Properties Of Amino Acids Research Articles

Effects of the physico-chemical properties of amino acids and chemically functionalized surfaces on DIOS-MS analysis.

Desorption ionisation on silicon mass spectrometry (DIOS-MS) allows for the detection of low molecular weight species from fluid samples. However, this method remains scarcely used for clinical diagnosis likely because of a lack of knowledge about the desorption/ionization mechanism as well as about the interplay between the surface and analyte properties which are effective in desorption/ionization, impeding the optimization of the DIOS-MS analysis. Herein, the normalized intensity of the DIOS-MS peaks at [M+H]+ of seven amino acids on four different porous silicon modified surfaces are investigated. These amino acids (arginine, phenylalanine, methionine, glutamine, leucine, cysteine and valine) have different isoelectric points, proton affinities, and octanol-water partition coefficients. The four selected surfaces were oxidized porous silicon (SiO2), the same porous silicon modified with a propyl dimethyl ethoxy silane, octadecyl dimethyl ethoxy silane or 3 amino propyl dimethyl ethoxy silane (CH3-short, CH3-long and NH3+, respectively). These surfaces present different electrical charges, alkyl chain lengths, and hydrophilic/hydrophobic properties. For each surface, the intensities of the protonated molecules ([M+H]+) are discussed with respect to the electrical charge and proton affinity of the amino acids, their z-distributions inside the pores (determined by time of flight secondary ion mass spectrometry profiling), their surface interaction energies (calculated by molecular dynamics simulations), the interfacial water content and the proton availability for each surface.

Thermodynamics Properties of Leucine and Isoleucine Peptides in Water.

Thermodynamic properties of amino acids explore the ideas about the energetic contribution in biomolecular interfaces. In our work, we have estimated the solvation free energy of leucine and isoleucine peptides with the variation of chain length or residues of different monomer units (n=1, 2, 4, 8 & 16) using molecular dynamic simulation. We modeled our system using OPLS-AA force field and TIP3P water model at 310 K temperature. Solvation free energy of both leucine and isoleucine peptides increases with increase in chain length, which have been reported by using TI, TI-CUBIC and BAR methods. The increase in solvation free energy with increase in chain length of both peptides is also supported by the increase in hydrogen bond and solvent accessible surface area (SASA) with the number of residues.

Detection and distinction of amino acids and post-translational modifications with gold nanojunctions.

Amino acids are fundamental building blocks of proteins, playing critical roles in medical diagnostics, environmental monitoring, and biomarker identification. The development of nanoscale electronic sensors capable of single-amino-acid recognition has gained significant attention due to their potential for label-free, real-time detection. In this study, we investigate the electronic transport properties of amino acids in two gold-based nanodevices with distinct architectures: a gold nanojunction and a gold-capacitor system. Using density functional theory (DFT) in combination with nonequilibrium Green's function (NEGF) calculations, we explore the sensing mechanism and conductance variations induced by different amino acids, including select phosphorylated variants. Each device was assigned a reference amino acid, F (M), for a capacitor (nanojunction) to differentiate its conductance from other molecules. Our results reveal distinct conductance that enables amino acid classification based on their electronic signatures, demonstrating that molecular discrimination is primarily governed by conductance variations as a function of the binding energy differences. The nanojunction exhibited remarkable differentiation for the amino acids S, pS, Y, and pY, rendering it especially proficient in distinguishing between structurally analogous molecules. These findings highlight the strong potential of gold-based nanodevices for precise amino acid detection, paving the way for advancements in biosensing technologies, molecular diagnostics, and biomedical applications.

Quantum chemical study of molecular properties of small branched-chain amino acids in water

Four aliphatic amino acids—α-aminobutyric acid (AABA), β-aminobutyric acid (BABA), α-aminoisobutyric acid (AAIBA) and β-aminoisobutyric acid (BAIBA) were investigated in water as a solvent by two quantum chemical methods. B3LYP hybrid version of DFT was used for geometry optimization and a full vibrational analysis of neutral molecules, their cations and anions in the canonical and zwitterionic forms (6 forms for each species). Ab initio DLPNO-CCSD(T) method was applied in the geometry pre-optimized by B3LYP. Calculated molecular descriptors involve dipole moment, quadrupole moment, dipole polarizability, energy of zero-point vibration and total entropic term which enter the standard Gibbs energy. In addition, a set of collective electronic and thermodynamic properties associated with redox process were evaluated: ionization energy, electron affinity, chemical hardness, molecular electronegativity, electrophilicity index, absolute oxidation and reduction potentials. A mutual comparison of these structural isomers including γ-aminobutyric acid (GABA) shows high degree of similarity in molecular descriptors. However, cluster analysis of 12 electro neutral, linear and branched amino acids with 2 – 6 carbon atoms discriminates them into five clusters. It is found that the electrophilicity index correlates with the absolute reduction potential along a straight line (24 items). The reduction potential for canonical structure varies between 1.21 V (glycine) and 1.45 V (AABA) whereas for the zwitterionic form it is visibly lower 0.52–1.11 V. The highest absolute reduction potential > 1.43 V is shown by α-amino acids: α-alanine, AABA (homoalanine) and AAIBA having 2-methyl or 2-ethyl functional group. The calculated absolute oxidation potential correlates with the adiabatic ionization energy and can be used as a criterion of the antioxidant capacity. According to thermodynamic data, the SPLET mechanism of the electron-proton coupled transfer is favored over the alternative SET-PT mechanism. This work contributes to the creation of a database of molecular properties of amino acids based on the same method and basis set.

Effect of critical amino acids’ properties on potential allergenicity of Ara h 2 epitopes

ABSTRACT Peanuts are one of the most common allergenic foods in the world and can cause severe allergic reactions. Ara h 2 is the most allergenic components of peanut allergens. Although critical amino acids at the epitopes were identified, the effect of their physicochemical properties remained unclear. The critical amino acids in epitopes of Ara h 2 (epitope 1: 26ELQGDRRCQSQLER39 and epitope 2: 62RDPYSPSQDPYSPS75). The critical amino acids were substituted by amino acid with different physicochemical properties. Then, the potential allergenicity of those peptides was assessed by IgE binding and cell model. Changing the properties of amino acids by mutation affects potential sensitisation. The charge distribution and molecular weight of critical amino acids in the epitope affected the IgE binding capacity significantly. The conclusion based on peptide might be different from that on whole protein, and our study provided direction of amino acids mutation in desensitisation therapy.

Amino Acid-Assisted Synthesis of Zeolites with Improved Catalytic Properties.

Conventional zeolites are limited in their ability to catalyze macromolecular reactions due to micropore constraints, resulting in sluggish reactant and product diffusion and subsequently pore clogging and catalyst deactivation. Consequently, the pore and textural refinement of zeolites to meet industrial demands has become a research hotspot. Herein, we review the amino acid-assisted methods in zeolite synthesis and scrutinize the principle and influential factors governing amino acid involvement in zeolite synthesis. Additionally, we analyze the advantages and challenges associated with the amino acid-assisted method. Certain amino acids can interact with zeolite precursors or crystal surface, thus altering the crystal growth rate and enabling precise control over the crystal size and shape. On the other hand, amino acids can serve as structure-directing agents to orchestrate the generation of mesoporous pores. These capabilities enable the production of zeolites with well-defined pores, particle sizes and/or crystal shapes that satisfy catalytic requirements. Moreover, the unique properties of amino acids allow their complete elimination from the solid product through a simple aqueous washing process, facilitating their recovery for subsequent usage. As result, the amino acid-assisted synthesis methods offer a convenient, green route to zeolites with modulated textual properties for high-performance catalysis.

AAindexNC: Estimating the Physicochemical Properties of Non-Canonical Amino Acids, Including Those Derived from the PDB and PDBeChem Databank.

The physicochemical properties of amino acid residues from the AAindex database are widely used as predictors in building models for predicting both protein structures and properties. It should be noted, however, that the AAindex database contains data only for the 20 canonical amino acids. Non-canonical amino acids, while less common, are not rare; the Protein Data Bank includes proteins with more than 1000 distinct non-canonical amino acids. In this study, we propose a method to evaluate the physicochemical properties from the AAindex database for non-canonical amino acids and assess the prediction quality. We implemented our method as a bioinformatics tool and estimated the physicochemical properties of non-canonical amino acids from the PDB with the chemical composition presentation using SMILES encoding obtained from the PDBechem databank. The bioinformatics tool and resulting database of the estimated properties are freely available on the author's website and available for download via GitHub.

Vector embeddings by sequence similarity and context for improved compression, similarity search, clustering, organization, and manipulation of cDNA libraries

This paper demonstrates the utility of organized numerical representations of genes in research involving flat string gene formats (i.e., FASTA/FASTQ5). By assigning a unique vector embedding to each short sequence, it is possible to more efficiently cluster and improve upon compression performance for the string representations of cDNA libraries. Furthermore, by studying alternative coordinate vector embeddings trained on the context of codon triplets, we can demonstrate clustering based on amino acid properties. Employing this sequence embedding method to encode barcodes and cDNA sequences, we can improve the time complexity of similarity searches. By pairing vector embeddings with an algorithm that determines the vector proximity in Euclidean space, this approach enables quicker and more flexible sequence searches.

Modular Design and Self-assembly of pH-Responsive Multidomain Peptides Incorporating Non-natural Ionic Amino Acids.

Stimuli-responsive peptides, particularly pH-responsive variants, hold significant promise in biomedical and technological applications by leveraging the broad pH spectrum inherent to biological environments. However, the limited number of natural pH-responsive amino acids within biologically relevant pH ranges presents challenges for designing rational pH-responsive peptide assemblies. In our study, we introduce a novel approach by incorporating a library of non-natural amino acids featuring chemically diverse tertiary amine side chains. Hydrophobic and ionic properties of these non-natural amino acids facilitate their incorporation into the assembly domain when uncharged, and electrostatic repulsion promotes disassembly under lower pH conditions. Furthermore, we observed a direct relationship between the number of substitutions and the hydrophobicity of these amino acids, influencing their pH-responsive properties and enabling rational design based on desired transitional pH ranges. The structure-activity relationship of these pH-responsive peptides was evaluated by assessing their antimicrobial properties, as their antimicrobial activity is triggered by the disassembly of peptides to release active monomers. This approach not only enhances the specificity and controllability of pH responsiveness but also broadens the scope of peptide materials in biomedical and technological applications.

Molecular dynamics simulations unveil the aggregation patterns and salting out of polyarginines at zwitterionic POPC bilayers in solutions of various ionic strengths

This study employs molecular dynamics (MD) simulations to investigate the adsorption and aggregation behavior of simple polyarginine cell-penetrating peptides (CPPs), specifically modeled as R9 peptides, at zwitterionic phosphocholine POPC membranes under varying ionic strengths of two peptide concentrations and two concentrations of NaCl and CaCl2. The results reveal an intriguing phenomenon of R9 aggregation at the membrane, which is dependent on the ionic strength, indicating a salting-out effect. As the peptide concentration and ionic strength increase, peptide aggregation also increases, with aggregate lifetimes and sizes showing a corresponding rise, accompanied by the total decrease of adsorbed peptides at the membrane surface. Notably, in high ionic strength environments, large R9 aggregates, such as octamers, are also observed occasionally. The salting-out, typically uncommon for short positively charged peptides, is attributed to the unique properties of arginine amino acid, specifically by its side chain containing amphiphilic guanidinium (Gdm+) ion which makes both intermolecular hydrophobic like-charge Gdm+ – Gdm+ and salt-bridge Gdm+ – C-terminus interactions, where the former are increased with the ionic strength, and the latter decreased due to electrostatic screening. The aggregation behavior of R9 peptides at membranes can also be linked to their CPP translocation properties, suggesting that aggregation may aid in translocation across cellular membranes.

Nutritional support for cancer patients during chemoradiation treatment

An analysis of publications in the medical databases e-Library, PubMed, and Medline (2005–2024) was conducted to evaluate the impact of nutritional support on the tolerability of antitumor chemoradiotherapy. Based on the data collected, it was revealed that chemoradiotherapy is linked to a high risk of developing protein-energy malnutrition. Consequently, the initial nutritional deficiency and sarcopenia result in either a refusal to administer chemotherapy in favor of radiation treatment alone, a forced reduction in the dose of a cytostatic medication, or the disruption of the chemoradiotherapy course. Patients with malignancies of the head and neck who have received chemoradiotherapy with previous induction chemotherapy are in the “red zone” for the risk of developing protein-energy malnutrition. Nutritional support is one of the instruments that enhances the likelihood of treatment implementation and mitigates the risk of drug and radiation toxicity. The fundamental principles of nutritional support during chemoradiotherapy include timely prescription, adequacy, composition of a specialized mixture, and the choice of the most physiological way of its implementation. Supplementing nutritional support with feasible strength physical exercise triggers protein synthesis in skeletal muscles, increases muscular sensitivity to the anabolic properties of amino acids, and mitigates insulin resistance.

Evidence of Intraspecific Adaptive Variation in the American Pika (Ochotona princeps) on a Continental Scale Using a Target Enrichment and Mitochondrial Genome Skimming Approach.

Montane landscapes present an array of abiotic challenges that drive adaptive evolution amongst organisms. These adaptations can promote habitat specialisation, which may heighten the risk of extirpation from environmental change. For example, higher metabolic rates in an endothermic species may contribute to heightened cold tolerance, whilst simultaneously limiting heat tolerance. Here, using the climate-sensitive American pika (Ochotona princeps), we test for evidence of intraspecific adaptive variation amongst environmental gradients across the Intermountain West of North America. We leveraged results from previous studies on pika adaptation to generate a custom nuclear target enrichment design to sequence several hundred candidate genes related to cold, hypoxia and dietary detoxification. We also applied a 'genome skimming' approach to sequence mitochondrial DNA. Using genotype-environment association tests, we identified rare genomic variants associated with elevation and temperature variation amongst populations. Amongst mitochondrial genes, we identified intraspecific variation in selective signals and significant changes to the amino acid property equilibrium constant, which may relate to electron transport chain efficiency. These results illustrate a complex dynamic of adaptive variation amongst O. princeps where lineages and populations have adapted to unique regional conditions. Some of the clearest signals of selection were in a genetic lineage that includes pikas of the Great Basin region, which is also where recent localised extirpations have taken place and highlights the risk of losing adaptive alleles during environmental change.

Freeze-drying-induced mutarotation of lactose detected by Raman spectroscopy

Freeze-drying enables delicate, heat-sensitive biomaterials to be stored in a dry form even at room temperature. However, exposure to physicochemical stress induced by freeze-drying presents challenges for maintaining material characteristics and functionality upon reconstitution, for which reason excipients are required. Although wide variety of different excipients are available for pharmaceutical applications, their protective role in the freeze-drying is not yet fully understood. In this study, our aim was to use molecular dynamics simulations to screen the properties of different sugars and amino acids, which could be combined with plant-based nanofibrillated cellulose (NFC) hydrogel to provide protective matrix system for future freeze-drying for pharmaceuticals and biologics. The changes in the NFC-based formulations before and after freeze-drying and reconstitution were evaluated using non-invasive Timegate PicoRaman spectroscopy and traditional characterization methods. We continued to the freeze-drying with the NFC hydrogel formulations including lactose with and without glycine, which showed the highest attraction preferences on NFC surface in silico. This formulation enabled successful freeze-drying and subsequent reconstitution with preserved physicochemical and rheological properties. Raman spectroscopy gave us insights of the molecular-level changes during freeze-drying, especially the mutarotation of lactose. This research showed the potential of integrating in silico screening and non-invasive spectroscopical method to design novel biomaterial-based formulations for freeze-drying. The research provided insights of the molecular-level interactions and orientational changes of the excipients, which might be crucial in future freeze-drying applications of pharmaceuticals and biologics.

Modulating the pH dependent photophysical properties of green fluorescent protein.

The photophysical properties of the β-barrel superfolder green fluorescent protein (sfGFP) arise from the chromophore that forms post-translationally in the interior of the protein. Specifically, the protonation state of the side chain of tyrosine 66 in the chromophore, in addition to the network of hydrogen bonds between the chromophore and surrounding residues, is directly related to the electronic absorbance and emission properties of the protein. The pH dependence of the photophysical properties of this protein were modulated by the genetic, site-specific incorporation of 3-nitro-l-tyrosine (mNO2Y) at site 66 in sfGFP. The altered photophysical properties of this noncanonical amino acid (ncAA) sfGFP construct were assessed by absorbance and fluorescence spectroscopies. Notably, a comparison of the pK a of the 3-nitrophenol side chain of mNO2Y incorporated in the protein relative to the phenol side chain of the tyrosine at site 66 in the native chromophore as well as the pK a of the 3-nitrophenol side chain of the free ncAA were measured and are compared. A structural analysis of the ncAA containing sfGFP construct is presented to yield molecular insight into the origin of the altered absorbance and fluorescence properties of the protein.

Statistical analysis of the unique characteristics of secondary structures in proteins

Protein folding is a complex process influenced by the primary sequence of amino acids. Early studies focused on understanding whether the specificity or the conservation of properties of amino acids was crucial for folding into secondary structures such as α-helices, β-sheets, turns, and coils. However, with the advent of artificial intelligence (AI) and machine learning (ML), the emphasis has shifted towards the precise nature and occurrence of specific amino acids. In our study, we analyzed a large set of proteins from diverse organisms to identify unique features of secondary structures, particularly in terms of the distribution of polar, non-polar, and charged amino acid residues. We found that α-helices tend to have a higher proportion of charged and non-polar groups compared to other secondary structures and that the presence of oppositely charged amino acid residues in helices stabilizes them, facilitating the formation of longer helices. These characteristics are distinct to α-helices. This study offers valuable insights for researchers in the field of protein design, enabling the de-novo creation of short helical peptides for a range of applications. We have also developed a web server for extensive analysis of proteins from different databases. The web server is housed at https://proseqanalyser.iitgn.ac.in/

An Analysis of Combined Molecular Weight and Hydrophobicity Similarity between the Amino Acid Sequences of Spike Protein Receptor Binding Domains of Betacoronaviruses and Functionally Similar Sequences from Other Virus Families

Recently, we proposed a new method, based on protein profiles derived from physicochemical dynamic time warping (PCDTW), to functionally/structurally classify coronavirus spike protein receptor binding domains (RBD). Our method, as used herein, uses waveforms derived from two physicochemical properties of amino acids (molecular weight and hydrophobicity (MWHP)) and is designed to reach into the twilight zone of homology, and therefore, has the potential to reveal structural/functional relationships and potentially homologous relationships over greater evolutionary time spans than standard primary sequence alignment-based techniques. One potential application of our method is inferring deep evolutionary relationships such as those between the RBD of the spike protein of betacoronaviruses and functionally similar proteins found in other families of viruses, a task that is extremely difficult, if not impossible, using standard multiple alignment-based techniques. Here, we applied PCDTW to compare members of four divergent families of viruses to betacoronaviruses in terms of MWHP physicochemical similarity of their RBDs. We hypothesized that some members of the families Arteriviridae, Astroviridae, Reoviridae (both from the genera rotavirus and orthoreovirus considered separately), and Toroviridae would show greater physicochemical similarity to betacoronaviruses in protein regions similar to the RBD of the betacoronavirus spike protein than they do to other members of their respective taxonomic groups. This was confirmed to varying degrees in each of our analyses. Three arteriviruses (the glycoprotein-2 sequences) clustered more closely with ACE2-binding betacoronaviruses than to other arteriviruses, and a clade of 33 toroviruses was found embedded within a clade of non-ACE2-binding betacoronaviruses, indicating potentially shared structure/function of RBDs between betacoronaviruses and members of other virus clades.

Gaining insights into the physicochemical properties and sequence space of blood–brain barrier penetrating peptides

The blood–brain barrier (BBB) poses a significant obstacle to the administration of drugs to the brain for the development of therapies of central nervous system (CNS) disorders. Blood-brain barrier penetrating peptides (BBBPps) are a group of peptides that can traverse the BBB by different processes without causing harm to the BBB. These peptides show promise as potential drugs for CNS ailments. Nevertheless, the process of identifying BBBPps using experimental approaches is both time-consuming and labour-intensive. To discover additional BBBPps as potential treatments for CNS diseases, it is critical to develop insilico methods that can distinguish BBBPps from non- BBBPps rapidly and precisely. In the current work, machine learning aided models are developed for accurate prediction of BBBPps using physicochemical and evolutionary information. The best model achieved an accuracy of 84.8 % by using 5-fold cross-validation and 79.8 % based on the holdout testing set on a reduced set of features. Further an extensive analysis is carried out for the black box models using model agnostic interpretation approaches to infer the physicochemical and sequence space of BBBPps. Basic amino acids and conservation of Arginine and Lysine are found to be more favoured in BBBPps. Further, basic amino acid property group and its interactions with other features are found to be prominent important interactions.

Impact of Multi-Factor Features on Protein Secondary Structure Prediction.

Protein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino acid sequences, position-specific score matrices (PSSM), amino acid properties, and secondary structure trend factors, to improve prediction accuracy is an important technical route for it. However, a comprehensive evaluation of the impact of these factor features in secondary structure prediction is lacking in the current work. This study quantitatively analyzes the impact of several major factors on secondary structure prediction models using a more explanatory four-class machine learning approach. The applicability of each factor in the different types of methods, the extent to which the different methods work on each factor, and the evaluation of the effect of multi-factor combinations are explored in detail. Through experiments and analyses, it was found that PSSM performs best in methods with strong high-dimensional features and complex feature extraction capabilities, while amino acid sequences, although performing poorly overall, perform relatively well in methods with strong linear processing capabilities. Also, the combination of amino acid properties and trend factors significantly improved the prediction performance. This study provides empirical evidence for future researchers to optimize multi-factor feature combinations and apply them to protein secondary structure prediction models, which is beneficial in further optimizing the use of these factors to enhance the performance of protein secondary structure prediction models.

Construction of amino acids reduced alphabets from molecular descriptors for interpretation of N-carbamylase, luciferase and PI3K mutations

The classification of amino acids has proven to be a useful tool for understanding the importance of sequence in protein function. The reduced amino acid alphabets are an example of these classifications, which, when built from physicochemical, structural and quantum characteristics of the amino acids, allow it to simplify the representation of the sequences, being useful in the modelling, design and understanding of proteins. So, an objective selection of amino acids properties is important, due classes formed in a reduced alphabet depend on the descriptors used for classification. In this research, based on a careful selection of descriptors for the 20 amino acids, through techniques such as the information content index and hierarchical cluster analysis with ties in proximity, 20,871,586 reduced amino acid alphabets were constructed. This large collection of reduced alphabets was been used to interpret alterations in the function of three proteins: N-carbamylase, Luciferase, and PI3K, caused by amino acid changes in their sequences. For this, the similar and different descriptors linked to these mutations were studied. Properties such as volume, hydrophobicity, charge and autocorrelation can be associated with variations in the behaviour of these proteins, while the frequency in specific secondary structures, the Gibbs free energy and some topological and quantum properties can be considered as the causes of preventing the deactivation of protein function. This work offers the most complete collection of reduced alphabets that promise to be a useful tool for the interpretation of alterations caused by amino acid mutations in the protein sequence.

Species-specific model based on sequence and structural information for ubiquitination sites prediction

Ubiquitination is a common post-translational modification of proteins in eukaryotic cells, and it is also a significant method of regulating protein biological function. Computational methods for predicting ubiquitination sites can serve as a cost-effective and time-saving alternative to experimental methods. Existing computational methods often build classifiers based on protein sequence information, physical and chemical properties of amino acids, evolutionary information, and structural parameters. However, structural information about most proteins cannot be found in existing databases directly. The features of proteins differ among species, and some species have small amounts of ubiquitinated proteins. Therefore, it is necessary to develop species-specific models that can be applied to datasets with small sample sizes. To solve these problems, we propose a species-specific model (SSUbi) based on a capsule network, which integrates proteins’ sequence and structural information. In this model, the feature extraction module is composed of two sub-modules that extract multi-dimensional features from sequence and structural information respectively. In the submodule, the convolution operation is used to extract encoding dimension features, and the channel attention mechanism is used to extract feature map dimension features. After integrating the multi-dimensional features from both types of information, the species-specific capsule network further converts the features into capsule vectors and classifies species-specific ubiquitination sites. The experimental results show that SSUbi can effectively improve the prediction performance of species with small sample sizes and outperform other models.