An Investigation of Hepatitis B Virus Genome using Markov Models

The human genome encodes a family of editing enzymes known as APOBEC3 (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3). Several family members, such as APO-BEC3G, APOBEC3F, and APOBEC3H haplotype II, exhibit activity against viruses such as HIV. These enzymes induce C-to-U mutations in the negative strand of viral genomes, resulting in multiple G-to-A changes, commonly referred to as 'hypermutation.' Mutations catalyzed by these enzymes are sequence context-dependent in the HIV genome; for instance, APOBEC3G preferen-tially mutates G within GG, TGG, and TGGG contexts, while other members mutate G within GA, TGA, and TGAA contexts. However, the same sequence context has not been explored in relation to these enzymes and HBV. In this study, our objective is to identify the mutational footprint of APOBEC3 enzymes in the HBV genome. To achieve this, we employ a multivariable data analytics technique to investigate motif preferences and potential sequence hierarchies of mutation by APOBEC3 enzymes using full genome HBV sequences from a diverse range of naturally infected patients. This approach allows us to distinguish between normal and hypermutated sequences based on the representation of mono- to tetra-nucleotide motifs. Additionally, we aim to identify motifs associated with hypermutation induced by different APOBEC3 enzymes in HBV genomes. Our analyses reveal that either APOBEC3 enzymes are not active against HBV, or the induction of G-to-A mutations by these enzymes is not sequence context-dependent in the HBV genome

Interocular Symmetry Analysis of Corneal Elevation Using the Fellow Eye as the Reference Surface and Machine Learning

Unilateral corneal indices and topography maps are routinely used in practice, however, although there is consensus that fellow-eye asymmetry can be clinically significant, symmetry studies are limited to local curvature and single-point thickness or elevation measures. To improve our current practices, there is a need to devise algorithms for generating symmetry colormaps, study and categorize their patterns, and develop reference ranges for new global discriminative indices for identifying abnormal corneas. In this work, we test the feasibility of using the fellow eye as the reference surface for studying elevation symmetry throughout the entire corneal surface using 9230 raw Pentacam files from a population-based cohort of 4613 middle-aged adults. The 140 × 140 matrix of anterior elevation data in these files were handled with Python to subtract matrices, create color-coded maps, and engineer features for machine learning. The most common pattern was a monochrome circle (“flat”) denoting excellent mirror symmetry. Other discernible patterns were named “tilt”, “cone”, and “four-leaf”. Clustering was done with different combinations of features and various algorithms using Waikato Environment for Knowledge Analysis (WEKA). Our proposed approach can identify cases that may appear normal in each eye individually but need further testing. This work will be enhanced by including data of posterior elevation, thickness, and common diagnostic indices

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

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 at https://github.com/abelavit/PepCNN.git

Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://github.com/MLBC-lab/iACP-RF

Programmable bio-ionic liquid functionalized hydrogels for in situ 3D bioprinting of electronics at the tissue interface

The increased demand for personalized wearable and implantable medical devices has created the need for the generation of electronics that interface with living systems. Current bioelectronics has not fully resolved mismatches between biological systems and engineered circuits, resulting in tissue injury and pain. Thus, there is an unmet need to develop materials for the fabrication of wearable electronics that are biocompatible at the tissue interface. Here, we developed a tailorable gelatin-based bio-ink functionalized with a choline bio-ionic liquid (BIL) for in situ 3D bioprinting of bioelectronics at the tissue interface. The resultant photocrosslinked polymer is programmable, transparent, ion conductive, and flexible. BILs are stably conjugated with a gelatin methacryloyl (GelMA) hydrogel using photocrosslinking to make BioGel, which routes ionic current with high resolution and enables localized electrical stimulation delivery. Controllable crosslinking, achieved by varying reactants composition, allows the BioGel bio-ink platform for easy and rapid in-situ 3D bioprinting of complex designs directly on skin tissue. Bio-ionic modified polymers thus represent a versatile and wide-applicable bio-ink solution for personalized bioelectronics fabrication that minimizes tissue damage