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. Here we report that IFITM1 inhibits EphA2-mediated EBV entry into ECs. RNA-sequencing and clinical sample analysis show reduced IFITM1 in EBV-positive ECs and a negative correlation between IFITM1 level and EBV copy number. IFITM1 depletion increases EBV infection and vice versa. Exogenous soluble IFITM1 effectively prevents EBV infection in vitro and in vivo. Furthermore, three-dimensional structure prediction and site-directed mutagenesis demonstrate that IFITM1 interacts with EphA2 via its two specific residues, competitively blocking EphA2 binding to EBV glycoproteins. Finally, YTHDF3, an m6A reader, suppresses IFITM1 via degradation-related DEAD-box protein 5 (DDX5). Thus, this study underscores IFITM1's crucial role in blocking EphA2-mediated EBV entry into ECs, indicating its potential in preventing EBV infection.

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. DockQ, a widely used metric, has some limitations. In this study, we propose a novel metric called BDM (Based on Distance difference Matrix) for assessing protein complex prediction structures. Our approach utilizes a distance difference matrix derived from comparing real and predicted protein structures, establishing a linear correlation with Root Mean Square Deviation (RMSD). BDM overcomes limitations associated with receptor-ligand differentiation and eliminates the requirement for structure alignment, making it a more effective and efficient metric. Evaluation of BDM using CASP14 and CASP15 test sets demonstrates superior performance compared to the official CASP scoring. BDM provides accurate and reasonable assessments of predicted protein complexes, wide adoption of BDM has the potential to advance protein complex structure prediction and facilitate related researches across scientific domains. Code is available at http://mialab.ruc.edu.cn/BDMServer/ .

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. We categorize the dataset based on docking difficulty, interface properties and structural characteristics, to provide a diverse set of cases for rigorous evaluation. Compared with Docking Benchmark 5.5, we have added 112 cases, including 14 single-domain antibody (sdAb) cases and 98 monoclonal antibody (mAb) cases, and also increased the proportion of Difficult cases. Our dataset contains diverse cases, including human/humanized antibodies, sdAbs, rodent antibodies and other types, opening the door to better algorithm development. Furthermore, we provide details on the process of building the benchmark dataset and introduce a pipeline for periodic updates to keep it up to date. We also utilize multiple complex prediction methods including ZDOCK, ClusPro, HDOCK and AlphaFold-Multimer for testing and analyzing this dataset. This benchmark serves as a valuable resource for evaluating and advancing docking computational methods in the analysis of antibody-antigen interaction, enabling researchers to develop more accurate and effective tools for predicting and designing antibody-antigen complexes. The non-redundant ABAG-docking structure benchmark dataset is available at https://github.com/Zhaonan99/Antibody-antigen-complex-structure-benchmark-dataset.

Computational methods for in situ structural studies with cryogenic electron tomography.

Cryo-electron tomography (cryo-ET) plays a critical role in imaging microorganisms in situ 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. Because only few near-atomic resolution structures have been reconstructed in cryo-ET, there is still much room to design new approaches to improve universal reconstruction resolutions. This review summarizes classical mathematical models and deep learning methods among general reconstruction steps. Moreover, we also discuss current limitations and prospects. This review can provide software and methods for each step of the entire procedure from tilt series by cryo-ET to 3D atomic structures. In addition, it can also help more experts in various fields comprehend a recent research trend in cryo-ET. Furthermore, we hope that more researchers can collaborate in developing computational methods and mathematical models for high-resolution three-dimensional structures from cryo-ET datasets.

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. However, structural analyses of D53 and the D53-D3-D14 holo-complex are challenging, and the biochemical mechanism underlying the complex assembly remains poorly understood. Here, we found that apo-D53 was rather flexible and reconstituted the holo-complex containing D53, S-phase kinase-associated protein 1 (SKP1), D3 and D14 with rac-GR24. The cryo-electron microscopy (cryo-EM) structure of SKP1-D3-D14 in the presence of D53 was analyzed and superimposed on the crystal structure of ASK1-D3-AtD14 without D53. No large conformational rearrangement was observed, but a 9Å rotation appeared between D14 and AtD14. Using hydrogen-deuterium exchange monitored by mass spectrometry, we analyzed dynamic motifs of D14, D3 and D53 in the D53-SKP1-D3-D14 complex assembly process and further identified two potential interfaces in D53 that are located in the N and D2 domains, respectively. Together, our results uncovered the dynamic conformational changes and built a model of the holo-complex D53-SKP1-D3-D14, offering valuable information for the biochemical and genetic mechanisms of SL perception and signal transduction.

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. The research has two parts: The first part is to predict the interaction between chains of the tetramer protein complex. In this part, we proposed a feature map to represent a sample generated by two chains of the tetramer protein complex, and constructed a Convolutional Neural Network (CNN) model to predict the interaction between chains of the tetramer protein complex. The AUC value of testing set is 0.6263, which indicates that our model can be used to predict the interaction between chains of the tetramer protein complex. The second part is to predict the tetramer protein complex interface residue pairs. In this part, we proposed a Support Vector Machine (SVM) ensemble method based on under-sampling and ensemble method to predict the tetramer protein complex interface residue pairs. In the top 10 predictions, when at least one protein-protein interaction interface is correctly predicted, the accuracy of our method is 82.14%. The result shows that our method is effective for the prediction of the tetramer protein complex interface residue pairs.

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. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

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. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/ .

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'-CPSCA-3' motif and SspE impedes phage propagation. SspE relies on PT modification in host DNA to exert antiphage activity. Here, structural and biochemical analyses reveal that SspE is preferentially recruited to PT sites mediated by the joint action of its N-terminal domain (NTD) hydrophobic cavity and C-terminal domain (CTD) DNA binding region. PT recognition enlarges the GTP-binding pocket, thereby increasing GTP hydrolysis activity, which subsequently triggers a conformational switch of SspE from a closed to an open state. The closed-to-open transition promotes the dissociation of SspE from self PT-DNA and turns on the DNA nicking nuclease activity of CTD, enabling SspE to accomplish self-nonself discrimination and limit phage predation, even when only a small fraction of modifiable consensus sequences is PT-protected in a bacterial genome.

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. For protein-protein non-interactions (PPNIs), the proposed method obtained accuracies of 86.23% for Homo sapiens and 85.34% for Mus musculus, and it performed well on three types of non-interaction networks. For protein-protein interactions (PPIs), we obtained accuracies of 99.20, 94.94, 98.56, 95.41, and 94.83% for Saccharomyces cerevisiae, Drosophila melanogaster, Helicobacter pylori, Homo sapiens, and Mus musculus, respectively. Furthermore, NVDT outperformed established sequence-based methods and demonstrated high prediction results for cross-species interactions. NVDT is expected to be an effective approach for predicting PPIs and PPNIs.

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. Through in vitro and in vivo studies, this nanocontainer, combining both immune escape and bioreceptor targeting, has greatly improved the drug bioavailability. Additionally, this nanocontainer shows good biocompatibility, and the targeted heart tissues and other important metabolic organs, such as liver and kidney, keep physiological structures and functions without the detection of side effects. Furthermore, the mechanism of immune escape for the developed nanocontainer has been investigated by lubrication test and molecular simulation. We anticipate that our study will establish a new perspective on the achievement of immune escape-based targeted drug delivery, which can provide a fundamental approach for the design of related biomaterials.

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. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers.

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. Although some previous studies have been devoted to the interaction interface of protein monomer and the interface residues between chains of protein dimers, very few studies about the interface residues prediction of protein multimers, including trimers, tetramer and even more monomers in a large protein complex. As we all know, a large number of proteins function with the form of multibody protein complexes. And the complexity of the protein multimers structure causes the difficulty of interface residues prediction on them. So, we hope to build a method for the prediction of protein tetramer interface residue pairs. RESULTS: Here, we developed a new deep network based on LSTM network combining with graph to predict protein tetramers interaction interface residue pairs. On account of the protein structure data is not the same as the image or video data which is well-arranged matrices, namely the Euclidean Structure mentioned in many researches. Because the Non-Euclidean Structure data can't keep the translation invariance, and we hope to extract some spatial features from this kind of data applying on deep learning, an algorithm combining with graph was developed to predict the interface residue pairs of protein interactions based on a topological graph building a relationship between vertexes and edges in graph theory combining multilayer Long Short-Term Memory network. First, selecting the training and test samples from the Protein Data Bank, and then extracting the physicochemical property features and the geometric features of surface residue associated with interfacial properties. Subsequently, we transform the protein multimers data to topological graphs and predict protein interaction interface residue pairs using the model. In addition, different types of evaluation indicators verified its validity.

The unidirectional causality influence of factors on PM2.5 in Shenyang city of China.

Air quality issue such as particulate matter pollution (PM2.5 and PM10) 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. The results demonstrated that variation trend of particles was almost similar, the annual, monthly and daily distribution had their own characteristics. Particles decreased gradually from south to north, from west to east. Correlation analysis showed that wind speed was the most important factor affecting particles, and temperature, air pressure and relative humidity were key factors in some seasons. Path analysis showed that there was complex unidirectional causal relationship between particles and individual or combined effects, and NO2 and CO were key factors affecting PM2.5. The hot and cold areas changed little with the seasons. All the above results suggests that planning the industrial layout, adjusting industrial structure, joint prevention and control were necessary measure to reduce particles concentration.

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. It is possible to predict protein-protein interaction from the perspective of network structure. Although there are many high-throughput experimental methods to detect protein-protein interactions, the cost of experiments is high, time-consuming, and there is a certain error rate meanwhile. Network-based approaches can provide candidates of protein pairs for high-throughput experiments and improve the accuracy rate. This paper presents a new link prediction approach "Sim" for PPI networks from the perspectives of proteins' complementary interfaces and gene duplication. By integrating our approach "Sim" with the state-of-art network-based approach "L3," the prediction accuracy and robustness are improved.

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. However, selecting the appropriate kernels and their parameters still needs further investigation. RESULTS: We utilized heterogeneous kernels from a specific kernel set including the Hadamard, RBF and linear kernels. The mixed coefficients of the heterogeneous kernel were computed by solving the standard convex quadratic programming problem of the quadratic constraints. The algorithm is named the heterogeneous multiple kernel learning (HMKL). Using the particle swarm optimization (PSO) in HMKL, we selected the kernel parameters, then we employed HMKL to perform the breast cancer outcome evaluation. By testing real-world microarray datasets, the HMKL method outperforms the methods of the random forest, decision tree, GA with Rotation Forest, BFA + RF, SVM and MKL. CONCLUSIONS: On one hand, HMKL is effective for the breast cancer evaluation and can be utilized by physicians to better understand the patient's condition. On the other hand, HMKL can choose the function and parameters of the kernel. At the same time, this study proves that the Hadamard kernel is effective in HMKL. We hope that HMKL could be applied as a new method to more actual problems.

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. To accurately compute the interface residue-residue contacts, we define a new constant called interface residue pairs frequency, which counts the atom contact numbers between two interface residues. We normalize interface residue pairs frequency to pick out the top residue-residue pairs from all the possible pairs preferential to be on correct protein-protein interaction interface. When tested on 37 protein dimers from the decoy set where most decoys are incorrect, our method successfully predicts 30 protein dimers with a success rate of up to 81.1%. Higher accuracy than some other state-of-the-art methods confirmed the performance of our method.

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. It is a NP-hard problem and the state-of-art algorithm IMM proposed by Youze Tang et al. achieves a ratio of 63.2% of the optimum with nearly linear time complexity. In recent years, there have been some works of maximizing influence on multilayer networks, either in the situation of single or multiple influences. But most of them study seed selection strategies to maximize their own influence from the perspective of participants. In fact, the problem from the perspective of network owners is also worthy of attention. Since network participants have not had access to all information of the network for reasons such as privacy protection and corporate interests, they may have access to only part of the social network. The owners of networks can get the whole picture of the networks, and they need not only to maximize the overall influence, but also to consider allocating seeds to their customers fairly, i.e., the Fair Seed Allocation (FSA) problem. As far as we know, FSA problem has been studied on a single network, but not on multilayer networks yet. From the perspective of network owners, we propose a multiple-influence diffusion model MMIC on multilayer networks and its FSA problem. Two solutions of FSA problem are given in this paper, and we prove theoretically that our seed allocation schemes are greedy. Subsequent experiments also validate the effectiveness of our approaches.

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. We show that HY5 physically interacts with and genetically acts through BIN2 to inhibit hypocotyl elongation. The interaction of HY5 with BIN2 enhances its kinase activity possibly by the promotion of BIN2 Tyr200 autophosphorylation, and subsequently represses the accumulation of the transcription factor BRASSINAZOLE-RESISTANT 1 (BZR1). Leu137 of HY5 is found to be important for the HY5-BIN2 interaction and HY5-mediated regulation of BIN2 activity, without affecting the transcriptional activity of HY5. HY5 levels increase with light intensity, which gradually enhances BIN2 activity. Thus, our work reveals an additional way in which HY5 promotes photomorphogenesis, and provides an insight into the regulation of GSK3 activity.

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. On the one hand, there are some deficiencies in LSTM, such as shallow layers, gradient explosion or vanishing, etc. With a dramatic data increasing, the imbalance between algorithm innovation and big data processing has been more serious and urgent. On the other hand, protein-protein interaction interface residue pairs prediction is an important problem in biology, but the low prediction accuracy compels us to propose new computational methods. RESULTS: In order to surmount aforementioned problems of LSTM, we adopt the residual architecture and add attention mechanism to LSTM. In detail, we redefine the block, and add a connection from front to back in every two layers and attention mechanism to strengthen the capability of mining information. Then we use it to predict protein-protein interaction interface residue pairs, and acquire a quite good accuracy over 72%. What's more, we compare our method with random experiments, PPiPP, standard LSTM, and some other machine learning methods. Our method shows better performance than the methods mentioned above. CONCLUSION: We present an attention mechanism enhanced LSTM with residual architecture, and make deeper network without gradient vanishing or explosion to a certain extent. Then we apply it to a significant problem- protein-protein interaction interface residue pairs prediction and obtain a better accuracy than other methods. Our method provides a new approach for protein-protein interaction computation, which will be helpful for related biomedical researches.