3D Structures
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2021 ◽  
Jérôme Tubiana ◽  
Dina Schneidman-Duhovny ◽  
Haim Wolfson

Abstract Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies sheds light on its function in vivo. Currently, two classes of methods prevail: Machine Learning (ML) models built on top of handcrafted features and comparative modeling. They are respectively limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy - including for unseen protein folds - and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful, and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/

2021 ◽  
Vol 8 ◽  
Pin Huang ◽  
Haoming Xing ◽  
Xun Zou ◽  
Qi Han ◽  
Ke Liu ◽  

We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites. In experiments on the OppA protein dataset used in previous studies and our selection of protein structures, our method achieves the highest model quality in terms of F1 score, compared to several previous studies.

2021 ◽  
Vol 28 ◽  
Antoni Planas

: The bacterial cell wall peptidoglycan (PG) is a dynamic structure that is constantly synthesized, re-modeled and degraded during bacterial division and growth. Post-synthetic modifications modulate the action of endogenous autolysis during PG lysis and remodeling for growth and sporulation, but also they are a mechanism used by pathogenic bacteria to evade the host innate immune system. Modifica-tions of the glycan backbone are limited to the C-2 amine and the C-6 hydroxyl moieties of either Glc-NAc or MurNAc residues. This paper reviews the functional roles and properties of peptidoglycan de-N-acetylases (distinct PG GlcNAc and MurNAc deacetylases) and recent progress through genetic stud-ies and biochemical characterization to elucidate their mechanism of action, 3D structures, substrate specificities and biological functions. Since they are virulence factors in pathogenic bacteria, peptidogly-can deacetylases are potential targets for the design of novel antimicrobial agents.

Insects ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 827
Long Liu ◽  
Yu-Shan Wei ◽  
Dun Wang

The gypsy moth, Lymantria dispar, is a polyphagous forest pest worldwide. The baculovirus, Lymantria dispar multiple nucleopolyhedrovirus (LdMNPV) is a natural pathogen of L. dispar. The Toll-like receptors (TLR) pathway plays a crucial role in both innate and adaptive immunity in animals. However, The TLR pathway and its underlying immune mechanism against baculovirus in L. dispar have not been explored. In this study, eleven TLRs and five downstream TLR pathway components were identified and characterized from L. dispar. Structural analysis indicated that intracellular Toll/interleukin-1 receptor (TIR) domains of LdTLRs and LdMyD88 contained three conserved motifs, and the 3D structures of TIR domains of LdTLRs possessed similar patterns in components arrangement and spatial conformation. The TLR proteins of L. dispar were placed into five monophyletic groups based on the phylogenetic analysis. LdTLR1, 2, 5, 6, 7, 8 and all identified downstream TLR pathway factors were highly induced upon LdMNPV infection, indicating that the TLR pathway of L. dispar was activated and might play a role in the immune response to LdMNPV infection. Collectively, these results help elucidate the crucial role of the TLR pathway in the immune response of L. dispar against LdMNPV, and offer a foundation for further understanding of innate immunity of the pest.

2021 ◽  
Gabriel Jabbour ◽  
Samantha Rego ◽  
Vincent Nguyenkhoa ◽  
Sivanesan Dakshanamurthy

The current COVID-19 pandemic continues to spread and devastate in the absence of effective treatments, warranting global concern and action. Despite progress in vaccine development, the rise of novel, increasingly infectious SARS-CoV-2 variants makes it clear that our response to the virus must continue to evolve along with it. The use of immunoinformatics provides an opportunity to rapidly and efficiently expand the tools at our disposal to combat the current pandemic and prepare for future outbreaks through epitope-based vaccine design. In this study, we validated and compared the currently available epitope prediction tools, and then used the best tools to predict T cell epitopes from SARS-CoV-2 spike and nucleocapsid proteins for use in an epitope-based vaccine. We combined the mouse MHC affinity predictor and clinical predictors such as HLA affinity, immunogenicity, antigenicity, allergenicity, toxicity and stability to select the highest quality CD8 and CD4 T cell epitopes for the common SARS-CoV-2 variants of concern suitable for further preclinical studies. We also identified variant-specific epitopes to more precisely target the Alpha, Beta, Gamma, Delta, Cluster 5 and US variants. We then modeled the 3D structures of our top 4 N and S epitopes to investigate the molecular interaction between peptide-MHC and peptide-MHC-TCR complexes. Following in vitro and in vivo validation, the epitopes identified by this study may be used in an epitope-based vaccine to protect across all current variants, as well as in variant-specific booster shots to target variants of concern. Immunoinformatics tools allowed us to efficiently predict epitopes in silico most likely to prove effective in vivo, providing a more streamlined process for vaccine development in the context of a rapidly evolving pandemic.

2021 ◽  
Yiyu Hong ◽  
Junsu Ko ◽  
Juyong Lee

In this study, we propose a new protein 3D structure modeling method, A-Prot, using MSA Transformer, one of the state-of-the-art protein lan-guage models. For a given MSA, an MSA feature tensor and row attention maps are extracted and converted into 2D residue-residue distance and dihedral angle predictions. We demonstrated that A-Prot predicts long-range contacts better than the existing methods. Additionally, we modeled the 3D structures of the free modeling and hard template-based modeling targets of CASP14. The assessment shows that the A-Prot models are more accurate than most top server groups of CASP14. These results imply that A-Prot captures evolutionary and structural infor-mation of proteins accurately with relatively low computational cost. Thus, A-Prot can provide a clue for the development of other protein prop-erty prediction methods.

2021 ◽  
Sandeep Kaur ◽  
Neblina Sikta ◽  
Andrea Schafferhans ◽  
Nicola Bordin ◽  
Mark J. Cowley ◽  

AbstractMotivationVariant analysis is a core task in bioinformatics that requires integrating data from many sources. This process can be helped by using 3D structures of proteins, which can provide a spatial context that can provide insight into how variants affect function. Many available tools can help with mapping variants onto structures; but each has specific restrictions, with the result that many researchers fail to benefit from valuable insights that could be gained from structural data.ResultsTo address this, we have created a streamlined system for incorporating 3D structures into variant analysis. Variants can be easily specified via URLs that are easily readable and writable, and use the notation recommended by the Human Genome Variation Society (HGVS). For example, ‘https://aquaria.app/SARS-CoV-2/S/?N501Y’ specifies the N501Y variant of SARS-CoV-2 S protein. In addition to mapping variants onto structures, our system provides summary information from multiple external resources, including COSMIC, CATH-FunVar, and PredictProtein. Furthermore, our system identifies and summarizes structures containing the variant, as well as the variant-position. Our system supports essentially any mutation for any well-studied protein, and uses all available structural data — including models inferred via very remote homology — integrated into a system that is fast and simple to use. By giving researchers easy, streamlined access to a wealth of structural information during variant analysis, our system will help in revealing novel insights into the molecular mechanisms underlying protein function in health and disease.AvailabilityOur resource is freely available at the project home page (https://aquaria.app). After peer review, the code will be openly available via a GPL version 2 license at https://github.com/ODonoghueLab/Aquaria. PSSH2, the database of sequence-to-structure alignments, is also freely available for download at https://zenodo.org/record/[email protected] informationNone.

2021 ◽  
Vol 3 ◽  
Patcharawee Jantimapornkij ◽  
Jörg Zerrer ◽  
Anna Buling

Lightweight structures produced by additive manufacturing (AM) technology such as the selective laser melting (SLM) process enable the fabrication of 3D structures with a high degree of freedom. A printed component can be tailored to have specific properties and render possible applications for industries such as the aerospace and automotive industries. Here, AlSi10Mg is one of the alloys that is currently used for SLM processes. Although the research with the aim improving the strength of AM aluminum alloy components is rapidly progressing, corrosion protection is scarcely addressed in this field. Plasma electrolytic oxidation (PEO) is an advanced electrolytical process for surface treatment of light metals such as aluminum, magnesium, and titanium. This process produces an oxide ceramic-like layer, which is extremely hard but also ductile, and significantly improves the corrosion and wear behavior. The aim of this study is to understand the corrosion behavior of 3D-printed AlSi10Mg alloy and to improve its corrosion resistance. For this reason, the properties of CERANOD®—PEO coating on an AlSi10Mg alloy produced by SLM were investigated on different AM surfaces, i.e., as-built, polished and stress relieved specimens. The corrosion performance of these surfaces was analyzed using electrochemical impedance spectroscopy (EIS), potentiodynamic polarization, and long-term immersion tests. Moreover, the microstructure and morphology of the resulting coatings were characterized by SEM/EDS, taking into account the corrosive attacks. The results exhibited a high amount of localized corrosion in the case of the uncoated specimens, while the PEO process conducted on the aluminum AM surfaces led to enclosed homogeneous coatings by protecting the material’s pores, which are typically observed in AM process. Thereby, high corrosion protection could be achieved using PEO surfaces, suggesting that this technology is a promising candidate for unleashing the full potential of 3D light metal printing.

2021 ◽  
Vol 12 ◽  
Jie Zheng ◽  
Xuan Xiao ◽  
Wang-Ren Qiu

Ion channels are the second largest drug target family. Ion channel dysfunction may lead to a number of diseases such as Alzheimer’s disease, epilepsy, cephalagra, and type II diabetes. In the research work for predicting ion channel–drug, computational approaches are effective and efficient compared with the costly, labor-intensive, and time-consuming experimental methods. Most of the existing methods can only be used to deal with the ion channels of knowing 3D structures; however, the 3D structures of most ion channels are still unknown. Many predictors based on protein sequence were developed to address the challenge, while most of their results need to be improved, or predicting web servers are missing. In this paper, a sequence-based classifier, called “iCDI-W2vCom,” was developed to identify the interactions between ion channels and drugs. In the predictor, the drug compound was formulated by SMILES-word2vec, FP2-word2vec, SMILES-node2vec, and ECFPs via a 1184D vector, ion channel was represented by the word2vec via a 64D vector, and the prediction engine was operated by the LightGBM classifier. The accuracy and AUC achieved by iCDI-W2vCom via the fivefold cross validation were 91.95% and 0.9703, which outperformed other existing predictors in this area. A user-friendly web server for iCDI-W2vCom was established at http://www.jci-bioinfo.cn/icdiw2v. The proposed method may also be a potential method for predicting target–drug interaction.

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