poorly understood. Furthermore, several chemokine receptors have highly truncated isoforms whose functional role is not yet clear. Here, we computationally show homo- and heterodimerization patterns of four human chemokine receptors, namely CXCR2, CXCR7, CCR2, and CCR7, along with their interaction patterns with their respective truncated isoforms. By combining the neural network-based AlphaFold2 and physics-based protein-protein docking tool ClusPro, we predicted 15 groups of complex structures and assessed the binding affinities in the context of atomistic molecular dynamics simulations. Our results are in agreement with previous experimental observations and support the dynamic and diverse nature of chemokine receptor dimerization, suggesting possible patterns of higher-order oligomerization. Additionally, we uncover the strong potential of truncated isoforms to block homo- and heterodimerization of chemokine receptors, also in a dynamic manner. Our study provides insights into the dimerization patterns of chemokine receptors and the functional significance of their truncated isoforms.

Rational Redesign of Chitosanase to Enhance Thermostability and Catalytic Activity to Produce Chitooligosaccharides with a Relatively High Degree of Polymerization

Chitooligosaccharides (hdpCOS) with a high degree of polymerization (hdp, DP 4-10) generally have greater biological activities than those of low-DP (ldp, DP 2-3) COS. Chitosanase from Bacillus amyloliquefaciens KCP2 (Csn46) can degrade chitosan to more hdpCOS at high temperature (70 °C), but low thermal stability at this temperature makes it unsuitable for industrial application; the wild-type enzyme can only produce COS (DP 2-4) at lower temperatures. Several thermostable mutants were obtained by modifying chitosanase using a comprehensive strategy based on a computer-aided mutant design. A combination of four beneficial single-point mutations (A129L/T175 V/K70T/D34G) to Csn46 was selected to obtain a markedly improved mutant, Mut4, with a half-life at 60 °C extended from 34.31 to 690.80 min, and the specific activity increased from 1671.73 to 3528.77 U/mg. Mut4 produced COS with DPs of 2-4 and 2-7 at 60 and 70 °C, respectively. Therefore, Mut4 has the potential to be applied to the industrial-scale preparation of hdpCOS with high biological activity.

Prediction of protein aggregation propensity employing SqFt-based logistic regression model

Here we present a novel machine-learning approach to predict protein aggregation propensity (PAP) which is a key factor in the formation of amyloid fibrils based on logistic regression (LR). Amyloid fibrils are associated with various neurodegenerative diseases (ND) such as Alzheimer's disease (AD) and Parkinson's disease (PD), which are caused by oxidative stress and impaired protein homeostasis. Accordingly, the paper uses a dataset of hexapeptides with known aggregation tendencies and eight physiochemical features to train and test the LR model. Also, it evaluates the performance of the LR model using F-measure and Matthews correlation coefficient (MCC) as metrics and compares it with other existing methods. Moreover, it investigates the effect of combining sequence and feature information in the prediction. In conclusion, the LR model with sequence and feature information achieves high F-measure (0.841) and MCC (0.6692), outperforming other methods and demonstrating its efficiency and reliability for PAP prediction. In addition, the overall performance of the concluded method was higher than the other known servers, for instance, Aggrescan, Metamyl, Foldamyloid, and PASTA 2.0. The LR model can be accessed at: https://github.com/KatherineEshari/Protein-aggregation-prediction.

CrMP-Sol database: classification, bioinformatic analyses and comparison of cancer-related membrane proteins and their water-soluble variant designs

Membrane proteins are critical mediators for tumor progression and present enormous therapeutic potentials. Although gene profiling can identify their cancer-specific signatures, systematic correlations between protein functions and tumor-related mechanisms are still unclear. We present here the CrMP-Sol database ( https://bio-gateway.aigene.org.cn/g/CrMP ), which aims to breach the gap between the two. Machine learning was used to extract key functional descriptions for protein visualization in the 3D-space, where spatial distributions provide function-based predictive connections between proteins and cancer types. CrMP-Sol also presents QTY-enabled water-soluble designs to facilitate native membrane protein studies despite natural hydrophobicity. Five examples with varying transmembrane helices in different categories were used to demonstrate the feasibility. Native and redesigned proteins exhibited highly similar characteristics, predicted structures and binding pockets, and slightly different docking poses against known ligands, although task-specific designs are still required for proteins more susceptible to internal hydrogen bond formations. The database can accelerate therapeutic developments and biotechnological applications of cancer-related membrane proteins.

A probabilistic view of protein stability, conformational specificity, and design

Various approaches have used neural networks as probabilistic models for the design of protein sequences. These "inverse folding" models employ different objective functions, which come with trade-offs that have not been assessed in detail before. This study introduces probabilistic definitions of protein stability and conformational specificity and demonstrates the relationship between these chemical properties and the [Formula: see text] Boltzmann probability objective. This links the Boltzmann probability objective function to experimentally verifiable outcomes. We propose a novel sequence decoding algorithm, referred to as "BayesDesign", that leverages Bayes' Rule to maximize the [Formula: see text] objective instead of the [Formula: see text] objective common in inverse folding models. The efficacy of BayesDesign is evaluated in the context of two protein model systems, the NanoLuc enzyme and the WW structural motif. Both BayesDesign and the baseline ProteinMPNN algorithm increase the thermostability of NanoLuc and increase the conformational specificity of WW. The possible sources of error in the model are analyzed.

Modulation of the catalytic activity and thermostability of a thermostable GH7 endoglucanase by engineering the key loop B3

The loop B3 of glycoside hydrolase family 7 (GH7) endoglucanases is confined into long and short types. TtCel7 is a thermophilic GH7 endoglucanase from Thermothelomyces thermophilus ATCC 42464 with a long loop B3. TtCel7 was distinct for the excellent thermostability (>30 % residual activity after 1-h incubation at 90 °C). The catalytic efficiency was reduced by removing the disulfide bond in loop B3 (C220A) and truncated the loop B3 (B3cut). However, B3cut exhibited improved thermostability, the remaining enzyme activity increased by 39 %-171 % compared toTtCel7 when treated at 70-90 °C for 1-h. Based on the analysis of molecular dynamics simulation, both loops B1 and A3 of B3cut swing toward the catalytic center, which contributed to the reduced cleft-space and increased structure-rigidity. Conversely, the deletion of disulfide bond resulted in a reduction of structural rigidity in C220A. Through structure-directed enzyme modulation, this study has identified two structural elements that are related to the catalysis and thermostability of TtCel7. The loop B3 of TtCel7 possibly stretches the catalytic pocket, thereby increases the openness of the catalytic tunnel and enhancing flexibility for efficient catalysis. Additionally, the disulfide bond within loop B3 serves to enhance structural stability and maintain a heightened level of activity.

Exploring Innovative Approaches to Isolate a One-Component c-di-GMP Transducer: A Pilot Study

Environmental nutrients control bacterial biofilm homeostasis, by regulating the intracellular levels of c-di-GMP. One component transducers can sense different classes of small molecules through a periplasmic domain; the nutrient recognition triggers the subsequent regulation of the downstream cytosolic diguanylate cyclase (GGDEF) or phosphodiesterase (EAL) domains, via transmembrane helix(ces), to finally change c-di-GMP levels.Protein studies on such transducers have been mainly carried out on isolated domains due to the presence of the transmembrane portion. Nevertheless, the cleavage of GGDEF and EAL-containing proteins could be detrimental since both tertiary and quaternary structures could be allosterically controlled; to by-pass this limitation, studies on the corresponding full-length proteins are highly desired.We have in silico selected a GGDEF-EAL transducer from Dyella thiooxydans (ann. A0A160N0B7), whose periplasmic binding domain was predicted to bind to arginine, a nutrient often associated with chronic infections and biofilm. This protein has been used as an in vitro tool for the identification of the best approach for its isolation, including (i) protein engineering to produce a water-soluble version via QTY (Glutamine, Threonine, and Tyrosine) code or (ii) nanodiscs assembly. The results on this "prototype" may represent the proof-of-concept for future isolation of other transmembrane proteins sharing the same architecture, including more complex nutrient-based transducers controlling c-di-GMP levels.

Like-Charge PISA: Polymerization-Induced Like-Charge Electrostatic Self-Assembly

We report the use of l-aspartic acid chiral ionic hydrogen bonds to drive liquid-liquid phase separation (LLPS) and precision two-dimensional electrostatic self-assembly in photo-RAFT aqueous polymerization-induced self-assembly (photo-PISA). Homopolymerization can yield salt-resistant, 3 nm ultrafine fibril-structured 5 nm ultrathin lamellae via LLPS, a left-to-right-handed chirality transition, and a droplets-to-lamellae transition. Like-charge block copolymerization leads to supercharged yet identical fibril-structured ultrathin lamellae, also, via LLPS, the left-to-right chirality transition and the droplets-to-lamellae transition. Ultrafine structures maintain intactness upon the seeded polymerization of the oppositely charged monomer. This work demonstrates that amino acid chiral ionic hydrogen bonds are powerful for the precision synthesis of salt-resistant ultrathin membrane nanomaterials.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Scalable biomimetic sensing system with membrane receptor dual-monolayer probe and graphene transistor arrays

Affinity-based biosensing can enable point-of-care diagnostics and continuous health monitoring, which commonly follows bottom-up approaches and is inherently constrained by bioprobes' intrinsic properties, batch-to-batch consistency, and stability in biofluids. We present a biomimetic top-down platform to circumvent such difficulties by combining a "dual-monolayer" biorecognition construct with graphene-based field-effect-transistor arrays. The construct adopts redesigned water-soluble membrane receptors as specific sensing units, positioned by two-dimensional crystalline S-layer proteins as dense antifouling linkers guiding their orientations. Hundreds of transistors provide statistical significance from transduced signals. System feasibility was demonstrated with rSbpA-ZZ/CXCR4QTY-Fc combination. Nature-like specific interactions were achieved toward CXCL12 ligand and HIV coat glycoprotein in physiologically relevant concentrations, without notable sensitivity loss in 100% human serum. The construct is regeneratable by acidic buffer, allowing device reuse and functional tuning. The modular and generalizable architecture behaves similarly to natural systems but gives electrical outputs, which enables fabrication of multiplex sensors with tailored receptor panels for designated diagnostic purposes.

Improving the soluble expression of difficult-to-express proteins in prokaryotic expression system via protein engineering and synthetic biology strategies

Solubility and folding stability are key concerns for difficult-to-express proteins (DEPs) restricted by amino acid sequences and superarchitecture, resolved by the precise distribution of amino acids and molecular interactions as well as the assistance of the expression system. Therefore, an increasing number of tools are available to achieve efficient expression of DEPs, including directed evolution, solubilization partners, chaperones, and affluent expression hosts, among others. Furthermore, genome editing tools, such as transposons and CRISPR Cas9/dCas9, have been developed and expanded to construct engineered expression hosts capable of efficient expression ability of soluble proteins. Accounting for the accumulated knowledge of the pivotal factors in the solubility and folding stability of proteins, this review focuses on advanced technologies and tools of protein engineering, protein quality control systems, and the redesign of expression platforms in prokaryotic expression systems, as well as advances of the cell-free expression technologies for membrane proteins production.

Basis for Accurate Protein pKa Prediction with Machine Learning

pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pK_a's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pK_a prediction is crucial. Here we present a theoretical pK_a data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pK_a orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pK_a prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pK_a predictor and thus can be applied immediately to, for example, pK_a database construction, protein design, drug discovery, and so on.

PP19128R, a Multiepitope Vaccine Designed to Prevent Latent Tuberculosis Infection, Induced Immune Responses In Silico and In Vitro Assays

Background: Latent tuberculosis infection (LTBI) is the primary source of active tuberculosis (ATB), but a preventive vaccine against LTBI is lacking. Methods: In this study, dominant helper T lymphocyte (HTL), cytotoxic T lymphocyte (CTL), and B-cell epitopes were identified from nine antigens related to LTBI and regions of difference (RDs). These epitopes were used to construct a novel multiepitope vaccine (MEV) based on their antigenicity, immunogenicity, sensitization, and toxicity. The immunological characteristics of the MEV were analyzed with immunoinformatics technology and verified by enzyme-linked immunospot assay and Th1/Th2/Th17 cytokine assay in vitro. Results: A novel MEV, designated PP19128R, containing 19 HTL epitopes, 12 CTL epitopes, 8 B-cell epitopes, toll-like receptor (TLR) agonists, and helper peptides, was successfully constructed. Bioinformatics analysis showed that the antigenicity, immunogenicity, and solubility of PP19128R were 0.8067, 9.29811, and 0.900675, respectively. The global population coverage of PP19128R in HLA class I and II alleles reached 82.24% and 93.71%, respectively. The binding energies of the PP19128R-TLR2 and PP19128R-TLR4 complexes were -1324.77 kcal/mol and -1278 kcal/mol, respectively. In vitro experiments showed that the PP19128R vaccine significantly increased the number of interferon gamma-positive (IFN-γ⁺) T lymphocytes and the levels of cytokines, such as IFN-γ, tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and IL-10. Furthermore, positive correlations were observed between PP19128R-specific cytokines in ATB patients and individuals with LTBI. Conclusions: The PP19128R vaccine is a promising MEV with excellent antigenicity and immunogenicity and no toxicity or sensitization that can induce robust immune responses in silico and in vitro. This study provides a vaccine candidate for the prevention of LTBI in the future.

Peptide Self-Assembled Nanocarriers for Cancer Drug Delivery

The design of novel cancer drug nanocarriers is critical in the framework of cancer therapeutics. Nanomaterials are gaining increased interest as cancer drug delivery systems. Self-assembling peptides constitute an emerging novel class of highly attractive nanomaterials with highly promising applications in drug delivery, as they can be used to facilitate drug release and/or stability while reducing side effects. Here, we provide a perspective on peptide self-assembled nanocarriers for cancer drug delivery and highlight the aspects of metal coordination, structure stabilization, and cyclization, as well as minimalism. We review particular challenges in nanomedicine design criteria and, finally, provide future perspectives on addressing a portion of the challenges via self-assembling peptide systems. We consider that the intrinsic advantages of such systems, along with the increasing progress in computational and experimental approaches for their study and design, could possibly lead to novel classes of single or multicomponent systems incorporating such materials for cancer drug delivery.

Advances in Peptide-Based Hydrogel for Tissue Engineering

The development of peptide-based materials has emerged as one of the most challenging aspects of biomaterials in recent years. It has been widely acknowledged that peptide-based materials can be used in a broad range of biomedical applications, particularly in tissue engineering. Among them, hydrogels have been attracting considerable interest in tissue engineering because they mimic tissue formation conditions by providing a three-dimensional environment and a high water content. It has been found that peptide-based hydrogels have received more attention due to mimicking proteins, particularly extracellular matrix proteins, as well as the wide variety of applications they are capable of serving. It is without a doubt that peptide-based hydrogels have become the leading biomaterials of today owing to their tunable mechanical stability, high water content, and high biocompatibility. Here, we discuss in detail various types of peptide-based materials, emphasizing peptide-based hydrogels, and then we examine in detail how hydrogels are formed, paying particular attention to the peptide structures that are incorporated into the final structure. Following that, we discuss the self-assembly and formation of hydrogels under various conditions, as well as the parameters to be considered as critical factors, which include pH, amino acid composi- tion within the sequence, and cross-linking techniques. Further, recent studies on the development of peptide-based hydrogels and their applications in tissue engineering are reviewed.

Racemic Amino Acid Assembly Enables Supramolecular β-Sheet Transition with Property Modulations

Amino acids are the most simplistic bio-building blocks and perform a variety of functions in metabolic activities. Increasing publications report that amino acid-based superstructures present amyloid-like characteristics, arising from their supramolecular β-sheet secondary structures driven by hydrogen-bonding-connected supramolecular β-strands, which are formed by head-to-tail hydrogen bonds between terminal amino and carboxyl groups of the adjacent residues. Therefore, the establishment of the structure-function relationships is critical for exploring the properties and applications of amino acid assemblies. Among the naturally encoded self-assembling amino acids, tyrosine (Y)-based superstructures have been found to show diverse properties and functions including high rigidity, promoting melanin formations, mood regulations, and preventing anxiety, thus showing promising potential as next-generation functional biomaterials for biomedical and bio-machine interface applications. However, the development of Y-based organizations of functional features is severely limited due to the intrinsic difficulty of modulating the energetically stable supramolecular β-sheet structures. Herein, we report that by the racemic assembly of l-Y and d-Y, the supramolecular secondary structures are modulated from the antiparallel β-sheets in the enantiomeric assemblies to the parallel ones in the racemate counterparts, thus leading to higher degrees of freedom, which finally induce distinct organization kinetics and modulation of the physicochemical properties including the optical shifts, elastic softening, and the piezoelectric outputs of the superstructures.

Combination of Enzymes with Materials to Give Them Antimicrobial Features: Modern Trends and Perspectives

Multidrug-resistant bacteria form serious problems in many areas, including medicine and the food industry. At the same time, great interest is shown in the transfer or enhancement of antimicrobial properties to various materials by modifying them with enzymes. The use of enzymes in biomaterials with antimicrobial properties is important because enzymes can be used as the main active components providing antimicrobial properties of functionalized composite biomaterials, or can serve as enhancers of the antimicrobial action of certain substances (antibiotics, antimicrobial peptides, metal nanoparticles, etc.) against cells of various microorganisms. Enzymes can simultaneously widen the spectrum of antimicrobial activity of biomaterials. This review presents the most promising enzymes recently used for the production of antibacterial materials, namely hydrolases and oxidoreductases. Computer modeling plays an important role in finding the most effective combinations between enzymes and antimicrobial compounds, revealing their possible interactions. The range of materials that can be functionalized using enzymes looks diverse. The physicochemical characteristics and functionalization methods of the materials have a significant impact on the activity of enzymes. In this context, fibrous materials are of particular interest. The purpose of this review is to analyze the current state of the art in this area.

Bioinformatics analysis and consistency verification of a novel tuberculosis vaccine candidate HP13138PB

Background

With the increasing incidence of tuberculosis (TB) and the shortcomings of existing TB vaccines to prevent TB in adults, new TB vaccines need to be developed to address the complex TB epidemic.

Method

The dominant epitopes were screened from antigens to construct a novel epitope vaccine termed HP13138PB. The immune properties, structure, and function of HP13138PB were predicted and analyzed with bioinformatics and immunoinformatics. Then, the immune responses induced by the HP13138PB were confirmed by enzyme-linked immunospot assay (ELISPOT) and Th1/Th2/Th17 multi-cytokine detection kit.

Result

The HP13138PB vaccine consisted of 13 helper T lymphocytes (HTL) epitopes, 13 cytotoxic T lymphocytes (CTL) epitopes, and 8 B-cell epitopes. It was found that the antigenicity, immunogenicity, and solubility index of the HP13138PB vaccine were 0.87, 2.79, and 0.55, respectively. The secondary structure prediction indicated that the HP13138PB vaccine had 31% of α-helix, 11% of β-strand, and 56% of coil. The tertiary structure analysis suggested that the Z-score and the Favored region of the HP13138PB vaccine were -4.47 88.22%, respectively. Furthermore, the binding energies of the HP13138PB to toll-like receptor 2 (TLR2) was -1224.7 kcal/mol. The immunoinformatics and real-world experiments showed that the HP13138PB vaccine could induce an innate and adaptive immune response characterized by significantly higher levels of cytokines such as interferon-gamma (IFN-γ), tumor necrosis factor-α (TNF-α), interleukin-4 (IL-4), and IL-10.

Conclusion

The HP13138PB is a potential vaccine candidate to prevent TB, and this study preliminarily evaluated the ability of the HP13138PB to generate an immune response, providing a precursor target for developing TB vaccines.

Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions

The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.