scholarly journals Influence of length and flexibility of spacers on the binding affinity of divalent ligands

2015 ◽  
Vol 11 ◽  
pp. 804-816 ◽  
Author(s):  
Susanne Liese ◽  
Roland R Netz
Keyword(s):  
Dissociation Constant ◽  
Binding Affinity ◽  
Receptor Binding ◽  
Binding Pocket ◽  
Critical Value ◽  
Quantitative Model ◽  
Second Step ◽  
Spacer Length ◽  
General Rules ◽  
Binding Pockets

We present a quantitative model for the binding of divalent ligand–receptor systems. We study the influence of length and flexibility of the spacers on the overall binding affinity and derive general rules for the optimal ligand design. To this end, we first compare different polymeric models and determine the probability to simultaneously bind to two neighboring receptor binding pockets. In a second step the binding affinity of divalent ligands in terms of the IC50 value is derived. We find that a divalent ligand has the potential to bind more efficiently than its monovalent counterpart only, if the monovalent dissociation constant is lower than a critical value. This critical monovalent dissociation constant depends on the ligand-spacer length and flexibility as well as on the size of the receptor. Regarding the optimal ligand-spacer length and flexibility, we find that the average spacer length should be equal or slightly smaller than the distance between the receptor binding pockets and that the end-to-end spacer length fluctuations should be in the same range as the size of a receptor binding pocket.

scholarly journals Mimicking the Nucleosomal Context in Peptide-Based Binders of a H3K36me Reader Increases Binding Affinity While Altering the Binding Mode

Molecules ◽  
2020 ◽  
Vol 25 (21) ◽  
pp. 4951
Author(s):  
Velten Horn ◽  
Seino A. K. Jongkees ◽  
Hugo van Ingen
Keyword(s):  
Histone Modification ◽  
Binding Affinity ◽  
Histone H3 ◽  
Binding Pocket ◽  
Binding Mode ◽  
Nmr Analysis ◽  
Dna Interactions ◽  
Nucleosomal Dna ◽  
Binding Pockets ◽  
Selection Of

Targeting of proteins in the histone modification machinery has emerged as a promising new direction to fight disease. The search for compounds that inhibit proteins that readout histone modification has led to several new epigenetic drugs, mostly for proteins involved in recognition of acetylated lysines. However, this approach proved to be a challenging task for methyllysine readers, which typically feature shallow binding pockets. Moreover, reader proteins of trimethyllysine K36 on the histone H3 (H3K36me3) not only bind the methyllysine but also the nucleosomal DNA. Here, we sought to find peptide-based binders of H3K36me3 reader PSIP1, which relies on DNA interactions to tightly bind H3K36me3 modified nucleosomes. We designed several peptides that mimic the nucleosomal context of H3K36me3 recognition by including negatively charged Glu-rich regions. Using a detailed NMR analysis, we find that addition of negative charges boosts binding affinity up to 50-fold while decreasing binding to the trimethyllysine binding pocket. Since screening and selection of compounds for reader domains is typically based solely on affinity measurements due to their lack of enzymatic activity, our case highlights the need to carefully control for the binding mode, in particular for the challenging case of H3K36me3 readers.

Antiproliferative and antiviral effects of mutated IFN-alpha with increased IFN receptor binding affinity

2011 ◽  
Vol 49 (01) ◽  
Author(s):  
MF Sprinzl ◽  
L Bührer ◽  
D Strand ◽  
G Schreiber ◽  
PR Galle ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Receptor Binding ◽  
Receptor Binding Affinity ◽  
Ifn Alpha ◽  
Antiviral Effects

GRG: KNOWLEDGE DISCOVERY USING INFORMATION GENERALIZATION, INFORMATION REDUCTION, AND RULE GENERATION

1996 ◽  
Vol 05 (01n02) ◽  
pp. 99-112 ◽  
Author(s):  
NING SHAN ◽  
HOWARD J. HAMILTON ◽  
NICK CERCONE
Keyword(s):  
Information System ◽  
Knowledge Discovery ◽  
Relational Databases ◽  
Decision Rules ◽  
Reduction Technique ◽  
Second Step ◽  
Rule Generation ◽  
Information Reduction ◽  
General Rules ◽  
Minimal Subset

We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascen sion technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, a reduction technique is applied to generate a minimalized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database.

[Aladan3]TIPP: A fluorescent ?-opioid antagonist with high ?-receptor binding affinity and ? selectivity

Biopolymers ◽  
2005 ◽  
Vol 80 (2-3) ◽  
pp. 325-331 ◽  
Author(s):  
Heru Chen ◽  
Nga N. Chung ◽  
Carole Lemieux ◽  
Bogumil Zelent ◽  
Jane M. Vanderkooi ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Receptor Binding ◽  
Opioid Antagonist ◽  
Receptor Binding Affinity

A hybrid predictive framework for evaluating P2P credit risks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Liang He ◽  
Haiyan Xu ◽  
Ginger Y. Ke
Keyword(s):  
Imbalanced Data ◽  
Sampling Technique ◽  
Critical Value ◽  
Quantitative Model ◽  
Support Vector ◽  
Content Type ◽  
Dynamic Selection ◽  
Credit Risks ◽  
Classification Feature ◽  
Selection Approach

PurposeDespite better accessibility and flexibility, peer-to-peer (P2P) lending has suffered from excessive credit risks, which may cause significant losses to the lenders and even lead to the collapse of P2P platforms. The purpose of this research is to construct a hybrid predictive framework that integrates classification, feature selection, and data balance algorithms to cope with the high-dimensional and imbalanced nature of P2P credit data.Design/methodology/approachAn improved synthetic minority over-sampling technique (IMSMOTE) is developed to incorporate the randomness and probability into the traditional synthetic minority over-sampling technique (SMOTE) to enhance the quality of synthetic samples and the controllability of synthetic processes. IMSMOTE is then implemented along with the grey relational clustering (GRC) and the support vector machine (SVM) to facilitate a comprehensive assessment of the P2P credit risks. To enhance the associativity and functionality of the algorithm, a dynamic selection approach is integrated with GRC and then fed in the SVM's process of parameter adaptive adjustment to select the optimal critical value. A quantitative model is constructed to recognize key criteria via multidimensional representativeness.FindingsA series of experiments based on real-world P2P data from Prosper Funding LLC demonstrates that our proposed model outperforms other existing approaches. It is also confirmed that the grey-based GRC approach with dynamic selection succeeds in reducing data dimensions, selecting a critical value, identifying key criteria, and IMSMOTE can efficiently handle the imbalanced data.Originality/valueThe grey-based machine-learning framework proposed in this work can be practically implemented by P2P platforms in predicting the borrowers' credit risks. The dynamic selection approach makes the first attempt in the literature to select a critical value and indicate key criteria in a dynamic, visual and quantitative manner.

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