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ABSTRACT Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host–pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low‐cost and high‐speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho‐serine (pS), phospho‐threonine (pT), and phospho‐tyrosine (pY) sites. DeepPhoPred incorporates a two‐headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep‐learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available athttps://github.com/faisalahm3d/DeepPhoPred. 

Abstract Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available athttps://github.com/abelavit/PepCNN.git. 

Abstract Pentameric ligand-gated ion channels (pLGICs) mediate synaptic transmission and are sensitive to their lipid environment. The mechanism of phospholipid modulation of any pLGIC is not well understood. We demonstrate that the model pLGIC, ELIC (Erwinialigand-gated ion channel), is positively modulated by the anionic phospholipid, phosphatidylglycerol, from the outer leaflet of the membrane. To explore the mechanism of phosphatidylglycerol modulation, we determine a structure of ELIC in an open-channel conformation. The structure shows a bound phospholipid in an outer leaflet site, and structural changes in the phospholipid binding site unique to the open-channel. In combination with streamlined alchemical free energy perturbation calculations and functional measurements in asymmetric liposomes, the data support a mechanism by which an anionic phospholipid stabilizes the activated, open-channel state of a pLGIC by specific, state-dependent binding to this site. 

Explainability is essential for AI models, especially in clinical settings where understanding the model’s decisions is crucial. Despite their impressive performance, black-box AI models are unsuitable for clinical use if their operations cannot be explained to clinicians. While deep neural networks (DNNs) represent the forefront of model performance, their explanations are often not easily interpreted by humans. On the other hand, hand-crafted features extracted to represent different aspects of the input data and traditional machine learning models are generally more understandable. However, they often lack the effectiveness of advanced models due to human limitations in feature design. To address this, we propose ExShall-CNN, a novel explainable shallow convolutional neural network for medical image processing. This model improves upon hand-crafted features to maintain human interpretability, ensuring that its decisions are transparent and understandable. We introduce the explainable shallow convolutional neural network (ExShall-CNN), which combines the interpretability of hand-crafted features with the performance of advanced deep convolutional networks like U-Net for medical image segmentation. Built on recent advancements in machine learning, ExShall-CNN incorporates widely used kernels while ensuring transparency, making its decisions visually interpretable by physicians and clinicians. This balanced approach offers both the accuracy of deep learning models and the explainability needed for clinical applications. 

Cranberries, native to North America, are known for their nutritional value and human health benefits. One hurdle to commercial production is losses due to fruit rot. Cranberry fruit rot results from a complex of more than ten filamentous fungi, challenging breeding for resistance. Nonetheless, our collaborative breeding program has fruit rot resistance as a significant target. This program currently relies heavily on manual sorting of sound vs. rotten cranberries. This process is labor-intensive and time-consuming, prompting the need for an automated classification (sound vs. rotten) system. Although many studies have focused on classifying different fruits and vegetables, no such approach has been developed for cranberries yet, partly because datasets are lacking for conducting the necessary image analyses. This research addresses this gap by introducing a novel image dataset comprising sound and rotten cranberries to facilitate computational analysis. In addition, we developed CARP (Cranberry Assessment for Rot Prediction), a convolutional neural network (CNN)-based model to distinguish sound cranberries from rotten ones. With an accuracy of 97.4%, a sensitivity of 97.2%, and a specificity of 97.2% on the training dataset and 94.8%, 95.4%, and 92.7% on the independent dataset, respectively, our proposed CNN model shows its effectiveness in accurately differentiating between sound and rotten cranberries. 

Enzymes play key roles in the biological functions of living organisms, which serve as catalysts to and regulate biochemical reaction pathways. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity and well-established methods for library synthesis. Experimental approaches to identify protein-binding peptides are time-consuming and costly. Hence, there is a demand to develop a fast and accurate computational approach to tackle this problem. Another challenge in developing a computational approach is the lack of a large and reliable dataset. In this study, we develop a new machine learning approach called PepBind-SVM to predict protein-binding peptides. To build this model, we extract different sequential and physicochemical features from peptides and use a Support Vector Machine (SVM) as the classification technique. We train this model on the dataset that we also introduce in this study. PepBind-SVM achieves 92.1% prediction accuracy, outperforming other classifiers at predicting protein-binding peptides.