Applying HT-SAXS to chemical ligand screening

Chemical probes are invaluable tools for investigating essential biological processes. Understanding how small-molecule probes engage biomolecular conformations is critical to developing their functional selectivity. High-throughput solution X-ray scattering is well-positioned to profile target-ligand complexes during probe development, bringing conformational insight and selection to traditional ligand binding assays. Access to high-quality synchrotron SAXS datasets and high-throughput data analysis now allows routine academic users to incorporate conformational information into small-molecule development pipelines. Here we describe a general approach for benchmarking and preparing HT-SAXS chemical screens from small fragment libraries. Using the allosteric oxidoreductase Apoptosis-Inducing Factor (AIF) as an exemplary system, we illustrate how HT-SAXS efficiently identifies an allosteric candidate among hits of a microscale thermophoresis ligand screen. We discuss considerations for pursuing HT-SAXS chemical screening with other systems of interest and reflect on advances to extend screening throughput and sensitivity.

Fragment-based approaches to the discovery of kinase inhibitors

Protein kinases are one of the most important families of drug targets, and aberrant kinase activity has been linked to a large number of disease areas. Although eminently targetable using small molecules, kinases present a number of challenges as drug targets, not least obtaining selectivity across such a large and relatively closely related target family. Fragment-based drug discovery involves screening simple, low-molecular weight compounds to generate initial hits against a target. These hits are then optimized to more potent compounds via medicinal chemistry, usually facilitated by structural biology. Here, we will present a number of recent examples of fragment-based approaches to the discovery of kinase inhibitors, detailing the construction of fragment-screening libraries, the identification and validation of fragment hits, and their optimization into potent and selective lead compounds. The advantages of fragment-based methodologies will be discussed, along with some of the challenges associated with using this route. Finally, we will present a number of key lessons derived both from our own experience running fragment screens against kinases and from a large number of published studies.

Explanatory chapter: next generation sequencing

Technological breakthroughs in sequencing technologies have driven the advancement of molecular biology and molecular genetics research. The advent of high-throughput Sanger sequencing (for information on the method, see Sanger Dideoxy Sequencing of DNA) in the mid- to late-1990s made possible the accelerated completion of the human genome project, which has since revolutionized the pace of discovery in biomedical research. Similarly, the advent of next generation sequencing is poised to revolutionize biomedical research and usher a new era of individualized, rational medicine. The term next generation sequencing refers to technologies that have enabled the massively parallel analysis of DNA sequence facilitated through the convergence of advancements in molecular biology, nucleic acid chemistry and biochemistry, computational biology, and electrical and mechanical engineering. The current next generation sequencing technologies are capable of sequencing tens to hundreds of millions of DNA templates simultaneously and generate >4 gigabases of sequence in a single day. These technologies have largely started to replace high-throughput Sanger sequencing for large-scale genomic projects, and have created significant enthusiasm for the advent of a new era of individualized medicine.

Structure based function prediction of proteins using fragment library frequency vectors

The function of the protein is primarily dictated by its structure. Therefore it is far more logical to find the functional clues of the protein in its overall 3-dimensional fold or its global structure. In this paper, we have developed a novel Support Vector Machines (SVM) based prediction model for functional classification and prediction of proteins using features extracted from its global structure based on fragment libraries. Fragment libraries have been previously used for abintio modelling of proteins and protein structure comparisons. The query protein structure is broken down into a collection of short contiguous backbone fragments and this collection is discretized using a library of fragments. The input feature vector is frequency vector that counts the number of each library fragment in the collection of fragments by all-to-all fragment comparisons. SVM models were trained and optimised for obtaining the best 10-fold Cross validation accuracy for classification. As an example, this method was applied for prediction and classification of Cell Adhesion molecules (CAMs). Thirty-four different fragment libraries with sizes ranging from 4 to 400 and fragment lengths ranging from 4 to 12 were used for obtaining the best prediction model. The best 10-fold CV accuracy of 95.25% was obtained for library of 400 fragments of length 10. An accuracy of 87.5% was obtained on an unseen test dataset consisting of 20 CAMs and 20 NonCAMs. This shows that protein structure can be accurately and uniquely described using 400 representative fragments of length 10.