Abstract Motivation Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of as drug candidates. The accurate prediction (HLPs) and its activity from the given is one challenging tasks in immunoinformatics, essential for development basic research. Although there are a few computational methods that have been proposed this aspect, none them able identify HLPs activities simultaneously. Results In...

The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number newly available peptide sequences in post-genomic era, development automated computational models for novel highly desirable.In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model predicting directly their amino acid sequence without using any structural information. To best our knowledge, first...

Abstract The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well immune cells for activating aggressive inflammation. IL-6 inducing are derived and can be used diagnostic biomarkers predicting various stages disease severity being inhibitors the suppression multi-signaling responses. Thus, accurate identification great importance investigating their mechanism action developing immunotherapeutic applications. This study proposes a novel stacking ensemble...

Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory via biological experiments is often a time-consuming expensive task. The development novel in silico predictors desired to classify potential prior vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor Anti-Inflammatory Peptides) was developed integrating multiple complementary features. We systematically...

Umami or the taste of monosodium glutamate represents one major attractive modalities in humans. Therefore, knowledge about biophysical and biochemical properties umami is important for both scientific research food industry. Experimental approaches predicting peptides are labor intensive, time consuming, expensive. To date, computational models prediction analysis as a function sequence information have not been developed yet. In this study, we proposed first sequence-based predictor named...

Abstract DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one the challenging tasks in genome analysis, leads to an understanding their biological functions. To date, several species-specific machine learning (ML)-based models have been proposed, but majority them did not test model other species. Hence, practical application plant species quite limited. In this study,...

A novel computational tool termed SuccinSite has been developed to predict protein succinylation sites using the amino acid patterns and properties based on a random forest classifier.

Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding viral coat proteins to host membrane receptors hijacking transcription machinery. However, few interspecies have been identified, because experimental methods including mass spectrometry are time-consuming expensive, molecular dynamic simulation is limited only whose 3D structures solved. Sequence-based machine learning expected overcome these...

The inhibition of dipeptidyl peptidase IV (DPP-IV, E.C.3.4.14.5) is well recognized as a new avenue for the treatment Type 2 diabetes (T2D). Until now, peptide-like DDP-IV inhibitors have been shown to normalize blood glucose concentration in T2D subjects. To best our knowledge, there yet no computational model predicting and analyzing DPP-IV inhibitory peptides using sequence information. In this study, we present first time simple easily interpretable sequence-based predictor scoring card...

Abstract Neuropeptides (NPs) are the most versatile neurotransmitters in immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid accurate large-scale identification of NPs is critical immunoinformatics, which indispensable basic research drug development. Although a few NP prediction tools have been developed, it mandatory to improve their NPs’ performances. In this study, we developed machine learning-based meta-predictor...

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, costly. As a result, it is preferable to develop computational tools the large-scale identification of available sequences order identify novel with properties. Although tool has developed this purpose, its predictive performance still insufficient. In...

Abstract As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning been proposed ACP identification. Although existing methods afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble that consequently leads to low interpretability and thus poses challenge biologists biochemists. Therefore, it is desirable develop simple, interpretable efficient...

As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C) plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications order to gain a deeper understanding other possible functional mechanisms. Although few computational methods have been proposed, their respective models developed using small training datasets. Hence, practical application quite limited genome-wide...

Abstract N6-methyladenine (6mA) is associated with important roles in DNA replication, repair, transcription, regulation of gene expression. Several experimental methods were used to identify modifications. However, these are costly and time-consuming. To detect the 6mA complement shortcomings methods, we proposed a novel, deep leaning approach called BERT6mA. compare BERT6mA other learning approaches, benchmark datasets including 11 species. The presented highest AUCs eight species...

Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which is responsible for molecular functions, including cell cycle regulation and genome rearrangements. Accurately identifying the subcellular location of lncRNAs from sequence information crucial a better understanding biological functions mechanisms. In contrast to traditional experimental methods, bioinformatics or computational methods can be applied annotation lncRNA locations in humans more...

This study investigates the utilization of waste iron slag (WIS) as a sustainable alternative in concrete production to reduce environmental impact and preserve natural resources. The experimental investigation WIS-incorporated focused on compressive tensile strength with machine learning (ML) models for prediction. Among tested ML algorithms, Decision Tree (DT) XGBoost showed highest accuracy (R2 = 0.95135) predicting properties, while like SVM Symbolic Regression underperformed....

Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup) tagging is functionally analogous ubiquitination order tag target for proteasomal degradation. To date, several experimental methods have been developed identify pupylated and their sites, but these generally laborious costly. Therefore, computational can accurately predict...

Lysine succinylation is one of the dominant post-translational modification protein that contributes to many biological processes including cell cycle, growth and signal transduction pathways. Identification sites an important step for understanding function proteins. The complicated sequence patterns revealed by proteomic studies highlight necessity developing effective species-specific in silico strategies global prediction sites. Here we have developed generic nine site classifiers...

N4-methylcytosine (4mC) is one of the most important DNA modifications and involved in regulating cell differentiations gene expressions. The accurate identification 4mC sites necessary to understand various biological functions. In this work, we developed a new computational predictor called i4mC-Mouse identify mouse genome. Herein, six encoding schemes k-space nucleotide composition (KSNC), k-mer (Kmer), mono binary (MBE), dinucleotide encoding, electron-ion interaction pseudo potentials...

Abstract Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription factors enhance the of related genes. Due to its sporadic distribution and similar fractions, identification enhancers from human genome seems a daunting task. Compared traditional experimental approaches, computational methods with easy-to-use platforms could be efficiently applied annotate enhancers’ functions physiological roles. In this aspect, several bioinformatics tools have been developed...