Representativity of target families in the Protein Data Bank: impact for family-directed structure-based drug discovery

Allosteric Regulation of 3CL Protease of SARS-CoV-2 and SARS-CoV Observed in the Crystal Structure Ensemble

The 3C-like protease (3CL^pro) of SARS-CoV-2 is a potential therapeutic target for COVID-19. Importantly, it has an abundance of structural information solved as a complex with various drug candidate compounds. Collecting these crystal structures (83 Protein Data Bank (PDB) entries) together with those of the highly homologous 3CL^pro of SARS-CoV (101 PDB entries), we constructed the crystal structure ensemble of 3CL^pro to analyze the dynamic regulation of its catalytic function. The structural dynamics of the 3CL^pro dimer observed in the ensemble were characterized by the motions of four separate loops (the C-loop, E-loop, H-loop, and Linker) and the C-terminal domain III on the rigid core of the chymotrypsin fold. Among the four moving loops, the C-loop (also known as the oxyanion binding loop) causes the order (active)-disorder (collapsed) transition, which is regulated cooperatively by five hydrogen bonds made with the surrounding residues. The C-loop, E-loop, and Linker constitute the major ligand binding sites, which consist of a limited variety of binding residues including the substrate binding subsites. Ligand binding causes a ligand size dependent conformational change to the E-loop and Linker, which further stabilize the C-loop via the hydrogen bond between the C-loop and E-loop. The T285A mutation from SARS-CoV 3CL^pro to SARS-CoV-2 3CL^pro significantly closes the interface of the domain III dimer and allosterically stabilizes the active conformation of the C-loop via hydrogen bonds with Ser1 and Gly2; thus, SARS-CoV-2 3CL^pro seems to have increased activity relative to that of SARS-CoV 3CL^pro.

Structure-based drug repositioning: Potential and limits

Drug repositioning, the assignment of new therapeutic purposes to known drugs, is an established strategy with many repurposed drugs on the market and many more at experimental stage. We review three use cases, a herpes drug with benefits in cancer, a cancer drug with potential in autoimmune disease, and a selective and an unspecific drug binding the same target (GPCR). We explore these use cases from a structural point of view focusing on a deep understanding of the underlying drug-target interactions. We review tools and data needed for such a drug-centric structural repositioning approach. Finally, we show that the availability of data on targets is an important limiting factor to realize the full potential of structural drug-repositioning.

Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity

To better understand and ultimately predict both the metabolic activities as well as the signaling functions of metabolites, a detailed understanding of the physical interactions of metabolites with proteins is highly desirable. Focusing in particular on protein binding specificity vs. promiscuity, we performed a comprehensive analysis of the physicochemical properties of compound-protein binding events as reported in the Protein Data Bank (PDB). We compared the molecular and structural characteristics obtained for metabolites to those of the well-studied interactions of drug compounds with proteins. Promiscuously binding metabolites and drugs are characterized by low molecular weight and high structural flexibility. Unlike reported for drug compounds, low rather than high hydrophobicity appears associated, albeit weakly, with promiscuous binding for the metabolite set investigated in this study. Across several physicochemical properties, drug compounds exhibit characteristic binding propensities that are distinguishable from those associated with metabolites. Prediction of target diversity and compound promiscuity using physicochemical properties was possible at modest accuracy levels only, but was consistently better for drugs than for metabolites. Compound properties capturing structural flexibility and hydrogen-bond formation descriptors proved most informative in PLS-based prediction models. With regard to diversity of enzymatic activities of the respective metabolite target enzymes, the metabolites benzylsuccinate, hypoxanthine, trimethylamine N-oxide, oleoylglycerol, and resorcinol showed very narrow process involvement, while glycine, imidazole, tryptophan, succinate, and glutathione were identified to possess broad enzymatic reaction scopes. Promiscuous metabolites were found to mainly serve as general energy currency compounds, but were identified to also be involved in signaling processes and to appear in diverse organismal systems (digestive and nervous system) suggesting specific molecular and physiological roles of promiscuous metabolites.

Identification of Similar Binding Sites to Detect Distant Polypharmacology

The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

Chemoisosterism in the proteome

The concept of chemoisosterism of protein environments is introduced as the complementary property to bioisosterism of chemical fragments. In the same way that two chemical fragments are considered bioisosteric if they can bind to the same protein environment, two protein environments will be considered chemoisosteric if they can interact with the same chemical fragment. The basis for the identification of chemoisosteric relationships among protein environments was the increasing amount of crystal structures available currently for protein-ligand complexes. It is shown that one can recover the right location and orientation of chemical fragments constituting the native ligand in a nuclear receptor structure by using only chemoisosteric environments present in enzyme structures. Examples of the potential applicability of chemoisosterism in fragment-based drug discovery are provided.

On the origins of drug polypharmacology

The ability of many drugs, unintended most often, to interact with multiple proteins is commonly referred to as polypharmacology. Could this be a reminiscent chemical signature of early protein evolution?

Functional repertoire, molecular pathways and diseases associated with 3D domain swapping in the human proteome

Background

3D domain swapping is a novel structural phenomenon observed in diverse set of protein structures in oligomeric conformations. A distinct structural feature, where structural segments in a protein dimer or higher oligomer were shared between two or more chains of a protein structure, characterizes 3D domain swapping. 3D domain swapping was observed as a key mediator of numerous functional mechanisms and play pathogenic role in various diseases including conformational diseases like amyloidosis, Alzheimer's disease, Parkinson's disease and prion diseases. We report the first study with a focus on identifying functional classes, pathways and diseases mediated by 3D domain swapping in the human proteome.

Methods

We used a panel of four enrichment tools with two different ontologies and two annotations database to derive biological and clinical relevant information associated with 3D domain swapping. Protein domain enrichment analysis followed by Gene Ontology (GO) term enrichment analysis revealed the functional repertoire of proteins involved in swapping. Pathway analysis using KEGG annotations revealed diverse pathway associations of human proteins involved in 3D domain swapping. Disease Ontology was used to find statistically significant associations with proteins in swapped conformation and various disease categories (P-value < 0.05).

Results

We report meta-analysis results of a literature-curated dataset of human gene products involved in 3D domain swapping and discuss new insights about the functional repertoire, pathway associations and disease implications of proteins involved in 3D domain swapping.

Conclusions

Our integrated bioinformatics pipeline comprising of four different enrichment tools, two ontologies and two annotations revealed new insights into the functional and disease correlations with 3D domain swapping. GO term enrichment were used to infer terms associated with three different GO categories. Protein domain enrichment was used to identify conserved domains enriched in swapped proteins. Pathway enrichment analysis using KEGG annotations revealed that proteins with swapped conformations are present in all six classes of KEGG BRITE hierarchy and significantly enriched KEGG pathways were observed in five classes. Five major classes of disease were found to be associated with 3D domain swapping using functional disease ontology based enrichment analysis. Five classes of human diseases: cancer, diseases of the respiratory or pulmonary system, degenerative diseases of the central nervous system, vascular disease and encephalitis were found to be significant. In conclusion, our study shows that bioinformatics based analytical approaches using curated data can enhance the understanding of functional and disease implications of 3D domain swapping.

Bacterial protein structures reveal phylum dependent divergence

Protein sequence space is vast compared to protein fold space. This raises important questions about how structures adapt to evolutionary changes in protein sequences. A growing trend is to regard protein fold space as a continuum rather than a series of discrete structures. From this perspective, homologous protein structures within the same functional classification should reveal a constant rate of structural drift relative to sequence changes. The clusters of orthologous groups (COG) classification system was used to annotate homologous bacterial protein structures in the Protein Data Bank (PDB). The structures and sequences of proteins within each COG were compared against each other to establish their relatedness. As expected, the analysis demonstrates a sharp structural divergence between the bacterial phyla Firmicutes and Proteobacteria. Additionally, each COG had a distinct sequence/structure relationship, indicating that different evolutionary pressures affect the degree of structural divergence. However, our analysis also shows the relative drift rate between sequence identity and structure divergence remains constant.

New opportunities for protease ligand-binding site comparisons using SitesBase

The rapid expansion of structural information for protein ligand-binding sites is potentially an important source of information in structure-based drug design and in understanding ligand cross-reactivity and toxicity. We have developed SitesBase, a comprehensive database of ligand-binding sites extracted automatically from the Macromolecular Structure Database. SitesBase is an easily accessible database which is simple to use and holds pre-calculated information about structural similarities between known ligand-binding sites. These similarities are presented to the wider community enabling binding-site comparisons for therapeutically interesting protein families, such as the proteases and for new proteins to enable the discovery of interesting new structure-function relationships. The database is available from http://www.modelling.leeds.ac.uk/sb/.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Ligand-based approach to in silico pharmacology: nuclear receptor profiling

Bioactive ligands are a valuable and increasingly accessible source of information about protein targets. On the basis of this statement, a list of 25 nuclear receptors was described by a series of bioactive ligands extracted directly from bibliographical sources, stored properly in an annotated chemical library, and mathematically represented using the recently reported SHED molecular descriptors. Analysis of this ligand information allowed for derivation of a threshold of nuclear receptor concern. If the similarity of one molecule to any of the molecules annotated to one particular nuclear receptor is below that threshold, the molecule receives an alert on the probability of having affinity below 10 microM for that nuclear receptor. On this basis, a linkage map was constructed that reveals the interaction network of nuclear receptors from the perspective of their active ligands. This ligand-based approach to nuclear receptor profiling was subsequently applied to four external chemical libraries of 10,000 molecules targeted to proteases, kinases, ion channels, and G protein-coupled receptors. The percentage of each library that returned an alert on at least one nuclear receptor was reasonably low and varied between 4.4 and 9.7%. In addition, ligand-based nuclear receptor profiling of a set of 2944 drugs provided an alert for 153 drugs. For some of them, namely, acitretin, telmisartan, phenyltoloxamine, tazarotene, and flumazenil, bibliographical evidence could be found indicating that those drugs may indeed have some potential off-target residual affinity for the nuclear receptors annotated. Overall, the present findings suggest that ligand-based approaches to protein family profiling appear as a promising means toward the establishment of novel tools for in silico pharmacology.

LigProf: a simple tool for in silico prediction of ligand-binding sites

With the increasing amount of data provided by both high-throughput sequencing and structural genomics studies, there is a growing need for tools to augment functional predictions for protein sequences. Broad descriptions of function can be provided by establishing the presence of protein domains associated with a particular function. To extend the domain-based annotation, LigProf provides predictions of potential ligands that bind to a protein, as well as critical residues that stabilize ligands. A P-value statistic for estimating the significance of motif occurrence is provided for all sites. Although the usefulness of the method will rise with increasing numbers of crystallographically solved molecules deposited in the PDB database, we show that it can already be applied successfully to the highly represented ligand-bound protein kinase domains of viral and human origin. The LigProf webserver is freely available at: http://www.cropnet.pl/ligprof . At present, LigProf descriptors annotate and extend major protein families from the PfamA database.

Potentials of phenolic molecules of natural origin and their derivatives as anti-HIV agents

Identification of phenolic compounds and their derivatives interfering the several steps of the viral life cycle of the human immunodeficiency virus type 1 (HIV-1) is focused for the development of novel molecules for the treatment of AIDS. Several phenolic compounds isolated and characterized from natural sources have been studied in detail and found to exhibit inhibitory effects against different steps of the HIV-1 life cycle, including virus-cell fusion and virus absorption, reverse transcription, integration (IN) and proteolytic cleavage. In the review, we are summarizing some strong evidences demonstrating several phenolic molecules and their derivatives from natural sources display promising anti-HIV-1 activities. The anti-HIV compounds have been organized in this review according to their mechanism of action in the life cycle of HIV. We also mentioned some findings using in silico approaches, like virtual screening, docking, neural network, etc., and even the chemogenomics and/or functional genomics approaches could be useful for the quick identifying promising new lead anti-HIV molecules without having any other unwanted pharmacological effects. Plants having large amount of phenolic compounds, can be considered as strong sources of molecules for the treatment of HIV-1. Despite the continuous advances made in antiretroviral combination therapy, AIDS has become the leading cause of death in Africa and the fourth worldwide. Today, many research groups are exploring the bio- and chemo-diversity of the plant kingdom to find new and better anti-HIV drugs with novel mechanisms of action.

Do Structurally Similar Ligands Bind in a Similar Fashion?

The scope of the current work is to investigate whether structurally similar ligands bind in a similar fashion by exhaustively analyzing experimental data from the protein database (PDB). The complete PDB was searched for pairs of structurally similar ligands binding to the same biological target. The binding sites of the pairs of proteins complexing structurally similar ligands were found to differ in 83% of the cases. The most recurrent structural change among the pairs involves different water molecule architecture. Side-chain movements are observed in half of the pairs, whereas backbone movements rarely occurred. However, two structurally similar ligands generally confirm a high degree of structural conservation. That is, a majority of the ligand pairs occupy the same region in the binding sites, providing support for the use of shape matching in the drug design process. We allow ourselves to draw general conclusions because our data set consists of ligands with drug-like physicochemical properties complexed to a broad spectrum of different protein classes.

Interrogating the druggable genome with structural informatics

Structural genomics projects are producing protein structure data at an unprecedented rate. In this paper, we present the Target Informatics Platform (TIP), a novel structural informatics approach for amplifying the rapidly expanding body of experimental protein structure information to enhance the discovery and optimization of small molecule protein modulators on a genomic scale. In TIP, existing experimental structure information is augmented using a homology modeling approach, and binding sites across multiple target families are compared using a clique detection algorithm. We report here a detailed analysis of the structural coverage for the set of druggable human targets, highlighting drug target families where the level of structural knowledge is currently quite high, as well as those areas where structural knowledge is sparse. Furthermore, we demonstrate the utility of TIP's intra- and inter-family binding site similarity analysis using a series of retrospective case studies. Our analysis underscores the utility of a structural informatics infrastructure for extracting drug discovery-relevant information from structural data, aiding researchers in the identification of lead discovery and optimization opportunities as well as potential "off-target" liabilities.

FCP: functional coverage of the proteome by structures

MOTIVATION

Tools and resources for translating the remarkable growth witnessed in recent years in the number of protein structures determined experimentally into actual gain in the functional coverage of the proteome are becoming increasingly necessary. We introduce FCP, a publicly accessible web tool dedicated to analyzing the current state and trends of the population of structures within protein families. FCP offers both graphical and quantitative data on the degree of functional coverage of enzymes and nuclear receptors by existing structures, as well as on the bias observed in the distribution of structures along their respective functional classification schemes.

AVAILABILITY

http://cgl.imim.es/fcp

CONTACT

jmestres@imim.es.