Transferability of Geometric Patterns from Protein Self-Interactions to Protein-Ligand Interactions.
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ABSTRACT:
SUBMITTER: Koehl A
PROVIDER: S-EPMC8669734 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundShape complementarity and non-covalent interactions are believed to drive protein-ligand interaction. To date protein-protein, protein-DNA, and protein-RNA interactions were systematically investigated, which is in contrast to interactions with small ligands. We investigate the role of covalent and non-covalent bonds in protein-small ligand interactions using a comprehensive dataset of 2,320 complexes.Methodology and principal findingsWe show that protein-ligand interactions are governed by different forces for different ligand types, i.e., protein-organic compound interactions are governed by hydrogen bonds, van der Waals contacts, and covalent bonds; protein-metal ion interactions are dominated by electrostatic force and coordination bonds; protein-anion interactions are established with electrostatic force, hydrogen bonds, and van der Waals contacts; and protein-inorganic cluster interactions are driven by coordination bonds. We extracted several frequently occurring atomic-level patterns concerning these interactions. For instance, 73% of investigated covalent bonds were summarized with just three patterns in which bonds are formed between thiol of Cys and carbon or sulfur atoms of ligands, and nitrogen of Lys and carbon of ligands. Similar patterns were found for the coordination bonds. Hydrogen bonds occur in 67% of protein-organic compound complexes and 66% of them are formed between NH- group of protein residues and oxygen atom of ligands. We quantify relative abundance of specific interaction types and discuss their characteristic features. The extracted protein-organic compound patterns are shown to complement and improve a geometric approach for prediction of binding sites.Conclusions and significanceWe show that for a given type (group) of ligands and type of the interaction force, majority of protein-ligand interactions are repetitive and could be summarized with several simple atomic-level patterns. We summarize and analyze 10 frequently occurring interaction patterns that cover 56% of all considered complexes and we show a practical application for the patterns that concerns interactions with organic compounds.
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Project description:Proteins can self-associate with copies of themselves to form symmetric complexes called homomers. Homomers are widespread in all kingdoms of life and allow for unique geometric and functional properties, as reflected in viral capsids or allostery. Once a protein forms a homomer, however, its internal symmetry can compound the effect of point mutations and trigger uncontrolled self-assembly into high-order structures. We identified mutation hot spots for supramolecular assembly, which are predictable by geometry. Here, we present a dataset of descriptors that characterize these hot spot positions both geometrically and chemically, as well as computer scripts allowing the calculation and visualization of these properties for homomers of choice. Since the biological relevance of homomers is not readily available from their X-ray crystallographic structure, we also provide reliability estimates obtained by methods we recently developed. These data have implications in the study of disease-causing mutations, protein evolution and can be exploited in the design of biomaterials.
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Project description:Many systems in biology rely on binding of ligands to target proteins in a single high-affinity conformation with a favorable ?G. Alternatively, interactions of ligands with protein regions that allow diffuse binding, distributed over multiple sites and conformations, can exhibit favorable ?G because of their higher entropy. Diffuse binding may be biologically important for multidrug transporters and carrier proteins. A fine-grained computational method for numerical integration of total binding ?G arising from diffuse regional interaction of a ligand in multiple conformations using a Markov Chain Monte Carlo (MCMC) approach is presented. This method yields a metric that quantifies the influence on overall ligand affinity of ligand binding to multiple, distinct sites within a protein binding region. This metric is essentially a measure of dispersion in equilibrium ligand binding and depends on both the number of potential sites of interaction and the distribution of their individual predicted affinities. Analysis of test cases indicates that, for some ligand/protein pairs involving transporters and carrier proteins, diffuse binding contributes greatly to total affinity, whereas in other cases the influence is modest. This approach may be useful for studying situations where "nonspecific" interactions contribute to biological function.
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Project description:Metal-ligand interactions with various proteins form in vivo metal assemblies. In recent years, metallosupramolecular approaches have been utilized to forge an assortment of fascinating two- and three-dimensional nano-architectures, and macroscopic materials, such as metal-ligand coordination polymeric materials, have promise in artificial systems. However to the best of our knowledge, the self-assembly of macroscopic materials through metal-ligand interactions has yet to be reported. Herein we demonstrate a gel assembly formed via metal-ligand interactions using polyacrylamide modified with Fe-porphyrin and L-histidine moieties. The stress values for the assembly increase as the concentration of Fe-porphyrin or L-histidine in the gels increases. Moreover, agitation of Fe-porphyrin gel, Zn-porphyrin gel, and L-histidine gel in an 80?mM Tris-acetate buffer (pH 9.0) results in selective adhesion of the Fe-porphyrin gel to the L-histidine gel based on the affinities of Fe-porphyrin and Zn-porphyrin with L-histidine.
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Project description:We introduce three assays for analyzing ligand-receptor interactions based on the specific conjugation of ligands to SNAP-tag fusion proteins. Conjugation of ligands to different SNAP-tag fusions permits the validation of suspected interactions in cell extracts and fixed cells as well as the establishment of high-throughput assays. The different assays allow the analysis of strong and weak interactions. Conversion of ligands into SNAP-tag substrates thus provides access to a powerful toolbox for the analysis of their interactions with proteins.
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Project description:BACKGROUND: Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions. RESULTS: Two test datasets from LigASite, each containing 388 bound and unbound structures, were used to predict binding regions using PLB-SAVE and two well-known prediction systems, SiteHound and MetaPocket2.0 (MPK2). PLB-SAVE outperformed the other programs with accuracy rates of 94.3% for unbound proteins and 95.5% for bound proteins via a tenfold cross-validation process. Additionally, because the parallel processing architecture was designed to enhance the computational efficiency, we obtained an average of 160-fold increase in computational time. CONCLUSIONS: In silico binding region prediction is considered the initial stage in structure-based drug design. To improve the efficacy of biological experiments for drug development, we developed PLB-SAVE, which uses only geometrical features of proteins and achieves a good overall performance for protein-ligand binding region prediction. Based on the same approach and rationale, this method can also be applied to predict carbohydrate-antibody interactions for further design and development of carbohydrate-based vaccines. PLB-SAVE is available at http://save.cs.ntou.edu.tw.
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