nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale.

However, most of them do not provide any structural information about the link, and for those providing it, the application at proteome scale for any query compound is unfeasible.

Here we introduced nAnno-Lyze a method for drug target interaction prediction that provides structural details at proteome scale.

A successful example of a method for predicting drug-like targets using the modelable human proteome with medical data integration is the Computational Analysis of Novel Drug Opportunities (CANDO) platform [43].

As a result, they provide structurally detailed information about the likely interaction between the compound and its target/ s. However, the computational requirements of such approaches make them not generally applicable at proteomic scales.

Our new method predicts interactions for any query compound against an entire 3D proteome by relying on a bipartite network of interactions and similarities.

Annolyze was used in an open source drug discovery initiative against neglected tropical diseases [30] while nAnnoLyze has been applied to a set of anti-tubercular drugs against the M ycobacterium tuberculosis proteome [31].

The average copy number of sequence—specific TFs per cell, as found in whole-cell proteomic studies, is around 2500 (S2 Fig), which results in a concentration of 3.5 nM [38—41].

Distribution of copy numbers were calculated for sequence specific TFs (blue), histone modifiers (green) and general TFs (red) based on the proteomic data from two mouse cell(line)s (Azimifar et al.

The corresponding UniProt IDs lists were checked against the corresponding ID lists provided in the proteomic measurement publications and copy number measurements for proteins found used to calculate the distributions.

Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms ( proteomic , genetic, etc) for system-wise drug target validation.

Classical methods for target identification like chemical proteomics rely on compound modification and immobilization to generate compound affinity matrixes that can be used to pull down associated proteins [4].

Additionally, CSNAP is capable of integrating with chemical and biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms ( proteomic , genetic, etc) for system-wise drug target validation.