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The Journal of Biological Chemistry Aug 2021RNA-binding proteins play crucial roles in various cellular functions and contain abundant disordered protein regions. The disordered regions in RNA-binding proteins are...
RNA-binding proteins play crucial roles in various cellular functions and contain abundant disordered protein regions. The disordered regions in RNA-binding proteins are rich in repetitive sequences, such as poly-K/R, poly-N/Q, poly-A, and poly-G residues. Our bioinformatic analysis identified a largely neglected repetitive sequence family we define as electronegative clusters (ENCs) that contain acidic residues and/or phosphorylation sites. The abundance and length of ENCs exceed other known repetitive sequences. Despite their abundance, the functions of ENCs in RNA-binding proteins are still elusive. To investigate the impacts of ENCs on protein stability, RNA-binding affinity, and specificity, we selected one RNA-binding protein, the ribosomal biogenesis factor 15 (Nop15), as a model. We found that the Nop15 ENC increases protein stability and inhibits nonspecific RNA binding, but minimally interferes with specific RNA binding. To investigate the effect of ENCs on sequence specificity of RNA binding, we grafted an ENC to another RNA-binding protein, Ser/Arg-rich splicing factor 3. Using RNA Bind-n-Seq, we found that the engineered ENC inhibits disparate RNA motifs differently, instead of weakening all RNA motifs to the same extent. The motif site directly involved in electrostatic interaction is more susceptible to the ENC inhibition. These results suggest that one of functions of ENCs is to regulate RNA binding via electrostatic interaction. This is consistent with our finding that ENCs are also overrepresented in DNA-binding proteins, whereas underrepresented in halophiles, in which nonspecific nucleic acid binding is inhibited by high concentrations of salts.
Topics: Amino Acid Sequence; Computational Biology; Intrinsically Disordered Proteins; Protein Binding; RNA-Binding Proteins
PubMed: 34246632
DOI: 10.1016/j.jbc.2021.100945 -
Biomolecules Dec 2022Intrinsically disordered proteins (IDPs) are important in both normal and disease states. Small molecules can be targeted to disordered regions, but we currently have...
Intrinsically disordered proteins (IDPs) are important in both normal and disease states. Small molecules can be targeted to disordered regions, but we currently have only a limited understanding of the nature of small-molecule binding sites in IDPs. Here, we show that a minimal small-molecule binding sequence of eight contiguous residues derived from the Myc protein can be ported into a different disordered protein and recapitulate small-molecule binding activity in the new context. We also find that the residue immediately flanking the binding site can have opposing effects on small-molecule binding in the different disordered protein contexts. The results demonstrate that small-molecule binding sites can act modularly and are portable between disordered protein contexts but that residues outside of the minimal binding site can modulate binding affinity.
Topics: Intrinsically Disordered Proteins; Binding Sites; Biophysical Phenomena; Protein Binding
PubMed: 36551315
DOI: 10.3390/biom12121887 -
Current Opinion in Structural Biology Jun 2022Protein-protein interactions (PPIs) govern numerous cellular functions in terms of signaling, transport, defense and many others. Designing novel PPIs poses a... (Review)
Review
Protein-protein interactions (PPIs) govern numerous cellular functions in terms of signaling, transport, defense and many others. Designing novel PPIs poses a fundamental challenge to our understanding of molecular interactions. The capability to robustly engineer PPIs has immense potential for the development of novel synthetic biology tools and protein-based therapeutics. Over the last decades, many efforts in this area have relied purely on experimental approaches, but more recently, computational protein design has made important contributions. Template-based approaches utilize known PPIs and transplant the critical residues onto heterologous scaffolds. De novo design instead uses computational methods to generate novel binding motifs, allowing for a broader scope of the sites engaged in protein targets. Here, we review successful design cases, giving an overview of the methodological approaches used for templated and de novo PPI design.
Topics: Computational Biology; Protein Binding; Proteins
PubMed: 35405427
DOI: 10.1016/j.sbi.2022.102370 -
Biochimica Et Biophysica Acta.... Mar 2022Acute kidney injury (AKI) is both a consequence and determinant of outcomes in COVID-19. The kidney is one of the major organs infected by the causative virus,...
BACKGROUND
Acute kidney injury (AKI) is both a consequence and determinant of outcomes in COVID-19. The kidney is one of the major organs infected by the causative virus, SARS-CoV-2. Viral entry into cells requires the viral spike protein, and both the virus and its spike protein appear in the urine of COVID-19 patients with AKI. We examined the effects of transfecting the viral spike protein of SARS-CoV-2 in kidney cell lines.
METHODS
HEK293, HEK293-ACE2 (stably overexpressing ACE2), and Vero E6 cells having endogenous ACE2 were transfected with SARS-CoV-2 spike or control plasmid. Assessment of gene and protein expression, and syncytia formation was performed, and the effects of quercetin on syncytia formation examined.
FINDINGS
Spike transfection in HEK293-ACE2 cells caused syncytia formation, cellular sloughing, and focal denudation of the cell monolayer; transfection in Vero E6 cells also caused syncytia formation. Spike expression upregulated potentially nephrotoxic genes (TNF-α, MCP-1, and ICAM1). Spike upregulated the cytoprotective gene HO-1 and relevant signaling pathways (p-Akt, p-STAT3, and p-p38). Quercetin, an HO-1 inducer, reduced syncytia formation and spike protein expression.
INTERPRETATION
The major conclusions of the study are: 1) Spike protein expression in kidney cells provides a relevant model for the study of maladaptive and adaptive responses germane to AKI in COVID-19; 2) such spike protein expression upregulates HO-1; and 3) quercetin, an HO-1 inducer, may provide a clinically relevant/feasible protective strategy in AKI occurring in the setting of COVID-19.
FUNDING
R01-DK119167 (KAN), R01-AI100911 (JPG), P30-DK079337; R01-DK059600 (AA).
Topics: Animals; COVID-19; Cell Line; Chlorocebus aethiops; HEK293 Cells; Heme Oxygenase-1; Host-Pathogen Interactions; Humans; Protein Binding; Quercetin; SARS-CoV-2; Signal Transduction; Spike Glycoprotein, Coronavirus; Up-Regulation; Vero Cells; Virus Internalization
PubMed: 34920080
DOI: 10.1016/j.bbadis.2021.166322 -
Scientific Reports Jul 2022What are the molecular determinants of protein-protein binding affinity and whether they are similar to those regulating fold stability are two major questions of...
What are the molecular determinants of protein-protein binding affinity and whether they are similar to those regulating fold stability are two major questions of molecular biology, whose answers bring important implications both from a theoretical and applicative point of view. Here, we analyze chemical and physical features on a large dataset of protein-protein complexes with reliable experimental binding affinity data and compare them with a set of monomeric proteins for which melting temperature data was available. In particular, we probed the spatial organization of protein (1) intramolecular and intermolecular interaction energies among residues, (2) amino acidic composition, and (3) their hydropathy features. Analyzing the interaction energies, we found that strong Coulombic interactions are preferentially associated with a high protein thermal stability, while strong intermolecular van der Waals energies correlate with stronger protein-protein binding affinity. Statistical analysis of amino acids abundances, exposed to the molecular surface and/or in interaction with the molecular partner, confirmed that hydrophobic residues present on the protein surfaces are preferentially located in the binding regions, while charged residues behave oppositely. Leveraging on the important role of van der Waals interface interactions in binding affinity, we focused on the molecular surfaces in the binding regions and evaluated their shape complementarity, decomposing the molecular patches in the 2D Zernike basis. For the first time, we quantified the correlation between local shape complementarity and binding affinity via the Zernike formalism. In addition, considering the solvent interactions via the residue hydropathy, we found that the hydrophobicity of the binding regions dictates their shape complementary as much as the correlation between van der Waals energy and binding affinity. In turn, these relationships pave the way to the fast and accurate prediction and design of optimal binding regions as the 2D Zernike formalism allows a rapid and superposition-free comparison between possible binding surfaces.
Topics: Amino Acids; Hydrophobic and Hydrophilic Interactions; Membrane Proteins; Protein Binding; Protein Stability; Thermodynamics
PubMed: 35840609
DOI: 10.1038/s41598-022-16338-5 -
Biophysical Journal Sep 2020We report the free-energy landscape and thermodynamics of the protein-protein association responsible for the drug-induced multimerization of HIV-1 integrase (IN)....
We report the free-energy landscape and thermodynamics of the protein-protein association responsible for the drug-induced multimerization of HIV-1 integrase (IN). Allosteric HIV-1 integrase inhibitors promote aberrant IN multimerization by bridging IN-IN intermolecular interactions. However, the thermodynamic driving forces and kinetics of the multimerization remain largely unknown. Here, we explore the early steps in the IN multimerization by using umbrella sampling and unbiased molecular dynamics simulations in explicit solvent. In direct simulations, the two initially separated dimers spontaneously associate to form near-native complexes that resemble the crystal structure of the aberrant tetramer. Most strikingly, the effective interaction of the protein-protein association is very short-ranged: the two dimers associate rapidly within tens of nanoseconds when their binding surfaces are separated by d ≤ 4.3 Å (less than two water diameters). Beyond this distance, the oligomerization kinetics appears to be diffusion controlled with a much longer association time. The free-energy profile also captured the crucial role of allosteric IN inhibitors in promoting multimerization and explained why several C-terminal domain mutations are remarkably resistant to the drug-induced multimerization. The results also show that at small separation, the protein-protein binding process contains two consecutive phases with distinct thermodynamic signatures. First, interprotein water molecules are expelled to the bulk, resulting in a small increase in entropy, as the solvent entropy gain from the water release is nearly cancelled by the loss of side-chain entropies as the two proteins approach each other. At shorter distances, the two dry binding surfaces adapt to each other to optimize their interaction energy at the expense of further protein configurational entropy loss. Although the binding interfaces feature clusters of hydrophobic residues, overall, the protein-protein association in this system is driven by enthalpy and opposed by entropy.
Topics: Entropy; Molecular Dynamics Simulation; Protein Binding; Proteins; Thermodynamics
PubMed: 32877664
DOI: 10.1016/j.bpj.2020.08.005 -
BMC Bioinformatics Sep 2022Protein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the...
Protein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the past two decades, it is still a challenging problem, characterized by the still limited understanding of the energetics between proteins and ligands, and the vast conformational space that has to be searched to find a satisfactory solution. In this project, we developed a novel reinforcement learning (RL) approach, the asynchronous advantage actor-critic model (A3C), to address the protein ligand docking problem. The overall framework consists of two models. During the search process, the agent takes an action selected by the actor model based on the current location. The critic model then evaluates this action and predict the distance between the current location and true binding site. Experimental results showed that in both single- and multi-atom cases, our model improves binding site prediction substantially compared to a naïve model. For the single-atom ligand, copper ion (Cu), the model predicted binding sites have a median root-mean-square-deviation (RMSD) of 2.39 Å to the true binding sites when starting from random starting locations. For the multi-atom ligand, sulfate ion (SO), the predicted binding sites have a median RMSD of 3.82 Å to the true binding sites. The ligand-specific models built in this study can be used in solvent mapping studies and the RL framework can be readily scaled up to larger and more diverse sets of ligands.
Topics: Binding Sites; Drug Design; Ligands; Molecular Docking Simulation; Protein Binding; Proteins
PubMed: 36076158
DOI: 10.1186/s12859-022-04912-7 -
Bioinformatics (Oxford, England) Aug 2023Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully...
MOTIVATION
Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand.
RESULTS
The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset.
AVAILABILITY AND IMPLEMENTATION
Code and data uploaded to https://github.com/abbiAR/PLBAffinity.
Topics: Molecular Docking Simulation; Ligands; Protein Binding; Proteins; Machine Learning
PubMed: 37572302
DOI: 10.1093/bioinformatics/btad502 -
Bioinformatics (Oxford, England) Jul 2019Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein-protein interactions, helps protein-protein...
MOTIVATION
Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein-protein interactions, helps protein-protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.
RESULTS
We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein-protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.
AVAILABILITY AND IMPLEMENTATION
SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Computational Biology; DNA; Humans; Protein Binding; Proteome; RNA; Sequence Analysis, Protein; Software
PubMed: 31510679
DOI: 10.1093/bioinformatics/btz324 -
Cellular and Molecular Life Sciences :... Nov 2021The central role of eukaryotic translation initiation factor 4E (eIF4E) in controlling mRNA translation has been clearly assessed in the last decades. eIF4E function is... (Review)
Review
The central role of eukaryotic translation initiation factor 4E (eIF4E) in controlling mRNA translation has been clearly assessed in the last decades. eIF4E function is essential for numerous physiological processes, such as protein synthesis, cellular growth and differentiation; dysregulation of its activity has been linked to ageing, cancer onset and progression and neurodevelopmental disorders, such as autism spectrum disorder (ASD) and Fragile X Syndrome (FXS). The interaction between eIF4E and the eukaryotic initiation factor 4G (eIF4G) is crucial for the assembly of the translational machinery, the initial step of mRNA translation. A well-characterized group of proteins, named 4E-binding proteins (4E-BPs), inhibits the eIF4E-eIF4G interaction by competing for the same binding site on the eIF4E surface. 4E-BPs and eIF4G share a single canonical motif for the interaction with a conserved hydrophobic patch of eIF4E. However, a second non-canonical and not conserved binding motif was recently detected for eIF4G and several 4E-BPs. Here, we review the structural features of the interaction between eIF4E and its molecular partners eIF4G and 4E-BPs, focusing on the implications of the recent structural and biochemical evidence for the development of new therapeutic strategies. The design of novel eIF4E-targeting molecules that inhibit translation might provide new avenues for the treatment of several conditions.
Topics: Amino Acid Sequence; Animals; Binding Sites; Eukaryotic Initiation Factor-4E; Humans; Neurodevelopmental Disorders; Protein Binding; Protein Biosynthesis
PubMed: 34541613
DOI: 10.1007/s00018-021-03938-z