Bahria University

Defensins, cationic antimicrobial peptides (AMPs) with broad-spectrum activity and a lower propensity for resistance development, represent promising candidates for combating multidrug-resistant bacteria and contributing to host-directed therapies. However, current computational models for defensin prediction often suffer from limited accuracy due to inadequate feature representation and model design. To address these limitations, we introduce GAC-BTCNN-Pred, a novel predictor for defensin identification. Our approach integrates advanced feature extraction with a hybrid model architecture. Initially, we derive evolutionary information using a segmented Position-Specific Scoring Matrix (seg-PSSM), which is subsequently enhanced through Discrete Cosine Transformation (DCT) to reduce noise and emphasize local sequence motifs, creating the Seg-PSSM-DCT descriptor. To capture complementary information, we incorporate ProtGen-LLM, a transformer-based protein language model, which effectively encodes contextual, physicochemical, and long-range dependencies within protein sequences. The fusion of Seg-PSSM-DCT and ProtGen-LLM yields a comprehensive, multi-faceted feature set termed PGL-PD. This rich feature representation is then fed into our proposed Generative Adversarial Capsule Bidirectional Temporal Convolutional Neural Network (GAC-BTCNN). Extensive comparative evaluations against traditional machine learning and existing deep learning methods demonstrate the superior performance of GAC-BTCNN-Pred across various evaluation metrics, establishing it as a scalable tool for accurate defensin identification within the context of novel antibacterial agent discovery.

The accurate prediction of antiviral peptides (AVPs) plays a crucial role in accelerating the development of peptide-based therapeutics. Despite extensive production of antiviral medications, viral diseases remain a major human health concern. AVPs have emerged as potential candidates for the development of novel antiviral drugs. However, the available traditional methods are labor-intensive, expensive, and cannot provide a deeper structural and contextual understanding of the peptide sequences. To address these problems, we propose a novel deep computational model, TargetAVP-DeepCaps, for the precise prediction of AVPs. In this model, multiple innovative feature representation strategies were presented by encoding the input peptides using a pretrained ProtGPT2 model for contextual embeddings. On the other hand, sequence-to-image transformations are performed using SMR and RECM matrices. Additionally, the produced 2D images were locally decomposed using the CLBP approach to obtain the SMR-CLBP and RECM-CLBP descriptors. A differential evolution mechanism was applied to form a weighted-feature-based multiperspective vector. The optimal features were selected using a hybrid MRMD + SFLA feature selection approach. Finally, a novel self-normalized capsule network (Sn-CapsNet) model was developed to achieve a superior predictive accuracy of 97.36%, outperforming the available predictors by approximately 12% with an area under the curve (AUC) of 0.98. To ensure the generalization of the TargetAVP-DeepCaps model, our training achieved an approximately 8% higher prediction than previous models using an independent data set. The demonstrated effectiveness and robustness of TargetAVP-DeepCaps provide an advanced therapeutic tool for understanding peptide mechanisms and related applications in drug discovery.

Environmental persistent free radicals (EPFRs) are emerging pollutants in atmospheric particulate matter (PM) with significant health implications. This study investigated the pollution characteristics, decay kinetics, and potential health risks of EPFRs in PM_2.5 and PM₁₀ collected from various sources and ambient air in Islamabad, Pakistan. Solid fuel combustion, including biomass, waste and residential/industrial coal burning, were identified as major sources of atmospheric EPFRs in Islamabad. Meanwhile, mineral particles from stone crushing exhibited inherently higher ·OH generation compared to other sources, highlighting their significant contribution to the oxidative potential of ambient dust. The decay lifetimes of EPFRs ranged in 43.5-63.0 days for PM_2.5 and 37.2-67.8 days for PM₁₀ in ambient air, and 38.4-51.6 days for PM_2.5 and 40.2-53.4 days for PM₁₀ from source emissions, respectively, closely resembling those of semiquinone-type radicals. For PM samples that were more prone to produce EPFRs under simulated sunlight irradiation, the associated EPFRs were more reactive and decayed faster, while EPFRs in those less prone to generate EPFRs generally exhibited slower decay. This source-oriented investigation of EPFRs in PM thereby provides critical insights for developing targeted PM control strategies to mitigate their health risks.