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Future Medicinal Chemistry Jul 2020Epigenetic protein-protein interactions (PPIs) play essential roles in regulating gene expression, and their dysregulations have been implicated in many diseases. These... (Review)
Review
Epigenetic protein-protein interactions (PPIs) play essential roles in regulating gene expression, and their dysregulations have been implicated in many diseases. These PPIs are comprised of reader domains recognizing post-translational modifications on histone proteins, and of scaffolding proteins that maintain integrities of epigenetic complexes. Targeting PPIs have become focuses for development of small-molecule inhibitors and anticancer therapeutics. Here we summarize efforts to develop small-molecule inhibitors targeting common epigenetic PPI domains. Potent small molecules have been reported for many domains, yet small domains that recognize methylated lysine side chains on histones are challenging in inhibitor development. We posit that the development of potent inhibitors for difficult-to-prosecute epigenetic PPIs may be achieved by interdisciplinary approaches and extensive explorations of chemical space.
Topics: Epigenesis, Genetic; Humans; Protein Binding; Protein Processing, Post-Translational; Proteins; Small Molecule Libraries
PubMed: 32551894
DOI: 10.4155/fmc-2020-0082 -
Journal of Chemical Information and... Jun 2023Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for...
Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, , for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. is available for free at: https://affinity.cuhk.edu.cn/.
Topics: Protein Binding; Proteins; Machine Learning; Amino Acids; Computers
PubMed: 37235532
DOI: 10.1021/acs.jcim.2c01499 -
Advanced Drug Delivery Reviews Jan 2023For systemically delivered nanoparticles to reach target tissues, they must first circulate long enough to reach the target and extravasate there. A challenge is that... (Review)
Review
For systemically delivered nanoparticles to reach target tissues, they must first circulate long enough to reach the target and extravasate there. A challenge is that the particles end up engaging with serum proteins and undergo immune cell recognition and premature clearance. The serum protein binding, also known as protein corona formation, is difficult to prevent, even with artificial protection via "stealth" coating. Protein corona may be problematic as it can interfere with the interaction of targeting ligands with tissue-specific receptors and abrogate the so-called active targeting process, hence, the efficiency of drug delivery. However, recent studies show that serum protein binding to circulating nanoparticles may be actively exploited to enhance their downstream delivery. This review summarizes known issues of protein corona and traditional strategies to control the corona, such as avoiding or overriding its formation, as well as emerging efforts to enhance drug delivery to target organs via nanoparticles. It concludes with a discussion of prevailing challenges in exploiting protein corona for nanoparticle development.
Topics: Humans; Protein Corona; Blood Proteins; Drug Delivery Systems; Protein Binding; Nanoparticles
PubMed: 36503885
DOI: 10.1016/j.addr.2022.114635 -
Biophysical Journal May 2021Accurate positioning of proteins on chromosomal DNA is crucial for its proper organization as well as gene transcription regulation. Recent experiments revealed...
Accurate positioning of proteins on chromosomal DNA is crucial for its proper organization as well as gene transcription regulation. Recent experiments revealed existence of periodic patterns of nucleoprotein complexes on DNA, which frequently cannot be explained by sequence-dependent binding of proteins. Previous theoretical studies suggest that such patterns typically emerge as a result of the proteins' volume-exclusion effect. However, the role of other physical factors in patterns' formation, such as the length of DNA, its sequence heterogeneity, and protein binding cooperativity/binding competition to DNA, remains unclear. To address these less understood yet important aspects, we investigated potential effects of these factors on protein positioning on finite-size DNA by using transfer-matrix calculations. It has been found that upon binding to DNA, proteins form oscillatory patterns that span over the length of up to ∼10 times the size of the protein binding site, with the shape of the patterns being strongly dependent on the length of DNA and the proteins' binding cooperativity to DNA. Furthermore, calculations showed that small variations in the proteins' affinity to DNA due to its sequence heterogeneity do not much change the main geometric characteristics of the observed protein patterns. Finally, competition between two different types of proteins for binding to DNA has been found to lead to formation of highly diverse and complex alternating positioning of the two proteins. Altogether, these results provide new insights into the roles of physicochemical properties of proteins, the DNA length, and DNA-binding competition between proteins in formation of protein positioning patterns on DNA.
Topics: Binding Sites; Binding, Competitive; DNA; DNA-Binding Proteins; Protein Binding
PubMed: 33771470
DOI: 10.1016/j.bpj.2021.03.016 -
Essays in Biochemistry Aug 2022Almost all interactions and reactions that occur in living organisms involve proteins. The various biological roles of proteins include, but are not limited to, signal...
Almost all interactions and reactions that occur in living organisms involve proteins. The various biological roles of proteins include, but are not limited to, signal transduction, gene transcription, cell death, immune function, structural support, and catalysis of all the chemical reactions that enable organisms to survive. The varied roles of proteins have led to them being dubbed 'the workhorses of all living organisms'. This article discusses the functions of proteins and how protein function is studied in a laboratory setting. In this article, we begin by examining the functions of protein domains, followed by a discussion of some of the major classes of proteins based on their function. We consider protein binding in detail, which is central to protein function. We then examine how protein function can be altered through various mechanisms including post-translational modification, and changes to environment, oligomerisation and mutations. Finally, we consider a handful of the techniques employed in the laboratory to understand and measure the function of proteins.
Topics: Protein Binding; Protein Processing, Post-Translational; Proteins; Signal Transduction
PubMed: 35946411
DOI: 10.1042/EBC20200108 -
The Journal of Physical Chemistry. B Oct 2019Cells of the vast majority of organisms are subject to temperature, pressure, pH, ionic strength, and other stresses. We discuss these effects in the light of protein... (Review)
Review
Cells of the vast majority of organisms are subject to temperature, pressure, pH, ionic strength, and other stresses. We discuss these effects in the light of protein folding and protein interactions , in complex environments, in cells, and . Protein phase diagrams provide a way of organizing different structural ensembles that occur under stress and how one can move among ensembles. Experiments that perturb biomolecules or in cells by stressing them have revealed much about the underlying forces that are competing to control protein stability, folding, and function. Two phenomena that emerge and serve to broadly classify effects of the cellular environment are crowding (mainly due to repulsive forces) and sticking (mainly due to attractive forces). The interior of cells is closely balanced between these emergent effects, and stress can tip the balance one way or the other. The free energy scale involved is small but significant on the scale of the "on/off switches" that control signaling in cells or of protein-protein association with a favorable function such as increased enzyme processivity. Quantitative tools from biophysical chemistry will play an important role in elucidating the world of crowding and sticking under stress.
Topics: Animals; Humans; Protein Binding; Protein Folding; Proteins; Stress, Physiological
PubMed: 31386813
DOI: 10.1021/acs.jpcb.9b05467 -
ELife Sep 2022Secreted proteins, which include cytokines, hormones, and growth factors, are extracellular ligands that control key signaling pathways mediating cell-cell communication...
Secreted proteins, which include cytokines, hormones, and growth factors, are extracellular ligands that control key signaling pathways mediating cell-cell communication within and between tissues and organs. Many drugs target secreted ligands and their cell surface receptors. Still, there are hundreds of secreted human proteins that either have no identified receptors ('orphans') or are likely to act through cell surface receptors that have not yet been characterized. Discovery of secreted ligand-receptor interactions by high-throughput screening has been problematic, because the most commonly used high-throughput methods for protein-protein interaction (PPI) screening are not optimized for extracellular interactions. Cell-based screening is a promising technology for the deorphanization of ligand-receptor interactions, because multimerized ligands can enrich for cells expressing low affinity cell surface receptors, and such methods do not require purification of receptor extracellular domains. Here, we present a proteo-genomic cell-based CRISPR activation (CRISPRa) enrichment screening platform employing customized pooled cell surface receptor sgRNA libraries in combination with a magnetic bead selection-based enrichment workflow for rapid, parallel ligand-receptor deorphanization. We curated 80 potentially high-value orphan secreted proteins and ultimately screened 20 secreted ligands against two cell sgRNA libraries with targeted expression of all single-pass (TM1) or multi-pass transmembrane (TM2+) receptors by CRISPRa. We identified previously unknown interactions in 12 of these screens, and validated several of them using surface plasmon resonance and/or cell binding assays. The newly deorphanized ligands include three receptor protein tyrosine phosphatase (RPTP) ligands and a chemokine-like protein that binds to killer immunoglobulin-like receptors (KIRs). These new interactions provide a resource for future investigations of interactions between the human-secreted and membrane proteomes.
Topics: Humans; Ligands; Proteome; Clustered Regularly Interspaced Short Palindromic Repeats; Receptors, Cell Surface; Protein Binding; Cytokines; Hormones; Immunoglobulins
PubMed: 36178190
DOI: 10.7554/eLife.81398 -
Briefings in Bioinformatics Nov 2023Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on...
Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.
Topics: Humans; Protein Binding; Spike Glycoprotein, Coronavirus; Mutation; Bias
PubMed: 38197311
DOI: 10.1093/bib/bbad491 -
Genomics, Proteomics & Bioinformatics Dec 2021The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity,...
The cellular functions of proteins are maintained by forming diverse complexes. The stability of these complexes is quantified by the measurement of binding affinity, and mutations that alter the binding affinity can cause various diseases such as cancer and diabetes. As a result, accurate estimation of the binding stability and the effects of mutations on changes of binding affinity is a crucial step to understanding the biological functions of proteins and their dysfunctional consequences. It has been hypothesized that the stability of a protein complex is dependent not only on the residues at its binding interface by pairwise interactions but also on all other remaining residues that do not appear at the binding interface. Here, we computationally reconstruct the binding affinity by decomposing it into the contributions of interfacial residues and other non-interfacial residues in a protein complex. We further assume that the contributions of both interfacial and non-interfacial residues to the binding affinity depend on their local structural environments such as solvent-accessible surfaces and secondary structural types. The weights of all corresponding parameters are optimized by Monte-Carlo simulations. After cross-validation against a large-scale dataset, we show that the model not only shows a strong correlation between the absolute values of the experimental and calculated binding affinities, but can also be an effective approach to predict the relative changes of binding affinity from mutations. Moreover, we have found that the optimized weights of many parameters can capture the first-principle chemical and physical features of molecular recognition, therefore reversely engineering the energetics of protein complexes. These results suggest that our method can serve as a useful addition to current computational approaches for predicting binding affinity and understanding the molecular mechanism of protein-protein interactions.
Topics: Protein Binding; Proteins
PubMed: 33838354
DOI: 10.1016/j.gpb.2021.03.004 -
Journal of Chemical Information and... Nov 2022The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology....
The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.
Topics: Neural Networks, Computer; Binding Sites; Protein Binding; Proteins; Machine Learning
PubMed: 36341715
DOI: 10.1021/acs.jcim.2c00832