AntiBP3: A hybrid method for predicting antibacterial peptides against gram-positive/negative/variable bacteria

Bajiya, Nisha; Choudhury, Shubham; Dhall, Anjali; Raghava, Gajendra P. S.

doi:10.1101/2023.07.25.550443

Cited by 3 publications

(2 citation statements)

References 59 publications

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“…Examples of these methods include AI4AMP, AMPDiscover, AMPScanner vr.2, AntiBP3, AntiFP and AntiMPmod [11] [12][13] [14][15] [16]. Similarly, species-specific predictive methods like AntiTbPred are designed to distinguish antituberculosis peptides [17].…”

Section: Figure 1: Graphical Representation Of the Working Of Antibac...mentioning

confidence: 99%

Prediction of inhibitory peptides againstE. coliwith desired MIC value

Bajiya,

Kumar,

Raghava

2024

Preprint

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In the past, several methods have been developed for predicting antibacterial and antimicrobial peptides, but only limited attempts have been made to predict their minimum inhibitory concentration (MIC) values. In this study, we trained our models on 3,143 peptides and validated them on 786 peptides whose MIC values have been determined experimentally against Escherichia coli (E. coli). The correlational analysis reveals that the Composition Enhanced Transition and Distribution (CeTD) attributes strongly correlate with MIC values. We initially employed the similarity search strategy utilizing BLAST to estimate MIC values of peptides but found it inadequate for prediction. Next, we developed machine learning techniques-based regression models using a wide range of features, including peptide composition, binary profile, and embeddings of large language models. We implemented feature selection techniques like minimum Redundancy Maximum Relevance (mRMR) to select the best relevant features for developing prediction models. Our Random forest-based regressor, based on selected features, achieved a correlation coefficient (R) of 0.78, R-squared (R2) of 0.59, and a root mean squared error (RMSE) of 0.53 on the validation dataset. Our best model outperforms the existing methods when benchmarked on an independent dataset of 498 inhibitory peptides of E. coli. One of the major features of the web-based platform EIPpred developed in this study is that it allows users to identify or design peptides that can inhibit E. coli with the desired MIC value (https://webs.iiitd.edu.in/raghava/eippred).

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Section: Figure 1: Graphical Representation Of the Working Of Antibac...mentioning

confidence: 99%

Prediction of inhibitory peptides againstE. coliwith desired MIC value

Bajiya,

Kumar,

Raghava

2024

Preprint

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“…In 2007, Raghava's research group proposed AntiBP, which employed support vector machines (SVM), quantitative matrix, and artificial neural networks to predict ABPs (Lata et al, 2007). Subsequently, upgraded versions of AntiBP, namely AntiBP2 and AntiBP3, were introduced, further enhancing ABP prediction performance (Bajiya et al, 2023; Lata et al, 2010). In 2015, Veltri proposed AMPScanner vr.…”

Section: Introductionmentioning

confidence: 99%

AMPActiPred: A three‐stage framework for predicting antibacterial peptides and activity levels with deep forest

Yao,

Guan,

Xie

et al. 2024

Protein Science

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The emergence and spread of antibiotic‐resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three‐stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state‐of‐the‐art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user‐friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.

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Investigating the Antimicrobial Effects of a Novel Peptide Derived From Listeriolysin S on S aureus, E coli, and L plantarum: An In Silico and In Vitro Study

Nobakht,

Bazyar,

Langeroudi

et al. 2024

Bioinform Biol Insights

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Aims: The emergence of antibiotic resistance is one of the most significant issues today. Modifying antimicrobial peptides (AMPs) can improve their effects. In this study, the active region of Listeriolysin S (LLS) as a peptidic toxin has been recognized, and its antibacterial properties have been evaluated by modifying that region. Methods: After extracting the sequence, the structure of LLS was predicted by PEP-FOLD3. AntiBP and AMPA servers identified its antimicrobial active site. It was modified by adding arginine residue to its 3- and N-terminal regions. Its antimicrobial properties on Staphylococcus aureus, Escherichia coli, and Lactobacillus Plantarum were estimated. Findings: The results of AntiBP and AntiBP servers demonstrated that a region of 15 amino acids has the most antimicrobial properties (score = 1.696). After adding arginine to the chosen region, the physicochemical evaluation and antimicrobial properties revealed that the designed peptide is a stable AMP with a positive charge of 4, which is not toxic to human erythrocyte cells and has antigenic properties. The results of in vitro and colony counting indicated that at different hours, it caused a significant reduction in the count of S aureus, E coli, and L Plantarum compared with the control sample. Conclusions: Upcoming research implies that identifying and enhancing the active sites of natural peptides can help combat bacteria.

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AntiBP3: A hybrid method for predicting antibacterial peptides against gram-positive/negative/variable bacteria

Cited by 3 publications

References 59 publications

Prediction of inhibitory peptides againstE. coliwith desired MIC value

Prediction of inhibitory peptides againstE. coliwith desired MIC value

AMPActiPred: A three‐stage framework for predicting antibacterial peptides and activity levels with deep forest

Investigating the Antimicrobial Effects of a Novel Peptide Derived From Listeriolysin S on S aureus, E coli, and L plantarum: An In Silico and In Vitro Study

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