Literature DB >> 22946447

Experimental-like affinity constants and enantioselectivity estimates from flexible docking.

N J Gumede1, P Singh, M I Sabela, K Bisetty, L Escuder-Gilabert, M J Medina-Hernández, S Sagrado.   

Abstract

Experimental-like affinity constants and enantioselectivity estimates, not predicted so far computationally, were obtained using a novel flexible modeling/docking combined strategy. The S- and R-warfarin-human serum albumin (HSA, site I) complexes were used as an interaction model. The process for a verified estimation includes the following: (i) ionized open chain forming at physiological pH (a recent focus); (ii) conformational search (molecular mechanics and Monte Carlo methods); (iii) rigid protein-flexible ligand docking (GlideXP) generating low energy paired S- and R-poses; (iv) graphical comparison against the X-ray crystal structure (unsatisfactory verification step); (v) quantum polarized ligand docking (insufficient verification step); (vi) induced fit docking (one pose satisfying the verification criterion; selection step); (vii) converting docking scores to affinity and enantioselectivity estimates (log K(S) = 5.43, log K(R) = 5.34, ES = K(S)/K(R) = 1.23) and numerical comparison against equivalent literature data from bioanalytical techniques (validation step); (viii) intermolecular forces explaining ES (hydrogen bonding and π-π interactions).

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Year:  2012        PMID: 22946447     DOI: 10.1021/ci300335m

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  1 in total

1.  Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning.

Authors:  Jianan Li; Keisuke Yanagisawa; Yasushi Yoshikawa; Masahito Ohue; Yutaka Akiyama
Journal:  Bioinformatics       Date:  2021-11-22       Impact factor: 6.937

  1 in total

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