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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 -
Cells Jul 2022Protein-protein interactions (PPIs) are responsible for various essential biological processes. This information can help develop a new drug against diseases. Various...
Protein-protein interactions (PPIs) are responsible for various essential biological processes. This information can help develop a new drug against diseases. Various experimental methods have been employed for this purpose; however, their application is limited by their cost and time consumption. Alternatively, computational methods are considered viable means to achieve this crucial task. Various techniques have been explored in the literature using the sequential information of amino acids in a protein sequence, including machine learning and deep learning techniques. The current efficiency of interaction-site prediction still has growth potential. Hence, a deep neural network-based model, ProB-site, is proposed. ProB-site utilizes sequential information of a protein to predict its binding sites. The proposed model uses evolutionary information and predicted structural information extracted from sequential information of proteins, generating three unique feature sets for every amino acid in a protein sequence. Then, these feature sets are fed to their respective sub-CNN architecture to acquire complex features. Finally, the acquired features are concatenated and classified using fully connected layers. This methodology performed better than state-of-the-art techniques because of the selection of the best features and contemplation of local information of each amino acid.
Topics: Amino Acids; Binding Sites; Neural Networks, Computer; Protein Binding; Proteins
PubMed: 35805201
DOI: 10.3390/cells11132117 -
Journal of Chemical Theory and... Mar 2022Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven...
Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed "PPI-GaMD", selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein's flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.
Topics: Kinetics; Molecular Dynamics Simulation; Protein Binding; Static Electricity; Thermodynamics
PubMed: 35099970
DOI: 10.1021/acs.jctc.1c00974 -
Cells May 2020Cisplatin is a widely used drug in the treatment of various solid tumors, such as ovarian cancer. However, while the acquired resistance significantly limits the success...
Cisplatin is a widely used drug in the treatment of various solid tumors, such as ovarian cancer. However, while the acquired resistance significantly limits the success of therapy, some tumors, such as colorectal cancer, are intrinsically insensitive to cisplatin. Only a small amount of intracellular platinum binds to the target-genomic DNA. The fate of the remaining drug is largely obscure. This work aimed to identify the cytosolic protein binding partners of cisplatin in ovarian and colorectal cancer cells and to evaluate their relevance for cell sensitivity to cisplatin and oxaliplatin. Using the fluorescent cisplatin analog BODIPY-cisplatin, two-dimensional gel electrophoresis, and mass spectrometry, we identified the protein binding partners in A2780 and cisplatin-resistant A2780cis ovarian carcinoma, as well as in HCT-8 and oxaliplatin-resistant HCT-8ox colorectal cell lines. Vimentin, only identified in ovarian cancer cells; growth factor receptor-bound protein 2, only identified in colorectal cancer cells; and glutathione-S-transferase π, identified in all four cell lines, were further investigated. The effect of pharmacological inhibition and siRNA-mediated knockdown on cytotoxicity was studied to assess the relevance of these binding partners. The silencing of glutathione-S-transferase π significantly sensitized intrinsically resistant HCT-8 and HCT-8ox cells to cisplatin, suggesting a possible involvement of the protein in the resistance of colorectal cancer cells to the drug. The inhibition of vimentin with FiVe1 resulted in a significant sensitization of A2780 and A2780cis cells to cisplatin, revealing new possibilities for improving the chemosensitivity of ovarian cancer cells.
Topics: Boron Compounds; Cell Death; Cell Line, Tumor; Cisplatin; Fluorescent Dyes; GRB2 Adaptor Protein; Gene Knockdown Techniques; Glutathione S-Transferase pi; Humans; Protein Binding; Vimentin
PubMed: 32466394
DOI: 10.3390/cells9061322 -
Clinical Microbiology and Infection :... Mar 2022The aim of this study was to develop a mechanistic protein-binding model to predict the unbound flucloxacillin concentrations in different patient populations. (Meta-Analysis)
Meta-Analysis
OBJECTIVES
The aim of this study was to develop a mechanistic protein-binding model to predict the unbound flucloxacillin concentrations in different patient populations.
METHODS
A mechanistic protein-binding model was fitted to the data using non-linear mixed-effects modelling. Data were obtained from four datasets, containing 710 paired total and unbound flucloxacillin concentrations from healthy volunteers, non-critically ill and critically ill patients. A fifth dataset with data from hospitalized patients was used for evaluation of our model. The predictive performance of the mechanistic model was evaluated and compared with the calculation of the unbound concentration with a fixed unbound fraction of 5%. Finally, we performed a fit-for-use evaluation, verifying whether the model-predicted unbound flucloxacillin concentrations would lead to clinically incorrect dose adjustments.
RESULTS
The mechanistic protein-binding model predicted the unbound flucloxacillin concentrations more accurately than assuming an unbound fraction of 5%. The mean prediction error varied between -26.2% to 27.8% for the mechanistic model and between -30.8% to 83% for calculation with a fixed factor of 5%. The normalized root mean squared error varied between 36.8% and 69% respectively between 57.1% and 134%. Predicting the unbound concentration with the use of the mechanistic model resulted in 6.1% incorrect dose adjustments versus 19.4% if calculated with a fixed unbound fraction of 5%.
CONCLUSIONS
Estimating the unbound concentration with a mechanistic protein-binding model outperforms the calculation with the use of a fixed protein binding factor of 5%, but neither demonstrates acceptable performance. When performing dose individualization of flucloxacillin, this should be done based on measured unbound concentrations rather than on estimated unbound concentrations from the measured total concentrations. In the absence of an assay for unbound concentrations, the mechanistic binding model should be preferred over assuming a fixed unbound fraction of 5%.
Topics: Anti-Bacterial Agents; Critical Illness; Floxacillin; Healthy Volunteers; Humans; Protein Binding
PubMed: 34245903
DOI: 10.1016/j.cmi.2021.06.039 -
Bioinformatics (Oxford, England) Nov 2021Mutations that alter protein-DNA interactions may be pathogenic and cause diseases. Therefore, it is extremely important to quantify the effect of mutations on...
MOTIVATION
Mutations that alter protein-DNA interactions may be pathogenic and cause diseases. Therefore, it is extremely important to quantify the effect of mutations on protein-DNA binding free energy to reveal the molecular origin of diseases and to assist the development of treatments. Although several methods that predict the change of protein-DNA binding affinity upon mutations in the binding protein were developed, the effect of DNA mutations was not considered yet.
RESULTS
Here, we report a new version of SAMPDI, the SAMPDI-3D, which is a gradient boosting decision tree machine learning method to predict the change of the protein-DNA binding free energy caused by mutations in both the binding protein and the bases of the corresponding DNA. The method is shown to achieve Pearson correlation coefficient of 0.76 and 0.80 in a benchmarking test against experimentally determined change of the binding free energy caused by mutations in the binding protein or DNA, respectively. Furthermore, three datasets collected from literature were used to do blind benchmark for SAMPDI-3D and it is shown that it outperforms all existing state-of-the-art methods. The method is very fast allowing for genome-scale investigations.
AVAILABILITYAND IMPLEMENTATION
It is available as a web server and a stand-code at http://compbio.clemson.edu/SAMPDI-3D/.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Software; Proteins; Mutation; Protein Binding; DNA
PubMed: 34343273
DOI: 10.1093/bioinformatics/btab567 -
International Journal of Molecular... Mar 2023The interaction between transcription factors (TFs) and DNA is the core process that determines the state of a cell's transcriptome [...].
The interaction between transcription factors (TFs) and DNA is the core process that determines the state of a cell's transcriptome [...].
Topics: Binding Sites; Transcription Factors; Protein Binding; DNA; Genetic Variation; Transcription, Genetic
PubMed: 36902467
DOI: 10.3390/ijms24055038 -
The Protein Journal Aug 2023Due to the importance of protein-protein interactions in defence mechanism of living body, attempts were made to investigate its attributes, including, but not limited...
Due to the importance of protein-protein interactions in defence mechanism of living body, attempts were made to investigate its attributes, including, but not limited to, binding affinity, and binding region. Contemporary strategies for binding site prediction largely resort to deep learning techniques but turned out to be low precision models. As laboratory experiments for drug discovery tasks utilize this information, increased false positives devalue the computational methods. This emphasize the need to develop enhanced strategies. DeepBindPPI employs deep learning technique to predict the binding regions of proteins, particularly antigen-antibody interaction sites. The results obtained are applied in a docking environment to confirm their correctness. An integration of graph convolutional network with attention mechanism predicts interacting amino acids with improved precision. The model learns the determining factors in interaction from a general pool of proteins and is then fine-tuned using antigen-antibody data. Comparison of the proposed method with existing techniques shows that the developed model has comparable performance. The use of a separate spatial network clearly improved the precision of the proposed method from 0.4 to 0.5. An attempt to utilize the interface information for docking using the HDOCK server gives promising results, with high-quality structures appearing in the top10 ranks.
Topics: Protein Binding; Binding Sites; Amino Acids; Drug Discovery; Protein Domains
PubMed: 37198346
DOI: 10.1007/s10930-023-10121-9 -
Journal of Pharmaceutical Sciences Oct 2021Over the last few decades, scientists and clinicians have often focused their attention on the unbound fraction of drugs as an indicator of efficacy and the eventual... (Review)
Review
Over the last few decades, scientists and clinicians have often focused their attention on the unbound fraction of drugs as an indicator of efficacy and the eventual outcome of drug treatments for specific illnesses. Typically, the total drug concentration (bound and unbound) in plasma is used in clinical trials to assess a compound's efficacy. However, the free concentration of a drug tends to be more closely related to its activity and interaction with the body. Thus far, measuring the unbound concentration has been a challenge. Several mechanistic models have attempted to solve this problem by estimating the free drug fraction from available data such as total drug and binding protein concentrations. The aims of this review are first, to give an overview of the methods that have been used to date to calculate the unbound drug fraction. Second, to assess the pharmacokinetic parameters affected by changes in drug protein binding in special populations such as pediatrics, the elderly, pregnancy, and obesity. Third, to review alterations in drug protein binding in some selected disease states and how these changes impact the clinical outcomes for the patients; the disease states include critical illnesses, transplantation, renal failure, chronic kidney disease, and epilepsy. And finally, to discuss how various disease states shift the ratio of unbound to total drug and the consequences of such shifts on dosing adjustments and reaching the therapeutic target.
Topics: Aged; Child; Female; Humans; Pharmaceutical Preparations; Plasma; Pregnancy; Protein Binding
PubMed: 34089711
DOI: 10.1016/j.xphs.2021.05.018 -
Journal of Chemical Information and... Sep 2022Herein, we introduce a new strategy to estimate binding free energies using end-state molecular dynamics simulation trajectories. The method is adopted from linear...
Herein, we introduce a new strategy to estimate binding free energies using end-state molecular dynamics simulation trajectories. The method is adopted from linear interaction energy (LIE) and ANI-2x neural network potentials (machine learning) for the atomic simulation environment (ASE). It predicts the single-point interaction energies between ligand-protein and ligand-solvent pairs at the accuracy of the wb97x/6-31G* level for the conformational space that is sampled by molecular dynamics (MD) simulations. Our results on 54 protein-ligand complexes show that the method can be accurate and have a correlation of = 0.87-0.88 to the experimental binding free energies, outperforming current end-state methods with reduced computational cost. The method also allows us to compare BFEs of ligands with different scaffolds. The code is available free of charge (documentation and test files) at https://github.com/otayfuroglu/deepQM.
Topics: Ligands; Molecular Dynamics Simulation; Protein Binding; Proteins; Thermodynamics
PubMed: 35972783
DOI: 10.1021/acs.jcim.2c00601