Identifying Druggable Targets by Protein Microenvironments Matching: Application to Transcription Factors

Liu, T; Altman, Russ B.

doi:10.1038/psp.2013.66

Cited by 34 publications

(29 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our study, a drug is actually represented by its binding site properties 44 , which are defined by protein residues within 6 Angstroms of the drug molecule. A target protein is represented by the binding site of the largest biological meaningful molecule co-crystalized with the protein.…”

Section: Methodsmentioning

confidence: 99%

Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

Liu

Altman

2015

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The molecular mechanism of many drug side-effects is unknown and difficult to predict. Previous methods for explaining side effects have focused on known drug targets and their pathways. However, low affinity binding to proteins that are not usually considered drug targets may also drive side-effects. In order to assess these alternative targets, we used the 3D structures of 563 essential human proteins systematically to predict binding to 216 drugs. We first benchmarked our affinity predictions with available experimental data. We then combined singular value decomposition and canonical component analysis (SVD-CCA) to predict side-effects based on these novel target profiles. Our method predicts side-effects with good accuracy (average AUC: 0.82 for side effects present in < 50% of drug labels). We also noted that side-effect frequency is the most important feature for prediction, and can confound efforts at elucidating mechanism; our method allows us to remove the contribution of frequency and isolate novel biological signals. In particular, our analysis produces 2768 triplet associations between 50 essential proteins, 99 drugs and 77 side-effects. Although experimental validation is difficult because many of our essential proteins do not have validated assays, we nevertheless attempted to validate a subset of these associations using experimental assay data. Our focus on essential proteins allows us to find potential associations that would likely be missed if we used recognized drug targets. Our associations provide novel insights about the molecular mechanisms of drug side-effects, and highlight the need for expanded experimental efforts to investigate drug binding to proteins more broadly.

show abstract

Section: Methodsmentioning

confidence: 99%

Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

Liu

Altman

2015

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…We evaluated the physical features of the predicted structure sites and the degree to which they shared similarity with the experimental structure sites. We previously developed PocketFEATURE (PF), an algorithm that evaluates similarity between two functional sites in terms of their physicochemical features . As part of this work, we applied the PF algorithm to assess the extent to which physicochemical features that are observed in experimental structures can be replicated by predicted structures.…”

Section: Introductionmentioning

confidence: 99%

Biological and functional relevance of CASP predictions

et al. 2017

View full text Add to dashboard Cite

Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo‐sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo‐sites), and Ten sites containing important motifs, loops, or key residues with important disease‐associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best‐ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand‐binding sites, most prediction methods have higher performance on apo‐sites than holo‐sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein‐protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein‐protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template.

show abstract

“…Neutralizing antibodies block CCL2 activity non-specifically in tissues [19, 20, 24]. Small interfering RNAs (siRNAs) could selectively inactivate critical oncogenes if coupled to a carrier that targeted carcinoma cells [25, 26]. The HIV-1 derived trans-activating transcriptor peptide (TAT 49–57 : RKKRRQRRR) exhibits unusual properties by efficiently penetrating cell membranes, independent of temperature or cell surface receptor expression [27].…”

Section: Introductionmentioning

confidence: 99%

The CCL2 chemokine is a negative regulator of autophagy and necrosis in luminal B breast cancer cells

Fang

Yao

Jokar

et al. 2015

Breast Cancer Res Treat

View full text Add to dashboard Cite

Luminal A and B breast cancers are the most prevalent forms of breast cancer diagnosed in women. Compared to luminal A breast cancer patients, patients with luminal B breast cancers experience increased disease recurrence and lower overall survival. The mechanisms that regulate the luminal B subtype remain poorly understood. The chemokine CCL2 is overexpressed in breast cancer, correlating with poor patient prognosis. The purpose of this study was to determine the role of CCL2 expression in luminal B breast cancer cells. Breast tissues, MMTV-PyVmT and MMTV-Neu transgenic mammary tumors forming luminal B-like lesions, were immunostained for CCL2 expression. To determine the role of CCL2 in breast cancer cells, CCL2 gene expression was silenced in mammary tumor tissues and cells using TAT cell-penetrating peptides non-covalently cross linked to siRNAs (Ca-TAT/siRNA). CCL2 expression was examined by ELISA and flow cytometry. Cell growth and survival were analyzed by flow cytometry, immunocytochemistry, and fluorescence microscopy. CCL2 expression was significantly increased in luminal B breast tumors, MMTV-PyVmT and MMTV-Neu mammary tumors, compared or normal breast tissue or luminal A breast tumors. Ca-TAT delivery of CCL2 siRNAs significantly reduced CCL2 expression in PyVmT mammary tumors, and decreased cell proliferation and survival. CCL2 gene silencing in PyVmT carcinoma cells or BT474 luminal B breast cancer cells decreased cell growth and viability associated with increased necrosis and autophagy. CCL2 expression is overexpressed in luminal B breast cancer cells and is important for regulating cell growth and survival by inhibiting necrosis and autophagy.

show abstract

Identifying Druggable Targets by Protein Microenvironments Matching: Application to Transcription Factors

Cited by 34 publications

References 36 publications

Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach

Biological and functional relevance of CASP predictions

The CCL2 chemokine is a negative regulator of autophagy and necrosis in luminal B breast cancer cells

Contact Info

Product

Resources

About