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Abstract Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

Abstract The entomopathogenic fungus (EPF), Beauveria bassiana, is reported as the most potent biological control agent against a wide range of insect families. This study aimed to isolate and characterize the native B. bassiana from various soil habitats in Bangladesh and to evaluate the bio-efficacy of these isolates against an important vegetable insect pest, Spodoptera litura. Seven isolates from Bangladeshi soils were characterized as B. bassiana using genomic analysis. Among the isolates, TGS2.3 showed the highest mortality rate (82%) against the 2nd instar larvae of S. litura at 7 days after treatment (DAT). This isolate was further bioassayed against different stages of S. litura and found that TGS2.3 induced 81, 57, 94, 84, 75, 65, and 57% overall mortality at egg, neonatal 1st, 2nd, 3rd, 4th, and 5th instar larvae, respectively, over 7 DAT. Interestingly, treatment with B. bassiana isolate TGS2.3 resulted in pupal and adult deformities as well as decreased adult emergence of S. litura. Taken together, our results suggest that a native isolate of B. bassiana TGS2.3 is a potential biocontrol agent against the destructive insect pest S. litura. However, further studies are needed to evaluate the bio-efficacy of this promising native isolate in planta and field conditions.