New Computational Protein Design Methods for De Novo Small Molecule Binding Sites

Protein binding to small molecules is fundamental to many biological processes, yet it remains challenging to predictively design this functionality de novo. Current state-of-the-art computational design methods typically rely on existing small molecule binding sites or protein scaffolds with existing shape complementarity for a target ligand. Here we introduce new methods that utilize pools of discrete contacts observed in the Protein Data Bank between protein residues and defined small molecule ligand substructures (ligand fragments). We use the Rosetta Molecular Modeling Suite to recombine protein residues in these contact pools to generate hundreds of thousands of energetically favorable binding sites for a target ligand. These composite binding sites are built into existing scaffold proteins matching the intended binding site geometry with high accuracy. In addition, we apply pools of rotamers interacting with the target ligand to augment Rosetta’s conventional design machinery and improve key metrics known to be predictive of design success. We demonstrate that our method reliably builds diverse binding sites into different scaffold proteins for a variety of target molecules. Our generalizable de novo ligand binding site design method will lay the foundation for versatile design of protein to interface previously unattainable molecules for applications in medical diagnostics and synthetic biology

Designing the molecular future

Approximately 25years ago the first computer applications were conceived for the purpose of automated ‘de novo' drug design, prominent pioneering tools being ALADDIN, CAVEAT, GENOA, and DYLOMMS. Many of these early concepts were enabled by innovative techniques for ligand-receptor interaction modeling like GRID, MCSS, DOCK, and CoMFA, which still provide the theoretical framework for several more recently developed molecular design algorithms. After a first wave of software tools and groundbreaking applications in the 1990s—expressly GROW, GrowMol, LEGEND, and LUDI representing some of the key players—we are currently witnessing a renewed strong interest in this field. Innovative ideas for both receptor and ligand-based drug design have recently been published. We here provide a personal perspective on the evolution of de novo design, highlighting some of the historic achievements as well as possible future developments of this exciting field of research, which combines multiple scientific disciplines and is, like few other areas in chemistry, subject to continuous enthusiastic discussion and compassionate disput

De novo design of a homo-trimeric amantadine-binding protein.

The computational design of a symmetric protein homo-oligomer that binds a symmetry-matched small molecule larger than a metal ion has not yet been achieved. We used de novo protein design to create a homo-trimeric protein that binds the C3 symmetric small molecule drug amantadine with each protein monomer making identical interactions with each face of the small molecule. Solution NMR data show that the protein has regular three-fold symmetry and undergoes localized structural changes upon ligand binding. A high-resolution X-ray structure reveals a close overall match to the design model with the exception of water molecules in the amantadine binding site not included in the Rosetta design calculations, and a neutron structure provides experimental validation of the computationally designed hydrogen-bond networks. Exploration of approaches to generate a small molecule inducible homo-trimerization system based on the design highlight challenges that must be overcome to computationally design such systems

Advances in De Novo Drug Design : From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.Peer reviewe

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties

Design and optimisation of novel Huperzine A analogues capable of modulating the acetylcholinesterase receptor for the management of Alzheimer’s disease

This is a de novo drug design study that aimed to create novel structures based on the alkaloid Huperzine A, capable of inhibiting the acetylcholinesterase (AChE) enzyme ligand binding pocket (AChE_LBP) for the management of Alzheimer’s disease. The X-ray crystallographic model of the Torpedo Californica AChE complexed to Huperzine A was identified from the Protein Data Bank (PDB ID 1VOT). Molecular visualisation and modelling was carried out using SYBYL® 1.2, in silico predicted ligand binding affinity (LBA) was quantified using XSCORE_V1.3 and de novo drug design was carried out using LIGBUILDER®V1.2. Two seed structures were constructed in SYBYL® 1.2 according to a methodology that took into account the relationship between molecular structure and biological activity as described in the literature. Based on SAR data derived from Huperzine A, the points considered to be critical for binding were retained in each seed and planted into the AChE_LBP with growth being allowed according to defined parameters of LIGBUILDER®V1.2. The implication of this study consequently is that novel structures compliant to Lipinski’s Rule of 5 may be promoted to second level drug design which could lead to identification of novel AChE inhibitors with better potency and a low side effect profile.peer-reviewe

Identification of Ligand Templates using Local Structure Alignment for Structure-based Drug Design

With a rapid increase in the number of high-resolution protein-ligand structures, the known protein-ligand structures can be used to gain insight into ligand-binding modes in a target protein. Based on the fact that the structurally similar binding sites share information about their ligands, we have developed a local structure alignment tool, G-LoSA (Graph-based Local Structure Alignment). In G-LoSA, the known protein-ligand binding-site structure library is searched to detect binding-site structures with similar geometry and physicochemical properties to a query binding-site structure regardless of sequence continuity and protein fold. Then, the ligands in the identified complexes are used as templates (i.e., template ligands) to predict/design a ligand for the target protein. The performance of G-LoSA is validated against 76 benchmark targets from the Astex diverse set. Using the currently available protein-ligand structure library, G-LoSA is able to identify a single template ligand (from a non-homologous protein complex) that is highly similar to the target ligand in more than half of the benchmark targets. In addition, our benchmark analyses show that an assembly of structural fragments from multiple template ligands with partial similarity to the target ligand can be used to design novel ligand structures specific to the target protein. This study clearly indicates that a template-based ligand modeling has potential for de novo ligand design and can be a complementary approach to the receptor structure based methods

Merging allosteric and active site binding motifs : de novo generation of target selectivity and potency via natural-product-derived fragments

The de novo design of molecules from scratch with tailored biological activity is still the major intellectual challenge in chemical biology and drug discovery. Herein we validate natural-product-derived fragments (NPDFs) as excellent molecular seeds for the targeted de novo discovery of lead structures for the modulation of therapeutically relevant proteins. The application of this de novo approach delivered, in synergy with the combination of allosteric and active site binding motifs, highly selective and ligand-efficient non-zinc-binding (3: 4-{[5-(2-{[(3-methoxyphenyl)methyl]carbamoyl}eth-1-yn-1-yl)-2,4-dioxo-1,2,3,4-tetrahydropyrimidin-1-yl]methyl}benzoic acid) as well as zinc-binding (4: 4-({5-[2-({[3-(3-carboxypropoxy)phenyl]methyl}carbamoyl)eth-1-yn-1-yl]-2,4-dioxo-1,2,3,4-tetrahydropyrimidin-1-yl}methyl)benzoic acid) uracil-based MMP-13 inhibitors presenting IC50 values of 11 nM (3: LE=0.35) and 6 nM (4: LE=0.31)

The effect of tightly-bound water molecules on scaffold diversity in computer-aided de novo ligand design of CDK2 inhibitors

We have determined the effects that tightly bound water molecules have on the de novo design of cyclin-dependent kinase-2 (CDK2) ligands. In particular, we have analyzed the impact of a specific structural water molecule on the chemical diversity and binding mode of ligands generated through a de novo structure-based ligand generation method in the binding site of CDK2. The tightly bound water molecule modifies the size and shape of the binding site and we have found that it also imposed constraints on the observed binding modes of the generated ligands. This in turn had the indirect effect of reducing the chemical diversity of the underlying molecular scaffolds that were able to bind to the enzyme satisfactorily