Physicochemical Properties Of Amino Acids Research Articles

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 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.

MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations.

The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs. To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR-pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset. The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.

DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning.

Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the peptide itself, such as its amino acid sequences or physicochemical properties, despite the fact that peptides detected by MS are dependent on many factors, including protein sample preparation, digestion, separation, ionization, and precursor selection during MS experiments. DbyDeep (Detectability by Deep learning) is an innovative end-to-end LSTM network model for peptide detectability prediction that incorporates sequence contexts of peptides and their cleavage sites (by protease). Utilizing the cleavage site contexts could improve the performance of prediction, and DbyDeep outperformed existing methods in predicting peptides recognizable from multiple MS/MS data sets with diverse species and MS instruments. We argue for the necessity of a learning model that encompasses several contexts associated with peptide detection, as opposed to depending just on peptide sequences. There is a Python implementation of DbyDeep at https://github.com/BISCodeRepo/DbyDeep.

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing.

Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-art deep-learning-based methods cannot provide interpretability from an energy perspective, affecting the accuracy of the design. Here we propose a new systematic approach, including both a posterior probability and a joint probability parts, to solve the two essential questions once for all. This approach takes the physicochemical property of amino acids into consideration and uses the joint probability model to ensure the convergence between structure and amino acid type. Our results demonstrated that this method could generate feasible, high-confidence sequences with low-energy side conformations. The designed sequences can fold into target structures with high confidence and maintain relatively stable biochemical properties. The side chain conformation has a significantly lower energy landscape without delegating to a rotamer library or performing the expensive conformational searches. Overall, we propose an end-to-end method that combines the advantages of both deep learning and energy-based methods. The design results of this model demonstrate high efficiency, and precision, as well as a low energy state and good interpretability.

Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides.

Anticancer peptides (ACPs) represent a promising new therapeutic approach in cancer treatment. They can target cancer cells without affecting healthy tissues or altering normal physiological functions. Machine learning algorithms have increasingly been utilized for predicting peptide sequences with potential ACP effects. This study analyzed four benchmark datasets based on a well-established random forest (RF) algorithm. The peptide sequences were converted into 566 physicochemical features extracted from the amino acid index (AAindex) library, which were then subjected to feature selection using four methods: light gradient-boosting machine (LGBM), analysis of variance (ANOVA), chi-squared test (Chi2), and mutual information (MI). Presenting and merging the identified features using Venn diagrams, 19 key amino acid physicochemical properties were identified that can be used to predict the likelihood of a peptide sequence functioning as an ACP. The results were quantified by performance evaluation metrics to determine the accuracy of predictions. This study aims to enhance the efficiency of designing peptide sequences for cancer treatment.

Deconstructing the Potency and Cell-Line Selectivity of Membranolytic Anticancer Peptides.

Current cancer treatments damage healthy cells and tissues, causing short-term and long-term side effects. New treatments are desired that show greater selectivity toward cancer cells and evade the common mechanisms of multidrug resistance. Membranolytic anticancer peptides (mACPs) hold promise against cancer and multidrug resistance. Amphipathicity, hydrophobicity, and net charge of mACPs participate in their respective interactions with cell membranes and their overall inhibition of cancer cells. To support the design of cell-line selective mACPs, we investigated the relationships that amino acid composition, physicochemical properties, sequence motifs, and sequence homology could have with their potency and selectivity towards several healthy and cancer cell lines. Sequence length and net charge are known to affect the selectivity of mACPs between cancer and healthy cell lines. Our study reveals that increasing the net charge or flexibility (i. e., small and aliphatic residues) influences their selectivity between cancer cell lines with comparable lipid compositions.

Amino acid profile, physico-chemical properties and fatty acids composition of some fruit seed kernels after detoxification

BackgroundThe use of food processing wastes and by-products, as well as the under-utilization of agricultural products, have recently received increased attention. Mango, apricot and peach are the three most significant fruits grown and processed in Egypt.ResultsThis work aimed to evaluate the amino acid composition, physio-chemical properties and fatty acids content of mango, apricot, and peach seed kernels after removal antinutritional components. According to the results, mango kernel flour contained all the essential amino acids with levels higher than those of the FAO/WHO reference protein. In addition, total essential amino acids were 28.88, 26.78 and 36.46 g/100 g protein for apricot, peach and mango kernel flours, respectively. The highest essential amino acids value was leucine, while the highest non-essential amino acids value was glutamic in all kernel protein. All kernel oils showed adequate values for acid and peroxide value. The main unsaturated fatty acids in all kernel oils were oleic and linoleic acids. Oleic acid contents ranged between 41.76% and 59.87%. On the other hand, linoleic acid contents varied between 5.25% and 26.61%.ConclusionsMango, apricot, and peach kernels are by-products that present a novel potential source of excellent protein and oil that might be used for food and other industrial applications after reduction of antinutritional matter. As a result, detoxified kernel flour might be used to enhance high-value food products with economical, high-quality sources of protein and oil.Graphical

Design of a machine learning-aided screening framework for antibiofilm peptides

Biofilms are formed by multicellular colonies of microorganisms that are protected by hard extracellular matrices. Eradication of biofilms is a challenging task due to their recalcitrant nature and thus biofilm formation poses a global threat to public health. In this regard, antibiofilm peptides are a promising class of therapeutics that are active against biofilms. However, large-scale experimental screening and testing of peptides for antibiofilm activity is a resource-intensive task. In this study, a machine learning-aided design framework is proposed to aid in screening of antibiofilm peptides. An SVM-based binary classification model is developed using amino acid compositions, sequence, and physicochemical properties of peptides as independent features. The physicochemical property-based model developed in this study achieved the highest accuracy of 97.9%, which is found to be substantially higher than the other feature representation techniques. The explainability of this model is performed using SHAP analysis. Results obtained show that amphiphilicity, aliphaticity and cationicity have positive correlation whereas steric parameter, length, and volume have negative correlation with antibiofilm activity of peptides. The developed model can be accessed freely via web tool: AntiBFP.