Building Blocks Of Proteins Research Articles

Design of a Genetically Programmable and Customizable Protein Scaffolding System for the Hierarchical Assembly of Robust, Functional Macroscale Materials.

Inspired by the properties of natural protein-based biomaterials, protein nanomaterials are increasingly designed with natural or engineered peptides or with protein building blocks. Few examples describe the design of functional protein-based materials for biotechnological applications that can be readily manufactured, are amenable to functionalization, and exhibit robust assembly properties for macroscale material formation. Here, we designed a protein-scaffolding system that self-assembles into robust, macroscale materials suitable for in vitro cell-free applications. By controlling the coexpression in Escherichia coli of self-assembling scaffold building blocks with and without modifications for covalent attachment of cross-linking cargo proteins, hybrid scaffolds with spatially organized conjugation sites are overproduced that can be readily isolated. Cargo proteins, including enzymes, are rapidly cross-linked onto scaffolds for the formation of functional materials. We show that these materials can be used for the in vitro operation of a coimmobilized two-enzyme reaction and that the protein material can be recovered and reused. We believe that this work will provide a versatile platform for the design and scalable production of functional materials with customizable properties and the robustness required for biotechnological applications.

Differential amino acid usage leads to ubiquitous edge effect in proteomes across domains of life that can be explained by amino acid secondary structure propensities

Amino acids are the building blocks of proteins and enzymes which are essential for life. Understanding amino acid usage offers insights into protein function and molecular mechanisms underlying life histories. However, genome-wide patterns of amino acid usage across domains of life remain poorly understood. Here, we analysed the proteomes of 5590 species across four domains and found that only a few amino acids are consistently the most and least used. This differential usage results in lower amino acid usage diversity at the most and least frequent ranks, creating a ubiquitous inverted U-shape pattern of amino acid diversity and rank which we call an ‘edge effect’ across proteomes and domains of life. This effect likely stems from protein secondary structural constraints, not the evolutionary chronology of amino acid incorporation into the genetic code, highlighting the functional rather than evolutionary influences on amino acid usage. We also tested other contemporary hypotheses regarding amino acid usage in proteomes and found that amino acid usage varies across life’s domains and is only weakly influenced by growth temperature. Our findings reveal a novel and pervasive amino acid usage pattern across genomes with the potential to help us probe deep evolutionary relationships and advance synthetic biology.

Umpolung Phosphorylation of Tyrosine via 1,2-Phospha-Brook Rearrangement.

Phosphorylated tyrosine is a fundamental building block of bioactive peptides and proteins. However, the chemoselective phosphorylation of tyrosine over other nucleophilic amino acid residues in unprotected peptides remains a significant challenge. Here we report an umpolung strategy that converts the C-terminal tyrosine into an electrophilic spirolactone cyclohexadienone motif through hypervalent iodine oxidation, followed by a 1,2-phospha-Brook rearrangement using phosphite diesters as nucleophilic phosphoryl donors. This reaction proceeds chemoselectively at the tyrosine phenol and is applicable to a wide range of peptide substrates containing various nucleophilic amino acid residues, including serine and threonine.

Aminoacyl-tRNA synthetases

Aminoacyl-tRNA synthetases hold the key to the genetic code and assign nucleic acid-based codons to amino acids, the building blocks of proteins. In their ability to recognize identity elements on transfer RNAs (tRNAs), some as simple as a single base pair, they ensure that the same proteins are formed each time information embedded in DNA is transcribed into messenger RNA (mRNA) and translated into proteins (Figure 1A). Thus, aminoacyl-tRNA synthetase active sites are conserved; however, since their evolutionary origin, their functions have been co-opted, expanded on and played novel roles during evolution. Below, we provide an overview of the many functions of aminoacyl-tRNA synthetases - from their role in translation, one of the most fundamental processes of all life, to newly discovered, diverse functions.

Stimulative effect of chitosan with amino acid to enhance growth, essential oil, and some physiochemical characteristics of two Mentha cultivars

Background Mint plants (Mentha spp.) are a member of the Lamiaceae family and it has long been used in medicine. Its applications include carminative, anti-inflammatory, antispasmodic, antiemetic, diaphoretic, analgesic, stimulant, emmenagogue, and anticatarrhal. Adding chitosan to medicinal plants has a major role in the process of secondary metabolism, as its addition stimulates the production of chemical compounds or essential oils in the plant. Amino acids are one of the possible strategies for increasing agricultural productivity. They are organic nitrogen polymers that are used as the building blocks of proteins and enzymes. Objective This study aimed to determine how chitosan, in combination with or without the foliar application of amino acids, affected the growth and physiological traits of two cultivars of mint. Materials and methods Two pot trail investigation studies were carried out during the two consecutive seasons 2021 and 2022 under the natural conditions of the greenhouse of the National Research Center (NRC), Dokki, Giza, Egypt. To study the effect of two levels of chitosan (1.5 and 3.0 g/l with amino acid at rates of 50 and 100 mg/l) as foliar application on growth, essential oil, and some physiochemical characteristics of two Mentha (Mentha viridis and Mentha longifolia L.) cultivars. Results and conclusion The results show significant differences between two mint cultivars in the growth parameters of mint plants. Plants of M. viridis variety were characterized by the highest significant values of herb fresh weight, number of branches/plants, essential oil (%), flavonoid content, and protein %, while the M. longifolia variety was superior in plant height, herb dry weight, chlorophyll a, chlorophyll b, carotenoids, total pigments, indole acetic acid, phenol, carbohydrates %, free amino acids, flavonoid, and proline contents. Using chitosan as foliar treatments at different concentrations with or without amino acid significantly increased all studied traits. The interaction between two cultivars and foliar treatments of high rates of chitosan and amino acid gave the maximum significant increase of plant height, photosynthetic pigments and indole acetic acid, phenol, protein, free amino acid, and proline contents as well as antioxidant activities (DPPH%).

Induction of Two Pseudomonas Strains Indole Acetic Acid Production by Synthetic and Biologically-Produced L-Tryptophan: a Comparative Study

Amino acids, serving as fundamental building blocks of proteins, can also act as inducers in essential metabolic pathways across various microorganisms. The metabolic effects of synthetic and biologically produced amino acid L-tryptophan on two Pseudomonas strains – Pseudomonas chlororaphis 1S4 and Pseudomonas yamanorum 1046 were compared in this study. Both strains belong to the plant growth-pro¬moting rhizobacteria. The inducers impacted the production of the primary auxin phytohormone in the plant kingdom, the indole-3-acetic acid. The biologically produced L-tryptophan exerted a more pronounced positive effect on its production in both strains, increasing the process efficiency.

Competition between cross-linking and force-induced local conformational changes determines the structure and mechanics of labile protein networks

Folded protein hydrogels are emerging as promising new materials for medicine and healthcare applications. Folded globular proteins can be modelled as colloids which exhibit site specific cross-linking for controlled network formation. However, folded proteins have inherent mechanical stability and unfolded in response to an applied force. It is not yet understood how colloidal network theory maps onto folded protein hydrogels and whether it models the impact of protein unfolding on network properties. To address this, we study a hybrid system which contains folded proteins (patchy colloids) and unfolded proteins (biopolymers). We use a model protein, bovine serum albumin (BSA), to explore network architecture and mechanics in folded protein hydrogels. We alter both the photo-chemical cross-linking reaction rate and the mechanical properties of the protein building block, via illumination intensity and redox removal of robust intra-protein covalent bonds, respectively. This dual approach, in conjunction with rheological and structural techniques, allows us to show that while reaction rate can ‘fine-tune’ the mechanical and structural properties of protein hydrogels, it is the force-lability of the protein which has the greatest impact on network architecture and rigidity. To understand these results, we consider a colloidal model which successfully describes the behaviour of the folded protein hydrogels but cannot account for the behaviour observed in force-labile hydrogels containing unfolded protein. Alternative models are needed which combine the properties of colloids (folded proteins) and biopolymers (unfolded proteins) in cross-linked networks. This work provides important insights into the accessible design space of folded protein hydrogels without the need for complex and costly protein engineering, aiding the development of protein-based biomaterials.

Construction of homologous branched oligomer megamolecules based on linker-directed protein assembly.

Utilizing the building blocks of recombinant proteins and synthetic linkers, we have obtained two distinct octameric megamolecules with diverse branched structures. This approach combines principles from both click chemistry and protein engineering technology, enabling the integration of functional domains within highly ordered protein assemblies for biomedical applications.

Applications of reinforcement learning, machine learning, and virtual screening in SARS-CoV-2-related proteins

Similarly, to all coronaviruses, SARS-CoV-2 uses the S glycoprotein to enter host cells, which contains two functional domains: S1 and S2 receptor binding domain (RBD). Angiotensin-converting enzyme 2 (ACE2) is recognizable by the S proteins on the surface of the SARS-CoV-2 virus. The SARS-CoV-2 virus causes SARS, but some mutations in the RBD of the S protein markedly enhance their binding affinity to ACE2. Searching for new compounds in COVID-19 is an important initial step in drug discovery and materials design. Still, the problem is that this search requires trial-and-error experiments, which are costly and time-consuming. In the automatic molecular design method based on deep reinforcement learning, it is possible to design molecules with optimized physical properties by combining a newly devised coarse-grained representation of molecules with deep reinforcement learning. Also, structured-based virtual screening uses protein 3D structure information to evaluate the binding affinity between proteins and compounds based on physicochemical interactions such as van der Waals forces, Coulomb forces, and hydrogen bonds, and select drug candidate compounds. In addition, AlphaFold can predict 3D protein structures, given the amino acid sequence, and the protein building blocks. Ensemble docking, in which multiple protein structures are generated using the molecular dynamics method and docking calculations are performed for each, is often performed independently of docking calculations. In the future, the AlphaFold algorithm can be used to predict various protein structures related to COVID-19.

ACP-ESM: A novel framework for classification of anticancer peptides using protein-oriented transformer approach

Anticancer peptides (ACPs) are a class of molecules that have gained significant attention in the field of cancer research and therapy. ACPs are short chains of amino acids, the building blocks of proteins, and they possess the ability to selectively target and kill cancer cells. One of the key advantages of ACPs is their ability to selectively target cancer cells while sparing healthy cells to a greater extent. This selectivity is often attributed to differences in the surface properties of cancer cells compared to normal cells. That is why ACPs are being investigated as potential candidates for cancer therapy. ACPs may be used alone or in combination with other treatment modalities like chemotherapy and radiation therapy. While ACPs hold promise as a novel approach to cancer treatment, there are challenges to overcome, including optimizing their stability, improving selectivity, and enhancing their delivery to cancer cells, continuous increasing in number of peptide sequences, developing a reliable and precise prediction model. In this work, we propose an efficient transformer-based framework to identify ACPs for by performing accurate a reliable and precise prediction model. For this purpose, four different transformer models, namely ESM, ProtBERT, BioBERT, and SciBERT are employed to detect ACPs from amino acid sequences. To demonstrate the contribution of the proposed framework, extensive experiments are carried on widely-used datasets in the literature, two versions of AntiCp2, cACP-DeepGram, ACP-740. Experiment results show the usage of proposed model enhances classification accuracy when compared to the literature studies. The proposed framework, ESM, exhibits 96.45% of accuracy for AntiCp2 dataset, 97.66% of accuracy for cACP-DeepGram dataset, and 88.51% of accuracy for ACP-740 dataset, thence determining new state-of-the-art. The code of proposed framework is publicly available at github (https://github.com/mstf-yalcin/acp-esm).

Construction of Smartphone-Integrated Nanozyme Sensor Based on Amino Acid-Modulated Gold Nanoparticles.

Amino acids are not only the building blocks of proteins but also lead to the development of novel nanomaterials with unique properties. Herein, we proposed a simple strategy to produce gold nanoparticles (Au NPs) with peroxidase-like (POD-like) activities by using a series of amino acids as reducing agents, named Au NPs@M (M represents different amino acids). The Au NPs@His was identified as the nanozyme with the most potent catalytic performance, which was used in combination with smartphones to achieve rapid detection of hydrogen peroxide with a detection limit of 0.966 μM. It also enables rapid detection of glucose with a detection limit of 2.904 μM, highlighting the significant contribution of Au NPs@His in expediting the detection of critical biomolecules. This work not only provides a convenient and highly efficient method to identify glucose but also shows the potential of histidine as a reducing agent in constructing Au nanomaterials exerting enzyme-like catalysis.

CoDIAC: A comprehensive approach for interaction analysis reveals novel insights into SH2 domain function and regulation.

Protein domains are conserved structural and functional units and are the functional building blocks of proteins. Evolutionary expansion means that domain families are often represented by many members in a species, which are found in various configurations with other domains, which have evolved new specificity for interacting partners. Here, we develop a structure-based interface analysis to comprehensively map domain interfaces from available experimental and predicted structures, including interfaces with other macromolecules and intraprotein interfaces (such as might exist between domains in a protein). We hypothesized that a comprehensive approach to contact mapping of domains could yield new insights. Specifically, we use it to gain information about how domains selectivity interact with ligands, whether domain-domain interfaces of repeated domain partnerships are conserved across diverse proteins, and identify regions of conserved post-translational modifications, using relationship to interaction interfaces as a method to hypothesize the effect of post-translational modifications (and mutations). We applied this approach to the human SH2 domain family, an extensive modular unit that is the foundation of phosphotyrosine-mediated signaling, where we identified a novel approach to understanding the binding selectivity of SH2 domains and evidence that there is coordinated and conserved regulation of multiple SH2 domain binding interfaces by tyrosine and serine/threonine phosphorylation and acetylation, suggesting that multiple signaling systems can regulate protein activity and SH2 domain interactions in a regulated manner. We provide the extensive features of the human SH2 domain family and this modular approach, as an open source Python package for COmprehensive Domain Interface Analysis of Contacts (CoDIAC).

Predicting the amino group pKa of amino acids using machine learning-QSPR methods

Amino acids are one of the main building blocks of proteins, and because of this connection and their main role in biochemistry, they are of great interest in pharmaceutical industries today. In nature, there are approximately 300 amino acids, out of which 20 are present in the human body and are responsible for protein synthesis. As a result, amino acids are widely used in various fields such as biological sciences, chemistry, medicine, and various industries. This research focuses on estimating the acid dissociation constant (pKa) of the amino group related to 52 amino acids using the quantitative structure–property relationship (QSPR) method, employing four different regression models: Genetic Algorithm-Multivariable Linear Regression (GA-MLR), Particle Swarm Optimization-Support Vector Machine (PSO-SVM), Feed Forward Neural Network (FFNN), and Decision Tree (DT). Using the GA-MLR, the optimal number of descriptors for estimating pKa was determined to be 9. By comparing different machine learning models, it can be observed that the coefficient of determination (R2) follows the trend R2 PSO-SVM = 0.9681 > R2 FFNN = 0.9539 > R2 DT = 0.9329 > R2 GA-MLR = 0.9231. Therefore, the PSO-SVM model provided the best prediction, while the FFNN, DT, and GA-MLR models ranked next in terms of precision in predicting the pKa of amino acids. By dividing the data into test/train sets in a ratio of approximately 20/80, the model's validity was confirmed due to the nearness of the R2 values between the training and testing sets. Ultimately, the best model is capable of predicting the pKa value of the amino group in amino acids with a mean absolute percent error of 1.18 %.

Engineering of microorganisms for high production of amino acids

Amino acids as the building blocks of proteins are widely applied in food, medicine, feed, and chemical industries. Amino acid production by microbial cell factories from renewable resources is praised for the environmental friendliness, mild reaction conditions, and high product purity, which helps to achieve the goal of carbon neutrality. Researchers have employed the methods of metabolic engineering and synthetic biology to engineer Escherichia coli and Corynebacterium glutamicum and optimized the culture conditions to construct the microbial cell factories with high performance for producing branched chain amino acids, amino acids of the aspartic acid and glutamic acid families, and aromatic amino acids. We review the engineering process of microbial cell factories for high production of amino acids, in the hope of providing a reference for the creation of high-performance microbial cell factories.

The contribution of Ca and Mg to the accumulation of amino acids in maize: from the response of physiological and biochemical processes

BackgroundThe quality of maize kernels is significantly enhanced by amino acids, which are the fundamental building blocks of proteins. Meanwhile, calcium (Ca) and magnesium (Mg), as important nutrients for maize growth, are vital in regulating the metabolic pathways and enzyme activities of amino acid synthesis. Therefore, our study analyzed the response process and changes of amino acid content, endogenous hormone content, and antioxidant enzyme activity in kernels to the coupling addition of sugar alcohol-chelated Ca and Mg fertilizers with spraying on maize.Result(1) The coupled addition of Ca and Mg fertilizers increased the Ca and Mg content, endogenous hormone components (indole-3-acetic acid, IAA; gibberellin, GA; zeatin riboside, ZR) content, antioxidant enzyme activity, and amino acid content of maize kernels. The content of Ca and Mg in kernels increased with the increasing levels of Ca and Mg fertilizers within a certain range from the filling to the wax ripening stage, and significantly positively correlated with antioxidant enzyme activities. (2) The contents of IAA, GA, and ZR continued to rise, and the activities of superoxide dismutase (SOD) and catalase (CAT) were elevated, which effectively enhanced the ability of cells to resist oxidative damage, promoted cell elongation and division, and facilitated the growth and development of maize. However, the malondialdehyde (MDA) content increased consistently, which would attack the defense system of the cell membrane plasma to some extent. (3) Leucine (LEU) exhibited the highest percentage of essential amino acid components and a gradual decline from the filling to the wax ripening stage, with the most substantial beneficial effect on essential amino acids. (4) CAT and SOD favorably governed essential amino acids, while IAA and MDA negatively regulated them. The dominant physiological driving pathway for the synthesis of essential amino acids was “IAA-CAT-LEU”, in which IAA first negatively drove CAT activity, and CAT then advantageously controlled LEU synthesis.ConclusionThese findings provide a potential approach to the physiological and biochemical metabolism of amino acid synthesis, and the nutritional quality enhancement of maize kernel.

The Role of Amino Acid Glycine on Cardiovascular Health and Its Beneficial Effects: A Narrative Review

Glycine, a simple amino acid, is not only essential due to its potential insufficiency in vivo, but also has significant metabolic functions. It serves as a crucial building block for proteins. At the same time, as a bioactive molecule, it regulates gene expression for cytoprotection, protein configuration and activity, and other critical biological processes, including glutathione synthesis. The intriguing, beneficial effects of glycine in medical applications have piqued the research community’s interest in recent decades. This work delves into the compelling discoveries about the pivotal role of glycine in cardiovascular health and its intricate mechanisms of action for alleviating several medical conditions. Glycine’s broad spectrum of impact spans numerous diseases, encompassing not only acute myocardial infarction, aortic dissection, and cardiac hypertrophy, but also transplant rejections of aortic allografts, insulin resistance, and endothelial dysfunction, thereby providing a comprehensive understanding of its medical applications.

A review on the green chemistry perspective of multipurpose use of cow urine

Abstract The use of cow urine (CU) in treating a variety of illness can be traced back to ancient ages. It has been referred as an important and integral component of Cowpathy – an age-old practice in Indian sub-continent since the Vedic period (1500 BC – 600 BC). The CU contains several important compounds that are essential in maintaining a balance between the well-being of human and the nature. It is composed of about 95 % water and other useful ingredients such as urea, hormones, lyase enzyme, and salts containing Fe, Ca, P, Mn, S, N, and K. In addition, it also contains lactose, cytokine, and amino acid which are the fundamental building blocks of protein. Researchers have developed various processes for the green synthesis of CU concentrate and evaluated its usefulness in treating various diseases owing to its antibacterial and antifungal properties. Further, CU has also shown promising immunity boosting and anti-oxidant characteristics. Even though human civilization has benefited from the use of CU in treating various types illness but the modern scientific understanding of the cause-effect relationships was partly developed in the last few decades. There exists a huge knowledge gap and a comprehensive study on exploring the science and application of CU hasn’t been reported. This paper reviews the historical perspective, compositional analysis, processing, applications, knowledge gap, and future research required in the field of therapeutic usage of CU.

Atomic Ruthenium‐Promoted Cadmium Sulfide for Photocatalytic Production of Amino Acids from Biomass Derivatives

AbstractAmino acids are the building blocks of proteins and are widely used as important ingredients for other nitrogen‐containing molecules. Here, we report the sustainable production of amino acids from biomass‐derived hydroxy acids with high activity under visible‐light irradiation and mild conditions, using atomic ruthenium‐promoted cadmium sulfide (Ru1/CdS). On a metal basis, the optimized Ru1/CdS exhibits a maximal alanine formation rate of 26.0 molAla ⋅ gRu−1 ⋅ h−1, which is 1.7 times and more than two orders of magnitude higher than that of its nanoparticle counterpart and the conventional thermocatalytic process, respectively. Integrated spectroscopic analysis and density functional theory calculations attribute the high performance of Ru1/CdS to the facilitated charge separation and O−H bond dissociation of the α‐hydroxy group, here of lactic acid. The operando nuclear magnetic resonance further infers a unique “double activation” mechanism of both the CH−OH and CH3−CH−OH structures in lactic acid, which significantly accelerates its photocatalytic amination toward alanine.

Atomic Ruthenium-Promoted Cadmium Sulfide for Photocatalytic Production of Amino Acids from Biomass Derivatives.

Amino acids are the building blocks of proteins and are widely used as important ingredients for other nitrogen-containing molecules. Here, we report the sustainable production of amino acids from biomass-derived hydroxy acids with high activity under visible-light irradiation and mild conditions, using atomic ruthenium-promoted cadmium sulfide (Ru1/CdS). On a metal basis, the optimized Ru1/CdS exhibits a maximal alanine formation rate of 26.0 molAla ⋅ gRu -1 ⋅ h-1, which is 1.7 times and more than two orders of magnitude higher than that of its nanoparticle counterpart and the conventional thermocatalytic process, respectively. Integrated spectroscopic analysis and density functional theory calculations attribute the high performance of Ru1/CdS to the facilitated charge separation and O-H bond dissociation of the α-hydroxy group, here of lactic acid. The operando nuclear magnetic resonance further infers a unique "double activation" mechanism of both the CH-OH and CH3-CH-OH structures in lactic acid, which significantly accelerates its photocatalytic amination toward alanine.

Reengineering of an Artificial Protein Cage for Efficient Packaging of Active Enzymes.

Protein cages that readily encapsulate active enzymes of interest present useful nanotools for delivery and catalysis, wherein those with programmable disassembly characteristics serve as particularly attractive platforms. Here, a general guest packaging system based on an artificial protein cage, TRAP-cage, the disassembly of which can be induced by the addition of reducing agents, is established. In this system, TRAP-cage with SpyCatcher moieties in the lumen is prepared using genetic modification of the protein building block and assembled into a cage structure with either monovalent gold ions or molecular crosslinkers. The resulting protein cage can efficiently capture guest proteins equipped with a SpyTag by simply mixing them in an aqueous solution. This post-assembly loading system, which circumvents the exposure of guests to thiol-reactive crosslinkers, enables the packaging of enzymes possessing a catalytic cysteine or a metal cofactor while retaining their catalytic activity.