The importance of discerning shape in molecular pharmacology

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Innovative synthesis of drug-like molecules using tetrazole as core building blocks

Tetrazole is widely utilized as a bioisostere for carboxylic acid in the field of medicinal chemistry and drug development, enhancing the drug-like characteristics of various molecules. Typically, tetrazoles are introduced from their nitrile precursors through late-stage functionalization. In this work, we propose a novel strategy involving the use of diversely protected, unprecedented tetrazole aldehydes as building blocks. This approach facilitates the incorporation of the tetrazole group into multicomponent reactions or other chemistries, aiding in the creation of a variety of complex, drug-like molecules. These innovative tetrazole building blocks are efficiently and directly synthesized using a Passerini three-component reaction (PT-3CR), employing cost-effective and readily available materials. We further showcase the versatility of these new tetrazole building blocks by integrating the tetrazole moiety into various multicomponent reactions (MCRs), which are already significantly employed in drug discovery. This technique represents a unique and complementary method to existing tetrazole synthesis processes. It aims to meet the growing demand for tetrazole-based compound libraries and novel scaffolds, which are challenging to synthesize through other methods.

A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap , an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFκB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.

Non-synonymous SNPs variants of PRKCG and its association with oncogenes predispose to hepatocellular carcinoma

BACKGROUND

PRKCG encodes PKC γ, which is categorized under the classical protein kinase C family. No studies have specifically established the relationship between PRKCG nsSNPs with structural and functional variations in PKC γ in the context of hepatocellular carcinoma (HCC). The present study aims to uncover this link through in-silico and experimental studies.

METHODS

The 3D structure of PKC γ was predicted. Molecular Dynamic (MD) Simulations were run and estimates were made for interactions, stability, conservation and post-translational alterations between wild and mutant structures. The association of PRKCG levels with HCC survival rate was determined. Genotyping analyses were conducted to investigate the deleterious PRKCG nsSNP association with HCC. mRNA expression of PKC γ, HIF-1 alpha, AKT, SOCS3 and VEGF in the blood of controls and HCC patients was analyzed and a genetic cascade was constructed depicting these interactions.

RESULTS

The expression level of studied oncogenes was compared to tumour suppressor genes. Through Alphafold, the 3D structure of PKC γ was explored. Fifteen SNPs were narrowed down for in-silico analyses that were identified in exons 5, 10 and 18 and the regulatory and kinase domain of PKC γ. Root mean square deviation and fluctuation along with the radius of gyration unveiled potential changes between the wild and mutated variant structures. Mutant genotype AA (homozygous) corresponding to nsSNP, rs386134171 had more frequency in patients with OR (2.446), RR (1.564) and P-values (< 0.0029) that highlights its significant association with HCC compared to controls in which the wild genotype GG was found more prevalent.

CONCLUSION

nsSNP rs386134171 can be a genetic marker for HCC diagnosis and therapeutic studies. This study has laid down a road map for future studies to be conducted on HCC.

Statistical methods for in silico tools used for risk assessment and toxicology

In silico toxicology is one type of toxicity assessment that uses computational methods to visualize, analyze, simulate, and predict the toxicity of chemicals. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. Animal studies for the type of toxicological information needed are both expensive and time-consuming, and to that, ethical consideration is added. Many different types of in silico methods have been developed to characterize the toxicity of chemical materials and predict their catastrophic consequences to humans and the environment. In light of European legislation such as Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) and the Cosmetics Regulation, in silico methods for predicting chemical toxicity have become increasingly important and used extensively worldwide e.g., in the USA, Canada, Japan, and Australia. A popular problem, concerning these methods, is the deficiency of the necessary data for assessing the hazards. REACH has called for increased use of in silico tools for non-testing data as structure-activity relationships, quantitative structure-activity relationships, and read-across. The main objective of the review is to refine the use of in silico tools in a risk assessment context of industrial chemicals.

Molecular engineering of confined space in metal–organic cages

The host–guest chemistry of metal–organic cages can be modified through tailoring of structural aspects such as size, shape and functionality. In this review, strategies, opportunities and challenges of such molecular engineering are discussed.

Design of Biopolymer-Based Interstitial Therapies for the Treatment of Glioblastoma

Glioblastoma multiforme (GBM) is the most common form of primary brain cancer and has the highest morbidity rate and current treatments result in a bleak 5-year survival rate of 5.6%. Interstitial therapy is one option to increase survival. Drug delivery by interstitial therapy most commonly makes use of a polymer implant encapsulating a drug which releases as the polymer degrades. Interstitial therapy has been extensively studied as a treatment option for GBM as it provides several advantages over systemic administration of chemotherapeutics. Primarily, it can be applied behind the blood–brain barrier, increasing the number of possible chemotherapeutic candidates that can be used and reducing systemic levels of the therapy while concentrating it near the cancer source. With interstitial therapy, multiple drugs can be released locally into the brain at the site of resection as the polymer of the implant degrades, and the release profile of these drugs can be tailored to optimize combination therapy or maintain synergistic ratios. This can bypass the blood–brain barrier, alleviate systemic toxicity, and resolve drug resistance in the tumor. However, tailoring drug release requires appropriate consideration of the complex relationship between the drug, polymer, and formulation method. Drug physicochemical properties can result in intermolecular bonding with the polymeric matrix and affect drug distribution in the implant depending on the formulation method used. This review is focused on current works that have applied interstitial therapy towards GBM, discusses polymer and formulation methods, and provides design considerations for future implantable biodegradable materials.

Piperine analogs arrest c-myc gene leading to downregulation of transcription for targeting cancer

G-quadruplex (G4) structures are considered a promising therapeutic target in cancer. Since Ayurveda, Piperine has been known for its medicinal properties. Piperine shows anticancer properties by stabilizing the G4 motif present upstream of the c-myc gene. This gene belongs to a group of proto-oncogenes, and its aberrant transcription drives tumorigenesis. The transcriptional regulation of the c-myc gene is an interesting approach for anticancer drug design. The present study employed a chemical similarity approach to identify Piperine similar compounds and analyzed their interaction with cancer-associated G-quadruplex motifs. Among all Piperine analogs, PIP-2 exhibited strong selectivity, specificity, and affinity towards c-myc G4 DNA as elaborated through biophysical studies such as fluorescence emission, isothermal calorimetry, and circular dichroism. Moreover, our biophysical observations are supported by molecular dynamics analysis and cellular-based studies. Our study showed that PIP-2 showed higher toxicity against the A549 lung cancer cell line but lower toxicity towards normal HEK 293 cells, indicating increased efficacy of the drug at the cellular level. Biological evaluation assays such as TFP reporter assay, quantitative real-time PCR (qRT- PCR), and western blotting suggest that the Piperine analog-2 (PIP-2) stabilizes the G-quadruplex motif located at the promoter site of c-myc oncogene and downregulates its expression. In conclusion, Piperine analog PIP-2 may be used as anticancer therapeutics as it affects the c-myc oncogene expression via G-quadruplex mediated mechanism.

Identification of 1,2,4-Triazolylthioethanone Scaffold for the Design of New Acetylcholinesterase Inhibitors

Acetylcholinesterase (AChE) inhibitors are the most effective drugs for Alzheimer's disease treatment. However, considering the potential and failure rates of AChE inhibitors, chemical scaffolds targeting cholinesterase specifically are still very limited. Herein, we report a new class of AChE inhibitors identified by employing a virtual screening approach that combines shape similarity with molecular docking calculations. Virtual screening followed by the evaluation of AChE inhibitory activity allowed us to identify 1,2,4-triazolylthioethanones as a novel class of AChE inhibitors. Thirteen compounds with 1,2,4-triazolylthiothanone core and IC₅₀ values in the range of 0.15±0.07 to 3.32±0.92 μM have been reported here. Our findings shed light into a class of AChE inhibitors that could be useful starting point for the development of novel therapeutics to tackle Alzheimer's disease.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Crystallization and self-assembly of shape-complementary sequence-defined peptoids

Shape complementarity between polymers is a hallmark of biological systems (e.g. DNA base pairing and protein binding interactions). Here we explore the role of shape complementarity between sequence-defined N-alkyl peptoids in crystal lattices.

Assessing the Intestinal Permeability and Anti-Inflammatory Potential of Sesquiterpene Lactones from Chicory

Cichorium intybus L. has recently gained major attention due to large quantities of health-promoting compounds in its roots, such as inulin and sesquiterpene lactones (SLs). Chicory is the main dietary source of SLs, which have underexplored bioactive potential. In this study, we assessed the capacity of SLs to permeate the intestinal barrier to become physiologically available, using in silico predictions and in vitro studies with the well-established cell model of the human intestinal mucosa (differentiated Caco-2 cells). The potential of SLs to modulate inflammatory responses through modulation of the nuclear factor of activated T-cells (NFAT) pathway was also evaluated, using a yeast reporter system. Lactucopicrin was revealed as the most permeable chicory SL in the intestinal barrier model, but it had low anti-inflammatory potential. The SL with the highest anti-inflammatory potential was 11β,13-dihydrolactucin, which inhibited up to 54% of Calcineurin-responsive zinc finger (Crz1) activation, concomitantly with the impairment of the nuclear accumulation of Crz1, the yeast orthologue of human NFAT.

Binding Site Switch by Dispersion Interactions: Rotational Signatures of Fenchone-Phenol and Fenchone-Benzene Complexes

Non-covalent interactions between molecules determine molecular recognition and the outcome of chemical and biological processes. Characterising how non-covalent interactions influence binding preferences is of crucial importance in advancing our understanding of these events. Here, we analyse the interactions involved in smell and specifically the effect of changing the balance between hydrogen-bonding and dispersion interactions by examining the complexes of the common odorant fenchone with phenol and benzene, mimics of tyrosine and phenylalanine residues, respectively. Using rotational spectroscopy and quantum chemistry, two isomers of each complex have been identified. Our results show that the increased weight of dispersion interactions in these complexes changes the preferred binding site in fenchone and sets the basis for a better understanding of the effect of different residues in molecular recognition and binding events.

sensaas: Shape-based Alignment by Registration of Colored Point-based Surfaces

sensaas is a tool developed for aligning and comparing molecular shapes and sub-shapes. Alignment is obtained by registration of 3D point-based representations of the van der Waals surface. The method uses local properties of the shape to identify the correspondence relationships between two point clouds containing up to several thousand colored (labeled) points. Our rigid-body superimposition method follows a two-stage approach. An initial alignment is obtained by matching pose-invariant local 3D descriptors, called FPFH, of the input point clouds. This stage provides a global superimposition of the molecular surfaces, without any knowledge of their initial pose in 3D space. This alignment is then refined by optimizing the matching of colored points. In our study, each point is colored according to its closest atom, which itself belongs to a user defined physico-chemical class. Finally, sensaas provides an alignment and evaluates the molecular similarity by using Tversky coefficients. To assess the efficiency of this approach, we tested its ability to reproduce the superimposition of X-ray structures of the benchmarking AstraZeneca (AZ) data set and, compared its results with those generated by the two shape-alignment approaches shaep and shafts. We also illustrated submatching properties of our method with respect to few substructures and bioisosteric fragments. The code is available upon request from the authors (demo version at https://chemoinfo.ipmc.cnrs.fr/SENSAAS).

Evaluation of 3-Dimensionality in Approved and Experimental Drug Space

The 3-dimensional (3D) structure of therapeutics and other bioactive molecules is an important factor in determining the strength and selectivity of their protein-ligand interactions. Previous efforts have considered the strain introduced and tolerated through conformational changes induced upon protein binding. Herein, we present an analysis of 3-dimentionality for energy-minimized structures from the DrugBank and ligands bound to proteins identified in the Protein Data Bank (PDB). This analysis reveals that the majority of molecules found in both the DrugBank and the PDB tend toward linearity and planarity, with few molecules having highly 3D conformations. Decidedly 3D geometries have been historically difficult to achieve, likely due to the synthetic challenge of making 3D organic molecules, and other considerations, such as adherence to the 'rule-of-five'. This has resulted in the dominance of planar and/or linear topologies of the molecules described here. Strategies to address the generally flat nature of these data sets are explored, including the use of 3D organic fragments and inorganic scaffolds as a means of accessing privileged 3D space. This work highlights the potential utility of libraries with greater 3D topological diversity so that the importance of molecular shape to biological behavior can be more fully understood in drug discovery campaigns.

Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (-)-Englerin A Mimetics

Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (-)-englerin A. Two of the top-ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (-)-englerin A. Intracellular calcium assays and electrophysiological whole-cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer-generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape-based molecular representation for molecular scaffold-hopping.

Discovery of a potent small molecule inhibiting Huntington's disease (HD) pathogenesis via targeting CAG repeats RNA and Poly Q protein

CAG repeats RNA causes various fatal neurodegenerative diseases exemplified by Huntington's disease (HD) and several spinocerebellar ataxias (SCAs). Although there are differences in the pathogenic mechanisms, these diseases share the common cause, i.e., expansion of CAG repeats. The shared cause of these diseases raises the possibility for the exploiting the common target as a potential therapeutic approach. Oligonucleotide-based therapeutics are designed earlier with the help of the base pairing rule but are not very promiscuous, considering the nonspecific stimulation of the immune system and the poor cellular delivery. Therefore, small molecules-based therapeutics are preferred for targeting the repeats expansion disorders. Here, we have used the chemical similarity search approach to discern the small molecules that selectively target toxic CAG RNA. The lead compounds showed the specificity towards AA mismatch in biophysical studies including CD, ITC, and NMR spectroscopy and thus aided to forestall the polyQ mediated pathogenicity. Furthermore, the lead compounds also explicitly alleviate the polyQ mediated toxicity in HD cell models and patient-derived cells. These findings suggest that the lead compound could act as a chemical probe for AA mismatch containing RNA as well as plays a neuroprotective role in fatal neurodegenerative diseases like HD and SCAs.

Perspective isomorphs - a new classification of molecular structures based on artistic and chemical concepts

Models are a quintessential part in research as well as for scientific communication in general. A special challenge is the visualization of the invisible, such as atoms and molecules. Visualizations are also deeply rooted in the discipline of art offering in the respect untapped potential in cross-fertilization with natural sciences. Here we show a new classification of molecular structures, so-called perspective isomorphs, applying an interdisciplinary crossing of epistemological concepts between chemistry and art. The idea is based on the notion that molecules can be classified, if they appear equivalent from one standpoint in a specific orientation. We termed such a group of such molecules perspective isomorphs. The general concept is outlined together with a nomenclature system. Furthermore, this concept has been visualized by artistic representations of molecules. The concept of perspective isomorphs and its discussions herein will extend current models and stimulate the discourse about the nature of atoms and molecules and especially their models.

Improving ligand 3D shape similarity-based pose prediction with a continuum solvent model

In order to improve the pose prediction performance of docking methods, we have previously developed the pose prediction using shape similarity (PoPSS) method. It identifies a ligand conformation of the highest shape similarity with target protein crystal ligands. The identified ligand conformation is then placed into the target protein binding pocket and refined using side-chain repacking and Monte Carlo energy minimization. Subsequently, we have reported a modification to PoPSS, named as PoPSS-Lite, using a simple grid-based energy minimization for side-chain repacking and Tversky correlation coefficient as the similarity metric. This modification has improved the pose prediction performance and PoPSS-Lite was one of the top performers in D3R GC3. Here we report a further modification to PoPSS that utilizes a continuum solvent model to account for water mediated protein ligand interactions. In this approach, named as PoPSS-PB, the ligand conformation of the highest shape similarity with crystal ligands is refined along with the target protein binding site by incorporating the Poisson-Boltzmann electrostatics. The performance of PoPSS-PB along with PoPSS and PoPSS-Lite was prospectively evaluated in D3R GC4. PoPSS-PB not only demonstrated excellent performance with mean and median RMSDs of 1.20 and 1.13 Å but also achieved improved performance over PoPSS and PoPSS-Lite. Furthermore, the comparison with other D3R GC4 pose prediction submissions revealed admirable performance. Our results showed that the binding poses of ligands with unknown binding modes can be successfully predicted by utilizing ligand 3D shape similarity with known crystallographic ligands and that taking the solvation into consideration improves pose prediction.

The need for mathematical modelling of spatial drug distribution within the brain

The blood brain barrier (BBB) is the main barrier that separates the blood from the brain. Because of the BBB, the drug concentration-time profile in the brain may be substantially different from that in the blood. Within the brain, the drug is subject to distributional and elimination processes: diffusion, bulk flow of the brain extracellular fluid (ECF), extra-intracellular exchange, bulk flow of the cerebrospinal fluid (CSF), binding and metabolism. Drug effects are driven by the concentration of a drug at the site of its target and by drug-target interactions. Therefore, a quantitative understanding is needed of the distribution of a drug within the brain in order to predict its effect. Mathematical models can help in the understanding of drug distribution within the brain. The aim of this review is to provide a comprehensive overview of system-specific and drug-specific properties that affect the local distribution of drugs in the brain and of currently existing mathematical models that describe local drug distribution within the brain. Furthermore, we provide an overview on which processes have been addressed in these models and which have not. Altogether, we conclude that there is a need for a more comprehensive and integrated model that fills the current gaps in predicting the local drug distribution within the brain.

Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3

To extend the utility of ligand 3D shape similarity into pose prediction and virtual screening, we have previously developed CDVS and PoPSS methods. Both of them utilize ligand 3D shape similarity with the crystallographic ligands to improve pose prediction. While CDVS utilizes shape similarity to select suitable receptor structures for molecular docking, PoPSS places a ligand conformation of the highest shape similarity with crystal ligands into the target protein binding pocket which is then refined by side-chain repacking and Monte Carlo energy minimization. Analyses of PoPSS revealed some drawbacks in ligand conformation generation and the scoring scheme used. Moreover, as PoPSS does not sample the ligand conformation after placing it in the binding pocket, it relies solely on conformation generation methods to produce native like conformations. To address these limitations of PoPSS method, we report here a modified approach named as PoPSS-Lite, where side-chain repacking was replaced by a simple grid-based energy minimization. This modification also allowed the sampling of terminal functional groups while keeping the core scaffold fixed. Furthermore, shape similarity calculations were improved by increasing the number of ligand conformations and using a different similarity metric. The performance of PoPSS-Lite was prospectively evaluated in D3R GC3. Comparison of PoPSS-Lite demonstrated superior performance over PoPSS and CDVS with lower mean and median RMSDs. Furthermore, comparison with other D3R GC3 pose prediction submissions revealed top performance for PoPSS-Lite. Our D3R GC3 result extends our perspective that ligand 3D shape similarity with known crystallographic information can be successfully used to predict the binding pose of ligands with unknown binding modes. Our D3R GC3 results further highlight the necessity for improvement in conformer generation methods in order to improve shape similarity guided pose prediction.

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

Optimization of a MT1-MMP-targeting Peptide and Its Application in Near-infrared Fluorescence Tumor Imaging

Membrane type 1 metalloproteinase (MT1-MMP) is an important regulator of cancer invasion, growth and angiogenesis, thus making it an attractive target for cancer imaging and therapy. A non-substrate peptide (MT1-AF7p) that bonded to the "MT-Loop" region of MT1-MMP was identified by using a phage-displayed peptide library and was used to image the MT1-MMP expression in vivo through optical imaging. However, the substrate in the screening did not have a 3D structure, thus resulting in a loose bonding of MT1-AF7p. To simulate the real conformation of the "MT-Loop" and improve the performance of MT1-AF7p, molecular simulations were performed, because this strategy provides multiple methods for predicting the conformation and interaction of proteinase in 3D. In view of the binding site of the receptor-ligand interactions, histidine 4 was selected for mutation to achieve an increased affinity effect. The optimized peptides were further identified and conformed by atomic force microscopy, isothermal titration calorimetry, cell fluorescence imaging in vitro, and near-infrared fluorescence tumor optical imaging in vivo. The results revealed that the optimized peptide with a mutation of histidine 4 to arginine has the highest affinity and specificity, and exhibited an increased fluorescence intensity in the tumor site in optical imaging.

Taste of phytocompounds: A better predictor for ethnopharmacological activities of medicinal plants than the phytochemical class?

ETHNOPHARMACOLOGICAL RELEVANCE

Understanding the patterns that shape traditional medical knowledge is essential for accelerating ethnopharmacological progress. According to Ayurveda, medicinal plants that belong to different taxa, but which have similar taste, may display similar (ethno)pharmacological activities (EPAs) (Bhishagratna, 1998; Sharma and Dash, 2006).

AIM OF THE STUDY

To understand the patterns that govern the distribution of herbal EPAs in Ayurveda and to evaluate the potential concordance between chemical class or taste of the constituent phytocompounds and EPAs.

MATERIAL AND METHODS

A mixed database (PhytoMolecularTasteDB) was constructed for Ayurvedic medicinal plants by integrating modern data (medicinal plant composition, phytochemical taste) with traditional data (ethnopharmacological activities of plant). PhytoMolecularTasteDB contains 431 Ayurvedic medicinal plants, 94 EPAs, 223 chemical classes of phytocompounds and 438 herbal tastants. Potential global or individual associations between chemical classes/taste of the phytoconstituents and EPAs were statistically analyzed.

RESULTS

There was no global statistical correlation between the various chemical classes of phytocompounds and EPAs, although there were several individual correlations. The results suggest the existence of a global statistical correlation (besides several individual correlations) between the plant "molecular taste" (various taste-based classes of phytocompounds) and EPAs.

CONCLUSIONS

These results suggest that phytochemical taste may be more relevant than chemical class for EPAs prediction.

From chemical metabolism to life: the origin of the genetic coding process

Looking for origins is so much rooted in ideology that most studies reflect opinions that fail to explore the first realistic scenarios. To be sure, trying to understand the origins of life should be based on what we know of current chemistry in the solar system and beyond. There, amino acids and very small compounds such as carbon dioxide, dihydrogen or dinitrogen and their immediate derivatives are ubiquitous. Surface-based chemical metabolism using these basic chemicals is the most likely beginning in which amino acids, coenzymes and phosphate-based small carbon molecules were built up. Nucleotides, and of course RNAs, must have come to being much later. As a consequence, the key question to account for life is to understand how chemical metabolism that began with amino acids progressively shaped into a coding process involving RNAs. Here I explore the role of building up complementarity rules as the first information-based process that allowed for the genetic code to emerge, after RNAs were substituted to surfaces to carry over the basic metabolic pathways that drive the pursuit of life.

On the origins of three-dimensionality in drug-like molecules

AIM

Many medicinal chemistry-relevant structures and core scaffolds tend toward geometric planarity, which hampers the optimization of physicochemical properties desirable in drug-like molecules. As challenging drug target classes emerge, the exploitation of molecular three-dimensionality in lead optimization is becoming increasingly important. While recent interest has emphasized the importance of enhanced three-dimensionality in molecular fragment designs, the extent to which this is required in core scaffolds remains unclear.

MATERIALS & METHODS

Three computational methods, Scaffold Tree deconstruction, Synthetic Disconnection Rules retrosynthetic deconstruction and virtual library enumeration, are applied, together with the descriptors plane of best fit and principal moments of inertia, to investigate the origins of three-dimensionality in drug-like molecules.

CONCLUSION

This study informs on the stage at which molecular three-dimensionality should be considered in drug design.

Modern approaches to accelerate discovery of new antischistosomal drugs

INTRODUCTION

The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects.

AREAS COVERED

Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors.

EXPERT OPINION

Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.

USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques

Ligand-based Virtual Screening (VS) methods aim at identifying molecules with a similar activity profile across phenotypic and macromolecular targets to that of a query molecule used as search template. VS using 3D similarity methods have the advantage of biasing this search toward active molecules with innovative chemical scaffolds, which are highly sought after in drug design to provide novel leads with improved properties over the query molecule (e.g. patentable, of lower toxicity or increased potency). Ultrafast Shape Recognition (USR) has demonstrated excellent performance in the discovery of molecules with previously-unknown phenotypic or target activity, with retrospective studies suggesting that its pharmacophoric extension (USRCAT) should obtain even better hit rates once it is used prospectively. Here we present USR-VS (http://usr.marseille.inserm.fr/), the first web server using these two validated ligand-based 3D methods for large-scale prospective VS. In about 2 s, 93.9 million 3D conformers, expanded from 23.1 million purchasable molecules, are screened and the 100 most similar molecules among them in terms of 3D shape and pharmacophoric properties are shown. USR-VS functionality also provides interactive visualization of the similarity of the query molecule against the hit molecules as well as vendor information to purchase selected hits in order to be experimentally tested.

Ultrafast protein structure-based virtual screening with Panther

Molecular docking is by far the most common method used in protein structure-based virtual screening. This paper presents Panther, a novel ultrafast multipurpose docking tool. In Panther, a simple shape-electrostatic model of the ligand-binding area of the protein is created by utilizing the protein crystal structure. The features of the possible ligands are then compared to the model by using a similarity search algorithm. On average, one ligand can be processed in a few minutes by using classical docking methods, whereas using Panther processing takes <1 s. The presented Panther protocol can be used in several applications, such as speeding up the early phases of drug discovery projects, reducing the number of failures in the clinical phase of the drug development process, and estimating the environmental toxicity of chemicals. Panther-code is available in our web pages (http://www.jyu.fi/panther) free of charge after registration.

Molecular dynamics study of naturally existing cavity couplings in proteins

Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100 ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results.

Which three-dimensional characteristics make efficient inhibitors of protein-protein interactions?

The specific properties of protein-protein interactions (PPI) (flat, large and hydrophobic) make them harder to tackle with low-molecular-weight compounds. Learning from the properties of successful examples of PPI interface inhibitors (iPPI) at earlier stages of developments, has been pinpointed as a powerful strategy to circumvent this trend. To this end, we have computationally analyzed the bioactive conformations of iPPI and those of inhibitors of conventional targets (e.g enzymes) to highlight putative iPPI 3D characteristics. Most noticeably, the essential property revealed by this study illustrates how efficiently iPPI manages to bind to the hydrophobic patch often present at the core of protein interfaces. The newly identified properties were further confirmed as characteristics of iPPI using much larger data sets (e.g iPPI-DB, www.ippidb.cdithem.fr ). Interestingly, the absence of correlation of such properties with the hydrophobicity and the size of the compounds opens new ways to design potent iPPI with better pharmacokinetic features.

Identification of the first inhibitor of the GBP1:PIM1 interaction. Implications for the development of a new class of anticancer agents against paclitaxel resistant cancer cells

Class III β-tubulin plays a prominent role in the development of drug resistance to paclitaxel by allowing the incorporation of the GBP1 GTPase into microtubules. Once in the cytoskeleton, GBP1 binds to prosurvival kinases such as PIM1 and initiates a signaling pathway that induces resistance to paclitaxel. Therefore, the inhibition of the GBP1:PIM1 interaction could potentially revert resistance to paclitaxel. A panel of 44 4-azapodophyllotoxin derivatives was screened in the NCI-60 cell panel. The result is that 31 are active and the comparative analysis demonstrated specific activity in paclitaxel-resistant cells. Using surface plasmon resonance, we were able to prove that NSC756093 is a potent in vitro inhibitor of the GBP1:PIM1 interaction and that this property is maintained in vivo in ovarian cancer cells resistant to paclitaxel. Through bioinformatics, molecular modeling, and mutagenesis studies, we identified the putative NSC756093 binding site at the interface between the helical and the LG domain of GBP1. According to our results by binding to this site, the NSC756093 compound is able to stabilize a conformation of GBP1 not suitable for binding to PIM1.

Cross-reactivity of steroid hormone immunoassays: clinical significance and two-dimensional molecular similarity prediction

Background

Immunoassays are widely used in clinical laboratories for measurement of plasma/serum concentrations of steroid hormones such as cortisol and testosterone. Immunoassays can be performed on a variety of standard clinical chemistry analyzers, thus allowing even small clinical laboratories to do analysis on-site. One limitation of steroid hormone immunoassays is interference caused by compounds with structural similarity to the target steroid of the assay. Interfering molecules include structurally related endogenous compounds and their metabolites as well as drugs such as anabolic steroids and synthetic glucocorticoids.

Methods

Cross-reactivity of a structurally diverse set of compounds were determined for the Roche Diagnostics Elecsys assays for cortisol, dehydroepiandrosterone (DHEA) sulfate, estradiol, progesterone, and testosterone. These data were compared and contrasted to package insert data and published cross-reactivity studies for other marketed steroid hormone immunoassays. Cross-reactivity was computationally predicted using the technique of two-dimensional molecular similarity.

Results

The Roche Elecsys Cortisol and Testosterone II assays showed a wider range of cross-reactivity than the DHEA sulfate, Estradiol II, and Progesterone II assays. 6-Methylprednisolone and prednisolone showed high cross-reactivity for the cortisol assay, with high likelihood of clinically significant effect for patients administered these drugs. In addition, 21-deoxycortisol likely produces clinically relevant cross-reactivity for cortisol in patients with 21-hydroxylase deficiency, while 11-deoxycortisol may produce clinically relevant cross-reactivity in 11β-hydroxylase deficiency or following metyrapone challenge. Several anabolic steroids may produce clinically significant false positives on the testosterone assay, although interpretation is limited by sparse pharmacokinetic data for some of these drugs. Norethindrone therapy may impact immunoassay measurement of testosterone in women. Using two-dimensional similarity calculations, all compounds with high cross-reactivity also showed a high degree of similarity to the target molecule of the immunoassay.

Conclusions

Compounds producing cross-reactivity in steroid hormone immunoassays generally have a high degree of structural similarity to the target hormone. Clinically significant interactions can occur with structurally similar drugs (e.g., prednisolone and cortisol immunoassays; methyltestosterone and testosterone immunoassays) or with endogenous compounds such as 21-deoxycortisol that can accumulate to very high concentrations in certain disease conditions. Simple similarity calculations can help triage compounds for future testing of assay cross-reactivity.

A monoclonal antibody against human MUDENG protein

MUDENG (mu-2-related death-inducing gene, MuD) encodes a predicted ∼54-kDa protein in humans, considered to be involved in trafficking proteins from endosomes toward other membranous compartments as well as in inducing cell death. Here we report on the generation of a mouse monoclonal antibody (MAb) against the middle domain of human (h) MuD. This IgG sub 1 MAb, named M3H9, recognizes residues 244-326 in the middle domain of the MuD protein. Thus, the MuD proteins expressed in an astroglioma cell line and primary astrocytes can be detected by the M3H9 MAb. We showed that M3H9 MAb can be useful in enzyme-linked immunosorbent assay (ELISA) and immunoblot experiments. In addition, M3H9 MAb can detect the expression of the MuD protein in formalin-fixed, paraffin-embedded mouse ovary and uterus tissues. These results indicate that the MuD MAb M3H9 could be useful as a new biomarker of hereditary spastic paraplegia and other related diseases.

Protein pocket and ligand shape comparison and its application in virtual screening

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

A leap into the chemical space of protein-protein interaction inhibitors

Protein-protein interactions (PPI) are involved in vital cellular processes and are therefore associated to a growing number of diseases. But working with them as therapeutic targets comes with some major hurdles that require substantial mutations from our way to design drugs on historical targets such as enzymes and G-Protein Coupled Receptor (GPCR). Among the numerous ways we could improve our methodologies to maximize the potential of developing new chemical entities on PPI targets, is the fundamental question of what type of compounds should we use to identify the first hits and among which chemical space should we navigate to optimize them to the drug candidate stage. In this review article, we cover different aspects on PPI but with the aim to gain some insights into the specific nature of the chemical space of PPI inhibitors. We describe the work of different groups to highlight such properties and discuss their respective approach. We finally discuss a case study in which we describe the properties of a set of 115 PPI inhibitors that we compare to a reference set of 1730 enzyme inhibitors. This case study highlights interesting properties such as the unfortunate price that still needs to be paid by PPI inhibitors in terms of molecular weight, hydrophobicity, and aromaticity in order to reach a critical level of activity. But it also shows that not all PPI targets are equivalent, and that some PPI targets can demonstrate a better druggability by illustrating the better drug likeness of their associated inhibitors.

Cross-reactivity studies and predictive modeling of "Bath Salts" and other amphetamine-type stimulants with amphetamine screening immunoassays

INTRODUCTION

The increasing abuse of amphetamine-like compounds presents a challenge for clinicians and clinical laboratories. Although these compounds may be identified by mass spectrometry-based assays, most clinical laboratories use amphetamine immunoassays that have unknown cross-reactivity with novel amphetamine-like drugs. To date, there has been a little systematic study of amphetamine immunoassay cross-reactivity with structurally diverse amphetamine-like drugs or of computational tools to predict cross-reactivity.

METHODS

Cross-reactivities of 42 amphetamines and amphetamine-like drugs with three amphetamines screening immunoassays (AxSYM(®) Amphetamine/Methamphetamine II, CEDIA(®) amphetamine/Ecstasy, and EMIT(®) II Plus Amphetamines) were determined. Two- and three-dimensional molecular similarity and modeling approaches were evaluated for the ability to predict cross-reactivity using receiver-operator characteristic curve analysis.

RESULTS

Overall, 34%-46% of the drugs tested positive on the immunoassay screens using a concentration of 20,000 ng/mL. The three immunoassays showed differential detection of the various classes of amphetamine-like drugs. Only the CEDIA assay detected piperazines well, while only the EMIT assay cross-reacted with the 2C class. All three immunoassays detected 4-substituted amphetamines. For the AxSYM and EMIT assays, two-dimensional molecular similarity methods that combined similarity to amphetamine/methamphetamine and 3,4-methylenedioxymethampetamine most accurately predicted cross-reactivity. For the CEDIA assay, three-dimensional pharmacophore methods performed best in predicting cross-reactivity. Using the best performing models, cross-reactivities of an additional 261 amphetamine-like compounds were predicted.

CONCLUSIONS

Existing amphetamines immunoassays unevenly detect amphetamine-like drugs, particularly in the 2C, piperazine, and β-keto classes. Computational similarity methods perform well in predicting cross-reactivity and can help prioritize testing of additional compounds in the future.

Virtual target screening: validation using kinase inhibitors

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term "Virtual Target Screening (VTS)", a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.