Build-up algorithm for atomic correspondence between chemical structures

Reaction rebalancing: a novel approach to curating reaction databases

PURPOSE

Reaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need.

METHODS

The SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities.

RESULTS

The rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively.

CONCLUSION

The SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning.

SCIENTIFIC CONTRIBUTION

SynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem.

Molecular fragmentation as a crucial step in the AI-based drug development pathway

The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

Structural Perspective of NR4A Nuclear Receptor Family and Their Potential Endogenous Ligands

There are 48 nuclear receptors in the human genome, and many members of this superfamily have been implicated in human diseases. The NR4A nuclear receptor family consisting of three members, NR4A1, NR4A2, and NR4A3 (formerly annotated as Nur77, Nurr1, and NOR1, respectively), are still orphan receptors but exert pathological effects on immune-related and neurological diseases. We previously reported that prostaglandin A1 (PGA1) and prostaglandin A2 (PGA2) are potent activators of NR4A3, which bind directly to the ligand-binding domain (LBD) of the receptor. Recently, the co-crystallographic structures of NR4A2-LBD bound to PGA1 and PGA2 were reported, followed by reports of the neuroprotective effects of these possible endogenous ligands in mouse models of Parkinson's disease. Based on these structures, we modeled the binding structures of the other two members (NR4A1 and NR4A3) with these potential endogenous ligands using a template-based modeling method, and reviewed the similarity and diversity of ligand-binding mechanisms in the nuclear receptor family.

A combined ligand and target-based virtual screening strategy to repurpose drugs as putrescine uptake inhibitors with trypanocidal activity

Chagas disease, also known as American trypanosomiasis, is a neglected tropical disease caused by the protozoa Trypanosoma cruzi, affecting nearly 7 million people only in the Americas. Polyamines are essential compounds for parasite growth, survival, and differentiation. However, because trypanosomatids are auxotrophic for polyamines, they must be obtained from the host by specific transporters. In this investigation, an ensemble of QSAR classifiers able to identify polyamine analogs with trypanocidal activity was developed. Then, a multi-template homology model of the dimeric polyamine transporter of T. cruzi, TcPAT12, was created with Rosetta, and then refined by enhanced sampling molecular dynamics simulations. Using representative snapshots extracted from the trajectory, a docking model able to discriminate between active and inactive compounds was developed and validated. Both models were applied in a parallel virtual screening campaign to repurpose known drugs as anti-trypanosomal compounds inhibiting polyamine transport in T. cruzi. Montelukast, Quinestrol, Danazol, and Dutasteride were selected for in vitro testing, and all of them inhibited putrescine uptake in biochemical assays, confirming the predictive ability of the computational models. Furthermore, all the confirmed hits proved to inhibit epimastigote proliferation, and Quinestrol and Danazol were able to inhibit, in the low micromolar range, the viability of trypomastigotes and the intracellular growth of amastigotes.

Pocket2Drug: An Encoder-Decoder Deep Neural Network for the Target-Based Drug Design

Computational modeling is an essential component of modern drug discovery. One of its most important applications is to select promising drug candidates for pharmacologically relevant target proteins. Because of continuing advances in structural biology, putative binding sites for small organic molecules are being discovered in numerous proteins linked to various diseases. These valuable data offer new opportunities to build efficient computational models predicting binding molecules for target sites through the application of data mining and machine learning. In particular, deep neural networks are powerful techniques capable of learning from complex data in order to make informed drug binding predictions. In this communication, we describe Pocket2Drug, a deep graph neural network model to predict binding molecules for a given a ligand binding site. This approach first learns the conditional probability distribution of small molecules from a large dataset of pocket structures with supervised training, followed by the sampling of drug candidates from the trained model. Comprehensive benchmarking simulations show that using Pocket2Drug significantly improves the chances of finding molecules binding to target pockets compared to traditional drug selection procedures. Specifically, known binders are generated for as many as 80.5% of targets present in the testing set consisting of dissimilar data from that used to train the deep graph neural network model. Overall, Pocket2Drug is a promising computational approach to inform the discovery of novel biopharmaceuticals.

Protein Data Bank Japan: Celebrating our 20th anniversary during a global pandemic as the Asian hub of three dimensional macromolecular structural data

Protein Data Bank Japan (PDBj), a founding member of the worldwide Protein Data Bank (wwPDB) has accepted, processed and distributed experimentally determined biological macromolecular structures for 20 years. During that time, we have continuously made major improvements to our query search interface of PDBj Mine 2, the BMRBj web interface, and EM Navigator for PDB/BMRB/EMDB entries. PDBj also serves PDB-related secondary database data, original web-based modeling services such as Homology modeling of complex structure (HOMCOS), visualization services and utility tools, which we have continuously enhanced and expanded throughout the years. In addition, we have recently developed several unique archives, BSM-Arc for computational structure models, and XRDa for raw X-ray diffraction images, both of which promote open science in the structural biology community. During the COVID-19 pandemic, PDBj has also started to provide feature pages for COVID-19 related entries across all available archives at PDBj from raw experimental data and PDB structural data to computationally predicted models, while also providing COVID-19 outreach content for high school students and teachers.

Maximum Common Substructure Searching in Combinatorial Make-on-Demand Compound Spaces

Commercial make-on-demand compound spaces have become increasingly popular within the past few years. Since these libraries are too large for enumeration, they are usually accessed using combinatorial fragment space technologies like FTrees-FS and SpaceLight. Although both search types are of high practical impact, they lack the ability to search for precise structural features on the atomic level. To address this important use case, we developed SpaceMACS enabling efficient and precise maximum common induced substructure (MCIS) similarity and substructure searches within chemical fragment spaces. SpaceMACS enumerates a user-defined number of compounds in a multistep procedure. First, substructures of the query are extracted and matched to all fragments of the space. Then partial results are combined to actual compounds of the space. In this way, SpaceMACS identifies common substructures even if they cross fragment borders. We applied SpaceMACS on three commercial fragment spaces searching for the 150 000 most similar analogs to a glucosyltransferase binder from literature. We were able to find almost all building blocks used for the synthesis of the 90 listed analogs and a plethora of additional results. SpaceMACS is the missing link to enable rational drug discovery on make-on-demand combinatorial catalogs. No matter whether initial compound suggestions come from a de novo design, an AI-based compound generation, or a medicinal chemist's drawing board, the method gives access to the structurally closest chemically available analogs in seconds to at most minutes.

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.

Bionoi: A Voronoi Diagram-Based Representation of Ligand-Binding Sites in Proteins for Machine Learning Applications

Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network architectures, reducing the development work without sacrificing the performance. When initially generating images of binding sites, users have the option to color the Voronoi cells based on either one of six structural, physicochemical, and evolutionary properties, or a blend of all six individual properties. Encouragingly, after inputting images generated by Bionoi into the convolutional autoencoder, the network was able to effectively learn the most salient features of binding pockets. The accuracy of the generated model is evaluated both visually and numerically through the reconstruction of binding site images from the latent feature space. The generated feature vectors capture well various properties of binding sites and thus can be applied in a multitude of machine learning projects. As a demonstration, we trained the ResNet-18 architecture from Microsoft on Bionoi images to show that it is capable to effectively classify nucleotide- and heme-binding pockets against a large dataset of control pockets binding a variety of small molecules. Bionoi is freely available to the research community at https://github.com/CSBG-LSU/BionoiNet.

Disconnected Maximum Common Substructures under Constraints

The maximum common substructure (MCS) problem is an important, well-studied problem in cheminformatics. It is applied in several application scenarios like molecule superimposition and scaffold detection or as a similarity measure in virtual screening and clustering. In many cases, the connected MCS is preferred since it is faster to calculate and a highly fragmented MCS is not very meaningful from a chemical point of view. Nevertheless, a disconnected MCS (dMCS) can be very instructive if it consists of reasonably sized molecular parts connected by variable groups. We present a new algorithm named RIMACS, which is able to calculate the dMCS under constraints. We can control the maximum number of connected components and their minimal size using a modified local substructure mapping approach. A formal proof of correctness is provided as well as extended runtime evaluations on chemical data. The evaluation of RIMACS shows that a small number of connected components helps us to improve MCS similarity in a meaningful way while keeping the runtime requirements in a reasonable range.

BionoiNet: ligand-binding site classification with off-the-shelf deep neural network

MOTIVATION

Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.

RESULTS

We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures.

AVAILABILITY AND IMPLEMENTATION

BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Crystal violet structural analogues identified by in silico drug repositioning present anti-Trypanosoma cruzi activity through inhibition of proline transporter TcAAAP069

BACKGROUND

Crystal violet (CV) was used for several years in blood banks to eliminate the parasite Trypanosoma cruzi in endemic areas in order to prevent transfusion-transmitted Chagas disease. One mechanism of action described for CV involves inhibition of proline uptake. In T. cruzi, proline is essential for host cell infection and intracellular differentiation among other processes, and can be obtained through the proline permease TcAAAP069.

METHODOLOGY/PRINCIPAL FINDINGS

CV inhibited proline transporter TcAAAP069 and parasites overexpressing this permease were 47-fold more sensitive to this compound than control parasites. Using CV as reference molecule, loratadine, cyproheptadine, olanzapine and clofazimine were identified as structurally related compounds to CV (structural analogues) by in silico drug repurposing through a similarity-based virtual screening protocol. All these already-approved drugs for clinical use inhibited TcAAAP069 activity with different efficacies and also presented trypanocidal action in epimastigotes, trypomastigotes and amastigotes of the Y, CL Brener and Dm28c T. cruzi strains. Finally, a synergistic effect between benznidazole and the CV chemical analogues was evidenced by combination and dose-reduction indexes values in epimastigotes and trypomastigotes of the Y strain.

CONCLUSIONS/SIGNIFICANCE

Loratadine, cyproheptadine and clofazimine inhibit TcAAAP069 proline transporter and also present trypanocidal effect against all T. cruzi life stages in strains from three different DTUs. These CV structural analogues could be a starting point to design therapeutic alternatives to treat Chagas disease by finding new indications for old drugs. This approach, called drug repurposing is a recommended strategy by the World Health Organization to treat neglected diseases, like Chagas disease, and combination therapy may improve the possibility of success of repositioned drugs.

Identification of Novel, Structurally Diverse, Small Molecule Modulators of GPR119

GPR119 drug discovery efforts in the pharmaceutical industry for the treatment of type 2 diabetes mellitus (T2DM) and obesity, were initiated based on its restricted distribution in pancreas and GI tract, and its possible role in glucose homeostasis. While a number of lead series have emerged, the pharmacological endpoints they provide have not been clear. In particular, many lead series have demonstrated loss of efficacy and significant toxic side effects. Thus, we sought to identify novel, potent, positive modulators of GPR119. In this study, we have successfully developed and optimized a high-throughput screening strategy to identify GPR119 modulators using a live cell assay format that utilizes a cyclic nucleotide-gated channel as a biosensor for cAMP production. Our high-throughput screening (HTS) approach is unique to that of previous HTS approaches targeting this receptor, as changes in cAMP were measured both in the presence and absence of an EC₁₀ of the endogenous ligand, oleoylethanolamide, enabling detection of both agonists and potential allosteric modulators in a single assay. From these efforts, we have identified positive modulators of GPR119 with similar as well as unique scaffolds compared to existing compounds and similar as well as unique signaling properties. Our compounds will not only serve as novel molecular probes to better understand GPR119 pleiotropic signaling and the underlying physiological consequences of receptor activation, but are also well-suited for translation as potential therapeutic agents.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Elucidating the druggability of the human proteome with eFindSite

Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and resources. Inability to properly measure druggability has accounted for a significant portion of failures in drug discovery. This problem is only further exacerbated by the large sample space of proteins involved in human diseases. With these barriers, the druggability space within the human proteome remains unexplored and has made it difficult to develop drugs for numerous diseases. Hence, we present a new feature developed in eFindSite that employs supervised machine learning to predict the druggability of a given protein. Benchmarking calculations against the Non-Redundant data set of Druggable and Less Druggable binding sites demonstrate that an AUC for druggability prediction with eFindSite is as high as 0.88. With eFindSite, we elucidated the human druggability space to be 10,191 proteins. Considering the disease space from the Open Targets Platform and excluding already known targets from the predicted data set reveal 2731 potentially novel therapeutic targets. eFindSite is freely available as a stand-alone software at https://github.com/michal-brylinski/efindsite .

Rubiscolins are naturally occurring G protein-biased delta opioid receptor peptides

The impact that β-arrestin proteins have on G protein-coupled receptor trafficking, signaling and physiological behavior has gained much appreciation over the past decade. A number of studies have attributed the side effects associated with the use of naturally occurring and synthetic opioids, such as respiratory depression and constipation, to excessive recruitment of β-arrestin. These findings have led to the development of biased opioid small molecule agonists that do not recruit β-arrestin, activating only the canonical G protein pathway. Similar G protein-biased small molecule opioids have been found to occur in nature, particularly within kratom, and opioids within salvia have served as a template for the synthesis of other G protein-biased opioids. Here, we present the first report of naturally occurring peptides that selectively activate G protein signaling pathways at δ opioid receptors, but with minimal β-arrestin recruitment. Specifically, we find that rubiscolin peptides, which are produced as cleavage products of the plant protein rubisco, bind to and activate G protein signaling at δ opioid receptors. However, unlike the naturally occurring δ opioid peptides leu-enkephalin and deltorphin II, the rubiscolin peptides only very weakly recruit β-arrestin 2 and have undetectable recruitment of β-arrestin 1 at the δ opioid receptor.

DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network

Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.

eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database

Background

The structural information on proteins in their ligand-bound conformational state is invaluable for protein function studies and rational drug design. Compared to the number of available sequences, not only is the repertoire of the experimentally determined structures of holo-proteins limited, these structures do not always include pharmacologically relevant compounds at their binding sites. In addition, binding affinity databases provide vast quantities of information on interactions between drug-like molecules and their targets, however, often lacking structural data. On that account, there is a need for computational methods to complement existing repositories by constructing the atomic-level models of drug-protein assemblies that will not be determined experimentally in the near future.

Results

We created eModel-BDB, a database of  200,005 comparative models of drug-bound proteins based on   1,391,403 interaction data obtained from the Binding Database and the PDB library of 31 January 2017. Complex models in eModel-BDB were generated with a collection of the state-of-the-art techniques, including protein meta-threading, template-based structure modeling, refinement and binding site detection, and ligand similarity-based docking. In addition to a rigorous quality control maintained during dataset generation, a subset of weakly homologous models was selected for the retrospective validation against experimental structural data recently deposited to the Protein Data Bank. Validation results indicate that eModel-BDB contains models that are accurate not only at the global protein structure level but also with respect to the atomic details of bound ligands.

Conclusions

Freely available eModel-BDB can be used to support structure-based drug discovery and repositioning, drug target identification, and protein structure determination.

Allosteric binding sites in Rab11 for potential drug candidates

Rab11 is an important protein subfamily in the RabGTPase family. These proteins physiologically function as key regulators of intracellular membrane trafficking processes. Pathologically, Rab11 proteins are implicated in many diseases including cancers, neurodegenerative diseases and type 2 diabetes. Although they are medically important, no previous study has found Rab11 allosteric binding sites where potential drug candidates can bind to. In this study, by employing multiple clustering approaches integrating principal component analysis, independent component analysis and locally linear embedding, we performed structural analyses of Rab11 and identified eight representative structures. Using these representatives to perform binding site mapping and virtual screening, we identified two novel binding sites in Rab11 and small molecules that can preferentially bind to different conformations of these sites with high affinities. After identifying the binding sites and the residue interaction networks in the representatives, we computationally showed that these binding sites may allosterically regulate Rab11, as these sites communicate with switch 2 region that binds to GTP/GDP. These two allosteric binding sites in Rab11 are also similar to two allosteric pockets in Ras that we discovered previously.

Large-scale computational drug repositioning to find treatments for rare diseases

Rare, or orphan, diseases are conditions afflicting a small subset of people in a population. Although these disorders collectively pose significant health care problems, drug companies require government incentives to develop drugs for rare diseases due to extremely limited individual markets. Computer-aided drug repositioning, i.e., finding new indications for existing drugs, is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Structure-based matching of drug-binding pockets is among the most promising computational techniques to inform drug repositioning. In order to find new targets for known drugs ultimately leading to drug repositioning, we recently developed eMatchSite, a new computer program to compare drug-binding sites. In this study, eMatchSite is combined with virtual screening to systematically explore opportunities to reposition known drugs to proteins associated with rare diseases. The effectiveness of this integrated approach is demonstrated for a kinase inhibitor, which is a confirmed candidate for repositioning to synapsin Ia. The resulting dataset comprises 31,142 putative drug-target complexes linked to 980 orphan diseases. The modeling accuracy is evaluated against the structural data recently released for tyrosine-protein kinase HCK. To illustrate how potential therapeutics for rare diseases can be identified, we discuss a possibility to repurpose a steroidal aromatase inhibitor to treat Niemann-Pick disease type C. Overall, the exhaustive exploration of the drug repositioning space exposes new opportunities to combat orphan diseases with existing drugs. DrugBank/Orphanet repositioning data are freely available to research community at https://osf.io/qdjup/.

Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

Background

Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.

Results

We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock.

Conclusions

Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs

About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.

ArbAlign: A Tool for Optimal Alignment of Arbitrarily Ordered Isomers Using the Kuhn-Munkres Algorithm

When assessing the similarity between two isomers whose atoms are ordered identically, one typically translates and rotates their Cartesian coordinates for best alignment and computes the pairwise root-mean-square distance (RMSD). However, if the atoms are ordered differently or the molecular axes are switched, it is necessary to find the best ordering of the atoms and check for optimal axes before calculating a meaningful pairwise RMSD. The factorial scaling of finding the best ordering by looking at all permutations is too expensive for any system with more than ten atoms. We report use of the Kuhn-Munkres matching algorithm to reduce the cost of finding the best ordering from factorial to polynomial scaling. That allows the application of this scheme to any arbitrary system efficiently. Its performance is demonstrated for a range of molecular clusters as well as rigid systems. The largely standalone tool is freely available for download and distribution under the GNU General Public License v3.0 (GNU_GPL_v3) agreement. An online implementation is also provided via a web server ( http://www.arbalign.org ) for convenient use.

Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag

Constructing high-quality libraries of molecular building blocks is essential for successful fragment-based drug discovery. In this communication, we describe eMolFrag, a new open-source software to decompose organic compounds into nonredundant fragments retaining molecular connectivity information. Given a collection of molecules, eMolFrag generates a set of unique fragments comprising larger moieties, bricks, and smaller linkers connecting bricks. These building blocks can subsequently be used to construct virtual screening libraries for targeted drug discovery. The robustness and computational performance of eMolFrag is assessed against the Directory of Useful Decoys, Enhanced database conducted in serial and parallel modes with up to 16 computing cores. Further, the application of eMolFrag in de novo drug design is illustrated using the adenosine receptor. eMolFrag is implemented in Python, and it is available as stand-alone software and a web server at www.brylinski.org/emolfrag and https://github.com/liutairan/eMolFrag.

Analysis of drug-endogenous human metabolite similarities in terms of their maximum common substructures

In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites.

Protein Data Bank Japan (PDBj): updated user interfaces, resource description framework, analysis tools for large structures

The Protein Data Bank Japan (PDBj, http://pdbj.org), a member of the worldwide Protein Data Bank (wwPDB), accepts and processes the deposited data of experimentally determined macromolecular structures. While maintaining the archive in collaboration with other wwPDB partners, PDBj also provides a wide range of services and tools for analyzing structures and functions of proteins. We herein outline the updated web user interfaces together with RESTful web services and the backend relational database that support the former. To enhance the interoperability of the PDB data, we have previously developed PDB/RDF, PDB data in the Resource Description Framework (RDF) format, which is now a wwPDB standard called wwPDB/RDF. We have enhanced the connectivity of the wwPDB/RDF data by incorporating various external data resources. Services for searching, comparing and analyzing the ever-increasing large structures determined by hybrid methods are also described.

New Inhibitors of Indoleamine 2,3-Dioxygenase 1: Molecular Modeling Studies, Synthesis, and Biological Evaluation

Indoleamine 2,3-dioxygenase 1 (IDO1) is an attractive target for anticancer therapy. Herein, we report a virtual screening study which led to the identification of compound 5 as a new IDO1 inhibitor. In order to improve the biological activity of the identified hit, arylthioindoles 6-30 were synthesized and tested. Among these, derivative 21 exhibited an IC₅₀ value of 7 μM, being the most active compound of the series. Furthermore, compounds 5 and 21 induced a dose-dependent growth inhibition in IDO1 expressing cancer cell lines HTC116 and HT29. Three-dimensional quantitative structure-activity relationship studies were carried out in order to rationalize obtained results and suggest new chemical modifications.

Assessing the similarity of ligand binding conformations with the Contact Mode Score

Structural and computational biologists often need to measure the similarity of ligand binding conformations. The commonly used root-mean-square deviation (RMSD) is not only ligand-size dependent, but also may fail to capture biologically meaningful binding features. To address these issues, we developed the Contact Mode Score (CMS), a new metric to assess the conformational similarity based on intermolecular protein-ligand contacts. The CMS is less dependent on the ligand size and has the ability to include flexible receptors. In order to effectively compare binding poses of non-identical ligands bound to different proteins, we further developed the eXtended Contact Mode Score (XCMS). We believe that CMS and XCMS provide a meaningful assessment of the similarity of ligand binding conformations. CMS and XCMS are freely available at http://brylinski.cct.lsu.edu/content/contact-mode-score and http://geaux-computational-bio.github.io/contact-mode-score/.

NLDB: a database for 3D protein-ligand interactions in enzymatic reactions

NLDB (Natural Ligand DataBase; URL: http://nldb.hgc.jp) is a database of automatically collected and predicted 3D protein–ligand interactions for the enzymatic reactions of metabolic pathways registered in KEGG. Structural information about these reactions is important for studying the molecular functions of enzymes, however a large number of the 3D interactions are still unknown. Therefore, in order to complement such missing information, we predicted protein–ligand complex structures, and constructed a database of the 3D interactions in reactions. NLDB provides three different types of data resources; the natural complexes are experimentally determined protein–ligand complex structures in PDB, the analog complexes are predicted based on known protein structures in a complex with a similar ligand, and the ab initio complexes are predicted by docking simulations. In addition, NLDB shows the known polymorphisms found in human genome on protein structures. The database has a flexible search function based on various types of keywords, and an enrichment analysis function based on a set of KEGG compound IDs. NLDB will be a valuable resource for experimental biologists studying protein–ligand interactions in specific reactions, and for theoretical researchers wishing to undertake more precise simulations of interactions.

HOMCOS: an updated server to search and model complex 3D structures

The HOMCOS server (http://homcos.pdbj.org) was updated for both searching and modeling the 3D complexes for all molecules in the PDB. As compared to the previous HOMCOS server, the current server targets all of the molecules in the PDB including proteins, nucleic acids, small compounds and metal ions. Their binding relationships are stored in the database. Five services are available for users. For the services “Modeling a Homo Protein Multimer” and “Modeling a Hetero Protein Multimer”, a user can input one or two proteins as the queries, while for the service “Protein-Compound Complex”, a user can input one chemical compound and one protein. The server searches similar molecules by BLAST and KCOMBU. Based on each similar complex found, a simple sequence-replaced model is quickly generated by replacing the residue names and numbers with those of the query protein. A target compound is flexibly superimposed onto the template compound using the program fkcombu. If monomeric 3D structures are input as the query, then template-based docking can be performed. For the service “Searching Contact Molecules for a Query Protein”, a user inputs one protein sequence as the query, and then the server searches for its homologous proteins in PDB and summarizes their contacting molecules as the predicted contacting molecules. The results are summarized in “Summary Bars” or “Site Table”display. The latter shows the results as a one-site-one-row table, which is useful for annotating the effects of mutations. The service “Searching Contact Molecules for a Query Compound” is also available.

Comprehensive analysis of the Co-structures of dipeptidyl peptidase IV and its inhibitor

Background

We comprehensively analyzed X-ray cocrystal structures of dipeptidyl peptidase IV (DPP-4) and its inhibitor to clarify whether DPP-4 alters its general or partial structure according to the inhibitor used and whether DPP-4 has a common rule for inhibitor binding.

Results

All the main and side chains in the inhibitor binding area were minimally altered, except for a few side chains, despite binding to inhibitors of various shapes. Some residues (Arg125, Glu205, Glu206, Tyr662 and Asn710) in the area had binding modes to fix a specific atom of inhibitor to a particular spatial position in DPP-4. We found two specific water molecules that were common to 92 DPP-4 structures. The two water molecules were close to many inhibitors, and seemed to play two roles: maintaining the orientation of the Glu205 and Glu206 side chains through a network via the water molecules, and arranging the inhibitor appropriately at the S2 subsite.

Conclusions

Our study based on high-quality resources may provide a necessary minimum consensus to help in the discovery of a novel DPP-4 inhibitor that is commercially useful.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-016-0062-8) contains supplementary material, which is available to authorized users.

Discovery of New Potential Anti-Infective Compounds Based on Carbonic Anhydrase Inhibitors by Rational Target-Focused Repurposing Approaches

In academia, compound recycling represents an alternative drug discovery strategy to identify new pharmaceutical targets from a library of chemical compounds available in house. Herein we report the application of a rational target-based drug-repurposing approach to find diverse applications for our in-house collection of compounds. The carbonic anhydrase (CA, EC 4.2.1.1) metalloenzyme superfamily was identified as a potential target of our compounds. The combination of a thoroughly validated docking screening protocol, together with in vitro assays against various CA families and isoforms, allowed us to identify two unprecedented chemotypes as CA inhibitors. The identified compounds have the capacity to preferentially bind pathogenic (bacterial/protozoan) CAs over human isoforms and represent excellent hits for further optimization in hit-to-lead campaigns.

Fluorometric High-Throughput Screening Assay for Secreted Phospholipases A2 Using Phospholipid Vesicles

There is interest in developing inhibitors of human group III secreted phospholipase A2 (hGIII-sPLA2) because this enzyme plays a role in mast cell maturation. There are no potent inhibitors for hGIII-sPLA2 reported to date, so we adapted a fluorescence-based enzyme activity monitoring method to a high-throughput screening format. We opted to use an assay based on phospholipid substrate present in phospholipid vesicles since this matrix more closely resembles the natural substrate of hGIII-sPLA2, as opposed to phospholipid/detergent mixed micelles. The substrate is a phospholipid analogue containing BODIPY fluorophores dispersed as a minor component in vesicles of nonfluorescent phospholipids. Action of hGIII-sPLA2 liberates a free fatty acid from the phospholipid, leading to a reduction in quenching of the fluorophore and hence an increase in fluorescence. The assay uses optical detection in a 1536-well plate format with an excitation wavelength far away from the UV range so as to minimize false-positive library hits that result from quenching of the fluorescence. The high-throughput screen was successfully carried out on a library of 370,276 small molecules. Several hits were discovered, and data have been uploaded to PubChem. This study describes the first high-throughput optical screening assay for secreted phospholipase A2 inhibitors based on a phospholipid vesicle substrate.

A graph-based approach to construct target-focused libraries for virtual screening

Background

Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family.

Methods

Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns.

Results

We conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated.

Conclusions

eSynth can successfully reconstruct chemically feasible molecules from molecular fragments. Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis.Graphical abstract

Smiles2Monomers: a link between chemical and biological structures for polymers

Background

The monomeric composition of polymers is powerful for structure comparison and synthetic biology, among others. Many databases give access to the atomic structure of compounds but the monomeric structure of polymers is often lacking. We have designed a smart algorithm, implemented in the tool Smiles2Monomers (s2m), to infer efficiently and accurately the monomeric structure of a polymer from its chemical structure.

Results

Our strategy is divided into two steps: first, monomers are mapped on the atomic structure by an efficient subgraph-isomorphism algorithm ; second, the best tiling is computed so that non-overlapping monomers cover all the structure of the target polymer. The mapping is based on a Markovian index built by a dynamic programming algorithm. The index enables s2m to search quickly all the given monomers on a target polymer. After, a greedy algorithm combines the mapped monomers into a consistent monomeric structure. Finally, a local branch and cut algorithm refines the structure. We tested this method on two manually annotated databases of polymers and reconstructed the structures de novo with a sensitivity over 90 %. The average computation time per polymer is 2 s.

Conclusion

s2m automatically creates de novo monomeric annotations for polymers, efficiently in terms of time computation and sensitivity. s2m allowed us to detect annotation errors in the tested databases and to easily find the accurate structures. So, s2m could be integrated into the curation process of databases of small compounds to verify the current entries and accelerate the annotation of new polymers. The full method can be downloaded or accessed via a website for peptide-like polymers at http://bioinfo.lifl.fr/norine/smiles2monomers.jsp.Graphical abstract:

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Electronic supplementary material

The online version of this article (doi:10.1186/s13321-015-0111-5) contains supplementary material, which is available to authorized users.

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GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field

Molecular docking is an important component of computer-aided drug discovery. In this communication, we describe GeauxDock, a new docking approach that builds on the ideas of ligand homology modeling. GeauxDock features a descriptor-based scoring function integrating evolutionary constraints with physics-based energy terms, a mixed-resolution molecular representation of protein-ligand complexes, and an efficient Monte Carlo sampling protocol. To drive docking simulations toward experimental conformations, the scoring function was carefully optimized to produce a correlation between the total pseudoenergy and the native-likeness of binding poses. Indeed, benchmarking calculations demonstrate that GeauxDock has a strong capacity to identify near-native conformations across docking trajectories with the area under receiver operating characteristics of 0.85. By excluding closely related templates, we show that GeauxDock maintains its accuracy at lower levels of homology through the increased contribution from physics-based energy terms compensating for weak evolutionary constraints. GeauxDock is available at http://www.institute.loni.org/lasigma/package/dock/.

Efficient heuristics for maximum common substructure search

Maximum common substructure search is a computationally hard optimization problem with diverse applications in the field of cheminformatics, including similarity search, lead optimization, molecule alignment, and clustering. Most of these applications have strict constraints on running time, so heuristic methods are often preferred. However, the development of an algorithm that is both fast enough and accurate enough for most practical purposes is still a challenge. Moreover, in some applications, the quality of a common substructure depends not only on its size but also on various topological features of the one-to-one atom correspondence it defines. Two state-of-the-art heuristic algorithms for finding maximum common substructures have been implemented at ChemAxon Ltd., and effective heuristics have been developed to improve both their efficiency and the relevance of the atom mappings they provide. The implementations have been thoroughly evaluated and compared with existing solutions (KCOMBU and Indigo). The heuristics have been found to greatly improve the performance and applicability of the algorithms. The purpose of this paper is to introduce the applied methods and present the experimental results.

Identification of small molecules that disrupt signaling between ABL and its positive regulator RIN1

Constitutively active BCR-ABL kinase fusions are causative mutations in the pathogenesis of hematopoietic neoplasias including chronic myelogenous leukemia (CML). Although these fusions have been successfully targeted with kinase inhibitors, drug-resistance and relapse continue to limit long-term survival, highlighting the need for continued innovative drug discovery. We developed a time-resolved Förster resonance energy transfer (TR-FRET) -based assay to identify compounds that disrupt stimulation of the ABL kinase by blocking its ability to bind the positive regulator RIN1. This assay was used in a high throughput screen (HTS) of two small molecule libraries totaling 444,743 compounds. 708 confirmed hits were counter-screened to eliminate off-target inhibitors and reanalyzed to prioritize compounds with IC₅₀ values below 10 μM. The CML cell line K562 was then used to identify five compounds that decrease MAPK1/3 phosphorylation, which we determined to be an indicator of RIN1-dependent ABL signaling. One of these compounds is a thiadiazole, and the other four are structurally related acyl piperidine amides. Notably, these five compounds lower cellular BCR-ABL1 kinase activity by blocking a positive regulatory interaction rather than directly inhibiting ABL catalytic function.

eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models

Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4–9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite.

3D flexible alignment using 2D maximum common substructure: dependence of prediction accuracy on target-reference chemical similarity

A protein-bound conformation of a target molecule can be predicted by aligning the target molecule on the reference molecule obtained from the 3D structure of the compound-protein complex. This strategy is called "similarity-based docking". For this purpose, we develop the flexible alignment program fkcombu, which aligns the target molecule based on atomic correspondences with the reference molecule. The correspondences are obtained by the maximum common substructure (MCS) of 2D chemical structures, using our program kcombu. The prediction performance was evaluated using many target-reference pairs of superimposed ligand 3D structures on the same protein in the PDB, with different ranges of chemical similarity. The details of atomic correspondence largely affected the prediction success. We found that topologically constrained disconnected MCS (TD-MCS) with the simple element-based atomic classification provides the best prediction. The crashing potential energy with the receptor protein improved the performance. We also found that the RMSD between the predicted and correct target conformations significantly correlates with the chemical similarities between target-reference molecules. Generally speaking, if the reference and target compounds have more than 70% chemical similarity, then the average RMSD of 3D conformations is <2.0 Å. We compared the performance with a rigid-body molecular alignment program based on volume-overlap scores (ShaEP). Our MCS-based flexible alignment program performed better than the rigid-body alignment program, especially when the target and reference molecules were sufficiently similar.

A rotation-translation invariant molecular descriptor of partial charges and its use in ligand-based virtual screening

Background

Measures of similarity for chemical molecules have been developed since the dawn of chemoinformatics. Molecular similarity has been measured by a variety of methods including molecular descriptor based similarity, common molecular fragments, graph matching and 3D methods such as shape matching. Similarity measures are widespread in practice and have proven to be useful in drug discovery. Because of our interest in electrostatics and high throughput ligand-based virtual screening, we sought to exploit the information contained in atomic coordinates and partial charges of a molecule.

Results

A new molecular descriptor based on partial charges is proposed. It uses the autocorrelation function and linear binning to encode all atoms of a molecule into two rotation-translation invariant vectors. Combined with a scoring function, the descriptor allows to rank-order a database of compounds versus a query molecule. The proposed implementation is called ACPC (AutoCorrelation of Partial Charges) and released in open source. Extensive retrospective ligand-based virtual screening experiments were performed and other methods were compared with in order to validate the method and associated protocol.

Conclusions

While it is a simple method, it performed remarkably well in experiments. At an average speed of 1649 molecules per second, it reached an average median area under the curve of 0.81 on 40 different targets; hence validating the proposed protocol and implementation.

Chemical structure elucidation from ¹³C NMR chemical shifts: efficient data processing using bipartite matching and maximal clique algorithms

Computer-assisted chemical structure elucidation has been intensively studied since the first use of computers in chemistry in the 1960s. Most of the existing elucidators use a structure-spectrum database to obtain clues about the correct structure. Such a structure-spectrum database is expected to grow on a daily basis. Hence, the necessity to develop an efficient structure elucidation system that can adapt to the growth of a database has been also growing. Therefore, we have developed a new elucidator using practically efficient graph algorithms, including the convex bipartite matching, weighted bipartite matching, and Bron-Kerbosch maximal clique algorithms. The utilization of the two matching algorithms especially is a novel point of our elucidator. Because of these sophisticated algorithms, the elucidator exactly produces a correct structure if all of the fragments are included in the database. Even if not all of the fragments are in the database, the elucidator proposes relevant substructures that can help chemists to identify the actual chemical structures. The elucidator, called the CAST/CNMR Structure Elucidator, plays a complementary role to the CAST/CNMR Chemical Shift Predictor, and together these two functions can be used to analyze the structures of organic compounds.

Identification of levothyroxine antichagasic activity through computer-aided drug repurposing

Cruzipain (Cz) is the major cysteine protease of the protozoan Trypanosoma cruzi, etiological agent of Chagas disease. A conformation-independent classifier capable of identifying Cz inhibitors was derived from a 163-compound dataset and later applied in a virtual screening campaign on the DrugBank database, which compiles FDA-approved and investigational drugs. 54 approved drugs were selected as candidates, 3 of which were acquired and tested on Cz and T. cruzi epimastigotes proliferation. Among them, levothyroxine, traditionally used in hormone replacement therapy in patients with hypothyroidism, showed dose-dependent inhibition of Cz and antiproliferative activity on the parasite.