MVCL-DTI: Predicting Drug-Target Interactions Using a Multiview Contrastive Learning Model on a Heterogeneous Graph.

Accurate prediction of drug-target interactions (DTIs) is pivotal for accelerating the processes of drug discovery and drug repurposing. MVCL-DTI, a novel model leveraging heterogeneous graphs for predicting DTIs, tackles the challenge of synthesizing information from varied biological subnetworks. It integrates neighbor view, meta-path view, and diffusion view to capture semantic features and employs an attention-based contrastive learning approach, along with a multiview attention-weighted fusion module, to effectively integrate and adaptively weight the information from the different views.

DSE-HNGCN: Predicting the frequencies of drug-side effects based on heterogeneous networks with mining interactions between drugs and side effects.

Evaluating the frequencies of drug-side effects is crucial in drug development and risk-benefit analysis. While existing deep learning methods show promise, they have yet to explore using heterogeneous networks to simultaneously model the various relationship between drugs and side effects, highlighting areas for potential enhancement. In this study, we propose DSE-HNGCN, a novel method that leverages heterogeneous networks to simultaneously model the various relationships between drugs and side effects.

DCMA: faster protein backbone dihedral angle prediction using a dilated convolutional attention-based neural network.

The dihedral angle of the protein backbone can describe the main structure of the protein, which is of great significance for determining the protein structure. Many computational methods have been proposed to predict this critically important protein structure, including deep learning. However, these heavyweight methods require more computational resources, and the training time becomes intolerable.

DTA-GTOmega: Enhancing Drug-Target Binding Affinity Prediction with Graph Transformers Using OmegaFold Protein Structures.

Understanding drug-protein interactions is crucial for elucidating drug mechanisms and optimizing drug development. However, existing methods have limitations in representing the three-dimensional structure of targets and capturing the complex relationships between drugs and targets. This study proposes a new method, DTA-GTOmega, for predicting drug-target binding affinity.

MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction.

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

Motivation: Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two.

The role of microstructure of extracellular proteins in dewaterability of alkaline pretreatment sludge during bioleaching.

Alkaline pre-treatment is known to enhance the acid production efficiency of sludge but adversely affects its dewatering performance. In this study, the improvement of sludge dewaterability by a novel bioleaching system with inoculating domesticated acidified sludge (AS) and its underlying mechanism were investigated. The results showed that although the addition of Fe and the reduction of pH improved the dewatering performance of sludge, their effects were inferior to that of AS + Fe.

DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features.

Phosphorylation, as one of the most important post-translational modifications, plays a key role in various cellular physiological processes and disease occurrences. In recent years, computer technology has been gradually applied to the prediction of protein phosphorylation sites. However, most existing methods rely on simple protein sequence features that provide limited contextual information.

TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer.

Accurate identification of RNA modification sites is of great significance in understanding the functions and regulatory mechanisms of RNAs. Recent advances have shown great promise in applying computational methods based on deep learning for accurate prediction of RNA modifications. However, those methods generally predicted only a single type of RNA modification.

Enhancement of sludge dewaterability by repeated inoculation of acidified sludge: Extracellular polymeric substances molecular structure and microbial community succession.

Improving the dewatering performance of sewage sludge is of great scientific and engineering significance in the context of accelerated urbanization and increasingly strict environmental regulations. Acidified sludge (AS) can improve sludge dewatering performance, but the dewatering effect of repeated inoculation is unclear. The effects of long-term repeated inoculation of AS on the sludge dewaterability were investigated.

DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations.

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure.

Simultaneous Prediction of Interaction Sites on the Protein and Peptide Sides of Complexes through Multilayer Graph Convolutional Networks.

Identifying the binding residues of protein-peptide complexes is essential for understanding protein function mechanisms and exploring drug discovery. Recently, many computational methods have been developed to predict the interaction sites of either protein or peptide. However, to our knowledge, no prediction method can simultaneously identify the interaction sites on both the protein and peptide sides.

CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism.

Motivation: Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.

Multisource Attention-Mechanism-Based Encoder-Decoder Model for Predicting Drug-Drug Interaction Events.

Many computational methods have been proposed to predict drug-drug interactions (DDIs), which can occur when combining drugs to treat various diseases, but most mainly utilize single-source features of drugs, which is inadequate for drug representation. To fill this gap, we propose two attention-mechanism-based encoder-decoder models that incorporate multisource information: one is MAEDDI, which can predict DDIs, and the other is MAEDDIE, which can make further DDI-associated event predictions for drug pairs with DDIs. To better express the drug feature, we used three encoding methods to encode the drugs, integrating the self-attention mechanism, cross-attention mechanism, and graph attention network to construct a multisource feature fusion network.

DeepNup: Prediction of Nucleosome Positioning from DNA Sequences Using Deep Neural Network.

Nucleosome positioning is involved in diverse cellular biological processes by regulating the accessibility of DNA sequences to DNA-binding proteins and plays a vital role. Previous studies have manifested that the intrinsic preference of nucleosomes for DNA sequences may play a dominant role in nucleosome positioning. As a consequence, it is nontrivial to develop computational methods only based on DNA sequence information to accurately identify nucleosome positioning, and thus intend to verify the contribution of DNA sequences responsible for nucleosome positioning.

Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence.

Motivation: Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene expression. However, the solutions for this task can be improved by exploiting multiple relationships from dataset and exploring designs of learning models, for example jointly learning technology.

TransPPMP: predicting pathogenicity of frameshift and non-sense mutations by a Transformer based on protein features.

Motivation: Protein structure can be severely disrupted by frameshift and non-sense mutations at specific positions in the protein sequence. Frameshift and non-sense mutation cases can also be found in healthy individuals. A method to distinguish neutral and potentially disease-associated frameshift and non-sense mutations is of practical and fundamental importance.

How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.

The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding.

SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model.

A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affect drug-DNA interactions, but they can promote or inhibit the expression of the critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However, the biological experimental techniques for measuring it are expensive and time-consuming.

ctPISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation.

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods.

Quantifying Intensities of Transcription Factor-DNA Binding by Learning From an Ensemble of Protein Binding Microarrays.

The control of the coordinated expression of genes is primarily regulated by the interactions between transcription factors (TFs) and their DNA binding sites, which are an integral part of transcriptional regulatory networks. There are many computational tools focused on determining TF binding or unbinding to a DNA sequence. However, other tools focused on further determining the relative preference of such binding are needed.

Identifying dust as the dominant source of exposure to heavy metals for residents around battery factories in the Battery Industrial Capital of China.

Heavy metals (HMs) are constantly released into the environment during the production and use of batteries. Battery manufacturing has been ongoing for over six decades in the "Battery Industrial Capital" (located in Xinxiang City) of China, but the potential exposure pathways of residents in this region to HMs remain unclear. To clarify the exposure pathways and health risk of human exposure to HMs, hand wipe samples (n=82) and fingernail samples (n=36) were collected from residents (including young children (0-6 years old), children (7-12 years old) and adults (30-60 years old)) living around battery factories.

Learning Useful Representations of DNA Sequences From ChIP-Seq Datasets for Exploring Transcription Factor Binding Specificities.

Deep learning has been successfully applied to surprisingly different domains. Researchers and practitioners are employing trained deep learning models to enrich our knowledge. Transcription factors (TFs)are essential for regulating gene expression in all organisms by binding to specific DNA sequences.

CoZnMoO/C Nanosheet Composite: Rational Construction via a One-Stone-Three-Birds Strategy and Superior Lithium Storage Performances for Lithium-Ion Batteries.

CoMoO has gained great attention as an anode material for lithium-ion batteries owing to its high theoretical capacity of 980 mAh g and relatively high electrochemical activity. Unfortunately, CoMoO anode also has some drawbacks such as low electronic/ionic conductivity, inferior cyclic stability, and relative severe volumetric expansion during the lithiation/delithiation process, greatly inhibiting its further development and application. Herein, we report CoZnMoO/C nanosheet composite constructed via a novel and facile one-stone-three-birds strategy.