Physicochemical Properties Of Amino Acids Research Articles

Analysis of protein determinants of host-specific infection properties of polyomaviruses using machine learning.

The large tumor antigen (LT-Ag) and major capsid protein VP1 are known to play important roles in determining the host-specific infection properties of polyomaviruses (PyVs). The objective of this study was to investigate the physicochemical properties of amino acids of LT-Ag and VP1 that have important effects on host specificity, as well as classification techniques used to predict PyV hosts. We collected and used reference sequences of 86 viral species for analysis. Based on the clustering pattern of the reconstructed phylogenetic tree, the dataset was divided into three groups: mammalian, avian, and fish. We then used random forest (RF), naïve Bayes (NB), and k-nearest neighbors (kNN) algorithms for host classification. Among the three algorithms, classification accuracy using kNN was highest in both LT-Ag (ACC = 98.83) and VP1 (ACC = 96.51). The amino acid physicochemical property most strongly correlated with host classification was charge, followed by solvent accessibility, polarity, and hydrophobicity in LT-Ag. However, in VP1, amino acid composition showed the highest correlation with host classification, followed by charge, normalized van der Waals volume, and solvent accessibility. The results of the present study suggest the possibility of determining or predicting the host range and infection properties of PyVs at the molecular level by identifying the host species of active and emerging PyVs that exhibit different infection properties among diverse host species. Structural and biochemical differences of LT-Ag and VP1 proteins in host species that reflect these amino acid properties can be considered primary factors that determine the host specificity of PyV.

A novel predictor of ACE2-binding ability among betacoronaviruses.

BackgroundCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in ~4.8 million deaths worldwide as of this writing. Almost all conceivable aspects of SARS-CoV-2 have been explored since the virus began spreading in the human population. Despite numerous proposals, it is still unclear how and when the virus gained the ability to efficiently bind to and infect human cells. In an effort to understand the evolution of receptor binding domain (RBD) of the spike protein of SARS-CoV-2, and specifically, how the ability of RBD to bind to angiotensin-converting enzyme 2 receptor (ACE2) of humans evolved in coronaviruses, we have applied an alignment-free technique to infer functional relatedness among betacoronaviruses. This technique, concurrently being optimized for identifying novel prions, was adapted to gain new insights into coronavirus evolution, specifically in the context of the ongoing COVID-19 pandemic. Novel methods for predicting the capacity for coronaviruses, in general, to infect human cells are urgently needed.Methodologyproposed method utilizes physicochemical properties of amino acids to develop fully dynamic waveform representations of proteins that encode both the amino acid content and the context of amino acids. These waveforms are then subjected to dynamic time warping (DTW) and distance evaluation to develop a distance metric that is relatively less sensitive to variation in sequence length and primary amino acid composition.Results and ConclusionsUsing our proposed method, we show that in contrast to alignment-based maximum likelihood (ML) and neighbor-joining (NJ) phylogenetic analyses, all bat betacoronavirus spike protein RBDs known to bind to the ACE2 receptor are found within a single physicochemical cluster. Further, other RBDs within that cluster are from pangolin coronaviruses, two of which have already been shown to bind to ACE2 while the others are suspected, yet unverified ACE2 binding domains. This finding is important because both severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2 use the host ACE2 receptor for cell entry. Surveillance for coronaviruses belonging to this cluster could potentially guide efforts to stifle or curtail potential and/or early zoonotic outbreaks with their associated deaths and financial devastation.Lay SummaryRobust methods for predicting human ACE2 receptor binding by the spike protein of coronaviruses are needed for the early detection of zoonotic coronaviruses and biosurveillance to prevent future outbreaks. Here we present a new waveform-based approach that utilizes the physicochemical properties of amino acids to determine the propensity of betacoronaviruses to infect humans. Comparison with the established phylogenetic methods demonstrates the usefulness of this new approach in the biosurveillance of coronaviruses.

Bioluminescent Proteins Prediction with Voting Strategy

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology. In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this study, a new bioluminescent proteins prediction method, based on a voting algorithm, is proposed. Four methods of feature extraction based on the amino acid sequence were used. 314 dimensional features in total were extracted from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, a voting algorithm was then used to build the model. To create the best performing model, the selection of base classifiers and vote counting rules are discussed. Results: The proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. A previous prediction of bioluminescent proteins in three lineages was also improved using the model building method, resulting in greatly improved accuracy.

Computational identification of ubiquitination sites in Arabidopsis thaliana using convolutional neural networks.

We developed two CNNs for predicting ubiquitination sites in Arabidopsis thaliana, demonstrated their competitive performance, analyzed amino acid physicochemical properties and the CNN structures, and predicted ubiquitination sites in Arabidopsis. As an important posttranslational protein modification, ubiquitination plays critical roles in plant physiology, including plant growth and development, biotic and abiotic stress, metabolism, and so on. A lot of ubiquitination site prediction models have been developed for human, mouse and yeast. However, there are few models to predict ubiquitination sites for the plant Arabidopsis thaliana. Based on this context, we proposed two convolutional neural network (CNN) based models for predicting ubiquitination sites in A. thaliana. The two models reach AUC (area under the ROC curve) values of 0.924 and 0.913 respectively in five-fold cross-validation, and 0.921 and 0.914 respectively in independent test, which outperform other models and demonstrate the competitive edge of them. We in-depth analyze the amino acid physicochemical properties in the neighboring sequence regions of the ubiquitination sites, and study the influence of the CNN structure to the prediction performance. Potential ubiquitination sites in the global Arbidopsis proteome are predicted using the two CNN models. To facilitate the community, the source code, training and test dataset, predicted ubiquitination sites in the Arbidopsis proteome are available at GitHub ( http://github.com/nongdaxiaofeng/CNNAthUbi ) for interest users.

MRMD-palm: A novel method for the identification of palmitoylated protein

Palmitoylated proteins are important proteins that regulate several biological processes in cells, including protein localization, protein trafficking, and protein-protein interactions. The identification of palmitoylated proteins may therefore contribute to our understanding of important molecular mechanisms and biological functions. In this paper, a simple but effective computational method named MRMD-Palm is proposed for identifying palmitoylated proteins. In developing the method, first, palmitoylated and non-palmitoylated proteins were collected to construct a benchmark dataset. Next, some typical sequence-based feature-encoding schemes related to the amino acid composition, pseudo amino acid composition and physico-chemical properties were extracted. To avoid noise and reduce information redundancy, a feature selection technique named MRMD (Max-Relevance-Max-Distance) was introduced to obtain the optimal feature subset. Finally, the random forest algorithm was utilized as the predictor. Under 10-fold cross-validation, the method achieved a sensitivity of 97.1%, a specificity of 95.9% and a MCC of 73.7%, representing better performance than most current counterparts. Additionally, the experimental results showed that the use of hydrophobicity to encode palmitoylated sequences can improve the performance of the predictor. This finding is consistent with the biological phenomenon that palmitoylation is highly hydrophobic. It is believed that the results of this study will lead to the determination of the protein palmitoylation mechanism.

Computational identification of human ubiquitination sites using convolutional and recurrent neural networks.

Ubiquitination is a very important protein post-translational modification in humans, which is closely related to many human diseases such as cancers. Although some methods have been elegantly proposed to predict human ubiquitination sites, the accuracy of these methods is generally not very satisfactory. In order to improve the prediction accuracy of human ubiquitination sites, we propose a new ensemble method HUbipPred, which takes the binary encoding and physicochemical properties of amino acids as training features, and integrates two intensively trained convolutional neural networks and two recurrent neural networks to build the model. Finally, HUbiPred achieves AUC values of 0.852 and 0.844 in five-fold cross-validation and independent tests, respectively, which greatly improves the prediction accuracy compared to previous predictors. We also analyze the physicochemical properties of amino acids around ubiquitination sites, study the important roles of architectures (i.e., convolution, long short-term memory (LSTM) and fully connected hidden layers) in the networks for prediction performance, and also predict potential ubiquitination sites in humans using HUbiPred. The training and test datasets, predicted human ubiquitination sites, and source codes of HUbiPred are publicly available at https://github.com/amituofo-xf/HUbiPred.

Quantitative elucidation of associations between nucleotide identity and physicochemical properties of amino acids and the functional insight

Studies on codon property would deepen our understanding of the origin of primitive life and enlighten biotechnical application. Here, we proposed a quantitative measurement of codon-amino acid association and found that seven out of 13 physicochemical properties have stronger associations with the nucleotide identity at the second codon position, indicating that protein structure and function may associate more closely with it than the other two sites. When extending the effect of codon-amino acid association to protein level, it was found that the correlation between the second codon position (measured by the relative frequencies of nucleobase T and A at this codon site) and hydrophobicity (by the form of GRAVY value) became stronger with 96% genomes having R > 0.90 and p < 1e-60. Furthermore, we revealed that informational genes encoding proteins have lower GRAVY values than operational proteins (p < 3e-37) in both prokaryotic and eukaryotic genomes. The above results reveal a complete link from codon identity (A2 versus T2) to amino acid property (hydrophilic versus hydrophobic) and then to protein functions (informational versus operational). Hence, our work may help to understand how the nucleotide sequence determines protein function.

Characterization of structural, functional and antioxidant properties and amino acid composition of pepsin-derived glutelin-1 hydrolysate from walnut processing by-products.

Glutelin-1 of defatted walnut meal protein (DWPG-1) was modified by pepsin enzymatic hydrolysis to improve its functional properties and antioxidant activities. The amino acid composition, structural characteristics, physicochemical and functional properties as well as antioxidant activities of the hydrolysate were compared with those of unmodified DWPG-1. The analysis of X-ray diffraction patterns, surface microstructure and particle size distribution indicated that enzymatic hydrolysis changed the structures of DWPG-1. Compared with the natural unhydrolyzed protein, the hydrolysate showed better physicochemical properties, such as surface hydrophobicity, solubility, emulsifying properties, foaming properties and water absorption capacity. In addition, the hydrolysate also exhibited significantly stronger antioxidant activities than DWPG-1. In conclusion, the results of this study prove that pepsin-mediated hydrolysis of walnut glutelin-1 can effectively modify the structure, function and antioxidant activity of DWPG-1, and could be used as an effective technology to produce bioactive multifunctional hydrolysates.

Benchmarking Datasets from Malaria Cytotoxic T-cell Epitopes Using Machine Learning Approach.

Background:Epitope prediction remains a major challenge in malaria due to the unique parasite biology, in addition to rapidly evolving parasite sequence variation in Plasmodium species. Although several models for epitope prediction exist, they are not useful in Plasmodium specific epitope development. Hence, it was proposed to use machine learning based methods to develop a peptide sequence based epitope predictor specific for malaria.Methods:Model datasets were developed and performance was tested using various machine learning algorithms. Machine learning classifiers were trained on epitope data using sequence features and comparison of amino acid physicochemical properties was done to yield a valid prediction model.Results:The findings from the analysis reveal that the model developed using selected classifiers after preprocessing by Waikato Environment for Knowledge Analysis (WEKA) performed better than other methods. The datasets for benchmarks of performance are deposited in the repository https://github.com/githubramaadiga/epitope_dataset .Conclusion:The study is the first in-silico study on benchmarking Plasmodium cytotoxic T cell epitope datasets using machine learning approach. The peptide based predictors have been used for the first time to classify cytotoxic T cell epitopes in malaria. Algorithms has been evaluated using real datasets from malaria to obtain the model.

Volumetric and acoustic properties of amino acids L-Leucine and L-Serine in aqueous solution of ammonium dihydrogen phosphate (ADP) at different temperatures and concentrations

The physicochemical properties of amino acids like L-Leucine and L-Serine in aqueous solution of ammonium dihydrogen Phosphate (ADP) at different temperature analysed by volumetric and acoustic methods. By using density data, apparent molar volume Vϕ, partial molar volume Vϕ0 and standard partial molar volume of transfer ΔVϕ0 have been evaluated. From speed of sound data, apparent molar isentropic compression Kϕ,s apparent partial molar isentropic compression Kϕ,s0 and partial molar isentropic compression of transfer ΔKϕ,so have been calculated. The pair and triplet interaction coefficient have also been calculated. The results have been interpreted in terms of solute–solute and solvent–solvent interactions.

A Sequence-segment Neighbor Encoding Schema for Protein Hotspot Residue Prediction

Background: Hotspots are those residues that contribute major free energy of binding in protein-protein interactions. Protein functions are frequently dependent on hotspot residues. At present, hotspot residues are always identified by Alanine scanning mutagenesis technology, which is costly, time-consuming and laborious. Objective: Therefore, more accurate and efficient methods have to be developed to identify protein hotspot residues. Methods: This paper proposed a novel encoding schema of sequence-segment neighbors and constructed a random forest-based model to identify hotspots in protein interaction interfaces. Firstly, 10 amino acid physicochemical properties, 16 features related to the PI and DI, and 25 features related to ASA were extracted. Different from the previous residue encoding schemas, such as auto correlation descriptor or triplet combination information, this paper employed the influence of amino acids neighbors to hotspot residues and amino acids with a certain distance in sequence to the hotspot. Results: Moreover, the proposed model was compared with other hotspot prediction methods, including APIS, Robetta, FOLDEF, KFC, MINERVA models, etc. Conclusion: The experimental results showed that the proposed model can improve the prediction ability of protein hotspot residues on the same test set.

A Generalized Iterative Map for Analysis of Protein Sequences.

The similarities comparison of biological sequences is an important task in bioinformatics. The methods of the similarities comparison for biological sequences are divided into two classes: sequence alignment method and alignment-free method. The graphical representation of biological sequences is a kind of alignment-free method, which constitutes a tool for analyzing and visualizing the biological sequences. In this article, a generalized iterative map of protein sequences was suggested to analyze the similarities of biological sequences. Based on the normalized physicochemical indexes of 20 amino acids, each amino acid can be mapped into a point in 5D space. A generalized iterative function system was introduced to outline a generalized iterative map of protein sequences, which can not only reflect various physicochemical properties of amino acids but also incorporate with different compression ratios of the component of a generalized iterative map. Several properties were proved to illustrate the advantage of the generalized iterative map. The mathematical description of the generalized iterative map was suggested to compare the similarities and dissimilarities of protein sequences. Based on this method, similarities/dissimilarities were compared among ND5 protein sequences, as well as ND6 protein sequences of ten different species. By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to show the utility of our approach. The comparison results show that our approach has better correlations with ClustalW for all species than other approaches and illustrate the effectiveness of our approach. Two examples show that our method not only has good performances and effects in the similarity/dissimilarity analysis of protein sequences but also does not require complex computation.

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction.

Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.

Genome-Wide Identification of Barley ABC Genes and Their Expression in Response to Abiotic Stress Treatment.

Adenosine triphosphate-binding cassette transporters (ABC transporters) participate in various plant growth and abiotic stress responses. In the present study, 131 ABC genes in barley were systematically identified using bioinformatics. Based on the classification method of the family in rice, these members were classified into eight subfamilies (ABCA–ABCG, ABCI). The conserved domain, amino acid composition, physicochemical properties, chromosome distribution, and tissue expression of these genes were predicted and analyzed. The results showed that the characteristic motifs of the barley ABC genes were highly conserved and there were great diversities in the homology of the transmembrane domain, the number of exons, amino acid length, and the molecular weight, whereas the span of the isoelectric point was small. Tissue expression profile analysis suggested that ABC genes possess non-tissue specificity. Ultimately, 15 differentially expressed genes exhibited diverse expression responses to stress treatments including drought, cadmium, and salt stress, indicating that the ABCB and ABCG subfamilies function in the response to abiotic stress in barley.

Structural Determinants within the Adenovirus Early Region 1A Protein Spacer Region Necessary for Tumorigenesis.

It has long been established that group A human adenoviruses (HAdV-A12, -A18, and -A31) can cause tumors in newborn rodents, with tumorigenicity related to the presence of a unique spacer region located between conserved regions 2 and 3 within the HAdV-A12 early region 1A (E1A) protein. Group B adenoviruses are weakly oncogenic, whereas most of the remaining human adenoviruses are nononcogenic. In an attempt to understand better the relationship between the structure of the AdE1A spacer region and oncogenicity of HAdVs, the structures of synthetic peptides identical or very similar to the adenovirus 12 E1A spacer region were determined and found to be α-helical using nuclear magnetic resonance (NMR) spectroscopy. This contrasts significantly with some previous suggestions that this region is unstructured. Using available predictive algorithms, the structures of spacer regions from other E1As were also examined, and the extent of the predicted α-helix was found to correlate reasonably well with the tumorigenicity of the respective virus. We suggest that this may represent an as-yet-unknown binding site for a partner protein required for tumorigenesis.IMPORTANCE This research analyzed small peptides equivalent to a region within the human adenovirus early region 1A protein that confers, in part, tumor-inducing properties to various degrees on several viral strains in rats and mice. The oncogenic spacer region is α-helical, which contrasts with previous suggestions that this region is unstructured. The helix is characterized by a stretch of amino acids rich in alanine residues that are organized into a hydrophobic, or "water-hating," surface that is considered to form a major site of interaction with cellular protein targets that mediate tumor formation. The extent of α-helix in E1A from other adenovirus species can be correlated to a limited degree to the tumorigenicity of that virus. Some serotypes show significant differences in predicted structural propensity, suggesting that the amino acid type and physicochemical properties are also of importance.

Are Nonsynonymous Transversions Generally More Deleterious than Nonsynonymous Transitions?

It has been suggested that, due to the structure of the genetic code, nonsynonymous transitions are less likely than transversions to cause radical changes in amino acid physicochemical properties so are on average less deleterious. This view was supported by some but not all mutagenesis experiments. Because laboratory measures of fitness effects have limited sensitivities and relative frequencies of different mutations in mutagenesis studies may not match those in nature, we here revisit this issue using comparative genomics. We extend the standard codon model of sequence evolution by adding the parameter that quantifies the ratio of the fixation probability of transitional nonsynonymous mutations to that of transversional nonsynonymous mutations. We then estimate from the concatenated alignment of all protein-coding DNA sequences of two closely related genomes. Surprisingly, ranges from 0.13 to 2.0 across 90 species pairs sampled from the tree of life, with 51 incidences of < 1 and 30 incidences of >1 that are statistically significant. Hence, whether nonsynonymous transversions are overall more deleterious than nonsynonymous transitions is species-dependent. Because the corresponding groups of amino acid replacements differ between nonsynonymous transitions and transversions, is influenced by the relative exchangeabilities of amino acid pairs. Indeed, an extensive search reveals that the large variation in is primarily explainable by the recently reported among-species disparity in amino acid exchangeabilities. These findings demonstrate that genome-wide nucleotide substitution patterns in coding sequences have species-specific features and are more variable among evolutionary lineages than are currently thought.

INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features.

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81, external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called “neoantigens.” Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool “INeo-Epp” was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.

Functional divergence and adaptive selection of KNOX gene family in plants.

KNOTTED-like homeodomain (KNOX) genes are transcriptional regulators that play an important role in morphogenesis. In the present study, a comparative analysis was performed to investigate the molecular evolution of the characteristics of the KNOX gene family in 10 different plant species. We identified 129 KNOX gene family members, which were categorized into two subfamilies based on multiple sequence alignment and phylogenetic tree reconstruction. Several segmental duplication pairs were found, indicating that different species share a common expansion model. Functional divergence analysis identified the 15 and 52 amino acid sites with significant changes in evolutionary rates and amino acid physicochemical properties as functional divergence sites. Additional selection analysis showed that 14 amino acid sites underwent positive selection during evolution, and two groups of co-evolutionary amino acid sites were identified by Coevolution Analysis using Protein Sequences software. These sites could play critical roles in the molecular evolution of the KNOX gene family in these species. In addition, the expression profiles of KNOX duplicated genes demonstrated functional divergence. Taken together, these results provide novel insights into the structural and functional evolution of the KNOX gene family.

Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle.

The prediction of ion ligand–binding residues in protein sequences is a challenging work that contributes to understand the specific functions of proteins in life processes. In this article, we selected binding residues of 14 ion ligands as research objects, including four acid radical ion ligands and 10 metal ion ligands. Based on the amino acid sequence information, we selected the composition and position conservation information of amino acids, the predicted structural information, and physicochemical properties of amino acids as basic feature parameters. We then performed a statistical analysis and reclassification for dihedral angle and proposed new methods on the extraction of feature parameters. The methods mainly included applying information entropy on the extraction of polarization charge and hydrophilic–hydrophobic information of amino acids and using position weight matrices on the extraction of position conservation information. In the prediction model, we used the random forest algorithm and obtained better prediction results than previous works. With the independent test, the Matthew's correlation coefficient and accuracy of 10 metal ion ligand–binding residues were larger than 0.07 and 52%, respectively; the corresponding evaluation values of four acid radical ion ligand–binding residues were larger than 0.15 and 86%, respectively. Further, we classified and combined the phi and psi angles and optimized prediction model for each ion ligand–binding residue.

Physicochemical and functional properties of proteins isolated from local beans of Turkey

Amino acid composition, physicochemical and functional properties of proteins isolated from five local beans of Turkey were investigated. Minimal variation was observed in amino acid composition of the bean protein isolates except for the hydrophobic amino acids. The isolates were found to be rich in essential amino acids leucine, lysine and phenylalanine while their methionine content was limiting. Denaturation temperature of isolates ranged between 90 and 152 °C and β-layer structure was found to be the dominant secondary structure. Maximum solubility (~97%) and net surface charge (~−23 mV) were observed at pH 9.0. Emulsion capacity and emulsifying activity index of bean protein isolates were found to be 402.7–468.5 g oil/g protein and 15.6–22.0 m2/g, respectively. The isolates with significantly higher net surface charge and solubility were observed to show better emulsifying and foaming properties compared to the other isolates studied. No significant correlation was observed between the thermal characteristics and gelation properties of isolates.