The use of small-molecule structures to complement protein-ligand crystal structures in drug discovery

Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning

Metalloproteins require metal ions as cofactors to catalyze specific reactions with remarkable efficiency and specificity. In various electron transfer reactions, metals in the active sites change their oxidation states to facilitate the biochemical reactions. Cryogenic electron microscopy, X-ray, and X-ray free electron laser (XFEL) crystallography are used to image metalloproteins to understand the reaction mechanisms. However, radiation damage in cryoEM and X-ray crystallography, and the challenge of generating homogeneous crystals and keeping the appropriate experimental conditions for all the crystals in XFEL crystallography, may alter the oxidation states. Here, we build machine learning models trained on a large data set from the Cambridge Crystallographic Data Center to evaluate the metal oxidation states. The models yield high accuracy scores (from 82% to 94%) for all metals in the small molecules. Then, they were used to predict the oxidation states of more than 30 000 metal clusters in metalloproteins with Fe, Mn, Co, and Cu in their active sites. We found that most of the metals exist in the lower oxidation states (Fe2+ 77%, Mn2+ 85%, Co2+ 65%, and Cu+ 64%), and these populations correlate with the standard reduction potentials of the metal ions. Furthermore, we found no clear correlation between these populations and the resolution of the structures, which suggests no significant dependence of these predictions on the resolution. Our models represent a valuable tool for evaluating the oxidation states of the metals in metalloproteins imaged with different techniques. The data files and the machine learning code are available in a public GitHub repository: https://github.com/mamin03/OxitationStatesMetalloprotein.git.

Structure-guided engineering of a receptor-agonist pair for inducible activation of the ABA adaptive response to drought

Strategies to activate abscisic acid (ABA) receptors and boost ABA signaling by small molecules that act as ABA receptor agonists are promising biotechnological tools to enhance plant drought tolerance. Protein structures of crop ABA receptors might require modifications to improve recognition of chemical ligands, which in turn can be optimized by structural information. Through structure-based targeted design, we have combined chemical and genetic approaches to generate an ABA receptor agonist molecule (iSB09) and engineer a CsPYL1 ABA receptor, named CsPYL1^5m, which efficiently binds iSB09. This optimized receptor-agonist pair leads to activation of ABA signaling and marked drought tolerance. No constitutive activation of ABA signaling and hence growth penalty was observed in transformed Arabidopsis thaliana plants. Therefore, conditional and efficient activation of ABA signaling was achieved through a chemical-genetic orthogonal approach based on iterative cycles of ligand and receptor optimization driven by the structure of ternary receptor-ligand-phosphatase complexes.

Graph-Convolutional Neural Net Model of the Statistical Torsion Profiles for Small Organic Molecules

We present a graph-convolutional neural network (GCNN)-based method for learning and prediction of statistical torsional profiles (STP) in small organic molecules based on the experimental X-ray structure data. A specialized GCNN torsion profile model is trained using the structures in the Crystallography Open Database (COD). The GCNN-STP model captures torsional preferences over a wide range of torsion rotor chemotypes and correctly predicts a variety of effects from the vicinal atoms and moieties. GCNN-STP statistical profiles also show good agreement with quantum chemically (DFT) calculated torsion energy profiles. Furthermore, we demonstrate the application of the GCNN-STP statistical profiles for conformer generation. A web server that allows interactive profile prediction and viewing is made freely available at https://www.molsoft.com/tortool.html.

Transferability of Geometric Patterns from Protein Self-Interactions to Protein-Ligand Interactions

There is significant interest in developing machine learning methods to model protein-ligand interactions but a scarcity of experimentally resolved protein-ligand structures to learn from. Protein self-contacts are a much larger source of structural data that could be leveraged, but currently it is not well understood how this data source differs from the target domain. Here, we characterize the 3D geometric patterns of protein self-contacts as probability distributions. We then present a flexible statistical framework to assess the transferability of these patterns to protein-ligand contacts. We observe that the level of transferability from protein self-contacts to protein-ligand contacts depends on contact type, with many contact types exhibiting high transferability. We then demonstrate the potential of leveraging information from these geometric patterns to aid in ligand pose-selection problems in protein-ligand docking. We publicly release our extracted data on geometric interaction patterns to enable further exploration of this problem.

The First Insight Into the Supramolecular System of D,L-α-Difluoromethylornithine: A New Antiviral Perspective

Targeting the polyamine biosynthetic pathway by inhibiting ornithine decarboxylase (ODC) is a powerful approach in the fight against diverse viruses, including SARS-CoV-2. Difluoromethylornithine (DFMO, eflornithine) is the best-known inhibitor of ODC and a broad-spectrum, unique therapeutical agent. Nevertheless, its pharmacokinetic profile is not perfect, especially when large doses are required in antiviral treatment. This article presents a holistic study focusing on the molecular and supramolecular structure of DFMO and the design of its analogues toward the development of safer and more effective formulations. In this context, we provide the first deep insight into the supramolecular system of DFMO supplemented by a comprehensive, qualitative and quantitative survey of non-covalent interactions via Hirshfeld surface, molecular electrostatic potential, enrichment ratio and energy frameworks analysis visualizing 3-D topology of interactions in order to understand the differences in the cooperativity of interactions involved in the formation of either basic or large synthons (Long-range Synthon Aufbau Modules, LSAM) at the subsequent levels of well-organized supramolecular self-assembly, in comparison with the ornithine structure. In the light of the drug discovery, supramolecular studies of amino acids, essential constituents of proteins, are of prime importance. In brief, the same amino-carboxy synthons are observed in the bio-system containing DFMO. DFT calculations revealed that the biological environment changes the molecular structure of DFMO only slightly. The ADMET profile of structural modifications of DFMO and optimization of its analogue as a new promising drug via molecular docking are discussed in detail.

A Proline-Based Tectons and Supramolecular Synthons for Drug Design 2.0: A Case Study of ACEI

Proline is a unique, endogenous amino acid, prevalent in proteins and essential for living organisms. It is appreciated as a tecton for the rational design of new bio-active substances. Herein, we present a short overview of the subject. We analyzed 2366 proline-derived structures deposited in the Cambridge Structure Database, with emphasis on the angiotensin-converting enzyme inhibitors. The latter are the first-line antihypertensive and cardiological drugs. Their side effects prompt a search for improved pharmaceuticals. Characterization of tectons (molecular building blocks) and the resulting supramolecular synthons (patterns of intermolecular interactions) involving proline derivatives, as presented in this study, may be useful for in silico molecular docking and macromolecular modeling studies. The DFT, Hirshfeld surface and energy framework methods gave considerable insight into the nature of close inter-contacts and supramolecular topology. Substituents of proline entity are important for the formation and cooperation of synthons. Tectonic subunits contain proline moieties characterized by diverse ionization states: -N and -COOH(-COO-), -N+ and -COOH(-COO-), -NH and -COOH(-COO-), -NH+ and -COOH(-COO-), and -NH2+ and -COOH(-COO-). Furthermore, pharmacological profiles of ACE inhibitors and their impurities were determined via an in silico approach. The above data were used to develop comprehensive classification, which may be useful in further drug design studies.

Frequency and hydrogen bonding of nucleobase homopairs in small molecule crystals

We used the high resolution and accuracy of the Cambridge Structural Database (CSD) to provide detailed information regarding base pairing interactions of selected nucleobases. We searched for base pairs in which nucleobases interact with each other through two or more hydrogen bonds and form more or less planar structures. The investigated compounds were either free forms or derivatives of adenine, guanine, hypoxanthine, thymine, uracil and cytosine. We divided our findings into categories including types of pairs, protonation patterns and whether they are formed by free bases or substituted ones. We found base pair types that are exclusive to small molecule crystal structures, some that can be found only in RNA containing crystal structures and many that are native to both environments. With a few exceptions, nucleobase protonation generally followed a standard pattern governed by pKa values. The lengths of hydrogen bonds did not depend on whether the nucleobases forming a base pair were charged or not. The reasons why particular nucleobases formed base pairs in a certain way varied significantly.

A Supramolecular Approach to Structure-Based Design with A Focus on Synthons Hierarchy in Ornithine-Derived Ligands: Review, Synthesis, Experimental and in Silico Studies

The success of innovative drugs depends on an interdisciplinary and holistic approach to their design and development. The supramolecular architecture of living systems is controlled by non-covalent interactions to a very large extent. The latter are prone to extensive cooperation and like a virtuoso play a symphony of life. Thus, the design of effective ligands should be based on thorough knowledge on the interactions at either a molecular or high topological level. In this work, we emphasize the importance of supramolecular structure and ligand-based design keeping the potential of supramolecular H-bonding synthons in focus. In this respect, the relevance of supramolecular chemistry for advanced therapies is appreciated and undisputable. It has developed tools, such as Hirshfeld surface analysis, using a huge data on supramolecular interactions in over one million structures which are deposited in the Cambridge Structure Database (CSD). In particular, molecular interaction surfaces are useful for identification of macromolecular active sites followed by in silico docking experiments. Ornithine-derived compounds are a new, promising class of multi-targeting ligands for innovative therapeutics and cosmeceuticals. In this work, we present the synthesis together with the molecular and supramolecular structure of a novel ornithine derivative, namely N-α,N-δ)-dibenzoyl-(α)-hydroxymethylornithine, 1. It was investigated by modern experimental and in silico methods in detail. The incorporation of an aromatic system into the ornithine core induces stacking interactions, which are vital in biological processes. In particular, rare C=O…π intercontacts have been identified in 1. Supramolecular interactions were analyzed in all structures of ornithine derivatives deposited in the CSD. The influence of substituent was assessed by the Hirshfeld surface analysis. It revealed that the crystal packing is stabilized mainly by H…O, O…H, C…H, Cl (Br, F)…H and O…O interactions. Additionally, π…π, C-H…π and N-O…π interactions were also observed. All relevant H-bond energies were calculated using the Lippincott and Schroeder H-bond model. A library of synthons is provided. In addition, the large synthons (Long-Range Synthon Aufbau Module) were considered. The DFT optimization either in vacuo or in solutio yields very similar molecular species. The major difference with the relevant crystal structure was related to the conformation of terminal benzoyl C15-C20 ring. Furthermore, in silico prediction of the extensive physicochemical ADME profile (absorption, distribution, metabolism and excretion) related to the drug-likeness and medicinal chemistry friendliness revealed that a novel ornithine derivative 1 has the potential to be a new drug candidate. It has shown good in silico absorption and very low toxicity.

Why is interoperability between the two fields of chemical crystallography and protein crystallography so difficult?

The interoperability of chemical and biological crystallographic data is a key challenge to research and its application to pharmaceutical design. Research attempting to combine data from the two disciplines, small-molecule or chemical crystallography (CX) and macromolecular crystallography (MX), will face unique challenges including variations in terminology, software development, file format and databases which differ significantly from CX to MX. This perspective overview spans the two disciplines and originated from the investigation of protein binding to model radiopharmaceuticals. The opportunities of interlinked research while utilizing the two databases of the CSD (Cambridge Structural Database) and the PDB (Protein Data Bank) will be highlighted. The advantages of software that can handle multiple file formats and the circuitous route to convert organometallic small-molecule structural data for use in protein refinement software will be discussed. In addition some pointers to avoid being shipwrecked will be shared, such as the care which must be taken when interpreting data precision involving small molecules versus proteins.

A Million Crystal Structures: The Whole Is Greater than the Sum of Its Parts

The founding in 1965 of what is now called the Cambridge Structural Database (CSD) has reaped dividends in numerous and diverse areas of chemical research. Each of the million or so crystal structures in the database was solved for its own particular reason, but collected together, the structures can be reused to address a multitude of new problems. In this Review, which is focused mainly on the last 10 years, we chronicle the contribution of the CSD to research into molecular geometries, molecular interactions, and molecular assemblies and demonstrate its value in the design of biologically active molecules and the solid forms in which they are delivered. Its potential in other commercially relevant areas is described, including gas storage and delivery, thin films, and (opto)electronics. The CSD also aids the solution of new crystal structures. Because no scientific instrument is without shortcomings, the limitations of CSD research are assessed. We emphasize the importance of maintaining database quality: notwithstanding the arrival of big data and machine learning, it remains perilous to ignore the principle of garbage in, garbage out. Finally, we explain why the CSD must evolve with the world around it to ensure it remains fit for purpose in the years ahead.

New developments in crystallography: exploring its technology, methods and scope in the molecular biosciences

Since the Protein Data Bank (PDB) was founded in 1971, there are now over 120,000 depositions, the majority of which are from X-ray crystallography and 90% of those made use of synchrotron beamlines. At the Cambridge Structure Database (CSD), founded in 1965, there are more than 800,000 'small molecule' crystal structure depositions and a very large number of those are relevant in the biosciences as ligands or cofactors. The technology for crystal structure analysis is still developing rapidly both at synchrotrons and in home labs. Determination of the details of the hydrogen atoms in biological macromolecules is well served using neutrons as probe. Large multi-macromolecular complexes cause major challenges to crystallization; electrons as probes offer unique advantages here. Methods developments naturally accompany technology change, mainly incremental but some, such as the tuneability, intensity and collimation of synchrotron radiation, have effected radical changes in capability of biological crystallography. In the past few years, the X-ray laser has taken X-ray crystallography measurement times into the femtosecond range. In terms of applications many new discoveries have been made in the molecular biosciences. The scope of crystallographic techniques is indeed very wide. As examples, new insights into chemical catalysis of enzymes and relating ligand bound structures to thermodynamics have been gained but predictive power is seen as not yet achieved. Metal complexes are also an emerging theme for biomedicine applications. Our studies of coloration of live and cooked lobsters proved to be an unexpected favourite with the public and schoolchildren. More generally, public understanding of the biosciences and crystallography's role within the field have been greatly enhanced by the United Nations International Year of Crystallography coordinated by the International Union of Crystallography. This topical review describes each of these areas along with illustrative results to document the scope of each methodology.