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.

A molecular evolution algorithm for ligand design in DOCK.

As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.

Application of Post Solid-Phase Oxime Ligation to Fine-Tune Peptide-Protein Interactions.

Protein-protein interactions (PPIs) represent an extremely attractive class of potential new targets for therapeutic intervention; however, the shallow extended character of many PPIs can render developing inhibitors against them as exceptionally difficult. Yet this problem can be made tractable by taking advantage of the fact that large interacting surfaces are often characterized by confined "hot spot" regions, where interactions contribute disproportionately to overall binding energies. Peptides afford valuable starting points for developing PPI inhibitors because of their high degrees of functional diversity and conformational adaptability. Unfortunately, contacts afforded by the 20 natural amino acids may be suboptimal and inefficient for accessing both canonical binding interactions and transient "cryptic" binding pockets. Oxime ligation represents a class of biocompatible "click" chemistry that allows the structural diversity of libraries of aldehydes to be rapidly evaluated within the context of a parent oxime-containing peptide platform. Importantly, oxime ligation represents a form of post solid-phase diversification, which provides a facile and empirical means of identifying unanticipated protein-peptide interactions that may substantially increase binding affinities and selectivity. The current review will focus on the authors' use of peptide ligation to optimize PPI antagonists directed against several targets, including tumor susceptibility gene 101 (Tsg101), protein tyrosine phosphatases (PTPases) and the polo-like kinase 1 (Plk1). This should provide insights that can be broadly directed against an almost unlimited range of physiologically important PPIs.

Comprehensive and High-Throughput Exploration of Chemical Space Using Broadband 19 F†NMR-Based Screening.

Fragment-based lead discovery has become a fundamental approach to identify ligands that efficiently interact with disease-relevant targets. Among the numerous screening techniques, fluorine-detected NMR has gained popularity owing to its high sensitivity, robustness, and ease of use. To effectively explore chemical space, a universal NMR experiment, a rationally designed fragment library, and a sample composition optimized for a maximal number of compounds and minimal measurement time are required. Here, we introduce a comprehensive method that enabled the efficient assembly of a high-quality and diverse library containing nearly 4000 fragments and screening for target-specific binders within days. At the core of the approach is a novel broadband relaxation-edited NMR experiment that covers the entire chemical shift range of drug-like 19 F motifs in a single measurement. Our approach facilitates the identification of diverse binders and the fast ligandability assessment of new targets.

Library Design Strategies To Accelerate Fragment-Based Drug Discovery.

Fragment-based drug discovery (FBDD) has become an established approach for the generation of early lead candidates. However, despite its success and inherent advantages, hit-to-candidate progression for FBDD is not necessarily faster than that of traditional high-throughput screening. Thus, new technology-driven library design strategies have emerged as a means to facilitate more efficient fragment screening and/or subsequent fragment-to-hit chemistry. This minireview discusses such strategies, which cover the use of labeled fragments for NMR spectroscopy, X-ray crystallographic screening of specialized fragments, covalent linkage for mass spectrometry, dynamic combinatorial chemistry, and fragments optimized for easy elaboration.

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets.

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.

Classifying shape coverage in fragment libraries using a fingerprinting approach.

Fragment screening is one approach to hit identification for early stage drug discovery projects. Like any screening library, diversity is needed in fragment libraries. This includes coverage of shape and electrostatic space, as well as chemotype diversity. A new, easily interpretable shape-based fingerprint is described and its utility in probing fragment library content is demonstrated using a Rule of Three library from Maybridge. This method explicitly considers size as a component of shape. It allows interrogation of shape space on both a per conformer and a per molecule level, and includes a measure of flexibility. This allows for the identification of highly flexible compounds and their exclusion from the analysis. A comparison with two literature methods, the triangle plot approach of Sauer and Schwarz and the plane of best fit method of Firth et al., is also included.

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.

Single-domain antibodies and their utility.

Engineered monoclonal antibody fragments have gained market attention due to their versatility and tailor-made potential and are now considered to be an important part of future immunobiotherapeutics. Single-domain antibodies (sdAbs), also known as nanobodies, are derived from VHHs [variable domains (V) of heavy-chain-only antibodies (HCAb)] of camelid heavy-chain antibodies. These nature-made sdAbs are well suited for various applications due to their favorable characteristics such as small size, ease of genetic manipulation, high affinity and solubility, overall stability, resistance to harsh conditions (e.g., low pH, high temperature), and low immunogenicity. Most importantly, sdAbs have the feature of penetrating into cavities and recognizing hidden epitopes normally inaccessible to conventional antibodies, mainly due to their protruding CDR3/H3 loops. In this unit, we will present and discuss comprehensive and step-by-step protocols routinely practiced in our laboratory for isolating sdAbs from immunized llamas (or other members of the Camelidae family) against target antigens using phage-display technology. Expression, purification, and characterization of the isolated sdAbs will then be described, followed by presentation of several examples of applications of sdAbs previously characterized in our laboratory and elsewhere.

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.