Integration and analysis of large scale data in chemical biology

much lower molecular weight than macromolecules like proteins or DNA. Small molecules are grouped into different families according to their physico-chemical or functional properties, and they can be either natural (like lipids) or synthetic (like drugs). Only a staggeringly low fraction of the small molecule universe has been characterize, and very little is known about it. For instance, we know that lipids can play the role of scaffolding and energy storage compounds, and that they differently compose biological membranes. However, we don’t know if it influences some biological functions, including protein recruitment to membranes and cellular transport. Chemical biology aims at utilizing chemicals in order to explore biological systems. Advances in synthesizing big chemical libraries as well as in high-throughput screenings have led to technologies capable of studying protein-lipid interactions at large scale and in physiological conditions. Therefore, answering such questions has become possible, but it presents many new computational challenges. For instance, establishing methods capable of automatically classifying interactions as binding or non-binding requiring a minimal interaction with human experts. Making use of unsupervised clustering methods to identify clusters of lipids and proteins exhibiting similar patterns and linking them to similar biological functions. To tackle these challenges, I have developed a computational pipeline performing a technical and functional analysis on the readouts produced by the high-throughput technology LiMA. Applied to a screen focusing on 94 proteins and 122 lipid combinations yielding more than 10,000 interactions, I have demonstrated that cooperativity was a key mechanism for membrane recruitment and that it could be applied to most PH domains. Furthermore, I have identified a conserved motif conferring PH domains the ability to be recruited to organellar membranes and which is linked to cellular transport functions. Two amino acids of this motif are found mutated in some human cancer, and we predicted and confirmed that these mutations could induce discrete changes in binding affinities in vitro and protein mis-localization in vivo. These results represent milestones in the field of protein-lipid interactions. While we are progressing toward a global understanding of protein-lipid interactions, data on the bioactivities of small molecules is accumulating at a tremendous speed. In vitro data on interactions with targets are complemented by other molecular and phenotypic readouts, such as gene expression profiles or toxicity readouts. The diversity of screening technologies accompanied by big efforts to collect the resulting data in public databases have created unprecedented opportunities for chemo-informatics work to integrate these data and make new inferences. For instance, is the protein target profile of a drug correlated with a given phenotype? Can we predict the side effects of a drug based on its toxicology readouts? In this context, I have developed CART: a computational platform with which we address major chemo-informatics challenges to answer such questions. CART integrates many resources covering molecular and phenotypical readouts, and annotates sets of chemical names with these integrated resources. CART includes state-of-the-art full-text search engine technologies in order to match chemical names at a very high speed and accuracy. Importantly, CART is a scalable resource that can cope with the increasing number of new chemical annotation resources, and therefore, constitutes a major contribution to chemical biology

CART—a chemical annotation retrieval toolkit

Motivation: Data on bioactivities of drug-like chemicals are rapidly accumulating in public repositories, creating new opportunities for research in computational systems pharmacology. However, integrative analysis of these data sets is difficult due to prevailing ambiguity between chemical names and identifiers and a lack of cross-references between databases. Results: To address this challenge, we have developed CART, a Chemical Annotation Retrieval Toolkit. As a key functionality, it matches an input list of chemical names into a comprehensive reference space to assign unambiguous chemical identifiers. In this unified space, bioactivity annotations can be easily retrieved from databases covering a wide variety of chemical effects on biological systems. Subsequently, CART can determine annotations enriched in the input set of chemicals and display these in tabular format and interactive network visualizations, thereby facilitating integrative analysis of chemical bioactivity data. Availability and Implementation: CART is available as a Galaxy web service (cart.embl.de). Source code and an easy-to-install command line tool can also be obtained from the web site. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

A protocol for the systematic and quantitative measurement of protein-lipid interactions using the liposome-microarray-based assay

Lipids organize the activity of the cell's proteome through a complex network of interactions. The assembly of comprehensive atlases embracing all protein-lipid interactions is an important challenge that requires innovative methods. We recently developed a liposome-microarray-based assay (LiMA) that integrates liposomes, microfluidics and fluorescence microscopy and which is capable of measuring protein recruitment to membranes in a quantitative and high-throughput manner. Compared with previous assays that are labor-intensive and difficult to scale up, LiMA improves the protein-lipid interaction assay throughput by at least three orders of magnitude. Here we provide a step-by-step LiMA protocol that includes the following: (i) the serial and generic production of the liposome microarray; (ii) its integration into a microfluidic format; (iii) the measurement of fluorescently labeled protein (either purified proteins or from cell lysate) recruitment to liposomal membranes using high-throughput microscopy; (iv) automated image analysis pipelines to quantify protein-lipid interactions; and (v) data quality analysis. In addition, we discuss the experimental design, including the relevant quality controls. Overall, the protocol-including device preparation, assay and data analysis-takes 6-8 d. This protocol paves the way for protein-lipid interaction screens to be performed on the proteome and lipidome scales

A quantitative liposome microarray to systematically characterize protein-lipid interactions

Lipids have a role in virtually all biological processes, acting as structural elements, scaffolds and signaling molecules, but they are still largely under-represented in known biological networks. Here we describe a liposome microarray-based assay (LiMA), a method that measures protein recruitment to membranes in a quantitative, automated, multiplexed and high-throughput manner.</p

A quantitative liposome microarray to systematically characterize protein-lipid interactions

Lipids have a role in virtually all biological processes, acting as structural elements, scaffolds and signaling molecules, but they are still largely under-represented in known biological networks. Here we describe a liposome microarray-based assay (LiMA), a method that measures protein recruitment to membranes in a quantitative, automated, multiplexed and high-throughput manner.</p

Lipid Cooperativity as a General Membrane-Recruitment Principle for PH Domains

Many cellular processes involve the recruitment of proteins to specific membranes, which are decorated with distinctive lipids that act as docking sites. The phosphoinositides form signaling hubs, and we examine mechanisms underlying recruitment. We applied a physiological, quantitative, liposome microarray-based assay to measure the membrane-binding properties of 91 pleckstrin homology (PH) domains, the most common phosphoinositide-binding target. 10,514 experiments quantified the role of phosphoinositides in membrane recruitment. For most domains examined, the observed binding specificity implied cooperativity with additional signaling lipids. Analyses of PH domains with similar lipid-binding profiles identified a conserved motif, mutations in which-including some found in human cancers-induced discrete changes in binding affinities in vitro and protein mislocalization in vivo. The data set reveals cooperativity as a key mechanism for membrane recruitment and, by enabling the interpretation of disease-associated mutations, suggests avenues for the design of small molecules targeting PH domains.publisher: Elsevier articletitle: Lipid Cooperativity as a General Membrane-Recruitment Principle for PH Domains journaltitle: Cell Reports articlelink: http://dx.doi.org/10.1016/j.celrep.2015.07.054 content_type: article copyright: Copyright © 2015 The Authors. Published by Elsevier Inc.status: publishe

Lipid Cooperativity as a General Membrane-Recruitment Principle for PH Domains

Many cellular processes involve the recruitment of proteins to specific membranes, which are decorated with distinctive lipids that act as docking sites. The phosphoinositides form signaling hubs, and we examine mechanisms underlying recruitment. We applied a physiological, quantitative, liposome microarray-based assay to measure the membrane-binding properties of 91 pleckstrin homology (PH) domains, the most common phosphoinositide-binding target. 10,514 experiments quantified the role of phosphoinositides in membrane recruitment. For most domains examined, the observed binding specificity implied cooperativity with additional signaling lipids. Analyses of PH domains with similar lipid-binding profiles identified a conserved motif, mutations in which—including some found in human cancers—induced discrete changes in binding affinities in vitro and protein mislocalization in vivo. The data set reveals cooperativity as a key mechanism for membrane recruitment and, by enabling the interpretation of disease-associated mutations, suggests avenues for the design of small molecules targeting PH domains