Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

Grid‐based molecular footprint comparison method for docking and de novo design: Application to HIVgp41

Scoring functions are a critically important component of computer‐aided screening methods for the identification of lead compounds during early stages of drug discovery. Here, we present a new multigrid implementation of the footprint similarity (FPS) scoring function that was recently developed in our laboratory which has proven useful for identification of compounds which bind to a protein on a per‐residue basis in a way that resembles a known reference. The grid‐based FPS method is much faster than its Cartesian‐space counterpart, which makes it computationally tractable for on‐the‐fly docking, virtual screening, or de novo design. In this work, we establish that: (i) relatively few grids can be used to accurately approximate Cartesian space footprint similarity, (ii) the method yields improved success over the standard DOCK energy function for pose identification across a large test set of experimental co‐crystal structures, for crossdocking, and for database enrichment, and (iii) grid‐based FPS scoring can be used to tailor construction of new molecules to have specific properties, as demonstrated in a series of test cases targeting the viral protein HIVgp41. The method is available in the program DOCK6. © 2013 Wiley Periodicals, Inc.     

Discovery of Small‐Molecule Interleukin‐2 Inhibitors from a DNA‐Encoded Chemical Library

Libraries of chemical compounds individually coupled to encoding DNA tags (DNA‐encoded chemical libraries) hold promise to facilitate exceptionally efficient ligand discovery. We constructed a high‐quality DNA‐encoded chemical library comprising 30 000 drug‐like compounds; this was screened in 170 different affinity capture experiments. High‐throughput sequencing allowed the evaluation of 120 million DNA codes for a systematic analysis of selection strategies and statistically robust identification of binding molecules. Selections performed against the tumor‐associated antigen carbonic anhydrase IX (CA IX) and the pro‐inflammatory cytokine interleukin‐2 (IL‐2) yielded potent inhibitors with exquisite target specificity. The binding mode of the revealed pharmacophore against IL‐2 was confirmed by molecular docking. Our findings suggest that DNA‐encoded chemical libraries allow the facile identification of drug‐like ligands principally to any protein of choice, including molecules capable of disrupting high‐affinity protein–protein interactions.     

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

Structure‐based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this “ab initio” approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT‐Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein structure alignment program SPalign. The method was found to yield a consistent performance in DUD and DUD‐E docking benchmarks even if model structures were employed. It improves over docking methods (DOCK6 and AUTODOCK Vina) and has a performance comparable to or better than other binding‐homology methods (FINDsite and PoLi) with higher computational efficiency. The server is available at http://sparks-lab.org . © 2016 Wiley Periodicals, Inc.     

20 years of DNA-encoded chemical libraries

The identification of specific binding molecules is a central problem in chemistry, biology and medicine. Therefore, technologies, which facilitate ligand discovery, may substantially contribute to a better understanding of biological processes and to drug discovery. DNA-encoded chemical libraries represent a new inexpensive tool for the fast and efficient identification of ligands to target proteins of choice. Such libraries consist of collections of organic molecules, covalently linked to a unique DNA tag serving as an amplifiable identification bar code. DNA-encoding enables the in vitro selection of ligands by affinity capture at sub-picomolar concentrations on virtually any target protein of interest, in analogy to established selection methodologies like antibody phage display. Multiple strategies have been investigated by several academic and industrial laboratories for the construction of DNA-encoded chemical libraries comprising up to millions of DNA-encoded compounds. The implementation of next generation high-throughput sequencing enabled the rapid identification of binding molecules from DNA-encoded libraries of unprecedented size. This article reviews the development of DNA-encoded library technology and its evolution into a novel drug discovery tool, commenting on challenges, perspectives and opportunities for the different experimental approaches.     

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compounds. Although these "random" methods sample larger areas of chemical space than traditional synthetic approaches, only a relatively small percentage of all possible compounds are practically accessible. It is therefore helpful to select regions of chemical space that have greater likelihood of yielding useful leads. When three-dimensional structural data are available for the target molecule this can be achieved by applying structure-based computational design methods to focus the combinatorial library. This is advantageous over the standard usage of computational methods to design a small number of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to determine a propensity for binding of certain chemical moieties in regions of the target molecule. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-based design tool, to design a focused combinatorial library for the class II MHC protein HLA-DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-ligand complex could not be determined, experimental results including cross-exclusion of a known HLA-DR4 peptide ligand (HA) by a compound from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.     

A genetic algorithm for structure-based de novo design

Genetic algorithms have properties which make them attractive in de novo drug design. Like other de novo design programs, genetic algorithms require a method to reduce the enormous search space of possible compounds. Most often this is done using information from known ligands. We have developed the ADAPT program, a genetic algorithm which uses molecular interactions evaluated with docking calculations as a fitness function to reduce the search space. ADAPT does not require information about known ligands. The program takes an initial set of compounds and iteratively builds new compounds based on the fitness scores of the previous set of compounds. We describe the particulars of the ADAPT algorithm and its application to three well-studied target systems. We also show that the strategies of enhanced local sampling and re-introducing diversity to the compound population during the design cycle provide better results than conventional genetic algorithm protocols.     

Targeting programmed cell death 4 (PDCD4) with biogenic compounds in ARDS by Gaussian process‐based QSAR virtual screening

The programmed cell death 4 (PDCD4) has recently been recognized as a new and attractive target of acute respiratory distress syndrome. Here, we attempted to discover new and potent PDCD4 mediator ligands from biogenic compounds using a synthetic strategy of statistical virtual screening and experimental affinity assay. In the procedure, a Gaussian process‐based quantitative structure‐activity relationship regression predictor was developed and validated statistically based on a curated panel of structure‐based protein‐ligand affinity data. The predictor was integrated with pharmacokinetics analysis, chemical redundancy reduction, and flexible molecular docking to perform high‐throughput virtual screening against a distinct library of chemically diverse, drug‐like biogenic compounds. Consequently, 6 hits with top scores were selected, and their binding affinities to the recumbent protein of human PDCD4 were identified, 3 out of which were determined to have high or moderate affinity with K_d at micromolar level. Structural analysis of protein‐ligand complexes revealed that hydrophobic interactions and van der Waals contacts are the primary chemical forces to stabilize the complex architecture of PDCD4 with these mediator ligands, while few hydrogen bonds, salt bridges, and/or π‐π stacking at the complex interfaces confer selectivity and specificity for the protein‐ligand recognition. It is suggested that the statistical Gaussian process‐based quantitative structure‐activity relationship screening strategy can be successfully applied to rational discovery of biologically active compounds. The newly identified molecular entities targeting PDCD4 are considered as promising lead scaffolds to develop novel chemical therapeutics for acute respiratory distress syndrome.     

Multi‐Objective Molecular De Novo Design by Adaptive Fragment Prioritization

We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on‐ and off‐target binding. The approach translates the nature‐inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure–activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype‐selective and multitarget‐modulating dopamine D₄ antagonists, as well as ligands selective for the sigma‐1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand‐based computer‐aided molecular design method may guide target‐focused combinatorial chemistry.     

Quality, not quantity: the role of natural products and chemical proteomics in modern drug discovery

Chemical genetics and reverse chemical genetics parallel classical genetics but target genes at the protein level and have proven useful in recent years for screening combinatorial libraries for compounds of biological interest. However, the performance of combinatorial chemistry in filling pharmaceutical pipelines has been lower than anticipated and the tide may be turning back to Nature in the search for new drug candidates. Even though diversity oriented synthesis is now producing molecules that are natural product-like in terms of size and complexity, these molecules have not evolved to interact with biomolecules. Natural products, on the other hand, have evolved to interact with biomolecules, which is why so many can be found in pharmacopoeias. However, the cellular targets and modes of action of these fascinating compounds are seldom known, hindering the drug development process. This review focuses on the emergence of chemical proteomics and reverse chemical proteomics as tools for the discovery of cellular receptors for natural products, thereby generating protein/ligand pairs that will prove useful in identifying new drug targets and new biologically active small molecule scaffolds. Such a system-wide approach to identifying new drugable targets and their small molecule ligands will help unblock the pharmaceutical product pipelines by speeding the process of target and lead identification.     

Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 10⁸ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure–property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.

Bayesian optimization can accelerate structure-based virtual screening campaigns by minimizing the total number of simulations performed while still identifying the vast majority of computational hits.     

Automated Docking and the Search for HIV Protease Inhibitors

Abstract

This article will discuss the motivations, technologies, and future directions of computational automated docking in the context of the structure-based rational design of HIV-1 protease inhibitors. Docking simulations are widely used for screening of compound libraries to identify new drug leads, employing a simple model for rapid testing of thousands of compounds. Docking simulations are also useful for lead enhancement, using more detailed models to analyze the atomic interactions between inhibitors and target macromolecules. Major advances have been reported in the development of empirical force fields, which now allow assessment of relative binding strength and drug specificity, and extensions of automated docking techniques allow de novo drug design.     

Towards the automatic design of synthetically accessible protein ligands: Peptides, amides and peptidomimetics

Summary The computer program LUDI for the de novo design of protein ligands was extended so that it is now able to take into account the synthetic accessibility of the constructed molecules. As an example, the design of peptides, amides and peptidomimetics using amino acids as building blocks is described. Two new libraries containing natural and non-natural amino acids were constructed for this purpose. Conformational flexibility is taken into account by using multiple conformers for each amino acid. The program was applied to the design of ligands for the enzymes elastase, renin and thermolysin.     

Virtual screening and library enumeration of new hydroxycinnamates based antioxidant compounds: A complete framework

Designing of molecules for drugs is important topic from many decades. The search of new drugs is very hard, and it is expensive process. Computer assisted framework can provide the fastest way to design and screen drug-like compounds. In present work, a multidimensional approach is introduced for the designing and screening of antioxidant compounds. Antioxidants play a crucial role in ensuring that the body's oxidizing and reducing species are kept in the proper balance, minimizing oxidative stress. Machine learning models are used to predict antioxidant activity. Three hydroxycinnamates are selected as standard antioxidants. Similar compounds are searched from ChEMBL database using chemical structural similarity method. The libraries of new compounds are generated using evolutionary method. New compounds are also designed using automatic decomposition and construction building blocks. The antioxidant activity of all designed and searched compounds is predicted using machine learning models. The chemical space of searched and generated compounds is envisioned using t-distributed stochastic neighbor embedding (t-SNE) method. Best compounds are shortlisted, and their synthetic accessibility is predicted to further facilitate the experimental chemists. The chemical similarity between standard and selected compounds is also studied using fingerprints and heatmap.     

Expanding medicinal chemistry into 3D space: metallofragments as 3D scaffolds for fragment-based drug discovery

Fragment-based drug discovery (FBDD) is a powerful strategy for the identification of new bioactive molecules. FBDD relies on fragment libraries, generally of modest size, but of high chemical diversity. Although good chemical diversity in FBDD libraries has been achieved in many respects, achieving shape diversity – particularly fragments with three-dimensional (3D) structures – has remained challenging. A recent analysis revealed that >75% of all conventional, organic fragments are predominantly 1D or 2D in shape. However, 3D fragments are desired because molecular shape is one of the most important factors in molecular recognition by a biomolecule. To address this challenge, the use of inert metal complexes, so-called ‘metallofragments’ (mFs), to construct a 3D fragment library is introduced. A modest library of 71 compounds has been prepared with rich shape diversity as gauged by normalized principle moment of inertia (PMI) analysis. PMI analysis shows that these metallofragments occupy an area of fragment space that is unique and highly underrepresented when compared to conventional organic fragment libraries that are comprised of orders of magnitude more molecules. The potential value of this metallofragment library is demonstrated by screening against several different types of proteins, including an antiviral, an antibacterial, and an anticancer target. The suitability of the metallofragments for future hit-to-lead development was validated through the determination of IC₅₀ and thermal shift values for select fragments against several proteins. These findings demonstrate the utility of metallofragment libraries as a means of accessing underutilized 3D fragment space for FBDD against a variety of protein targets.

Fragment-based drug discovery (FBDD) using 3-dimensional metallofragments is a new strategy for the identification of bioactive molecules.     

Quantum probability ranking principle for ligand-based virtual screening

Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.     

Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site

Eg5, a mitotic kinesin exclusively involved in the formation and function of the mitotic spindle has attracted interest as an anticancer drug target. Eg5 is co-crystallized with several inhibitors bound to its allosteric binding pocket. Each of these occupies a pocket formed by loop 5/helix α2 (L5/α2). Recently designed inhibitors additionally occupy a hydrophobic pocket of this site. The goal of the present study was to explore this hydrophobic pocket with our MED-SuMo fragment-based protocol, and thus discover novel chemical structures that might bind as inhibitors. The MED-SuMo software is able to compare and superimpose similar interaction surfaces upon the whole protein data bank (PDB). In a fragment-based protocol, MED-SuMo retrieves MED-Portions that encode protein-fragment binding sites and are derived from cross-mining protein-ligand structures with libraries of small molecules. Furthermore we have excluded intra-family MED-Portions derived from Eg5 ligands that occupy the hydrophobic pocket and predicted new potential ligands by hybridization that would fill simultaneously both pockets. Some of the latter having original scaffolds and substituents in the hydrophobic pocket are identified in libraries of synthetically accessible molecules by the MED-Search software. Ksenia Oguievetskaia and Laetitia Martin-Chanas contributed equally to this work.