Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies.
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ABSTRACT:
SUBMITTER: Xu X
PROVIDER: S-EPMC8625032 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Retinoids are a class of compounds that are chemically related to vitamin A, which is an essential nutrient that plays a key role in vision, cell growth and differentiation. In vivo, retinoids must bind with specific proteins to perform their necessary functions. Plasma retinol-binding protein (RBP) and epididymal retinoic acid binding protein (ERABP) carry retinoids in bodily fluids, while cellular retinol-binding proteins (CRBPs) and cellular retinoic acid-binding proteins (CRABPs) carry retinoids within cells. Interestingly, although all of these transport proteins possess similar structures, the modes of binding for the different retinoid ligands with their carrier proteins are different. METHODOLOGY/PRINCIPAL FINDINGS: In this work, we analyzed the various retinoid transport mechanisms using structure and sequence comparisons, binding site analyses and molecular dynamics simulations. Our results show that in the same family of proteins and subcellular location, the orientation of a retinoid molecule within a binding protein is same, whereas when different families of proteins are considered, the orientation of the bound retinoid is completely different. In addition, none of the amino acid residues involved in ligand binding is conserved between the transport proteins. However, for each specific binding protein, the amino acids involved in the ligand binding are conserved. The results of this study allow us to propose a possible transport model for retinoids. CONCLUSIONS/SIGNIFICANCE: Our results reveal the differences in the binding modes between the different retinoid-binding proteins.
| S-EPMC3344936 | biostudies-literature
Project description:BackgroundMany computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power.ResultsHere we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction.ConclusionsUsing the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.
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Project description:It is often assumed that in the Protein Data Bank (PDB), two proteins with similar sequences will also have similar structures. Accordingly, it has proved useful to develop subsets of the PDB from which "redundant" structures have been removed, based on a sequence-based criterion for similarity. Similarly, when predicting protein structure using homology modeling, if a template structure for modeling a target sequence is selected by sequence alone, this implicitly assumes that all sequence-similar templates are equivalent. Here, we show that this assumption is often not correct and that standard approaches to create subsets of the PDB can lead to the loss of structurally and functionally important information. We have carried out sequence-based structural superpositions and geometry-based structural alignments of a large number of protein pairs to determine the extent to which sequence similarity ensures structural similarity. We find many examples where two proteins that are similar in sequence have structures that differ significantly from one another. The source of the structural differences usually has a functional basis. The number of such proteins pairs that are identified and the magnitude of the dissimilarity depend on the approach that is used to calculate the differences; in particular sequence-based structure superpositioning will identify a larger number of structurally dissimilar pairs than geometry-based structural alignments. When two sequences can be aligned in a statistically meaningful way, sequence-based structural superpositioning provides a meaningful measure of structural differences. This approach and geometry-based structure alignments reveal somewhat different information and one or the other might be preferable in a given application. Our results suggest that in some cases, notably homology modeling, the common use of nonredundant datasets, culled from the PDB based on sequence, may mask important structural and functional information. We have established a data base of sequence-similar, structurally dissimilar protein pairs that will help address this problem (http://luna.bioc.columbia.edu/rachel/seqsimstrdiff.htm).
| S-EPMC2673347 | biostudies-literature
Project description:Growth differentiation factor 11 (GDF11) and myostatin (MSTN) are closely related TGFβ family members that are often believed to serve similar functions due to their high homology. However, genetic studies in animals provide clear evidence that they perform distinct roles. While the loss of Mstn leads to hypermuscularity, the deletion of Gdf11 results in abnormal skeletal patterning and organ development. The perinatal lethality of Gdf11-null mice, which contrasts with the long-term viability of Mstn-null mice, has led most research to focus on utilizing recombinant GDF11 proteins to investigate the postnatal functions of GDF11. However, the reported outcomes of the exogenous application of recombinant GDF11 proteins are controversial partly because of the different sources and qualities of recombinant GDF11 used and because recombinant GDF11 and MSTN proteins are nearly indistinguishable due to their similar structural and biochemical properties. Here, we analyze the similarities and differences between GDF11 and MSTN from an evolutionary point of view and summarize the current understanding of the biological processing, signaling, and physiological functions of GDF11 and MSTN. Finally, we discuss the potential use of recombinant GDF11 as a therapeutic option for a wide range of medical conditions and the possible adverse effects of GDF11 inhibition mediated by MSTN inhibitors.
| S-EPMC8080601 | biostudies-literature
Project description:Solving the structure of an antibody-antigen complex gives atomic level information of the interactions between an antibody and its antigen, but such structures are expensive and hard to obtain. Alternative experimental sources include epitope mapping and binning experiments, which can be used as a surrogate to identify key interacting residues. However, their resolution is usually not sufficient to identify if two antibodies have identical interactions. Computational approaches to this problem have so far been based on the premise that antibodies with similar sequences behave similarly. Such approaches will fail to identify sequence-distant antibodies that target the same epitope. Here, we present Ab-Ligity, a structure-based similarity measure tailored to antibody-antigen interfaces. Using predicted paratopes on model antibody structures, we assessed its ability to identify those antibodies that target highly similar epitopes. Most antibodies adopting similar binding modes can be identified from sequence similarity alone, using methods such as clonotyping. In the challenging subset of antibodies whose sequences differ significantly, Ab-Ligity is still able to predict antibodies that would bind to highly similar epitopes (precision of 0.95 and recall of 0.69). We compared Ab-Ligity's performance to an existing tool for comparing general protein interfaces, InterComp, and showed improved performance on antibody cases achieved in a substantially reduced time. These results suggest that Ab-Ligity will allow the identification of diverse (sequence-dissimilar) antibodies that bind to the same epitopes from large datasets such as immune repertoires. The tool is available at http://opig.stats.ox.ac.uk/resources.
| S-EPMC7833755 | biostudies-literature
Project description:For a more detailed description of the interaction between residues, this paper proposes an amino acid network model, which contains two types of weight--similar weight and dissimilar weight. The weight of the link is based on a self-consistent statistical contact potential between different types of amino acids. In this model, we can get a more reasonable representation of the distance between residues. Furthermore, with the network parameter, average shortest path length, we can get a more accurate reflection of the molecular size. This amino acid network is a "small-world" network, and the network parameter is sensitive to the conformation change of protein. For some disease-related proteins, the highly central residues of the amino acid network are highly correlated with the hot spots. In the compound with the related drug, these residues either interacted directly with the drug or with the residue which is in contact with the drug.
| S-EPMC3549380 | biostudies-literature
Project description:BackgroundScaffolding proteins of the intersectin (ITSN) family, ITSN1 and ITSN2, are crucial for the initiation stage of clathrin-mediated endocytosis. These proteins are closely related but have implications in distinct pathologies. To determine how these proteins could be separated in certain cell pathways we performed a comparative study of ITSNs.Methodology/principal findingsWe have shown that endogenous ITSN1 and ITSN2 colocalize and form a complex in cells. A structural comparison of five SH3 domains, which mediated most ITSNs protein-protein interactions, demonstrated a similarity of their ligand-binding sites. We showed that the SH3 domains of ITSN2 bound well-established interactors of ITSN1 as well as newly identified ITSNs protein partners. A search for a novel interacting interface revealed multiple tyrosines that could be phosphorylated in ITSN2. Phosphorylation of ITSN2 isoforms but not ITSN1 short isoform was observed in various cell lines. EGF stimulation of HeLa cells enhanced tyrosine phosphorylation of ITSN2 isoforms and enabled their recognition by the SH2 domains of the Fyn, Fgr and Abl1 kinases, the regulatory subunit of PI3K, the adaptor proteins Grb2 and Crk, and phospholipase C gamma. The SH2 domains mentioned were unable to bind ITSN1 short isoform.Conclusions/significanceOur results indicate that during evolution of vertebrates ITSN2 acquired a novel protein-interaction interface that allows its specific recognition by the SH2 domains of signaling proteins. We propose that these data could be important to understand the functional diversity of paralogous ITSN proteins.
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Project description:The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.
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Project description:Synthetic equivalents of phosphoprotein-specific antibodies would be valuable reagents for biological research, since these antibodies can often be difficult to produce. Protein phosphorylation is thought to result in significant conformational changes in most substrate proteins. Therefore, one approach might be to simply screen combinatorial libraries for ligands to the phosphorylated state in the hope of isolating a ligand that binds to a pocket created by the conformational shift. In this study, we probe this strategy by screening a peptoid library for ligands to the phosphorylated form of the Brd4 chromatin adaptor and transcriptional coactivator protein. We find that peptoids with high selectivity for binding to the phosphorylation form of Brd4 can indeed be isolated in this screen. Moreover, these ligands do not bind promiscuously to other phospho-proteins. However, attempts to employ these reagents as antibody substitutes in an immunoaffinity purification-like application showed that they do not perform as well as bona fide antibodies and that significant optimization will be required. This study highlights the potential and current limitations of a naïve library screening strategy for phosphoprotein-specific antibody surrogates.
| S-EPMC3591469 | biostudies-literature
Project description:Many bacterial pathogens express small G5 domains that exist in the context of various membrane-anchored proteins and these G5 domains have been associated with colonization, cellular adhesion, and biofilm formation. However, despite over a decade since the computational prediction of these G5 domains, many remain uncharacterized, particularly those from Streptococcus pneumoniae. Of five previously predicted G5 domains we found that four of these, all derived from S. pneumoniae, are independently folded modules. As one of these exhibits extreme line broadening due to self-association, we were able to use NMR solution studies to probe the potential ligand interactions of the remaining three G5 domains. None of these G5 domains engage N-acetylglucosamine (NAG) as previously predicted but do interact with other small molecules that may modulate adherence to both bacteria and host cells. Specifically, while all G5 domains tested engage Zn, only one of these G5 domains engage heparin. NMR solution structural studies of the IgA1 Protease G5 (IgA1P-G5) and endo-beta-N-acetylglucosaminidase-D G5 (ENDD-G5) also facilitated identification of the ligand binding sites and confirm the typical G5 fold that comprises two connected β-sheets with no canonical core. NMR relaxation experiments indicate flexibility on both ends and within the connecting regions between the β-sheets. Our studies thus establish a basis for future biological experiments to test whether the ligands presented here are involved in bacterial adherence, either to bacteria or to host cells.
| S-EPMC6740092 | biostudies-literature