Physicochemical Properties Of Amino Acids Research Articles

Identification and characterization of azoreductase enzyme AzoR2 from Bacillus velezensis for biodegradation of azo dyes

Azoreductase presents an interesting group of enzymes due to its widespread occurrence in all living forms, high sequence heterogeneity and debatable physiological role. Some bacteria have been reported to contain multiple azoreductase enzymes with differing properties, raising questions about their phylogenetic, functional and structural relationships. A YhdA azoreductase was characterized from Bacillus velezensis strain AB earlier. The present investigation reports another azoreductase AzoR2 from this culture. The amino acid sequence, physico-chemical and kinetic properties of AzoR2 were very different from YhdA, indicating that these enzymes were phylogenetically distant. AzoR2 was a flavoprotein containing FMN with subunit molecular weight of 25 kDa. It preferentially utilized NADH as electron donor with Vmax and Km of 400 U/mg and 40.2 μM respectively for methyl red decolourization. It also decolourized methyl orange and congo red effectively. The highest activity was observed at pH 7. The optimum temperature for enzyme activity was 20 °C with 84.8% activity at 60 °C. The 3-D enzyme structure was modeled, where the FMN binding residues were found to be conserved and the enzyme was predicted to be homodimeric. It is the first report on AzoR2-type enzyme from B. velezensis according to our extensive literature review.

Prediction of Protein Sites and Physicochemical Properties Related to Functional Specificity.

Specificity Determining Positions (SDPs) are protein sites responsible for functional specificity within a family of homologous proteins. These positions are extracted from a family’s multiple sequence alignment and complement the fully conserved positions as predictors of functional sites. SDP analysis is now routinely used for locating these specificity-related sites in families of proteins of biomedical or biotechnological interest with the aim of mutating them to switch specificities or design new ones. There are many different approaches for detecting these positions in multiple sequence alignments. Nevertheless, existing methods report the potential SDP positions but they do not provide any clue on the physicochemical basis behind the functional specificity, which has to be inferred a-posteriori by manually inspecting these positions in the alignment. In this work, a new methodology is presented that, concomitantly with the detection of the SDPs, automatically provides information on the amino-acid physicochemical properties more related to the change in specificity. This new method is applied to two different multiple sequence alignments of homologous of the well-studied RasH protein representing different cases of functional specificity and the results discussed in detail.

ReRF-Pred: predicting amyloidogenic regions of proteins based on their pseudo amino acid composition and tripeptide composition

BackgroundAmyloids are insoluble fibrillar aggregates that are highly associated with complex human diseases, such as Alzheimer’s disease, Parkinson’s disease, and type II diabetes. Recently, many studies reported that some specific regions of amino acid sequences may be responsible for the amyloidosis of proteins. It has become very important for elucidating the mechanism of amyloids that identifying the amyloidogenic regions. Accordingly, several computational methods have been put forward to discover amyloidogenic regions. The majority of these methods predicted amyloidogenic regions based on the physicochemical properties of amino acids. In fact, position, order, and correlation of amino acids may also influence the amyloidosis of proteins, which should be also considered in detecting amyloidogenic regions.ResultsTo address this problem, we proposed a novel machine-learning approach for predicting amyloidogenic regions, called ReRF-Pred. Firstly, the pseudo amino acid composition (PseAAC) was exploited to characterize physicochemical properties and correlation of amino acids. Secondly, tripeptides composition (TPC) was employed to represent the order and position of amino acids. To improve the distinguishability of TPC, all possible tripeptides were analyzed by the binomial distribution method, and only those which have significantly different distribution between positive and negative samples remained. Finally, all samples were characterized by PseAAC and TPC of their amino acid sequence, and a random forest-based amyloidogenic regions predictor was trained on these samples. It was proved by validation experiments that the feature set consisted of PseAAC and TPC is the most distinguishable one for detecting amyloidosis. Meanwhile, random forest is superior to other concerned classifiers on almost all metrics. To validate the effectiveness of our model, ReRF-Pred is compared with a series of gold-standard methods on two datasets: Pep-251 and Reg33. The results suggested our method has the best overall performance and makes significant improvements in discovering amyloidogenic regions.ConclusionsThe advantages of our method are mainly attributed to that PseAAC and TPC can describe the differences between amyloids and other proteins successfully. The ReRF-Pred server can be accessed at http://106.12.83.135:8080/ReRF-Pred/.

A bioinformatics approach for identifying the probable cause of the cross-interaction of antibodies to the antigenic protein HPV16 L1 with the HPV6 L1 protein.

This paper describes an attempt to analyze, with the aid of bioinformatics resources (programs and databases), the probable cause of the cross-interaction of antibodies against HPV16 L1 with antigenic protein HPV6 L1, which has been revealed in the investigation of the candidate vaccine obtained on the base of a plant expression system (tomato plants). In our opinion, the most likely reason for the cross-interaction of antibodies with antigens of different pathogenic HPV types is the similarity of their antigenic determinants. In this work, the amino acid sequences of HPV16 L1 and HPV6 L1 used for the development of a binary vaccine against cervical cancer and anogenital papillomatosis have been analyzed. For the analysis of antigenic determinants, the programs BepiPred-2.0: Sequential B-Cell Epitope Predictor, DiscoTope 2.0 Server and SYFPEITHI have been used. As a result of the analysis of probable B-cell linear determinants (epitopes), it has been found that in both types of HPV the proteins have approximately the same location and size of linear antigenic determinants; the difference is observed only in the form of small shifts in the size of several amino acid residues. However, there are some differences in the amino acid composition of epitopes; therefore, the possibility for cross-interaction of the antibodies with the antigens due to the similarity of linear antigenic determinants for B-cells is very small. The analysis of potential threedimensional epitopes for B-cells has shown that due to little difference between them the HPV16 L1 and HPV6 L1 proteins have no prerequisites for cross-interaction of the antibodies with the antigens belonging to the two different pathogenic HPV types. The analysis of probable linear epitopes for T-cells has revealed a common antigenic determinant in the two protein sequences. According to the rank made with the SYFPEITHI program, the amino acid sequence AQL(I)FNKPYWL is the second most likely antigenic determinant for T-cells. Meanwhile, the amino acid sequences of this determinant in HPV16 L1 and HPV6 L1 are virtually identical. There is a difference in only one position, but it is not critical due to the similarity of the physicochemical properties of amino acids, for which there is a replacement in the amino acid sequence of antigenic determinants. Consequently, some moderate cross-interaction of the antibodies to HPV16 L1 with the antigens of HPV6 L1 may be expected.

Ensemble Machine Learning Model to Predict SARS-CoV-2 T-Cell Epitopes as Potential Vaccine Targets.

An ongoing outbreak of coronavirus disease 2019 (COVID-19), caused by a single-stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a worldwide pandemic that continues to date. Vaccination has proven to be the most effective technique, by far, for the treatment of COVID-19 and to combat the outbreak. Among all vaccine types, epitope-based peptide vaccines have received less attention and hold a large untapped potential for boosting vaccine safety and immunogenicity. Peptides used in such vaccine technology are chemically synthesized based on the amino acid sequences of antigenic proteins (T-cell epitopes) of the target pathogen. Using wet-lab experiments to identify antigenic proteins is very difficult, expensive, and time-consuming. We hereby propose an ensemble machine learning (ML) model for the prediction of T-cell epitopes (also known as immune relevant determinants or antigenic determinants) against SARS-CoV-2, utilizing physicochemical properties of amino acids. To train the model, we retrieved the experimentally determined SARS-CoV-2 T-cell epitopes from Immune Epitope Database and Analysis Resource (IEDB) repository. The model so developed achieved accuracy, AUC (Area under the ROC curve), Gini, specificity, sensitivity, F-score, and precision of 98.20%, 0.991, 0.994, 0.971, 0.982, 0.990, and 0.981, respectively, using a test set consisting of SARS-CoV-2 peptides (T-cell epitopes and non-epitopes) obtained from IEDB. The average accuracy of 97.98% was recorded in repeated 5-fold cross validation. Its comparison with 05 robust machine learning classifiers and existing T-cell epitope prediction techniques, such as NetMHC and CTLpred, suggest the proposed work as a better model. The predicted epitopes from the current model could possess a high probability to act as potential peptide vaccine candidates subjected to in vitro and in vivo scientific assessments. The model developed would help scientific community working in vaccine development save time to screen the active T-cell epitope candidates of SARS-CoV-2 against the inactive ones.

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction.

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

Prioritizing the candidate genes related to cervical cancer using the moment of inertia tensor.

It is well known that cervical cancer poses the fourth most malignancy threat to women worldwide among all cancer types. There is a tremendous improvement in realizing the underlying molecular associations in cervical cancer. Several studies reported pieces of evidence for the involvement of various genes in the disease progression. However, with the ever-evolving bioinformatics tools, there has been an upsurge in predicting numerous genes responsible for cervical cancer progression and making it highly complex to target the genes for further evaluation. In this article, we prioritized the candidate genes based on the sequence similarity analysis with known cancer genes. For this purpose, we used the concept of the moment of inertia tensor, which reveals the similarities between the protein sequences more efficiently. Tensor for moment of inertia explores the similarity of the protein sequences based on the physicochemical properties of amino acids. From our analysis, we obtained 14 candidate cervical cancer genes, which are highly similar to known cervical cancer genes. Further, we analyzed the GO terms and prioritized these genes based on the number of hits with biological process, molecular functions, and their involvement in KEGG pathways. We also discussed the evidence-based involvement of the prioritized genes in other cancers and listed the available drugs for those genes.

RF‐SVM: Identification ofDNA‐binding proteins based on comprehensive feature representation methods and support vector machine

Protein-DNA interactions play an important role in biological progress, such as DNA replication, repair, and modification processes. In order to have a better understanding of its functions, the one of the most important steps is the identification of DNA-binding proteins. We propose a DNA-binding protein predictor, namely, RF-SVM, which contains four types features, that is, pseudo amino acid composition (PseAAC), amino acid distribution (AAD), adjacent amino acid composition frequency (ACF) and Local-DPP. Random Forest algorithm is utilized for selecting top 174 features, which are established the predictor model with the support vector machine (SVM) on training dataset UniSwiss-Tr. Finally, RF-SVM method is compared with other existing methods on test dataset UniSwiss-Tst. The experimental results demonstrated that RF-SVM has accuracy of 84.25%. Meanwhile, we discover that the physicochemical properties of amino acids for OOBM770101(H), CIDH920104(H), MIYS990104(H), NISK860101(H), VINM940103(H), and SNEP660101(A) have contribution to predict DNA-binding proteins. The main code and datasets can gain in https://github.com/NiJianWei996/RF-SVM.

EightyDVec: a method for protein sequence similarity analysis using physicochemical properties of amino acids

ABSTRACT Similarity analysis of protein sequences can expose the evolutionary relationship among them. It is required to design effective computational algorithms that can compare the similarities among the colossal amount of sequences. Alignment-based approaches to this problem are often computationally expensive, especially when the number of sequences is large. This research aims to develop an efficient alignment-free tool in the field of protein sequence comparison and phylogenetic study. The proposed method, namely EightyDVec, performs a feature generation process based on the physiochemical properties of amino acids that best describe the evolutionary relationship among the species in a protein family. Using EightyDVec, protein sequences are transformed into 80-dimensional feature vectors and the comparisons between sequences are performed conveniently through these vectors. Four different datasets are used to validate the accuracy of EightyDVec, and the obtained results have shown the great effectiveness of the proposed method in the similarity analysis of protein sequences.

Identification and characterization of pectin lyase gene as a virulence factor in Colletotrichumgloeosporioides

C. gloeosporioides, a filamentous phytopathogenic fungus, secrets several enzymes during infection and degrades cell wall of the host plant causing anthracnose disease in plants worldwide. In the present study, leaves from four different infected plants (Litchi chinensis, Aegle marmelos, Mangifera indica and Syzygium jambolanum) were collected and four different fungi strains were isolated. They were confirmed as Colletotrichum gloeosporioides by 28s rDNA gene sequencing. The strains were screened for pectin lyase secretion by observing a zone of pectin utilization on pectin-agar plate assay using Congo red staining. The pectin lyase (pnl-1) gene from each strain was amplified using a specific primer and sequenced. The obtained sequences were analyzed for multiple sequence alignment, phylogenetic tree, sequence similarity matrix, pair-wise identity (using a heatmap), amino acid composition, conservation of amino acid residues and physicochemical properties. I-TASSER was used to elucidate its three-dimensional structure. Remarkable amino acid diversity was observed in pnl-1 gene in all the fungi studied. From the results of the physicochemical property of the protein it is observed that fungal pectin lyase have different pI values and amino acid compositions. The probable role of this pnl-1 gene in mechanisms of pathogenicity with the different hosts is discussed.

The Mutational Robustness of the Genetic Code and Codon Usage in Environmental Context: A Non-Extremophilic Preference?

The genetic code was evolved, to some extent, to minimize the effects of mutations. The effects of mutations depend on the amino acid repertoire, the structure of the genetic code and frequencies of amino acids in proteomes. The amino acid compositions of proteins and corresponding codon usages are still under selection, which allows us to ask what kind of environment the standard genetic code is adapted to. Using simple computational models and comprehensive datasets comprising genomic and environmental data from all three domains of Life, we estimate the expected severity of non-synonymous genomic mutations in proteins, measured by the change in amino acid physicochemical properties. We show that the fidelity in these physicochemical properties is expected to deteriorate with extremophilic codon usages, especially in thermophiles. These findings suggest that the genetic code performs better under non-extremophilic conditions, which not only explains the low substitution rates encountered in halophiles and thermophiles but the revealed relationship between the genetic code and habitat allows us to ponder on earlier phases in the history of Life.

MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network

Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for disease treatment and research of related drugs. However, most existing computational methods for the prediction of succinylation sites are still based on machine learning. With the increasing volume of data and complexity of feature representations, it is necessary to explore effective deep learning methods to recognize succinylation sites. In this paper, we propose a multilane dense convolutional attention network, MDCAN-Lys. MDCAN-Lys extracts sequence information, physicochemical properties of amino acids, and structural properties of proteins using a three-way network, and it constructs feature space. For each sub-network, MDCAN-Lys uses the cascading model of dense convolutional block and convolutional block attention module to capture feature information at different levels and improve the abstraction ability of the network. The experimental results of 10-fold cross-validation and independent testing show that MDCAN-Lys can recognize more succinylation sites, which is consistent with the conclusion of the case study. Thus, it is worthwhile to explore deep learning-based methods for the recognition of succinylation sites.

FEGS: a novel feature extraction model for protein sequences and its applications

BackgroundFeature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions.ResultsIn this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods.ConclusionThe FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses.

Phylogenetic analysis of mutational robustness based on codon usage supports that the standard genetic code does not prefer extreme environments

The mutational robustness of the genetic code is rarely discussed in the context of biological diversity, such as codon usage and related factors, often considered as independent of the actual organism’s proteome. Here we put the living beings back to picture and use distortion as a metric of mutational robustness. Distortion estimates the expected severities of non-synonymous mutations measuring it by amino acid physicochemical properties and weighting for codon usage. Using the biological variance of codon frequencies, we interpret the mutational robustness of the standard genetic code with regards to their corresponding environments and genomic compositions (GC-content). Employing phylogenetic analyses, we show that coding fidelity in physicochemical properties can deteriorate with codon usages adapted to extreme environments and these putative effects are not the artefacts of phylogenetic bias. High temperature environments select for codon usages with decreased mutational robustness of hydrophobic, volumetric, and isoelectric properties. Selection at high saline concentrations also leads to reduced fidelity in polar and isoelectric patterns. These show that the genetic code performs best with mesophilic codon usages, strengthening the view that LUCA or its ancestors preferred lower temperature environments. Taxonomic implications, such as rooting the tree of life, are also discussed.

Examining the amino acid composition, secondary structure, and physicochemical and functional properties of proteins isolated from local lentil landraces of Anatolia

AbstractBackground and objectivesProviding information on protein structure and functionality is essential for evaluating the potential of specific agricultural commodities to be utilized as plant‐based protein sources. Amino acid composition, secondary structure, surface charge, thermal, and functional properties of proteins isolated from five local lentil landraces were investigated. Secondary objective of the study was to investigate if a relationship exists between the amino acid composition and the physicochemical or functional properties of the isolates.FindingsAspartic acid and glutamic acid were found to be the dominant amino acids in the isolates while they were poor in methionine. Denaturation temperatures of isolates ranged between 96–108°C and their secondary structure mainly consisted of β‐sheet. Emulsion capacity, emulsifying activity (EAI), and emulsion stability indices (ESI) of isolates were found to be between 403.8–468.2 g oil/g protein, 18.8–24.2 m2/g, and 43.4–125.5 min, respectively. Foaming capacity of the isolates changed between 71% and 91% whereas their least gelation concentration varied between 110 and 150 g/L.ConclusionsYusufhan isolate which had relatively higher acidic amino acid content showed the highest net charge and solubility at acidic pH. Tigris isolate which had higher hydrophobic amino acid content compared to the other isolates showed relatively high thermal stability, EAI and ESI.Significance and noveltyFindings of the study make a contribution toward the potential use of local lentil landraces as sources for extracting protein‐based ingredients.

N-semble-based method for identifying Parkinson’s disease genes

Parkinson’s disease (PD) genes identification plays an important role in improving the diagnosis and treatment of the disease. A number of machine learning methods have been proposed to identify disease-related genes, but only few of these methods are adopted for PD. This work puts forth a novel neural network-based ensemble (n-semble) method to identify Parkinson’s disease genes. The artificial neural network is trained in a unique way to ensemble the multiple model predictions. The proposed n-semble method is composed of four parts: (1) protein sequences are used to construct feature vectors using physicochemical properties of amino acid; (2) dimensionality reduction is achieved using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method, (3) the Jaccard method is applied to find likely negative samples from unknown (candidate) genes, and (4) gene prediction is performed with n-semble method. The proposed n-semble method has been compared with Smalter’s, ProDiGe, PUDI and EPU methods using various evaluation metrics. It has been concluded that the proposed n-semble method outperforms the existing gene identification methods over the other methods and achieves significantly higher precision, recall and F Score of 88.9%, 90.9% and 89.8%, respectively. The obtained results confirm the effectiveness and validity of the proposed framework.

Non-Neutral Cytochrome b Variability in the Saker Falco cherrug Grey, 1834 and Gyrfalcon Falco rusticolus L.

Mitochondrial DNA variability affects the characteristics of cell metabolism and serves as marker of evolutionary processes. The mitochondrial cytochrome b gene sequences of saker and gyrfalcon were examined. Five amino acid substitutions were identified, and for two of these, changes in amino acid physicochemical properties were revealed. Suggestions on the phylogenetic origin and functional influence of the detected amino acid substitutions were made.

Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms

In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.

Prediction of protein‐protein interactions using stacked auto‐encoder

AbstractProtein‐protein interactions (PPIs) play essential roles in understanding the protein functions and the corresponding pathways which are involved in various biological processes, as well as help in understanding the cause and growth of diseases. Several computational methods such as Support Vector Machine and decision tree are popularly used along with the experimental methods to address the PPIs problem. Such algorithms consider different protein features, including protein sequence, genomes, protein structure, function, topology of the PPIs network, and those that combine multiple aspects. Nowadays, Deep learning (DL) algorithms are successfully used in solving problems in different domains. So, in this paper, we have used stacked auto‐encoder as one of the DL methods in solving the problem of PPIs. This model takes the input 92‐length feature vector, which is the integration of features extracted from the protein sequence using different methods. The feature vector consists of evolutionary features obtained by PSI‐BLAST algorithm, predicted structural properties obtained by SPIDER2, and seven physicochemical properties of amino acids. The key novelty of the current study lies in extracting useful features to solve the PPI problem. The results obtained by our method of feature extraction are compared with those obtained by other feature extraction methods such as Autocovariance and Conjoint‐triad, and our proposed feature extraction method is found to be relatively more accurate.

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.