J Chem Inf Model - Large-scale mining for similar protein binding pockets: with RAPMAD retrieval on the fly becomes real.

Tópicos

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Resumo

Determination of structural similarities between protein binding pockets is an important challenge in in silico drug design. It can help to understand selectivity considerations, predict unexpected ligand cross-reactivity, and support the putative annotation of function to orphan proteins. To this end, Cavbase was developed as a tool for the automated detection, storage, and classification of putative protein binding sites. In this context, binding sites are characterized as sets of pseudocenters, which denote surface-exposed physicochemical properties, and can be used to enable mutual binding site comparisons. However, these comparisons tend to be computationally very demanding and often lead to very slow computations of the similarity measures. In this study, we propose RAPMAD (RApid Pocket MAtching using Distances), a new evaluation formalism for Cavbase entries that allows for ultrafast similarity comparisons. Protein binding sites are represented by sets of distance histograms that are both generated and compared with linear complexity. Attaining a speed of more than 20 000 comparisons per second, screenings across large data sets and even entire databases become easily feasible. We demonstrate the discriminative power and the short runtime by performing several classification and retrieval experiments. RAPMAD attains better success rates than the comparison formalism originally implemented into Cavbase or several alternative approaches developed in recent time, while requiring only a fraction of their runtime. The pratical use of our method is finally proven by a successful prospective virtual screening study that aims for the identification of novel inhibitors of the NMDA receptor.

Resumo Limpo

determin structur similar protein bind pocket import challeng silico drug design can help understand select consider predict unexpect ligand crossreact support putat annot function orphan protein end cavbas develop tool autom detect storag classif putat protein bind site context bind site character set pseudocent denot surfaceexpos physicochem properti can use enabl mutual bind site comparison howev comparison tend comput demand often lead slow comput similar measur studi propos rapmad rapid pocket match use distanc new evalu formal cavbas entri allow ultrafast similar comparison protein bind site repres set distanc histogram generat compar linear complex attain speed comparison per second screen across larg data set even entir databas becom easili feasibl demonstr discrimin power short runtim perform sever classif retriev experi rapmad attain better success rate comparison formal origin implement cavbas sever altern approach develop recent time requir fraction runtim pratic use method final proven success prospect virtual screen studi aim identif novel inhibitor nmda receptor

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