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Current Pharmaceutical Design 2018In recent decades, drug-protein interactions have been widely studied and several methods of analysis of these phenomena have been developed and improved. These can be... (Review)
Review
In recent decades, drug-protein interactions have been widely studied and several methods of analysis of these phenomena have been developed and improved. These can be classified into separation, physical, chromatographic and electrophoretic methods. This review depicts the assumptions and mechanisms of methods from each group, details their strengths and weaknesses, and presents examples of their usage from the literature. Equilibrium dialysis, ultrafiltration, Hummel-Dreyer method or high performance affinity chromatography are given as representative examples, but this issue is far more expanded. Nowadays, increasing attention is paid to the computational methods and molecular modeling which are convenient tools to estimate protein binding affinity based on the physicochemical properties of compounds. To gain a broader overview, the study also examines the protein binding ability and pharmacotherapy of drugs against a range of substrates such as plasma, skin, tissue and human milk.
Topics: Animals; Chromatography, High Pressure Liquid; Humans; Models, Molecular; Pharmaceutical Preparations; Protein Binding; Proteins
PubMed: 30088445
DOI: 10.2174/1381612824666180808145320 -
Antimicrobial Agents and Chemotherapy Feb 2018This review summarizes evidence that the impact of protein binding of the activity of antibiotics is multifaceted and more complex than indicated by the numerical value... (Review)
Review
This review summarizes evidence that the impact of protein binding of the activity of antibiotics is multifaceted and more complex than indicated by the numerical value of protein binding alone. A plethora of studies has proven that protein binding of antibiotics matters, as the free fraction only is antibacterially active and governs pharmacokinetics. Several studies have indicated that independent from protein binding of immunoglobulin G, albumin, α-acid-glycoprotein, and pulmonary surfactant acted synergistically with antibacterial agents, thus suggesting that some intrinsic properties of serum proteins may have mediated serum-antibiotic synergisms. It has been demonstrated that IgG and albumin permeabilized Gram-negative and Gram-positive bacteria and facilitated the uptake of poorly penetrating antibiotics. Alpha-1-acid-glycoprotein and pulmonary surfactant also exerted a permeabilizing activity, but proof that this property results in a sensitizing effect is missing. The permeabilizing effect of serum proteins may explain why serum-antibiotic synergisms do not represent a general phenomenon but are limited to specific drug-bug associations only. Although evidence has been generated to support the hypothesis that native serum proteins interact synergistically with antibiotics, systematic and well-controlled studies have to be performed to substantiate this phenomenon. The interactions between serum proteins and bacterial surfaces are driven by physicochemical forces. However, preparative techniques, storage conditions, and incubation methods have a significant impact on the intrinsic activities of these serum proteins affecting serum-antibiotic synergisms, so these techniques have to be standardized; otherwise, contradictory data or even artifacts will be generated.
Topics: Animals; Anti-Bacterial Agents; Blood Proteins; Humans; Protein Binding
PubMed: 29158276
DOI: 10.1128/AAC.01663-17 -
Molecular Informatics Dec 2016Structure-based drug design starts with the collection, preparation, and initial analysis of protein structures. With more than 115,000 structures publically available... (Review)
Review
Structure-based drug design starts with the collection, preparation, and initial analysis of protein structures. With more than 115,000 structures publically available in the Protein Data Bank (PDB), fully automated processes reliably performing these important preprocessing steps are needed. Several tools are available for these tasks, however, most of them do not address the special needs of scientists interested in protein-ligand interactions. In this paper, we summarize our research activities towards an automated processing pipeline from raw PDB data towards ready-to-use protein binding site ensembles. Starting from a single protein structure, the pipeline covers the following phases: Extracting structurally related binding sites from the PDB, aligning disconnected binding site sequences, resolving tautomeric forms and protonation, orienting hydrogens and flippable side-chains, structurally aligning the multitude of binding sites, and performing a reasonable reduction of ensemble structures. The pipeline, named SIENA, creates protein-structural ensembles for the analysis of protein flexibility, molecular design efforts like docking or de novo design within seconds. For the first time, we are able to process the whole PDB in order to create a large collection of protein binding site ensembles. SIENA is available as part of the ZBH ProteinsPlus webserver under http://proteinsplus.zbh.uni-hamburg.de.
Topics: Binding Sites; Databases, Protein; Drug Design; Ligands; Protein Binding; Proteins; Software
PubMed: 27870245
DOI: 10.1002/minf.201600043 -
Proceedings of the National Academy of... Dec 2019Protein multivalency can provide increased affinity and specificity relative to monovalent counterparts, but these emergent biochemical properties and their mechanistic...
Protein multivalency can provide increased affinity and specificity relative to monovalent counterparts, but these emergent biochemical properties and their mechanistic underpinnings are difficult to predict as a function of the biophysical properties of the multivalent binding partners. Here, we present a mathematical model that accurately simulates binding kinetics and equilibria of multivalent protein-protein interactions as a function of the kinetics of monomer-monomer binding, the structure and topology of the multidomain interacting partners, and the valency of each partner. These properties are all experimentally or computationally estimated a priori, including approximating topology with a worm-like chain model applicable to a variety of structurally disparate systems, thus making the model predictive without parameter fitting. We conceptualize multivalent binding as a protein-protein interaction network: ligand and receptor valencies determine the number of interacting species in the network, with monomer kinetics and structural properties dictating the dynamics of each species. As predicted by the model and validated by surface plasmon resonance experiments, multivalent interactions can generate several noncanonical macroscopic binding dynamics, including a transient burst of high-energy configurations during association, biphasic equilibria resulting from interligand competition at high concentrations, and multiexponential dissociation arising from differential lifetimes of distinct network species. The transient burst was only uncovered when extending our analysis to trivalent interactions due to the significantly larger network, and we were able to predictably tune burst magnitude by altering linker rigidity. This study elucidates mechanisms of multivalent binding and establishes a framework for model-guided analysis and engineering of such interactions.
Topics: Computational Biology; Computer Simulation; Kinetics; Models, Molecular; Protein Binding; Protein Interaction Maps; Surface Plasmon Resonance
PubMed: 31776263
DOI: 10.1073/pnas.1902909116 -
Metallomics : Integrated Biometal... Nov 2020Proteomics has played an important role in elucidating the fundamental processes occuring in living cells. Translating these methods to metallodrug research... (Review)
Review
Proteomics has played an important role in elucidating the fundamental processes occuring in living cells. Translating these methods to metallodrug research ('metalloproteomics') has provided a means for molecular target identification of metal-based anticancer agents which should signifcantly advance the research field. In combination with biological assays, these techniques have enabled the mechanisms of action of metallodrugs to be linked to their interactions with molecular targets and aid understanding of their biological properties. Such investigations have profoundly increased our knowledge of the complex and dynamic nature of metallodrug-biomolecule interactions and have provided, at least for some compound types, a more detailed picture on their specific protein-binding patterns. This perspective highlights the progression of metallodrug proteomics research for the identification of non-DNA targets from standard analytical techniques to powerful metallodrug pull-down methods.
Topics: Animals; Antineoplastic Agents; Humans; Metabolic Networks and Pathways; Metalloproteins; Metals; Protein Binding; Proteomics
PubMed: 33063808
DOI: 10.1039/d0mt00196a -
Annals of the New York Academy of... Nov 1973
Topics: Pharmaceutical Preparations; Protein Binding
PubMed: 4520403
DOI: No ID Found -
Nature Reviews. Drug Discovery Dec 2010Data from in vitro plasma protein binding experiments that determine the fraction of protein-bound drug are frequently used in drug discovery to guide structure design... (Review)
Review
Data from in vitro plasma protein binding experiments that determine the fraction of protein-bound drug are frequently used in drug discovery to guide structure design and to prioritize compounds for in vivo studies. However, we consider that these practices are usually misleading, because in vivo efficacy is determined by the free (unbound) drug concentration surrounding the therapeutic target, not by the free drug fraction. These practices yield no enhancement of the in vivo free drug concentration. So, decisions based on free drug fraction could result in the wrong compounds being advanced through drug discovery programmes. This Perspective provides guidance on the application of plasma protein binding information in drug discovery.
Topics: Animals; Blood Proteins; Drug Discovery; Humans; Pharmaceutical Preparations; Protein Binding
PubMed: 21119731
DOI: 10.1038/nrd3287 -
Seminars in Dialysis 2009Protein-bound uremic retention solutes constitute a group whose common characteristic is their difficult removal by dialysis. In 2003, the EUTox group described 25... (Review)
Review
Protein-bound uremic retention solutes constitute a group whose common characteristic is their difficult removal by dialysis. In 2003, the EUTox group described 25 protein-bound solutes. They comprised six advanced glycation end products (AGE), four phenols (including p-cresol), six indoles (including indoxylsulfate), two hippurates, three polyamines, and two peptides, homocysteine and 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF). As then, three new compounds have been added to the list: phenylacetic acid, dinucleoside polyphosphates, and IL-18. During the last years, protein-bound compounds have been identified as some of the main toxins involved in vascular lesions of chronic kidney disease. The removal of these solutes by conventional hemodialysis (HD) is low because only the free fraction of the solute is available for diffusion. The increase in the convective part with hemodiafiltration improves the performance of depuration but convection only applies to the free fraction and its benefit is limited. One possibility to improve the removal of a protein-bound solute would be to stimulate its dissociation from the binding protein. This could be obtained in experiments by setting the dialysate flow rate and the dialyzer mass transfer area coefficient (KoA) at much higher levels than the plasma flow rate, or by adding to the dialysate a sorbent such as activated charcoal or albumin. In the future, specific adsorbents may be developed. Today, the only possibility is to use approaches such as daily HD and long HD which could allow better equilibration between extravascular and vascular compartments and consequently result in greater removal of protein-bound compounds.
Topics: Humans; Kidney Diseases; Protein Binding; Renal Dialysis; Toxins, Biological
PubMed: 19708977
DOI: 10.1111/j.1525-139X.2009.00576.x -
Journal of Molecular Biology Jun 2022Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter...
Protein-carbohydrate interactions play an important role in several biological processes. The mutation of amino acid residues in carbohydrate-binding proteins may alter the binding affinity, affect the functions and lead to diseases. Elucidating the factors influencing the binding affinity change (ΔΔG) of protein-carbohydrate complexes upon mutation is a challenging task. In this work, we have collected the experimental data for the binding affinity change of 318 unique mutants and related with sequence and structural features of amino acid residues at the mutant sites. We found that accessible surface area, secondary structure, mutation preference, conservation score, hydrophobicity and contact energies are important to understand the binding affinity change upon mutation. We have developed multiple regression equations for predicting the binding affinity change upon mutation and our method showed an average correlation of 0.74 and a mean absolute error of 0.70 kcal/mol between experimental and predicted ΔΔG on a 10-fold cross-validation. Further, we have validated our method using an independent test data set of 124 (62 unique) mutations, which showed a correlation and MAE of 0.79 and 0.56 kcal/mol, respectively. We have developed a web server PCA-MutPred, Protein-CArbohydrate complex Mutation affinity Predictor, for predicting the change in binding affinity of protein-carbohydrate complexes and it is freely accessible at https://web.iitm.ac.in/bioinfo2/pcamutpred. We suggest that the method could be a useful resource for designing protein-carbohydrate complexes with desired affinities.
Topics: Amino Acids; Carbohydrates; Mutation, Missense; Protein Binding; Protein Structure, Secondary; Thermodynamics
PubMed: 35662456
DOI: 10.1016/j.jmb.2022.167526 -
IEEE/ACM Transactions on Computational... 2023Computational prediction of the RBP bound sites using features learned from existing annotation knowledge is an effective method because high-throughput experiments are...
Computational prediction of the RBP bound sites using features learned from existing annotation knowledge is an effective method because high-throughput experiments are complex, expensive and time-consuming. Many methods have been proposed to predict RNA-protein binding sites. However, the partial information of RNA sequence is not fully used. In this study, we propose multiple convolutional neural networks (MCNN) method, which predicts RNA-protein binding sites by integrating multiple convolutional neural networks constructed by RNA sequence information extracted from windows with different lengths. First, MCNN trains multiple CNNs base on RNA sequences extracted by different window lengths. Second, MCNN can extract more binding patterns of RBPs by combining these trained multiple CNNs previously. Third, MCNN only uses RNA base sequence information for RNA-protein binding sites prediction, which extracts sequence binding features and predicts the result with same architecture. This avoids the information loss of feature extraction step. Our proposed MCNN demonstrates a competitive performance comparing with other methods on a large-scale dataset derived from CLIP-seq, which is an effective method for RNA-protein binding sites prediction. The source code of our proposed MCNN method can be found in https://github.com/biomg/MCNN.
Topics: Protein Binding; RNA; RNA-Binding Proteins; Binding Sites; Neural Networks, Computer
PubMed: 35471886
DOI: 10.1109/TCBB.2022.3170367