Dataset Information

A multifaceted secondary structure mimic based on piperidine-piperidinones.

ABSTRACT: Minimalist secondary structure mimics are typically made to resemble one interface in a protein-protein interaction (PPI), and thus perturb it. We recently proposed suitable chemotypes can be matched with interface regions directly, without regard for secondary structures. Here we describe a modular synthesis of a new chemotype 1, simulation of its solution-state conformational ensemble, and correlation of that with ideal secondary structures and real interface regions in PPIs. Scaffold 1 presents amino acid side-chains that are quite separated from each other, in orientations that closely resemble ideal sheet or helical structures, similar non-ideal structures at PPI interfaces, and regions of other PPI interfaces where the mimic conformation does not resemble any secondary structure. 68 different PPIs where conformations of 1 matched well were identified. A new method is also presented to determine the relevance of a minimalist mimic crystal structure to its solution conformations. Thus DLD-1?faf crystallized in a conformation that is estimated to be 0.91?kcal?mol(-1) above the minimum energy solution state.

SUBMITTER: Xin D

PROVIDER: S-EPMC4019009 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A multifaceted secondary structure mimic based on piperidine-piperidinones.

Xin Dongyue D Perez Lisa M LM Ioerger Thomas R TR Burgess Kevin K

Angewandte Chemie (International ed. in English) 20140303 14

Minimalist secondary structure mimics are typically made to resemble one interface in a protein-protein interaction (PPI), and thus perturb it. We recently proposed suitable chemotypes can be matched with interface regions directly, without regard for secondary structures. Here we describe a modular synthesis of a new chemotype 1, simulation of its solution-state conformational ensemble, and correlation of that with ideal secondary structures and real interface regions in PPIs. Scaffold 1 presen ...[more]

PMID: 24591004

Similar Datasets

Project description:Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure predictor evaluation, a fair comparison of predictors is challenging. A detailed examination of factors that contribute to higher accuracy is also lacking. In this paper, we present: (1) new test sets, Test2018, Test2019, and Test2018-2019, consisting of proteins from structures released in 2018 and 2019 with less than 25% identity to any protein published before 2018; (2) a 4-layer convolutional neural network, SecNet, with an input window of ±14 amino acids which was trained on proteins ≤25% identical to proteins in Test2018 and the commonly used CB513 test set; (3) an additional test set that shares no homologous domains with the training set proteins, according to the Evolutionary Classification of Proteins (ECOD) database; (4) a detailed ablation study where we reverse one algorithmic choice at a time in SecNet and evaluate the effect on the prediction accuracy; (5) new 4- and 5-label prediction alphabets that may be more practical for tertiary structure prediction methods. The 3-label accuracy (helix, sheet, coil) of the leading predictors on both Test2018 and CB513 is 81-82%, while SecNet's accuracy is 84% for both sets. Accuracy on the non-homologous ECOD set is only 0.6 points (83.9%) lower than the results on the Test2018-2019 set (84.5%). The ablation study of features, neural network architecture, and training hyper-parameters suggests the best accuracy results are achieved with good choices for each of them while the neural network architecture is not as critical as long as it is not too simple. Protocols for generating and using unbiased test, validation, and training sets are provided. Our data sets, including input features and assigned labels, and SecNet software including third-party dependencies and databases, are downloadable from dunbrack.fccc.edu/ss and github.com/sh-maxim/ss.

Project description:The homomeric α7 nicotinic receptor (α7 nAChR) is widely expressed in the human brain that could be activated to suppress neuroinflammation, oxidative stress and neuropathic pain. Consequently, a number of α7 nAChR agonists have entered clinical trials as anti-Alzheimer's or anti-psychotic therapies. However, high-resolution crystal structure of the full-length α7 receptor is thus far unavailable. Since acetylcholine-binding protein (AChBP) from Lymnaea stagnalis is most closely related to the α-subunit of nAChRs, it has been used as a template for the N-terminal domain of α-subunit of nAChR to study the molecular recognition process of nAChR-ligand interactions, and to identify ligands with potential nAChR-like activities.Here we report the discovery and optimization of novel acetylcholine-binding protein ligands through screening, structure-activity relationships and structure-based design. We manually screened in-house CNS-biased compound library in vitro and identified compound 1, a piperidine derivative, as an initial hit with moderate binding affinity against AChBP (17.2% inhibition at 100 nmol/L). During the 1st round of optimization, with compound 2 (21.5% inhibition at 100 nmol/L) as the starting point, 13 piperidine derivatives with different aryl substitutions were synthesized and assayed in vitro. No apparent correlation was demonstrated between the binding affinities and the steric or electrostatic effects of aryl substitutions for most compounds, but compound 14 showed a higher affinity (Ki=105.6 nmol/L) than nicotine (Ki=777 nmol/L). During the 2nd round of optimization, we performed molecular modeling of the putative complex of compound 14 with AChBP, and compared it with the epibatidine-AChBP complex. The results suggested that a different piperidinyl substitution might confer a better fit for epibatidine as the reference compound. Thus, compound 15 was designed and identified as a highly affinitive acetylcholine-binding protein ligand. In this study, through two rounds of optimization, compound 15 (Ki=2.8 nmol/L) has been identified as a novel, piperidine-based acetylcholine-binding protein ligand with a high affinity.

Project description:BACKGROUND: When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult. RESULTS: In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an n-gram pattern of amino acids that reflects the sequence variation in a protein's evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction.On a large non-redundant dataset of 8,297 protein chains (DsspNr-25), the average Q3 of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (EVA Set_1 and EVA_Set2), the average Q3 of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases. CONCLUSIONS: Our experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at http://bio-cluster.iis.sinica.edu.tw/SymPred/.

Dataset Information

A multifaceted secondary structure mimic based on piperidine-piperidinones.

Publications

A multifaceted secondary structure mimic based on piperidine-piperidinones.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets