Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications

Abstract

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

“…Docking methods are evaluated by predicting the correct pose/binding mode (evaluated using RMSD or TMScore of the coordinates of the atoms) or by measuring predicted binding affinities 4,8,11,12,16 . Application to protein targets involved in disease holds the promise of discovering new therapeutics using traditional single target approaches or by virtually measuring the interactions of a compound with the proteins from multi-organism proteome [17][18][19][20][21][22] . The resulting chemo-proteome interactions can be interrogated to study polypharmacology 19 and investigate the effect drugs and agents have on protein classes in a disease-specific context 19,22 .…”

“…Application to protein targets involved in disease holds the promise of discovering new therapeutics using traditional single target approaches or by virtually measuring the interactions of a compound with the proteins from multi-organism proteome [17][18][19][20][21][22] . The resulting chemo-proteome interactions can be interrogated to study polypharmacology 19 and investigate the effect drugs and agents have on protein classes in a disease-specific context 19,22 . In previous works, we have used the algorithm presented herein to combat Ebola 20 , determine the toxicity of potential diabetes therapeutics 21 , and rank the affinity of kinase inhibitors for the treatment of Acute Myeloid Leukemia 23 .…”

A Deep-Learning Proteomic-Scale Approach for Drug Design

“…Computational methods are efficient, accurate, holistic (i.e., take into account the entire interaction space of chemical entities), and have breadth in terms of chemical space exploration necessary to overcome the limitations of traditional approaches [2,6,12,13,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. To expand compound libraries utilized in screening, combinatorial chemistry and machine-learning design pipelines have been developed to generate libraries of compounds likely to bind to a given target [35][36][37].…”

Fingerprinting CANDO: Increased Accuracy with Structure- and Ligand-Based Shotgun Drug Repurposing

“…The goal of the Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun drug discovery and repurposing is to screen every human use compound/drug against every indication/disease. 46−49 The tenets of CANDO include docking with dynamics and multitargeting, which have been developed over the past decade and a half. 50−52 The first version of CANDO (v1) applied a bioinformatic docking protocol on large libraries of compound and protein structures.…”