Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum.

EnsembleDL-ATG: Identifying autophagy proteins by integrating their sequence and evolutionary information using an ensemble deep learning framework.

Autophagy is a primary mechanism for maintaining cellular homeostasis. The synergistic actions of autophagy-related (ATG) proteins strictly regulate the whole autophagic process. Therefore, accurate identification of ATGs is a first and critical step to reveal the molecular mechanism underlying the regulation of autophagy.

Fast and Efficient Design of Deep Neural Networks for Predicting N-Methylguanosine Sites Using autoBioSeqpy.

N-Methylguanosine (mG) is a crucial post-transcriptional RNA modification that plays a pivotal role in regulating gene expression. Accurately identifying mG sites is a fundamental step in understanding the biological functions and regulatory mechanisms associated with this modification. While whole-genome sequencing is the gold standard for RNA modification site detection, it is a time-consuming, expensive, and intricate process.

Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy.

Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (mU).

layerUMAP: A tool for visualizing and understanding deep learning models in biological sequence classification using UMAP.

Despite the impressive success of deep learning techniques in various types of classification and prediction tasks, interpreting these models and explaining their predictions are still major challenges. In this article, we present an easy-to-use command line tool capable of visualizing and analyzing alternative representations of biological observations learned by deep learning models. This new tool, namely, layerUMAP, integrates autoBioSeqpy software and the UMAP library to address learned high-level representations.

The applications of deep learning algorithms on in silico druggable proteins identification.

Introduction: The top priority in drug development is to identify novel and effective drug targets. In vitro assays are frequently used for this purpose; however, traditional experimental approaches are insufficient for large-scale exploration of novel drug targets, as they are expensive, time-consuming and laborious. Therefore, computational methods have emerged in recent decades as an alternative to aid experimental drug discovery studies by developing sophisticated predictive models to estimate unknown drugs/compounds and their targets.

Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning.

DNA N-methylcytosine (4mC) is a pivotal epigenetic modification that plays an essential role in DNA replication, repair, expression and differentiation. To gain insight into the biological functions of 4mC, it is critical to identify their modification sites in the genomics. Recently, deep learning has become increasingly popular in recent years and frequently employed for the 4mC site identification.

DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework.

Type III secretion systems (T3SSs) are bacterial membrane-embedded nanomachines that allow a number of humans, plant and animal pathogens to inject virulence factors directly into the cytoplasm of eukaryotic cells. Export of effectors through T3SSs is critical for motility and virulence of most Gram-negative pathogens. Current computational methods can predict type III secreted effectors (T3SEs) from amino acid sequences, but due to algorithmic constraints, reliable and large-scale prediction of T3SEs in Gram-negative bacteria remains a challenge.

DeepT3_4: A Hybrid Deep Neural Network Model for the Distinction Between Bacterial Type III and IV Secreted Effectors.

Gram-negative bacteria can deliver secreted proteins (also known as secreted effectors) directly into host cells through type III secretion system (T3SS), type IV secretion system (T4SS), and type VI secretion system (T6SS) and cause various diseases. These secreted effectors are heavily involved in the interactions between bacteria and host cells, so their identification is crucial for the discovery and development of novel anti-bacterial drugs. It is currently challenging to accurately distinguish type III secreted effectors (T3SEs) and type IV secreted effectors (T4SEs) because neither T3SEs nor T4SEs contain N-terminal signal peptides, and some of these effectors have similar evolutionary conserved profiles and sequence motifs.

DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm.

Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task.

In silico identification of Gram-negative bacterial secreted proteins from primary sequence.

In this study, we focus on different types of Gram-negative bacterial secreted proteins, and try to analyze the relationships and differences among them. Through an extensive literature search, 1612 secreted proteins have been collected as a standard data set from three data sources, including Swiss-Prot, TrEMBL and RefSeq. To explore the relationships among different types of secreted proteins, we model this data set as a sequence similarity network.

Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributes.

Signal peptides play a crucial role in various biological processes, such as localization of cell surface receptors, translocation of secreted proteins and cell-cell communication. However, the amino acid mutation in signal peptides, also called non-synonymous single nucleotide polymorphisms (nsSNPs or SAPs) may lead to the loss of their functions. In the present study, a computational method was proposed for predicting deleterious nsSNPs in signal peptides based on random forest (RF) by incorporating position specific scoring matrix (PSSM) profile, SignalP score and physicochemical properties.

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions.

Background: The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.

Results: We found that SAPs can be well characterized by network topological features.

SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition.

Protein secretion plays an important role in bacterial lifestyles. Secreted proteins are crucial for bacterial pathogenesis by making bacteria interact with their environments, particularly delivering pathogenic and symbiotic bacteria into their eukaryotic hosts. Therefore, identification of bacterial secreted proteins becomes an important process for the study of various diseases and the corresponding drugs.

An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition.

The purpose of this article is to identify protein structural classes by using support vector machine (SVM) ensemble classifier, which is very efficient in enhancing prediction performance. Firstly, auto covariance (AC) and pseudo-amino acid composition (PseAAC) were used in protein representation. AC focuses on adjacent effects and PseAA composition takes sequence order patterns into account.

SecretP: a new method for predicting mammalian secreted proteins.

In contrast to a large number of classically secreted proteins (CSPs) and non-secreted proteins (NSPs), only a few proteins have been experimentally proved to enter non-classical secretory pathways. So it is difficult to identify non-classically secreted proteins (NCSPs), and no methods are available for distinguishing the three types of proteins simultaneously. In order to solve this problem, a data mining has been taken firstly, and mammalian proteins exported via ER-Golgi-independent pathways are collected through extensive literature searches.

Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach.

The submitochondria location of a mitochondrial protein is very important for further understanding the structure and function of this protein. Hence, it is of great practical significance to develop an automated and reliable method for timely identifying the submitochondria locations of novel mitochondrial proteins. In this study, a sequence-based algorithm combining the augmented Chou's pseudo amino acid composition (Chou's PseAA) based on auto covariance (AC) is developed to predict protein submitochondria locations and membrane protein types in mitochondria inner membrane.

Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences.

Compared to the available protein sequences of different organisms, the number of revealed protein-protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins.