Migratory insertion in N-heterocyclic carbene complexes of palladium; an experimental and DFT studyElectronic supplementary information (ESI) available: experimental; structural data for 1; computational data. See http://www.rsc.org/suppdata/cc/b2/b212453j/

2003 ◽  
pp. 756-757 ◽  
Author(s):  
Andreas A. Danopoulos ◽  
Nikolaos Tsoureas ◽  
Jennifer C. Green ◽  
Michael B. Hursthouse
Keyword(s):  
Structural Data ◽  
Supplementary Information ◽  
Carbene Complexes ◽  
Computational Data ◽  
Migratory Insertion

Applications of the Cambridge Structural Database to molecular inorganic chemistry

2002 ◽  
Vol 58 (3) ◽  
pp. 398-406 ◽  
Author(s):  
A. Guy Orpen
Keyword(s):  
Inorganic Chemistry ◽  
Structural Data ◽  
Cambridge Structural Database ◽  
High Quality ◽  
Computational Data ◽  
Inorganic Complexes ◽  
Fundamental Research ◽  
Structural Database ◽  
Structure Correlation ◽  
Molecular Dimensions

Applications of the data in the Cambridge Structural Database (CSD) to knowledge acquisition and fundamental research in molecular inorganic chemistry are reviewed. Various classes of application are identified, including the derivation of typical molecular dimensions and their variability and transferability, the derivation and testing of theories of molecular structure and bonding, the identification of reaction paths and related conformational analyses based on the structure correlation hypothesis, and the identification of common and presumably energetically favourable intermolecular interactions. In many of these areas, the availability of plentiful structural data from the CSD is set against the emergence of high-quality computational data on the geometry and energy of inorganic complexes.

scholarly journals SphereCon—a method for precise estimation of residue relative solvent accessible area from limited structural information

2020 ◽  
Vol 36 (11) ◽  
pp. 3372-3378
Author(s):  
Alexander Gress ◽  
Olga V Kalinina
Keyword(s):  
Protein Function ◽  
Structural Information ◽  
Solvent Accessibility ◽  
Three Dimensional ◽  
Structural Data ◽  
Supplementary Information ◽  
Dimensional Structure ◽  
Relative Solvent Accessibility ◽  
Precise Measure ◽  
The Impact

Abstract Motivation In proteins, solvent accessibility of individual residues is a factor contributing to their importance for protein function and stability. Hence one might wish to calculate solvent accessibility in order to predict the impact of mutations, their pathogenicity and for other biomedical applications. A direct computation of solvent accessibility is only possible if all atoms of a protein three-dimensional structure are reliably resolved. Results We present SphereCon, a new precise measure that can estimate residue relative solvent accessibility (RSA) from limited data. The measure is based on calculating the volume of intersection of a sphere with a cone cut out in the direction opposite of the residue with surrounding atoms. We propose a method for estimating the position and volume of residue atoms in cases when they are not known from the structure, or when the structural data are unreliable or missing. We show that in cases of reliable input structures, SphereCon correlates almost perfectly with the directly computed RSA, and outperforms other previously suggested indirect methods. Moreover, SphereCon is the only measure that yields accurate results when the identities of amino acids are unknown. A significant novel feature of SphereCon is that it can estimate RSA from inter-residue distance and contact matrices, without any information about the actual atom coordinates. Availability and implementation https://github.com/kalininalab/spherecon. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Streamlined use of protein structures in variant analysis

2021 ◽  
Author(s):  
Sandeep Kaur ◽  
Neblina Sikta ◽  
Andrea Schafferhans ◽  
Nicola Bordin ◽  
Mark J. Cowley ◽  
...  
Keyword(s):  
Protein Function ◽  
Molecular Mechanisms ◽  
Structural Information ◽  
Protein Structures ◽  
Structural Data ◽  
Supplementary Information ◽  
3D Structures ◽  
Link Type ◽  
Variant Analysis ◽  
Many Sources

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.

scholarly journals MeLAD: an integrated resource for metalloenzyme-ligand associations

2019 ◽  
Author(s):  
Gen Li ◽  
Yu Su ◽  
Yu-Hang Yan ◽  
Jia-Yi Peng ◽  
Qing-Qing Dai ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Metal Binding ◽  
Metal Ion ◽  
Structural Data ◽  
Structural Similarity ◽  
Functional Class ◽  
Supplementary Information ◽  
Biological Processes ◽  
Ligand Interactions ◽  
Data Source

Abstract Motivation Metalloenzymes are attractive targets for therapeutic intervention owing to their central roles in various biological processes and pathological situations. The fast-growing body of structural data on metalloenzyme-ligand interactions is facilitating efficient drug discovery targeting metalloenzymes. However, there remains a shortage of specific databases that can provide centralized, interconnected information exclusive to metalloenzyme-ligand associations. Results We created a Metalloenzyme-Ligand Association Database (MeLAD), which is designed to provide curated structural data and information exclusive to metalloenzyme-ligand interactions, and more uniquely, present expanded associations that are represented by metal-binding pharmacophores (MBPs), metalloenzyme structural similarity (MeSIM) and ligand chemical similarity (LigSIM). MeLAD currently contains 6086 structurally resolved interactions of 1416 metalloenzymes with 3564 ligands, of which classical metal-binding, non-classical metal-binding, non-metal-binding and metal water-bridging interactions account for 63.0%, 2.3%, 34.4% and 0.3%, respectively. A total of 263 monodentate, 191 bidentate and 15 tridentate MBP chemotypes were included in MeLAD, which are linked to different active site metal ions and coordination modes. 3726 and 52 740 deductive metalloenzyme-ligand associations by MeSIM and LigSIM analyses, respectively, were included in MeLAD. An online server is provided for users to conduct metalloenzyme profiling prediction for small molecules of interest. MeLAD is searchable by multiple criteria, e.g. metalloenzyme name, ligand identifier, functional class, bioinorganic class, metal ion and metal-containing cofactor, which will serve as a valuable, integrative data source to foster metalloenzyme related research, particularly involved in drug discovery targeting metalloenzymes. Availability and implementation MeLAD is accessible at https://melad.ddtmlab.org. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Antibody complementarity determining region design using high-capacity machine learning

2019 ◽  
Vol 36 (7) ◽  
pp. 2126-2133 ◽  
Author(s):  
Ge Liu ◽  
Haoyang Zeng ◽  
Jonas Mueller ◽  
Brandon Carter ◽  
Ziheng Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Capacity ◽  
Structural Data ◽  
Supplementary Information ◽  
Proper Function ◽  
Integrative Approach ◽  
Target Specificity ◽  
Sequencing Data ◽  
Therapeutic Molecules ◽  
Complementarity Determining Regions

Abstract Motivation The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Results Here, we present Ens-Grad, a machine learning method that can design complementarity determining regions of human Immunoglobulin G antibodies with target affinities that are superior to candidates derived from phage display panning experiments. We also demonstrate that machine learning can improve target specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data. Availability and implementation Sequencing data of the phage panning experiment are deposited at NIH’s Sequence Read Archive (SRA) under the accession number SRP158510. We make our code available at https://github.com/gifford-lab/antibody-2019. Supplementary information Supplementary data are available at Bioinformatics online.

GeoMine: interactive pattern mining of protein–ligand interfaces in the Protein Data Bank

2020 ◽  
Author(s):  
Konrad Diedrich ◽  
Joel Graef ◽  
Katrin Schöning-Stierand ◽  
Matthias Rarey
Keyword(s):  
Protein Data Bank ◽  
Web Application ◽  
Pattern Mining ◽  
Structural Data ◽  
Data Bank ◽  
Supplementary Information ◽  
User Friendliness ◽  
Iterative Search ◽  
Potential Applications ◽  
Query Generation

Abstract Summary The searching of user-defined 3D queries in molecular interfaces is a computationally challenging problem that is not satisfactorily solved so far. Most of the few existing tools focused on that purpose are desktop based and not openly available. Besides that, they show a lack of query versatility, search efficiency and user-friendliness. We address this issue with GeoMine, a publicly available web application that provides textual, numerical and geometrical search functionality for protein–ligand binding sites derived from structural data contained in the Protein Data Bank (PDB). The query generation is supported by a 3D representation of a start structure that provides interactively selectable elements like atoms, bonds and interactions. GeoMine gives full control over geometric variability in the query while performing a deterministic, precise search. Reasonably selective queries are processed on the entire set of protein–ligand complexes in the PDB within a few minutes. GeoMine offers an interactive and iterative search process of successive result analyses and query adaptations. From the numerous potential applications, we picked two from the field of side-effect analyze showcasing the usefulness of GeoMine. Availability and implementation GeoMine is part of the ProteinsPlus web application suite and freely available at https://proteins.plus. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document