TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment.

Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient and effective quality assessment (QA) or estimation of model accuracy models that can evaluate the quality of the predicted protein-complexes without knowing their native structures are of key importance for protein structure generation and model selection. In this paper, we leverage persistent homology (PH) to capture the atomic-level topological information around residues and design a topological deep learning-based QA method, TopoQA, to assess the accuracy of protein complex interfaces.

Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity.

Background: The binding between proteins and ligands plays a crucial role in the field of drug discovery. However, this area currently faces numerous challenges. On one hand, existing methods are constrained by the limited availability of labeled data, often performing inadequately when addressing complex protein-ligand interactions.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging.

Interferon-induced transmembrane protein-1 competitively blocks Ephrin receptor A2-mediated Epstein-Barr virus entry into epithelial cells.

Epstein-Barr virus (EBV) can infect both B cells and epithelial cells (ECs), causing diseases such as mononucleosis and cancer. It enters ECs via Ephrin receptor A2 (EphA2). The function of interferon-induced transmembrane protein-1 (IFITM1) in EBV infection of ECs remains elusive.

BDM: An Assessment Metric for Protein Complex Structure Models Based on Distance Difference Matrix.

Protein complex structure prediction is an important problem in computational biology. While significant progress has been made for protein monomers, accurate evaluation of protein complexes remains challenging. Existing assessment methods in CASP, lack dedicated metrics for evaluating complexes.

ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking.

Accurate prediction of antibody-antigen complex structures is pivotal in drug discovery, vaccine design and disease treatment and can facilitate the development of more effective therapies and diagnostics. In this work, we first review the antibody-antigen docking (ABAG-docking) datasets. Then, we present the creation and characterization of a comprehensive benchmark dataset of antibody-antigen complexes.

Computational methods for structural studies with cryogenic electron tomography.

Cryo-electron tomography (cryo-ET) plays a critical role in imaging microorganisms in terms of further analyzing the working mechanisms of viruses and drug exploitation, among others. A data processing workflow for cryo-ET has been developed to reconstruct three-dimensional density maps and further build atomic models from a tilt series of two-dimensional projections. Low signal-to-noise ratio (SNR) and missing wedge are two major factors that make the reconstruction procedure challenging.

Conformational Dynamics of the D53-D3-D14 Complex in Strigolactone Signaling.

Strigolactones (SLs) play fundamental roles in regulating plant architecture, which is a major factor determining crop yield. The perception and signal transduction of SLs require the formation of a complex containing the receptor DWARF14 (D14), an F-box protein D3 and a transcriptional regulator D53 in an SL-dependent manner. Structural and biochemical analyses of D14 and its orthologs DAD2 and AtD14, D3 and the complexes of ASK1-D3-AtD14 and D3CTH-D14 have made great contributions to understanding the mechanisms of SL perception.

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions.

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery.

Prediction of the tetramer protein complex interaction based on CNN and SVM.

Protein-protein interactions play an important role in life activities. The study of protein-protein interactions helps to better understand the mechanism of protein complex interaction, which is crucial for drug design, protein function annotation and three-dimensional structure prediction of protein complexes. In this paper, we study the tetramer protein complex interaction.

ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction.

Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs.

Nicking mechanism underlying the DNA phosphorothioate-sensing antiphage defense by SspE.

DNA phosphorothioate (PT) modification, with a nonbridging phosphate oxygen substituted by sulfur, represents a widespread epigenetic marker in prokaryotes and provides protection against genetic parasites. In the PT-based defense system Ssp, SspABCD confers a single-stranded PT modification of host DNA in the 5'-CCA-3' motif and SspE impedes phage propagation. SspE relies on PT modification in host DNA to exert antiphage activity.

Protein-protein interaction and non-interaction predictions using gene sequence natural vector.

Predicting protein-protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting protein pairs. We proposed a gene sequence-based method, named NVDT (Natural Vector combine with Dinucleotide and Triplet nucleotide), for the prediction of interaction and non-interaction.

Polymeric coating lubricates nanocontainers to escape macrophage uptake for bioreceptor recognition.

Accurate drug delivery to the lesion has been deliberated for several decades, but one important phenomenon is usually neglected that the immune system can prevent smooth transportation of nanomedicine. Although injection would reduce first-pass effect, macrophages in the blood can still recognize and phagocytose nanomedicine. Here we show that a lubricated nanocontainer, which is prepared based on polyelectrolytes and mesoporous silica nanoparticles, can accurately target muscarinic bioreceptor while escaping from the identification of macrophages.

A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers.

Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein-protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results.

Tetramer protein complex interface residue pairs prediction with LSTM combined with graph representations.

Motivation: Protein-protein interactions are important for many biological processes. Theoretical understanding of the structurally determining factors of interaction sites will help to understand the underlying mechanism of protein-protein interactions. Taking advantage of advanced mathematical methods to correctly predict interaction sites will be useful.

The unidirectional causality influence of factors on PM in Shenyang city of China.

Air quality issue such as particulate matter pollution (PM and PM) has become one of the biggest environmental problem in China. As one of the most important industrial base and economic core regions of China, Northeast China is facing serious air pollution problems in recent years, which has a profound impact on the health of local residents and atmospheric environment in some part of East Asia. Therefore, it is urgent to understand temporal-spatial characteristics of particles and analyze the causality factors.

Heterogeneous multiple kernel learning for breast cancer outcome evaluation.

Background: Breast cancer is one of the common kinds of cancer among women, and it ranks second among all cancers in terms of incidence, after lung cancer. Therefore, it is of great necessity to study the detection methods of breast cancer. Recent research has focused on using gene expression data to predict outcomes, and kernel methods have received a lot of attention regarding the cancer outcome evaluation.

Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network.

Protein-protein interactions are the foundations of cellular life activities. At present, the already known protein-protein interactions only account for a small part of the total. With the development of experimental and computing technology, more and more PPI data are mined, PPI networks are more and more dense.

Modulation of BIN2 kinase activity by HY5 controls hypocotyl elongation in the light.

ELONGATED HYPOCOTYL 5 (HY5), a basic domain/leucine zipper (bZIP) transcription factor, acts as a master regulator of transcription to promote photomorphogenesis. At present, it's unclear whether HY5 uses additional mechanisms to inhibit hypocotyl elongation. Here, we demonstrate that HY5 enhances the activity of GSK3-like kinase BRASSINOSTEROID-INSENSITIVE 2 (BIN2), a key repressor of brassinosteroid signaling, to repress hypocotyl elongation.

A Novel Index of Contact Frequency from Noise Protein-Protein Interaction Data Help for Accurate Interface Residue Pair Prediction.

Protein-protein interactions are important for most biological processes and have been studied for decades. However, the detailed formation mechanism of protein-protein interaction interface is still ambiguous, which makes it difficult to accurately predict the protein-protein interaction interface residue pairs. Here, we extract the interface residue-residue contacts from the decoys in the ZDOCK protein-protein complex decoy set with RMSD mostly larger than 3 Å.

Maximizing multiple influences and fair seed allocation on multilayer social networks.

The dissemination of information on networks involves many important practical issues, such as the spread and containment of rumors in social networks, the spread of infectious diseases among the population, commercial propaganda and promotion, the expansion of political influence and so on. One of the most important problems is the influence-maximization problem which is to find out k most influential nodes under a certain propagate mechanism. Since the problem was proposed in 2001, many works have focused on maximizing the influence in a single network.

Attention mechanism enhanced LSTM with residual architecture and its application for protein-protein interaction residue pairs prediction.

Background: Recurrent neural network(RNN) is a good way to process sequential data, but the capability of RNN to compute long sequence data is inefficient. As a variant of RNN, long short term memory(LSTM) solved the problem in some extent. Here we improved LSTM for big data application in protein-protein interaction interface residue pairs prediction based on the following two reasons.

Cideb controls sterol-regulated ER export of SREBP/SCAP by promoting cargo loading at ER exit sites.

SREBPs are master regulators of lipid homeostasis and undergo sterol-regulated export from ER to Golgi apparatus for processing and activation via COPII-coated vesicles. While COPII recognizes SREBP through its escort protein SCAP, factor(s) specifically promoting SREBP/SCAP loading to the COPII machinery remains unknown. Here, we show that the ER/lipid droplet-associated protein Cideb selectively promotes the loading of SREBP/SCAP into COPII vesicles.

S-Nitrosylation Targets GSNO Reductase for Selective Autophagy during Hypoxia Responses in Plants.

Nitric oxide (NO) regulates diverse cellular signaling through S-nitrosylation of specific Cys residues of target proteins. The intracellular level of S-nitrosoglutathione (GSNO), a major bioactive NO species, is regulated by GSNO reductase (GSNOR), a highly conserved master regulator of NO signaling. However, little is known about how the activity of GSNOR is regulated.