Affinity Ligands from Biological Combinatorial Libraries

2011 ◽  
pp. 269-278 ◽  
Author(s):  
Per-Åke Nygren
Keyword(s):  
Combinatorial Libraries ◽  
Affinity Ligands

Mimicking nature: Phosphopeptide enrichment using combinatorial libraries of affinity ligands

2016 ◽  
Vol 1457 ◽  
pp. 76-87 ◽  
Author(s):  
Iris L. Batalha ◽  
Houjiang Zhou ◽  
Kathryn Lilley ◽  
Christopher R. Lowe ◽  
Ana C.A. Roque
Keyword(s):  
Combinatorial Libraries ◽  
Phosphopeptide Enrichment ◽  
Affinity Ligands

NMR-based screening in drug discovery

1999 ◽  
Vol 32 (3) ◽  
pp. 211-240 ◽  
Author(s):  
Philip J. Hajduk ◽  
Robert P. Meadows ◽  
Stephen W. Fesik
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Chemical Shifts ◽  
Combinatorial Libraries ◽  
Screening Methods ◽  
Binding Interaction ◽  
Widespread Application ◽  
Affinity Ligands ◽  
High Affinity ◽  
Technological Advances

1. Introduction 2112. Screening methods 2132.1 Chemical shifts 2132.2 Diffusion 2142.3 Transverse relaxation 2182.4 Nuclear Overhauser effects 2183. Strategies for drug discovery and design 2213.1 Fragment-based methods 2213.1.1 Linked-fragment approach 2213.1.2 Directed combinatorial libraries 2223.1.3 Modification of high-affinity ligands 2233.1.4 Solvent mapping techniques 2233.2 High-throughput NMR-based screening 2243.3 Enzymatic assays 2264. Discovery of novel ligands 2274.1 High-affinity ligands for FKBP 2274.2 Potent inhibitors of stromelysin 2294.3 Ligands for the DNA-binding domain of the E2 protein 2334.4 Discovery of Erm methyltransferase inhibitors 2334.5 Phosphotyrosine mimetics for SH2 domains 2365. Conclusions 2376. References 237A critical step in the drug discovery process is the identification of high-affinity ligands for macromolecular targets. Traditionally, the identification of such lead compounds has been accomplished through the high-throughout screening (HTS) of corporate compound repositories. Conventional HTS methodology has enjoyed widespread application and success in the pharmaceutical industry and, through recent technological advances in screening (Fernandes, 1998; Oldenburg et al. 1998; Silverman et al. 1998) and combinatorial chemistry (Fauchere et al. 1998; Fecik et al. 1998), these programs will continue to have a prominent role in drug discovery. However, suitable leads cannot always be found using conventional methods. This is not surprising since typical corporate libraries contain fewer than 106 compounds compared with the estimated 1050–1080 universe of compounds (Martin, 1997). In addition, most conventional assays are limited to screening libraries of compounds against proteins with known function, excluding the large number of targets becoming available from genomics research.Recently, a number of NMR-based screening methods have been employed to identify and design lead ligands for protein targets (see Table 1). These NMR-based strategies can augment ongoing conventional HTS for identifying leads and can be used to aid in lead optimization. All of these techniques take advantage of the fact that upon complex formation between a target molecule and a ligand, significant perturbations can be observed in NMR-sensitive parameters of either the target or the ligand. These perturbations can be used qualitatively to detect ligand binding or quantitatively to assess the strength of the binding interaction. In addition, some of the techniques allow the identification of the ligand binding site or which part of the ligand is responsible for interacting with the target. In this article, the current state of NMR-based screening is reviewed.

scholarly journals Recombinant human factor VIII-specific affinity ligands selected from phage-displayed combinatorial libraries of protein A

2001 ◽  
Vol 268 (15) ◽  
pp. 4269-4277 ◽  
Author(s):  
Karin Nord ◽  
Olof Nord ◽  
Mathias Uhlén ◽  
Brian Kelley ◽  
Charlotta Ljungqvist ◽  
...  
Keyword(s):  
Factor Viii ◽  
Human Factor ◽  
Protein A ◽  
Combinatorial Libraries ◽  
Affinity Ligands ◽  
Human Factor Viii ◽  
Specific Affinity

scholarly journals Improving MALDI-TOF MS sample preparation for the analysis of affinity ligands selected from one bead-one peptide combinatorial libraries screening

2009 ◽  
Vol 25 ◽  
pp. S174
Author(s):  
M.C. Martínez Ceron ◽  
S.L. Giudicessi ◽  
M.M. Marani ◽  
F. Albericio ◽  
O. Cascone ◽  
...  
Keyword(s):  
Sample Preparation ◽  
Combinatorial Libraries ◽  
Affinity Ligands ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Tof Ms

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

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