Ahmed Moustafa

Aug 11, 2024 | nature.com | Ian R. Monk |Ying-Tsun Cheng |Camilla Predella |Jack Boylan |Ahmed Moustafa |Gaurav Kumar Lohia | +5 more

AbstractStaphylococcus aureus is a pulmonary pathogen associated with substantial human morbidity and mortality. As vaccines targeting virulence determinants have failed to be protective in humans, other factors are likely involved in pathogenesis. Here we analysed transcriptomic responses of human clinical isolates of S. aureus from initial and chronic infections. We observed upregulated collagenase and proline transporter gene expression in chronic infection isolates.

Jun 5, 2024 | nature.com | Md. Azahar Ali |Kawsar Ahmed |Md. Mariful Hasan |Ahmed Moustafa |Fahad Al-Zahrani |Md. Shazzad Hossain Shaon | +3 more

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/ .

Sep 25, 2023 | softpedia.com | Ahmed Moustafa

JAligner is a versatile tool primarily used for sequence alignment, a fundamental task in bioinformatics and computational biology. The idea behind the tool is to provide a flexible and memory-efficient solution for comparing biological sequences, aiding researchers and scientists in various fields, such as genomics, proteomics, and evolutionary biology, to make sense of complex sequence data.