A Survey of Current Status in AI-Based Topology Prediction of Transmembrane Proteins.

Most of the sequence-based structure prediction and experimental structure determination research have been focused on soluble proteins, with relatively less attention paid to the transmembrane proteins. With the availability of high-quality methods for the structure prediction of the soluble proteins, there is a need to fill the gaps for the prediction of transmembrane proteins, motivated by the fact that they are the primary targets of many drug design efforts. We provide a systematic survey of machine learning methods that predict various aspects of topological properties of transmembrane proteins, including helical and beta-barrel proteins.

A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).

Protein function prediction from sequence, structure, gene expression profiles, and published literature are needed to understand all biological processes. Natural language processing of biological text and large language model (LLM)-based encoding of sequence and structure opens powerful paths to rapid function annotation and novel training models. In this survey, we take a look at the available models for function prediction, especially the NLP- and LLM-based models.

Differential genome organization revealed by comparative topological analysis of strains H37Rv and H37Ra.

Recent studies have shown that three-dimensional architecture of bacterial chromatin plays an important role in gene expression regulation. However, genome topological organization in , the etiologic agent of tuberculosis, remains unknown. On the other hand, the exact mechanism of differential pathogenesis in the canonical strains of H37Rv and H37Ra remains poorly understood in terms of their raw sequences.

Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis.

Differentially expressed genes in a cellular context may be co-regulated by the same transcription factor. However, in the absence of a concurrent transcription factor binding data, such interactions are difficult to detect, especially at the single cell expression level. Motif enrichments in such genes can be used to gain insight into differential expressions caused by the shared upstream TFs.

Global and local genomic features together modulate the spontaneous single nucleotide mutation rate.

Spontaneous mutations are evolutionary engines as they generate variants for the evolutionary downstream processes that give rise to speciation and adaptation. Single nucleotide mutations (SNM) are the most abundant type of mutations among them. Here, we perform a meta-analysis to quantify the influence of selected global genomic parameters (genome size, genomic GC content, genomic repeat fraction, number of coding genes, gene count, and strand bias in prokaryotes) and local genomic features (local GC content, repeat content, CpG content and the number of SNM at CpG islands) on spontaneous SNM rates across the tree of life (prokaryotes, unicellular eukaryotes, multicellular eukaryotes) using wild-type sequence data in two different taxon classification systems.

Predictive modeling of moonlighting DNA-binding proteins.

Moonlighting proteins are multifunctional, single-polypeptide chains capable of performing multiple autonomous functions. Most moonlighting proteins have been discovered through work unrelated to their multifunctionality. We believe that prediction of moonlighting proteins from first principles, that is, using sequence, predicted structure, evolutionary profiles, and global gene expression profiles, for only one functional class of proteins in a single organism at a time will significantly advance our understanding of multifunctional proteins.

MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis.

Whole-genome sequencing (WGS) provides a comprehensive tool to analyze the bacterial genomes for genotype-phenotype correlations, diversity of single-nucleotide variant (SNV), and their evolution and transmission. Several online pipelines and standalone tools are available for WGS analysis of () complex (MTBC). While they facilitate the processing of WGS data with minimal user expertise, they are either too general, providing little insights into bacterium-specific issues such as gene variations, INDEL/synonymous/PE-PPE (IDP family), and drug resistance from sample data, or are limited to specific objectives, such as drug resistance.

Host-pathogen protein-nucleic acid interactions: A comprehensive review.

Recognition of pathogen-derived nucleic acids by host cells is an effective host strategy to detect pathogenic invasion and trigger immune responses. In the context of pathogen-specific pharmacology, there is a growing interest in mapping the interactions between pathogen-derived nucleic acids and host proteins. Insight into the principles of the structural and immunological mechanisms underlying such interactions and their roles in host defense is necessary to guide therapeutic intervention.

Unraveling molecular mechanisms of head and neck cancer.

Malignancies that develop from mucosal epithelium of the upper aerodigestive tract are known as head and neck squamous cell carcinomas (HNSCC). Heterogeneity, late stage diagnosis and high recurrence rate are big hurdles in head and neck treatment regimen. Presently, the biomarkers available for diagnosis and prognosis of HNSCC are based on smoking as the major risk habit.

Inadequacy of Evolutionary Profiles Vis-a-vis Single Sequences in Predicting Transient DNA-Binding Sites in Proteins.

Sequence-based prediction of DNA-binding residues in a protein is a widely studied problem for which machine learning methods with continuously improving predictive power have been developed. Concatenated rows within a sliding window of a Position Specific Substitution Matrix (PSSM) of the protein are currently used as the primary feature set in almost all the methods of predicting DNA-binding residues. Here we report that these evolutionary profiles are powerful, only for identifying conserved binding sites and fall short for the residue positions which undergo binding to non-binding transitions in closely related proteins.

Molecular Characterization of HOXA2 and HOXA3 Binding Properties.

The highly conserved HOX homeodomain (HD) transcription factors (TFs) establish the identity of different body parts along the antero-posterior axis of bilaterian animals. Segment diversification and the morphogenesis of different structures is achieved by generating precise patterns of HOX expression along the antero-posterior axis and by the ability of different HOX TFs to instruct unique and specific transcriptional programs. However, HOX binding properties in vitro, characterised by the recognition of similar AT-rich binding sequences, do not account for the ability of different HOX to instruct segment-specific transcriptional programs.

Current Applications of Artificial Intelligence in Cleft Care: A Scoping Review.

This scoping review aims to identify the various areas and current status of the application of artificial intelligence (AI) for aiding individuals with cleft lip and/or palate. Cleft lip and/or palate contributes significantly toward the global burden on the healthcare system. Artificial intelligence is a technology that can help individuals with cleft lip and/or palate, especially those in areas with limited access to receive adequate care.

Deciphering the Role of Residues Involved in Disorder-To-Order Transition Regions in Archaeal tRNA Methyltransferase 5.

tRNA methyltransferase 5 (Trm5) enzyme is an S-adenosyl methionine (AdoMet)-dependent methyltransferase which methylates the G37 nucleotide at the N atom of the tRNA. The free form of Trm5 enzyme has three intrinsically disordered regions, which are highly flexible and lack stable three-dimensional structures. These regions gain ordered structures upon the complex formation with tRNA, also called disorder-to-order transition (DOT) regions.

Understanding disorder-to-order transitions in protein-RNA complexes using molecular dynamics simulations.

Intrinsically disordered regions (IDRs) in proteins are characterized by their flexibilities and low complexity regions, which lack unique 3 D structures in solution. IDRs play a significant role in signaling, regulation, and binding multiple partners, including DNA, RNA, and proteins. Although various experiments have shown the role of disordered regions in binding with RNA, a detailed computational analysis is required to understand their binding and recognition mechanism.

Feature Selection for Topological Proximity Prediction of Single-Cell Transcriptomic Profiles in Embryo Using Genetic Algorithm.

Single-cell transcriptomics data, when combined with in situ hybridization patterns of specific genes, can help in recovering the spatial information lost during cell isolation. Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium conducted a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC) to predict the masked locations of single cells from a set of 60, 40 and 20 genes out of 84 in situ gene patterns known in embryo. We applied a genetic algorithm (GA) to predict the most important genes that carry positional and proximity information of the single-cell origins, in combination with the base distance mapping algorithm DistMap.

Intestinal Epithelium-Derived Luminally Released Extracellular Vesicles in Sepsis Exhibit the Ability to Suppress TNF-a and IL-17A Expression in Mucosal Inflammation.

Sepsis is a systemic inflammatory disorder induced by a dysregulated immune response to infection resulting in dysfunction of multiple critical organs, including the intestines. Previous studies have reported contrasting results regarding the abilities of exosomes circulating in the blood of sepsis mice and patients to either promote or suppress inflammation. Little is known about how the gut epithelial cell-derived exosomes released in the intestinal luminal space during sepsis affect mucosal inflammation.

Potential of age distribution profiles for the prediction of COVID-19 infection origin in a patient group.

The COVID-19 pandemic is a serious and global public health concern. It is now well known that COVID-19 cases may result in mild symptoms leading to patient recovery. However, severity of infection, fatality rates, and treatment responses across different countries, age groups, and demographic groups suggest that the nature of infection is diverse, and a timely investigation of the same is needed for evolving sound treatment and preventive strategies.

Publisher Correction: Successional trajectory of bacterial communities in soil are shaped by plant-driven changes during secondary succession.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Successional trajectory of bacterial communities in soil are shaped by plant-driven changes during secondary succession.

This study investigated the potential role of a nitrogen-fixing early-coloniser Alnus Nepalensis D. Don (alder) in driving the changes in soil bacterial communities during secondary succession. We found that bacterial diversity was positively associated with alder growth during course of ecosystem development.

Role of disordered regions in transferring tyrosine to its cognate tRNA.

Aminoacyl tRNA synthetase (AARS) plays an important role in transferring each amino acid to its cognate tRNA. Specifically, tyrosyl tRNA synthetase (TyrRS) is involved in various functions including protection from DNA damage due to oxidative stress, protein synthesis and cell signaling and can be an attractive target for controlling the pathogens by early inhibition of translation. TyrRS has two disordered regions, which lack a stable 3D structure in solution, and are involved in tRNA synthetase catalysis and stability.

A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images.

Stroke causes behavioral deficits in multiple cognitive domains and there is a growing interest in predicting patient performance from neuroimaging data using machine learning techniques. Here, we investigated a deep learning approach based on convolutional neural networks (CNNs) for predicting the severity of language disorder from 3D lesion images from magnetic resonance imaging (MRI) in a heterogeneous sample of stroke patients. CNN performance was compared to that of conventional (shallow) machine learning methods, including ridge regression (RR) on the images' principal components and support vector regression.

Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.

Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied to unequally sized protein sequences. PSSM summaries representing column averages and their amino-acid wise versions have been effectively used for the task, but it remains unclear if these features carry all the PSSM's predictive power, traditionally harnessed for binding site predictions.