Defining and navigating macrocycle chemical space.
Ontology highlight
ABSTRACT:
SUBMITTER: Viarengo-Baker LA
PROVIDER: S-EPMC8179434 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:DNA-encoded library (DEL) technology is a novel ligand identification strategy that allows the synthesis and screening of unprecedented chemical diversity more efficiently than conventional methods. However, no reports have been published to systematically study how to increase the diversity and improve the molecular property space that can be covered with DEL. This report describes the development and application of eDESIGNER, an algorithm that comprehensively generates all possible library designs, enumerates and profiles samples from each library and evaluates them to select the libraries to be synthesized. This tool utilizes suitable on-DNA chemistries and available building blocks to design and identify libraries with a pre-defined molecular weight distribution and maximal diversity compared with compound collections from other sources.
| S-EPMC9814293 | biostudies-literature
Project description:This study represents the first large-scale study on the chemical space of inhibitors of dipeptidyl peptidase-4 (DPP4), which is a potential therapeutic protein target for the treatment of diabetes mellitus. Herein, a large set of 2,937 compounds evaluated for their ability to inhibit DPP4 was compiled from the literature. Molecular descriptors were generated from the geometrically optimized low-energy conformers of these compounds at the semiempirical AM1 level. The origins of DPP4 inhibitory activity were elucidated from computed molecular descriptors that accounted for the unique physicochemical properties inherently present in the active and inactive sets of compounds as defined by their respective half maximal inhibitory concentration values of less than 1 μM and greater than 10 μM, respectively. Decision tree analysis revealed the importance of molecular weight, total energy of a molecule, topological polar surface area, lowest unoccupied molecular orbital, and number of hydrogen-bond donors, which correspond to molecular size, energy, surface polarity, electron acceptors, and hydrogen bond donors, respectively. The prediction model was subjected to rigorous independent testing via three external sets. Scaffold and chemical fragment analysis was also performed on these active and inactive sets of compounds to shed light on the distinguishing features of the functional moieties. Docking of representative active DPP4 inhibitors was also performed to unravel key interacting residues. The results of this study are anticipated to be useful in guiding the rational design of novel and robust DPP4 inhibitors for the treatment of diabetes.
| S-EPMC4539085 | biostudies-literature
Project description:Antimicrobial resistance is a global health threat that requires innovative strategies against drug-resistant bacteria. Our study focuses on enoyl-acyl carrier protein reductases (ENRs), in particular FabI, FabK, FabV, and InhA, as potential antimicrobial agents. Despite their promising potential, the lack of clinical approvals for inhibitors such as triclosan and isoniazid underscores the challenges in achieving preclinical success. In our study, we curated and analyzed a dataset of 1412 small molecules recognized as ENR inhibitors, investigating different structural variants. Using advanced cheminformatic tools, we mapped the physicochemical landscape and identified specific structural features as key determinants of bioactivity. Furthermore, we investigated whether the compounds conform to Lipinski rules, PAINS, and Brenk filters, which are crucial for the advancement of compounds in development pipelines. Furthermore, we investigated structural diversity using four different representations: Chemotype diversity, molecular similarity, t-SNE visualization, molecular complexity, and cluster analysis. By using advanced bioinformatics tools such as matched molecular pairs (MMP) analysis, machine learning, and SHAP analysis, we were able to improve our understanding of the activity cliques and the precise effects of the functional groups. In summary, this chemoinformatic investigation has unraveled the FAB inhibitors and provided insights into rational antimicrobial design, seamlessly integrating computation into the discovery of new antimicrobial agents.
| S-EPMC10967325 | biostudies-literature
Project description:Terpenoid synthases create diverse carbon skeletons by catalyzing complex carbocation rearrangements, making them particularly challenging for enzyme function prediction. To begin to address this challenge, we have developed a computational approach for the systematic enumeration of terpenoid carbocations. Application of this approach allows us to systematically define a nearly complete chemical space for the potential carbon skeletons of products from monoterpenoid synthases. Specifically, 18758 carbocations were generated, which we cluster into 74 cyclic skeletons. Five of the 74 skeletons are found in known natural products; some of the others are plausible for new functions, either in nature or engineered. This work systematizes the description of function for this class of enzymes, and provides a basis for predicting functions of uncharacterized enzymes. To our knowledge, this is the first computational study to explore the complete product chemical space of this important class of enzymes.
| S-EPMC4982680 | biostudies-literature
Project description:Identification of turn motifs that are stabilized by intramolecular hydrogen bonds can be useful in describing the conformation of peptide systems. However, this approach is somewhat insufficient for cyclic peptides because peptide regions that are not positioned within a hydrogen bond can be left with no description. Furthermore, non-regular secondary structures and other rarely-observed conformations can be left without detailed evaluation. Herein, we describe "higher-order" ϕ/ψ plots termed macrocycle conformational maps (MCMs) as a tool for evaluating and comparing the conformations of a series of structurally related macrocyclic peptides.
| S-EPMC9175111 | biostudies-literature
Project description:Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyond chemical rules-based framework for CPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred .
| S-EPMC8027643 | biostudies-literature
Project description:The monoamine oxidase (MAO) enzyme class is a prevalent target for many neurodegenerative and depressive disorders. Even though scrutinization of many promising drugs for the treatment of MAO inhibition has been carried out in recent times, a conclusive structural requirement for potent activity needs to be developed. Numerous approaches have been examined for the identification of structural features for potent MAO inhibitors (MAOIs) that mainly involve an array of computational studies, synthetic approaches, and biological evaluation. In this paper, we have analyzed ∼2200 well-known MAOIs to expand perceptions in the chemical space of MAOIs. The physicochemical properties of the MAOIs disclosed a discernible hydrophobic feature making a bunch discrete from the central nervous system (CNS) acting drugs, as exposed using the principal component analysis (PCA). The Murcko scaffold structure study revealed unfavorable and favorable scaffold structures, in both data sets, with the highest biological activity shown by the 3-phenyl-2H-chromen-2-one scaffold. This scaffold showed a polypharmacological effect. R-group disintegration and automatic structure-activity relationship (SAR) study resulted in identification of substructures responsible for the inhibitory bioactivity of the MAO-A and MAO-B enzymes. Moreover, with activity cliff analysis, significant biological activity was detected by simple molecular conversion in the chemical compound structure. In addition, we used the machine learning tool to generate a hypothesis wherein pyrazole, benzene ring, and amide containing structural functionalities can exhibit potential biological activities. This hypothesis revealed that CNS target drugs, C4155, C13390, C21265, C43862, C31524, C24810, C37100, C42075, and C43644, could be repurposed as valuable candidates for the MAO-B enzyme. For researchers, this study will bring new perceptions in the discovery and development of MAOIs and direct lead and hit optimization for the progress of small molecules beneficial for MAO-targeting associated diseases.
| S-EPMC8444296 | biostudies-literature
Project description:Eremophila is the largest genus in the plant tribe Myoporeae (Scrophulariaceae) and exhibits incredible morphological diversity across the Australian continent. The Australian Aboriginal Peoples recognize many Eremophila species as important sources of traditional medicine, the most frequently used plant parts being the leaves. Recent phylogenetic studies have revealed complex evolutionary relationships between Eremophila and related genera in the tribe. Unique and structurally diverse metabolites, particularly diterpenoids, are also a feature of plants in this group. To assess the full dimension of the chemical space of the tribe Myoporeae, we investigated the metabolite diversity in a chemo-evolutionary framework applying a combination of molecular phylogenetic and state-of-the-art computational metabolomics tools to build a dataset involving leaf samples from a total of 291 specimens of Eremophila and allied genera. The chemo-evolutionary relationships are expounded into a systematic context by integration of information about leaf morphology (resin and hairiness), environmental factors (pollination and geographical distribution), and medicinal properties (traditional medicinal uses and antibacterial studies), augmenting our understanding of complex interactions in biological systems.
| S-EPMC9292440 | biostudies-literature
Project description:The success of biological signal pattern recognition depends crucially on the selection of relevant features. Across signal and imaging modalities, a large number of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that, due to the inherent biological variability, even the same classification problem on different datasets can display variations in the respective optimal sets, casting doubts on the generalizability of relevant features. Here, we approach this problem by leveraging topological tools to create charts of features spaces. These charts highlight feature sub-groups that encode similar information (and their respective similarities) allowing for a principled and interpretable choice of features for classification and analysis. Using multiple electromyographic (EMG) datasets as a case study, we use this feature chart to identify functional groups among 58 state-of-the-art EMG features, and to show that they generalize across three different forearm EMG datasets obtained from able-bodied subjects during hand and finger contractions. We find that these groups describe meaningful non-redundant information, succinctly recapitulating information about different regions of feature space. We then recommend representative features from each group based on maximum class separability, robustness and minimum complexity.
| S-EPMC5746577 | biostudies-literature
Project description:Despite the global pandemic that recently affected human and cargo transportation, the emissions of the maritime sector are projected to keep growing steadily. The International Maritime Organization focused on boosting the fleets' efficiency to improve their environmental performance, while more sustainable fuels are currently under investigation. Here, we assess the economic, technical, and environmental feasibility of an interim solution for low-carbon shipping using state-of-the-art CO2 capture technology, namely, chemical absorption, on-board cargo ships. We compute the carbon footprint of this alternative and perform an absolute sustainability study based on seven planetary boundaries. Our results show that the capture on-board scenario can achieve 94% efficiency on the net CO2 emissions at 85 $/tCO2 while substantially reducing impacts on core planetary boundaries (relative to the business as usual) and outperforming a direct air capture scenario in global warming and all planetary boundaries, except for the nitrogen flow. Hence, capture on-board seems an appealing solution to decarbonize shipping in the short term while alternative carbon-free fuels and related infrastructure are developed and deployed.
| S-EPMC9795543 | biostudies-literature