Physicochemical Properties Of Amino Acids Research Articles

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.

Post-translational modifications in the Protein Data Bank.

Proteins frequently undergo covalent modification at the post-translational level, which involves the covalent attachment of chemical groups onto amino acids. This can entail the singular or multiple addition of small groups, such as phosphorylation; long-chain modifications, such as glycosylation; small proteins, such as ubiquitination; as well as the interconversion of chemical groups, such as the formation of pyroglutamic acid. These post-translational modifications (PTMs) are essential for the normal functioning of cells, as they can alter the physicochemical properties of amino acids and therefore influence enzymatic activity, protein localization, protein-protein interactions and protein stability. Despite their inherent importance, accurately depicting PTMs in experimental studies of protein structures often poses a challenge. This review highlights the role of PTMs in protein structures, as well as the prevalence of PTMs in the Protein Data Bank, directing the reader to accurately built examples suitable for use as a modelling reference.

Unlocking the puzzle: non-defining mutations in SARS-CoV-2 proteome may affect vaccine effectiveness.

SARS-CoV-2 variants are defined by specific genome-wide mutations compared to the Wuhan genome. However, non-clade-defining mutations may also impact protein structure and function, potentially leading to reduced vaccine effectiveness. Our objective is to identify mutations across the entire viral genome rather than focus on individual mutations that may be associated with vaccine failure and to examine the physicochemical properties of the resulting amino acid changes. Whole-genome consensus sequences of SARS-CoV-2 from COVID-19 patients were retrieved from the GISAID database. Analysis focused on Dataset_1 (7,154 genomes from Italy) and Dataset_2 (8,819 sequences from Spain). Bioinformatic tools identified amino acid changes resulting from codon mutations with frequencies of 10% or higher, and sequences were organized into sets based on identical amino acid combinations. Non-defining mutations in SARS-CoV-2 genomes belonging to clades 21 L (Omicron), 22B/22E (Omicron), 22F/23A (Omicron) and 21J (Delta) were associated with vaccine failure. Four sets of sequences from Dataset_1 were significantly linked to low vaccine coverage: one from clade 21L with mutations L3201F (ORF1a), A27- (S) and G30- (N); two sets shared by clades 22B and 22E with changes A27- (S), I68- (S), R346T (S) and G30- (N); and one set shared by clades 22F and 23A containing changes A27- (S), F486P (S) and G30- (N). Booster doses showed a slight improvement in protection against Omicron clades. Regarding 21J (Delta) two sets of sequences from Dataset_2 exhibited the combination of non-clade mutations P2046L (ORF1a), P2287S (ORF1a), L829I (ORF1b), T95I (S), Y145H (S), R158- (S) and Q9L (N), that was associated with vaccine failure. Vaccine coverage associations appear to be influenced by the mutations harbored by marketed vaccines. An analysis of the physicochemical properties of amino acid revealed that primarily hydrophobic and polar amino acid substitutions occurred. Our results suggest that non-defining mutations across the proteome of SARS-CoV-2 variants could affect the extent of protection of the COVID-19 vaccine. In addition, alteration of the physicochemical characteristics of viral amino acids could potentially disrupt protein structure or function or both.

Non-specific yet selective interactions contribute to small molecule condensate partitioning behavior.

Biomolecular condensates are essential in various cellular processes, and their mis-regulation has been demonstrated to be underly disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by non-specific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity towards specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

An efficient and accurate approach to identify similarities between biological sequences using pair amino acid composition and physicochemical properties

An efficient and accurate approach to identify similarities between biological sequences using pair amino acid composition and physicochemical properties

Predicting antibacterial activity, efficacy, and hemotoxicity of peptides using an explainable machine learning framework

Antimicrobial peptides have emerged as a potential alternative to combat the growing threat towards antimicrobial resistance. Owing to a large number of possible combinations of twenty naturally occurring amino acids, it is extremely resource intensive to experimentally identify whether a given peptide has desired therapeutic properties. To expedite the screening of therapeutic peptides, we propose a classification framework that can simultaneously predict the antibacterial activity, hemotoxicity, and efficacy against three most common pathogens i.e., Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa for any given peptide. The proposed framework uses support vector machine algorithm with amino acid compositions, sequence analysis, and physicochemical properties as features to develop three binary classifiers. Our models resulted in accuracies of 97.3 %, 86.2 %, and 84.1 % for antibacterial activity, combined efficacy against all three pathogens, and hemotoxicity, respectively. Explainable machine learning algorithm was implemented for each model to elucidate meaningful insights. It was evident that physicochemical properties along with the occurrence of certain amino acids play the most important role in determining antibacterial activity, efficacy, and hemolytic activity of peptides. The entire framework is made accessible freely in form of a web tool, which will further aid in rapid screening of antibacterial peptides with high therapeutic potential.

Theories of the origin of the genetic code: Strong corroboration for the coevolution theory

I analyzed all the theories and models of the origin of the genetic code, and over the years, I have considered the main suggestions that could explain this origin. The conclusion of this analysis is that the coevolution theory of the origin of the genetic code is the theory that best captures the majority of observations concerning the organization of the genetic code. In other words, the biosynthetic relationships between amino acids would have heavily influenced the origin of the organization of the genetic code, as supported by the coevolution theory. Instead, the presence in the genetic code of physicochemical properties of amino acids, which have also been linked to the physicochemical properties of anticodons or codons or bases by stereochemical and physicochemical theories, would simply be the result of natural selection. More explicitly, I maintain that these correlations between codons, anticodons or bases and amino acids are in fact the result not of a real correlation between amino acids and codons, for example, but are only the effect of the intervention of natural selection. Specifically, in the genetic code table we expect, for example, that the most similar codons - that is, those that differ by only one base - will have more similar physicochemical properties. Therefore, the 64 codons of the genetic code table ordered in a certain way would also represent an ordering of some of their physicochemical properties. Now, a study aimed at clarifying which physicochemical property of amino acids has influenced the allocation of amino acids in the genetic code has established that the partition energy of amino acids has played a role decisive in this. Indeed, under some conditions, the genetic code was found to be approximately 98% optimized on its columns. In this same work, it was shown that this was most likely the result of the action of natural selection. If natural selection had truly allocated the amino acids in the genetic code in such a way that similar amino acids also have similar codons - this, not through a mechanism of physicochemical interaction between, for example, codons and amino acids - then it might turn out that even different physicochemical properties of codons (or anticodons or bases) show some correlation with the physicochemical properties of amino acids, simply because the partition energy of amino acids is correlated with other physicochemical properties of amino acids. It is very likely that this would inevitably lead to a correlation between codons (or anticodons or bases) and amino acids. In other words, since the codons (anticodons or bases) are ordered in the genetic code, that is to say, some of their physicochemical properties should also be ordered by a similar order, and given that the amino acids would also appear to have been ordered in the genetic code by selection natural, then it should inevitably turn out that there is a correlation between, for example, the hydrophobicity of anticodons and that of amino acids. Instead, the intervention of natural selection in organizing the genetic code would appear to be highly compatible with the main mechanism of structuring the genetic code as supported by the coevolution theory. This would make the coevolution theory the only plausible explanation for the origin of the genetic code.

Meat‐reduced sausage substituted with germinated mung bean flour: proximate composition, physicochemical properties, and sensory acceptability

SummaryThe effects of substituting meat with germinated mung bean flour (GMF) was evaluated as a possible way to reduce the meat content of sausages. Replacement ratios of 10%, 15%, 20%, and 25% were used and the proximate composition, amino acid profile, physicochemical properties (cooking yield and emulsion stability, instrumental texture, and colour), microstructure (by scanning electron microscopy), and overall liking of the sausage products were evaluated. The results showed that elevating the meat substitution ratio from 0% to 25% using GMF increased the protein, carbohydrate, and ash content by 1.14, 2.49, and 1.16 times, while reducing moisture and fat content by 1.16 and 1.2 times. Simultaneously, the total phenolic content (TPC) increased by 46%, accompanied by a 20% rise in antioxidant capacity. Incorporating GMF into the meat‐reduced sausages not only increased the amount of total amino acid and ratio of essential amino acid (EAA) but also enhanced their emulsion stability and cooking yield. The 10%–15% meat substitute samples had the closest colour and textural characteristics to the control and were accepted by the consumers.

SAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides.

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization relies on approaches based on (a) amino acid (AA) composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP-VGG16.

A multi-source molecular network representation model for protein–protein interactions prediction

The prediction of potential protein–protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein–protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein–protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein–protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs.

PseAAC2Vec protein encoding for TCR protein sequence classification

The classification and prediction of T-cell receptors (TCRs) protein sequences are of significant interest in understanding the immune system and developing personalized immunotherapies. In this study, we propose a novel approach using Pseudo Amino Acid Composition (PseAAC) protein encoding for accurate TCR protein sequence classification. The PseAAC2Vec encoding method captures the physicochemical properties of amino acids and their local sequence information, enabling the representation of protein sequences as fixed-length feature vectors. By incorporating physicochemical properties such as hydrophobicity, polarity, charge, molecular weight, and solvent accessibility, PseAAC2Vec provides a comprehensive and informative characterization of TCR protein sequences. To evaluate the effectiveness of the proposed PseAAC2Vec encoding approach, we assembled a large dataset of TCR protein sequences with annotated classes. We applied the PseAAC2Vec encoding scheme to each sequence and generated feature vectors based on a specified window size. Subsequently, we employed state-of-the-art machine learning algorithms, such as support vector machines (SVM) and random forests (RF), to classify the TCR protein sequences. Experimental results on the benchmark dataset demonstrated the superior performance of the PseAAC2Vec-based approach compared to existing methods. The PseAAC2Vec encoding effectively captures the discriminative patterns in TCR protein sequences, leading to improved classification accuracy and robustness. Furthermore, the encoding scheme showed promising results across different window sizes, indicating its adaptability to varying sequence contexts.

In-silico comparison of fungal and bacterial asparaginase enzymes.

L-asparaginase is a commercial enzyme with a wide variety of applications. Asparaginase is known as an anti-cancer agent that is effective for the treatment of certain lymphomas and leukemias by growth inhibition of human cancer cells. Additionally, asparaginase is used in the food industry in a pretreatment process to decrease the accumulation of carcinogenic acrylamide. In this paper, different aspects of bacterial and fungal asparaginases such as mass, hydrophobicity and hydrophilicity of pseudo amino acid composition (PseAAC), physicochem-ical properties, and structural motifs were studied, and ROC curve statistical analysis was used for the comparison. The results showed that none of the physicochemical properties of fungal and bacterial asparaginase could not be differed, except molecular weight and sequence length. MEME Suite analysis demonstrated that there was a motif that was specific for bacterial asparaginases. However, analysis based on the concept of PseACC indicated a differentiation line between fungal and bacterial asparaginases. In conclusion, although there was not any specific demonstration to separate the bacterial and fungal asparaginases in the case of physicochemical properties, PseAAC analysis can be an appropriate and usable method to differentiate between them.

IN SILICO STUDY OF EUCALYPTOL FROM EUCALYPTUS GLOBULUS LABILL. AGAINST ANGIOTENSIN-CONVERTING ENZYME AS AN ANTIHYPERTENSIVE IN COVID-19 COMORBID

Objective: This study aimed to determine the best compound from the 62 compounds of Eucalyptus globulus Labill. as an antihypertensive based on its interaction with angiotensin-converting enzyme (ACE) using the in silico study.
 Methods: The study was carried out in silico through molecular docking simulations, analysis of potential compounds using Lipinski’s rule, and ligand-based ADMET prediction on 62 compounds of the E. globulus.
 Results: It was found that eucalyptol (1,8-cineole) had the best interaction with the ACE as indicated by a bond energy value (∆G) of-6.40 kcal/mol with an inhibition constant of 20.82 µM, and interacted with key amino acid residues in captopril, namely HIS513, HIS353, TYR523, and ALA354. Eucalyptol also had good physicochemical properties by fulfilling Lipinski’s rule and had the best ADMET profile compared to other compounds.
 Conclusion: Eucalyptol was the best antihypertensive against ACE based on amino acid residue interaction, physicochemical properties, and ADMET profile.

IDPpred: a new sequence-based predictor for identification of intrinsically disordered protein with enhanced accuracy

Discovery of intrinsically disordered proteins (IDPs) and protein hybrids that contain both intrinsically disordered protein regions (IDPRs) along with ordered regions has changed the sequence–structure–function paradigm of protein. These proteins with lack of persistently fixed structure are often found in all organisms and play vital roles in various biological processes. Some of them are considered as potential drug targets due to their overrepresentation in pathophysiological processes. The major bottlenecks for characterizing such proteins are their occasional overexpression, difficulty in getting purified homogeneous form and the challenge of investigating them experimentally. Sequence-based prediction of intrinsic disorder remains a useful strategy especially for many large-scale proteomic investigations. However, worst accuracy still occurs for short disordered regions with less than ten residues, for the residues close to order–disorder boundaries, for regions that undergo coupled folding and binding in presence of partner, and for prediction of fully disordered proteins. Annotation of fully disordered proteins mostly relies on the far-UV circular dichroism experiment which gives overall secondary structure composition without residue-level resolution. Current methods including that using secondary structure information failed to predict half of target IDPs correctly in the recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment. This study utilized profiles of random sequential appearance of physicochemical properties of amino acids and random sequential appearance of order and disorder promoting amino acids in protein together with the existing CIDER feature for the prediction of IDP from sequence input. Our method was found to significantly outperform the existing predictors across different datasets.Communicated by Ramaswamy H. Sarma

An Overview on Physico-Chemical Properties of Amino Acids upon Interactions with Solution Components: A Volumetric and Viscometric Approach

An Overview on Physico-Chemical Properties of Amino Acids upon Interactions with Solution Components: A Volumetric and Viscometric Approach

Comparison of Evolutionary Relationships between Branchiostoma floridae, Ciona intestinalis, and Homo sapiens Globins Provide Evidence of Gene Co-Option and Convergent Evolution.

Globins have been studied as model proteins to elucidate the principles of protein evolution. This was achieved by understanding the relationship between amino acid sequence, three-dimensional structure, physicochemical properties, and physiological function. Previous molecular phylogenies of chordate globin genes revealed the monophyletic evolution of urochordate globins and suggested convergent evolution. However, to provide evidence of convergent evolution, it is necessary to determine the physicochemical and functional similarities between vertebrates and urochordate globins. In this study, we determined the expression patterns of Ciona globin genes using real-time RT-PCR. Two genes (Gb-1 and Gb-2) were predominantly expressed in the branchial sac, heart, and hemocytes and were induced under hypoxia. Combined with the sequence analysis, our findings suggest that Gb-1/-2 correspond to vertebrate hemoglobin-α/-β. However, we did not find a robust similarity between Gb-3, Gb-4, and vertebrate globins. These results suggested that, even though Ciona globins obtained their unique functions differently from vertebrate globins, the two of them shared some physicochemical features and physiological functions. Our findings offer a good example for understanding the molecular mechanisms underlying gene co-option and convergence, which could lead to evolutionary innovations.

Comprehensive analysis of the mitochondrial genome of Ziziphus mauritiana Lam.: Investigating sequence structure, repeat-mediated recombination, chloroplast-derived transfer, RNA editing, and evolution

Ziziphus mauritiana Lam. is a valuable semi-deciduous tropical fruit tree with economic, medicinal, nutritional, and environmental significance. While the chloroplast genome of Z. mauritiana has been studied, the decoding of its mitochondrial genome (mtGenome) is crucial for various research areas, including molecular phylogeny and functional genomics. In this study, we extracted high-quality genomic DNA from the green leaves of Z. mauritiana and assembled the mtGenome by integrating sequencing data from both long and short reads. The Z. mauritiana mtGenome spans 324,124 bp with a 45.43 % GC content and exhibits six distinct conformations alongside the main circular sequence due to three pairs of repetitive fragments that facilitate recombination. RNA editing events resulted in changes in the physicochemical properties of amino acids, with genes expressed at higher levels showing an increased frequency of editing sites. Chloroplast-to-mitochondria horizontal gene transfers occurred in Z. mauritiana; however, these transferred fragments had few mutations, posing a challenge in determining the exact timing of these transfers. Phylogenetic analysis highlights a slower evolution rate of mitochondrial genes compared to chloroplast genes. These insights greatly enhance our understanding of the dynamics of the Z. mauritiana mtGenome, including recombination, gene expression, evolutionary processes, and more. The data and results of this study will serve as valuable mtGenome data resources within the Rhamnaceae family, thereby facilitating further genomics-based studies on Z. mauritiana.

Physicochemical properties and antimicrobial activities of MytiLec-1, a member from the mytilectin family of mussels

MytiLec-1, the recombinant form of a mussel lectin from Mytillus galloprovincialis, was purified by affinity chromatography and showed the maximum hemagglutination activity at a temperature range of 10 °C to 40 °C and at pH 7.0 to 9.0. Denaturants like urea and acidic-guanidine inhibited its hemagglutination activity significantly. MytiLec-1 was found to be metal-independent though Ca2+ slightly increased the activity of chelated MytiLec-1. The lectin suppressed 65 % growth of Pseudomonas aeruginosa (ATCC 47085) at 200 μg/ml and reduced the formation of biofilm (15 % at 200 μg/ml). Comparing to Shigella sonnei (ATCC 29930), Shigella boydii (ATCC 231903) and Shigella dysenteriae (ATCC 238135), Bacillus cereus (ATCC 14579) was slightly more sensitive to MytiLec-1. At a concentration of 200 μg/disc and 100 μg/ml, MytiLec-1 prevented the growth of Aspergillus niger and agglutinated the spores of Aspergillus niger and Trichoderma reesei, respectively. Amino acid sequences, physicochemical properties and antimicrobial activities of MytiLec-1 were compared with three other lectins (CGL, MTL and MCL from Crenomytilus grayanus, Mytilus trossulas and Mytilus californianus, respectively) from the mytilectin family of bivalve mollusks. It reconfirms the function of these lectins to recognize pathogens and perform important roles in innate immune response of mussels.

HIV-1 subtypes maintain distinctive physicochemical signatures in Nef domains associated with immunoregulation

BackgroundHIV subtype is associated with varied rates of disease progression. The HIV accessory protein, Nef, continues to be present during antiretroviral therapy (ART) where it has numerous immunoregulatory effects. In this study, we analyzed Nef sequences from HIV subtypes A1, B, C, and D using a machine learning approach that integrates functional amino acid information to identify if unique physicochemical features are associated with Nef functional/structural domains in a subtype-specific manner. Methods2253 sequences representing subtypes A1, B, C, and D were aligned and domains with known functional properties were scored based on amino acid physicochemical properties. Following feature generation, we used statistical pruning and evolved neural networks (ENNs) to determine if we could successfully classify subtypes. Next, we used ENNs to identify the top five key Nef physicochemical features applied to specific immunoregulatory domains that differentiated subtypes. A signature pattern analysis was performed to the assess amino acid diversity in sub-domains that differentiated each subtype. ResultsIn validation studies, ENNs successfully differentiated each subtype at A1 (87.2%), subtype B (89.5%), subtype C (91.7%), and subtype D (85.1%). Our feature-based domain scoring, followed by t-tests, and a similar ENN identified subtype-specific domain-associated features. Subtype A1 was associated with alterations in Nef CD4 binding domain; subtype B was associated with alterations with the AP-2 Binding domain; subtype C was associated with alterations in a structural Alpha Helix domain; and, subtype D was associated with alterations in a Beta-Sheet domain. ConclusionsRecent studies have focused on HIV Nef as a driver of immunoregulatory disease in those HIV infected and on ART. Nef acts through a complex mixture of interactions that are directly linked to the key features of the subtype-specific domains we identified with the ENN. The study supports the hypothesis that varied Nef subtypes contribute to subtype-specific disease progression.

Comparative of the complete chloroplast genome and RNA editing of Eucalyptus camaldulensis T5 clone, an elite variety in Thailand

Abstract. Racharak P, Suttangkakul A, Vuttipongchaikij S. 2023. Comparative of the complete chloroplast genome and RNA editing of Eucalyptus camaldulensis T5 clone, an elite variety in Thailand. Biodiversitas24: 3774-3784. Eucalyptus camaldulensis, T5 clone, the excellent fast-growing tree plantation in Thailand, was analyzed for the complete chloroplast genome and RNA editing. The complete chloroplast genome revealed a total genome size of 160,204 bp that divided into a large single copy (LSC) (88,904 bp) and a small single copy region (SSC) (18,506 bp) by inverted repeat regions (IR) containing 26,397 bp. A circular mapping genome and gene order showed the circle antiparallel mapping gene of 135 genes, including 37 tRNAs, 10 rRNAs, and 1 pseudogene. GC content of the genome was 36.87%. The comparative genomes analysis between the T5 clone and E. camaldulensis from the NCBI database suggested that the thymine (T) and adenine (A) strongly impacted the indel and transversion process, which could be a point of mutation in the genome. Furthermore, 24 specific genes were used to investigate RNA editing. From all genes, only 11 genes were edited with C to U conversion.Triplet codons, tUA, tUt, tUg and Ugg were the most frequently edited codon and expressions; the crucial influence of amino acid alterations. Due to RNA editing events, the physicochemical properties of amino acids were changed from polar to nonpolar amino acids and from hydrophilic to hydrophobic amino acids. Physicochemical properties conversion is necessary to form complete amino acid sequences for several essential chloroplast proteins. The event might be the accumulation of amino acid alterations causing phenotypic variation for plant adaptation and evolution.