DrugDevCovid19: An Atlas of Anti-COVID-19 Compounds Derived by Computer-Aided Drug Design

Since the outbreak of SARS-CoV-2, numerous compounds against COVID-19 have been derived by computer-aided drug design (CADD) studies. They are valuable resources for the development of COVID-19 therapeutics. In this work, we reviewed these studies and analyzed 779 compounds against 16 target proteins from 181 CADD publications. We performed unified docking simulations and neck-to-neck comparison with the solved co-crystal structures. We computed their chemical features and classified these compounds, aiming to provide insights for subsequent drug design. Through detailed analyses, we recommended a batch of compounds that are worth further study. Moreover, we organized all the abundant data and constructed a freely available database, DrugDevCovid19, to facilitate the development of COVID-19 therapeutics.     

Catalytic Activity of Enzymes Altered at Their Active Sites

Protein engineering has as its goals the design and construction of new peptides and proteins with novel binding and catalytic properties. In one approach to protein engineering, new active sites have been introduced into naturally occurring proteins either by site-directed mutagenesis or by chemical modification. Providing that important changes in the tertiary structures do not result from such alterations, at least a portion of the binding site of the original protein should be available for the formation of complexes between the altered enzyme and its substrates. Many examples of active-site mutations have been described, including the generation by us of a cysteine mutant of alkaline phosphatase. A fundamental limitation of the site-directed mutagenesis methodology is that replacements of residues are restricted to the twenty naturally occurring amino acids. The alternative, chemical modification, is difficult to carry out for the specific replacement of one amino acid by another. However, we have shown that through such modification coenzyme analogues can be introduced covalently into appropriate positions in proteins, allowing us to produce semisynthetic enzymes with catalytic activities radically altered from those of their precursor proteins. In another approach to protein engineering efforts have focused on the construction of systems where, as a first approximation, folding can be neglected and the preparation of secondary structural units is the target. Examples of the successful design of biologically active peptides and proteins along such lines, taken from our own work, include molecules mimicking apolipoproteins, toxins, and many hormones. In recent studies we have progressed to the stage where we are starting to combine the two general approaches to protein engineering we have described and are able to construct small enzymes like ribonuclease T₁ and its structural analogues.     

Integration of virtual and high throughput screening in lead discovery settings

In the last decade mass screening strategies became the main source of leads in drug discovery settings. Although high throughput (HTS) and virtual screening (VS) realize the same concept the different nature of these lead discovery strategies (experimental vs theoretical) results that they are typically applied separately. The majority of drug leads are still identified by hit-to-lead optimization of screening hits. Structural information on the target as well as on bound ligands, however, make structure-based and ligand-based virtual screening available for the identification of alternative chemical starting points. Although, the two techniques have rarely been used together on the same target, here we review the existing prominent studies on their true integration. Various approaches have been shown to apply the combination of HTS and VS and to better use them in lead generation. Although several attempts on their integration have only been considered at a conceptual level, there are numerous applications underlining its relevance that early-stage pharmaceutical drug research could benefit from a combined approach.     

SPROUT: A program for structure generation

Summary SPROUT is a new computer program for constrained structure generation that is designed to generate molecules for a range of applications in molecular recognition. It uses artificial intelligence techniques to moderate the combinatorial explosion that is inherent in structure generation. The program is presented here for the design of enzyme inhibitors. Structure generation is divided into two phases: (i) primary structure generation to produce molecular graphs to fit the steric constraints; and (ii) secondary structure generation which is the process of introducing appropriate functionality to the graphs to produce molecules that satisfy the secondary constraints, e.g., electrostatics and hydrophobicity. Primary structure generation has been tested on two enzyme receptor sites; the p-amidino-phenyl-pyruvate binding site of trypsin and the acetyl pepstatin binding site of HIV-1 protease. The program successfully generates structures that resemble known substrates and, more importantly, the predictive power of the program has been demonstrated by its ability to suggest novel structures.     

Virtual Screening and Structure Generation Applied to Drug Design

The methods of computer-aided drug design can be divided into two categories according to whether or not the structures of receptors are known1, corresponding to two principal strategies: (1) searching the bio-active ligands against virtual combinatorial libraries and calculating the affinity energy between ligand and receptor by docking ; (2) QSAR and 3D-structure data-mining. 3D-QSAR method is now applied widely to drug discovery, but this method is generally limited to refine the structu…     

Internet resources in GPCR modelling1

G-Protein coupled receptors (GPCRs), one of the most important families of drug targets, belong to the super family of integral membrane proteins characterized by seven transmembrane helices. Because they are difficult to crystallize, the three dimensional structure of these receptors have not yet been determined by X-ray crystallography, except one. In the absence of a 3-D structure, in-silico approaches for solving the structure of this class of proteins are widely used and provide valuable information for structure based drug design. There are several web servers and computer programs available that automate the modelling process of GPCRs. Some of these include Modeller, Swiss-Model server, Homer, etc. Using these tools reliable homology models of human histamine H1 receptor (HRH1) and thrombin receptor (PAR-1) have been generated which explain the binding mode of the standard antagonists of these receptors and may be useful in designing their novel antagonists.     

Internet resources in GPCR modelling

G-Protein coupled receptors (GPCRs), one of the most important families of drug targets, belong to the super family of integral membrane proteins characterized by seven transmembrane helices. Because they are difficult to crystallize, the three dimensional structure of these receptors have not yet been determined by X-ray crystallography, except one. In the absence of a 3-D structure, in-silico approaches for solving the structure of this class of proteins are widely used and provide valuable information for structure based drug design. There are several web servers and computer programs available that automate the modelling process of GPCRs. Some of these include Modeller, Swiss-Model server, Homer, etc. Using these tools reliable homology models of human histamine H1 receptor (HRH1) and thrombin receptor (PAR-1) have been generated which explain the binding mode of the standard antagonists of these receptors and may be useful in designing their novel antagonists.     

High-throughput, accurate mass liquid chromatography/tandem mass spectrometry on a quadrupole time-of-flight system as a 'first-line' approach for metabolite identification studies

Throughput for drug metabolite identification studies has been increased significantly by the combined use of accurate mass liquid chromatography/tandem mass spectrometry (LC/MS/MS) data on a quadrupole time-of-flight (QTOF) system and targeted data analysis procedures. Employed in concert, these tools have led to the implementation of a semi-automated high-throughput metabolite identification strategy that has been incorporated successfully into lead optimization efforts in drug discovery. The availability of elemental composition data on precursor and all fragment ions in each spectrum has greatly enhanced confidence in ion structure assignments, while computer-based algorithms for defining sites of biotransformation based upon mass shifts of diagnostic fragment ions have facilitated identification of positions of metabolic transformation in drug candidates. Adoption of this technology as the 'first-line' approach for in vitro metabolite profiling has resulted in the analysis of as many as 21 new chemical entities on one day from diverse structural classes and therapeutic programs.     

Engineering of Challenging G Protein-Coupled Receptors for Structure Determination and Biophysical Studies

Membrane proteins such as G protein-coupled receptors (GPCRs) exert fundamental biological functions and are involved in a multitude of physiological responses, making these receptors ideal drug targets. Drug discovery programs targeting GPCRs have been greatly facilitated by the emergence of high-resolution structures and the resulting opportunities to identify new chemical entities through structure-based drug design. To enable the determination of high-resolution structures of GPCRs, most receptors have to be engineered to overcome intrinsic hurdles such as their poor stability and low expression levels. In recent years, multiple engineering approaches have been developed to specifically address the technical difficulties of working with GPCRs, which are now beginning to make more challenging receptors accessible to detailed studies. Importantly, successfully engineered GPCRs are not only valuable in X-ray crystallography, but further enable biophysical studies with nuclear magnetic resonance spectroscopy, surface plasmon resonance, native mass spectrometry, and fluorescence anisotropy measurements, all of which are important for the detailed mechanistic understanding, which is the prerequisite for successful drug design. Here, we summarize engineering strategies based on directed evolution to reduce workload and enable biophysical experiments of particularly challenging GPCRs.     

Computer-assisted studies of molecular structure-biological activity relationships

Computer-assisted methods can be used to investigate the relationships between the molecular structures of compounds and their biological activity. A number of approaches have been reported in the literature, including correlations of activity with substituent constants, conformational analysis and display, quantum mechanical methods, and methods relying on discriminant development and pattern-recognition techniques. Application areas for this technology include drug design, agricultural chemical design, and studies of chemical toxicity and genetic toxicity (mutagenic or carcinogenic potential). These structure-activity methods are introduced, and citations are given. Several current structure-activity relationship (SAR) studies using pattern recognition are presented as examples of typical projects that are feasible with this approach. These include the investigation of a set of 122 antiinflammatory steroids, a study of 153 retinoids for cancer prevention, and a study of chemicals that have been tested in a sister chromatid exchange mutagen screen.     

Chemoinformatics: a new field with a long tradition

Chemoinformatics is the application of informatics methods to solve chemical problems. Although this term was introduced only a few years ago, this field has a long history with its roots going back more than 40 years. Work on chemical structure representation and searching, quantitative structure–activity relationships, chemometrics, molecular modeling as well as computer-assisted structure elucidation and synthesis design was initiated in the 1960s. These different origins have now merged into a discipline of its own that is in full bloom. All areas of chemistry from analytical chemistry to drug design can benefit from chemoinformatics methods. And there are still many challenging chemical problems waiting for solutions through the further development of chemoinformatics.     

From pharmacophore to leads: Bioactive compound discovery via computer-aided techniques

Based on the concept of Green Chemistry, a new procedure of finding bioactive compounds and their synthetic routes by computer-aided techniques was proposed. The procedure consists of pharmacophore search against a 3D structural database of natural products for lead discovery and computer-aided synthesis design for avoiding unuseful synthetic experiments. This work demonstrated that computer aided drug design and synthesis design would help us to make the consideration of environmental concerns systematically, rather than having to deal later with the unnecessary waste chemicals. Thus, it is shown that chemical computer-aided design (CAD) is an indispensable part of Green Chemistry.     

Generation of statin drug metabolites through electrochemical and enzymatic oxidations

The generation of key drug metabolites for the purpose of their complete structural characterization, toxicity testing, as well as to serve as standards for quantitative studies, is a critical step in the pharmaceutical discovery and development cycle. Here, we utilized electrochemistry/mass spectrometry for the detection and subsequent generation of six phase I metabolites of simvastatin and lovastatin. Both simvastatin and lovastatin are widely used for the treatment of hypercholesterolemia. There are known drug–drug interaction issues of statin therapy, and it has been suggested that the oxidative metabolites may contribute to the cholesterol-lowering effect of both statins. Of the known phase I metabolites of simvastatin and lovastatin, none are commercially available, and chemical means for the synthesis of a very few of them have been previously reported. Here, we report that electrochemical oxidation of less than 1 mg each of simvastatin and lovastatin led to the generation of three oxidative metabolites of each parent to allow complete nuclear magnetic resonance characterization of all six metabolites. The yields obtained by the electrochemical approach were also compared with incubation of parent drug with commercially available bacterial mutant CYP102A1 enzymes, and it was found that the electrochemical approach gave higher yields than the enzymatic oxidations for the generation of most of the observed oxidative metabolites in this study.

Rational design of nanocarriers for mitochondria-targeted drug delivery

Well-developed mitochondria-targeted nanocarriers for function regulation are highly desirable. Numerous studies have been conducted on the treatment of mitochondria-related diseases; however, further improvements are required to develop more effective drug delivery methods. Herein, we comprehensively introduce recent developments progress in rational design of mitochondria-targeted nanocarriers, and discuss the different strategies of available nanocarriers for targeting mitochondria. We also highlight the advantages and disadvantages of various carrier systems that are currently in use. Finally, perspective on new generation for mitochondria-targeted delivery systems in the emerging area of drug-based therapeutics is also discussed.     

Virtual darwinian drug design: QSAR inverse problem, virtual combinatorial chemistry, and computational screening

The generation of diversity and its further selection by an external system is a common mechanism for the evolution of the living species and for the current drug design methods. This assumption allows us to label the methods based on generation and selection of molecular diversity as "Darwinian" ones, and to distinguish them from the structure-based, structure-modulation approaches. An example of a Darwinian method is the inverse QSAR. It consists of the computational generation of candidate chemical structures and their selection according to a previously established QSAR model. New trends in the field of combinatorial chemical syntheses comprise the concepts of virtual combinatorial synthesis and virtual or computational screening. Virtual combinatorial synthesis, closely related to inverse QSAR, can be defined as the computational simulation of the generation of new chemical structures by using a combinatorial strategy to generate a virtual library. Virtual screening is the selection of chemical structures having potential desirable properties from a database or virtual library in order to be synthesized and assayed. This review is mainly focused on graph theoretical drug design approaches, but a survey with key references is provided that covers other simulation methods.     

High-throughput protein crystallography and drug discovery

Single crystal X-ray diffraction is the technique of choice for studying the interactions of small organic molecules with proteins by determining their three-dimensional structures; however the requirement for highly purified protein and lack of process automation have traditionally limited its use in this field. Despite these shortcomings, the use of crystal structures of therapeutically relevant drug targets in pharmaceutical research has increased significantly over the last decade. The application of structure-based drug design has resulted in several marketed drugs and is now an established discipline in most pharmaceutical companies. Furthermore, the recently published full genome sequences of Homo sapiens and a number of micro-organisms have provided a plethora of new potential drug targets that could be utilised in structure-based drug design programs. In order to take maximum advantage of this explosion of information, techniques have been developed to automate and speed up the various procedures required to obtain protein crystals of suitable quality, to collect and process the raw X-ray diffraction data into usable structural information, and to use three-dimensional protein structure as a basis for drug discovery and lead optimisation.This tutorial review covers the various technologies involved in the process pipeline for high-throughput protein crystallography as it is currently being applied to drug discovery. It is aimed at synthetic and computational chemists, as well as structural biologists, in both academia and industry, who are interested in structure-based drug design.     

Solution-phase combinatorial libraries: modulating cellular signaling by targeting protein-protein or protein-DNA interactions

The high-throughput synthesis and screening of compound libraries hold tremendous promise for drug discovery and powerful methods for both solid-phase and solution-phase library preparation have been introduced. The question of which approach (solution-phase versus solid-phase) is best for the preparation of chemical libraries has been replaced by which approach is most appropriate for a particular target or screen. Herein we highlight distinctions in the two approaches that might serve as useful considerations at the onset of new programs. This is followed by a more personal account of our own focus on solution-phase techniques for the preparation of libraries designed to modulate cellular signaling by targeting protein-protein or protein-DNA interactions. The screening of our libraries against a prototypical set of extracellular and intracellular targets, using a wide range of assay formats, provided the first small-molecule modulators of the protein-protein interactions studied, and a generalized approach for conducting such studies.     

A very large diversity space of synthetically accessible compounds for use with drug design programs

We have constructed a very large virtual diversity space containing more than 10¹³ chemical compounds. The diversity space is built from about 400 combinatorial libraries, which have been expanded by choosing sizeable collections of suitable R-groups that can be attached to each link point of their scaffolds. These R-group collections have been created by selecting reagents that have drug-like properties from catalogs of available chemicals. As members of known combinatorial libraries, the compounds in the diversity space are in general synthetically accessible and useful as potential drug leads. Hence, the diversity space can be used as a vast source of compounds by a de novo drug design program. For example, we have used such a program to generate inhibitors of HIV integrase enzyme that exhibited activity in the micromolar range.     

In-vitro assays for determination of oestrogenic activity

At present, in-vitro bioassays are predominantly being seen as tools to identify, through screening programs, whether or not individual chemical compounds have an effect on the endocrine system. However, as the techniques mature, they are likely to find use in the future in monitoring of discharges to the environment for any biological effect and will compliment the range of chemical and biological techniques also available for monitoring environmental quality. Such an approach has already been utilised by a number of workers to fractionate mixtures (e.g. final effluents from STW), to isolate the oestrogenically active components and subsequently identify the compounds which are active. This paper reviews the present state of in-vitro techniques for determination of oestrogenic activity and discusses present approaches to their use in environmental monitoring in conjunction with chemical analyses in toxicity identification and evaluation.