ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria

Ruiz‐Blanco, Yasser B.; Agüero-Chapin, Guillermin; Romero-Molina, Sandra; Antunes, Agostinho; Olari, Lia-Raluca; Spellerberg, Barbara; Münch, Jan; Sánchez-García, Elsa

doi:10.3390/antibiotics11121708

Cited by 7 publications

(9 citation statements)

References 60 publications

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“…We collected the ABPs from multiple repositories that specialize in antibacterial peptides for gram-positive, gram-negative, and gram-variable bacteria. These public repositories include APD3, AntiBP2, dbAMP 2.0, CAMPR3, DRAMP, and ABP-Finder, as depicted in Figure 7 [12,24,[33][34][35][36]. We removed sequences containing non-standard amino acids (BJOUXZ), as well as eliminating sequences shorter than eight and longer than 50 amino acids [37].…”

Section: Datasetmentioning

confidence: 99%

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AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Bajiya,

Choudhury,

Dhall

et al. 2024

Antibiotics

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Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.

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Section: Datasetmentioning

confidence: 99%

“…Though experimental methods are highly accurate, they are not suitable for high throughput, as these are time-consuming, labour-intensive, and expensive (Figure 1) [9][10][11][12]. To address this challenge, numerous in silico methods have been developed for predicting antibacterial peptides.…”

Section: Introductionmentioning

confidence: 99%

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Bajiya,

Choudhury,

Dhall

et al. 2024

Antibiotics

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“…Furthermore, models may be used for design by optimisation and in de novo design of antimicrobial peptides [141]. Machine learning models may be used as aids in screening during the early stages of designing peptide-based antimicrobials [142][143][144][145][146][147][148][149][150]. Machine learning algorithms allow rapid in silico screening and classification of antimicrobial peptides for investigating activity against specific species, providing savings and replacing or enhancing time-consuming conventional methods [151].…”

Section: Drug Discoverymentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

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Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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“…Machine learning models have emerged as a time-saving and cost-effective tool for screening large data sets to identify potential AMPs. To date, machine learning models − based on amino acid sequences have mainly been built using traditional − and deep learning (DL) − techniques, as well as using similarity networks. , However, the outstanding results of deep neural network-based approaches, such as trRosetta, AlphaFold, RoseTTAFold, ESMFold, and HelixFold-Single, in the prediction of tertiary (3D) structures of proteins from their amino acid sequences have unlocked new opportunities to build better predictive models. In this regard, non-DL based models using 3D protein descriptors − as well as Graph Neural Network-based models , (e.g., equivariant network) are promissory strategies to be developed.…”

Section: Introductionmentioning

confidence: 99%

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

Cordoves-Delgado,

García-Jacas

2024

J. Chem. Inf. Model.

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Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.

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ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria

Cited by 7 publications

References 60 publications

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

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