ProLIF: a library to encode molecular interactions as fingerprints.
Ontology highlight
ABSTRACT:
SUBMITTER: Bouysset C
PROVIDER: S-EPMC8466659 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Chemical communication underpins virtually all aspects of vertebrate social life, yet remains poorly understood because of its highly complex mechanistic basis. We therefore used chemical fingerprinting of skin swabs and genetic analysis to explore the chemical cues that may underlie mother-offspring recognition in colonially breeding Antarctic fur seals. By sampling mother-offspring pairs from two different colonies, using a variety of statistical approaches and genotyping a large panel of microsatellite loci, we show that colony membership, mother-offspring similarity, heterozygosity, and genetic relatedness are all chemically encoded. Moreover, chemical similarity between mothers and offspring reflects a combination of genetic and environmental influences, the former partly encoded by substances resembling known pheromones. Our findings reveal the diversity of information contained within chemical fingerprints and have implications for understanding mother-offspring communication, kin recognition, and mate choice.
| S-EPMC4568685 | biostudies-literature
Project description:Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.
| S-EPMC5028207 | biostudies-literature
Project description:Cell membranes contain hundreds of different proteins and lipids in an asymmetric arrangement. Our current understanding of the detailed organization of cell membranes remains rather elusive, because of the challenge to study fluctuating nanoscale assemblies of lipids and proteins with the required spatiotemporal resolution. Here, we use molecular dynamics simulations to characterize the lipid environment of 10 different membrane proteins. To provide a realistic lipid environment, the proteins are embedded in a model plasma membrane, where more than 60 lipid species are represented, asymmetrically distributed between the leaflets. The simulations detail how each protein modulates its local lipid environment in a unique way, through enrichment or depletion of specific lipid components, resulting in thickness and curvature gradients. Our results provide a molecular glimpse of the complexity of lipid-protein interactions, with potentially far-reaching implications for our understanding of the overall organization of real cell membranes.
| S-EPMC6028153 | biostudies-literature
Project description:BackgroundThe discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called Drug-Protein Interaction Analysis (D-PIA) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.MethodGiven a database of drug-protein interactions, D-PIA performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.ResultsTo evaluate the effectiveness of D-PIA, we have tested it with real drug-protein interaction data. D-PIA has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if D-PIA can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.ConclusionsD-PIA has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and D-PIA is therefore very fast to compute. D-PIA has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.
| S-EPMC3372454 | biostudies-literature
Project description:Drug-drug interactions (DDIs) constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints (IPFs) with successful application to DDI detection. IPFs were generated based on the DrugBank database, which provided 9,454 well-established DDIs as a primary source of interaction data. The model uses IPFs to measure the similarity of pairs of drugs and generates new putative DDIs from the non-intersecting interactions of a pair. We described as part of our analysis the pharmacological and biological effects associated with the putative interactions; for example, the interaction between haloperidol and dicyclomine can cause increased risk of psychosis and tardive dyskinesia. First, we evaluated the method through hold-out validation and then by using four independent test sets that did not overlap with DrugBank. Precision for the test sets ranged from 0.4-0.5 with more than two fold enrichment factor enhancement. In conclusion, we demonstrated the usefulness of the method in pharmacovigilance as a DDI predictor, and created a dataset of potential DDIs, highlighting the etiology or pharmacological effect of the DDI, and providing an exploratory tool to facilitate decision support in DDI detection and patient safety.
| S-EPMC3592896 | biostudies-other
Project description:Biological processes are carried out through molecular conformational transitions, ranging from the structural changes within biomolecules to the formation of macromolecular complexes and the associations between the complexes themselves. These transitions cover a vast range of timescales and are governed by a tangled network of molecular interactions. The resulting hierarchy of interactions, in turn, becomes encoded in the experimentally measurable "mechanical fingerprints" of the biomolecules, their force-extension curves. However, how can we decode these fingerprints so that they reveal the kinetic barriers and the associated timescales of a biological process? Here, we show that this can be accomplished with a simple, model-free transformation that is general enough to be applicable to molecular interactions involving an arbitrarily large number of kinetic barriers. Specifically, the transformation converts the mechanical fingerprints of the system directly into a map of force-dependent rate constants. This map reveals the kinetics of the multitude of rate processes in the system beyond what is typically accessible to direct measurements. With the contributions from individual barriers to the interaction network now "untangled", the map is straightforward to analyze in terms of the prominent barriers and timescales. Practical implementation of the transformation is illustrated with simulated biomolecular interactions that comprise different patterns of complexity--from a cascade of activation barriers to competing dissociation pathways.
| S-EPMC3799329 | biostudies-other
Project description:Hemoglobin is an iron carrying protein in erythrocytes and also an essential element to transfer oxygen from the lungs to the tissues. Abnormalities in hemoglobin concentration are closely correlated with health status and many diseases, including thalassemia, anemia, leukemia, heart disease, and excessive loss of blood. Particularly in resource-constrained settings existing blood analyzers are not readily applicable due to the need for high-level instrumentation and skilled personnel, thereby inexpensive, easy-to-use, and reliable detection methods are needed. Herein, a molecular fingerprints of hemoglobin on a nanofilm chip was obtained for real-time, sensitive, and selective hemoglobin detection using a surface plasmon resonance system. Briefly, through the photopolymerization technique, a template (hemoglobin) was imprinted on a monomeric (acrylamide) nanofilm on-chip using a cross-linker (methylenebisacrylamide) and an initiator-activator pair (ammonium persulfate-tetramethylethylenediamine). The molecularly imprinted nanofilm on-chip was characterized by atomic force microscopy and ellipsometry, followed by benchmarking detection performance of hemoglobin concentrations from 0.0005 mg mL-1 to 1.0 mg mL-1. Theoretical calculations and real-time detection implied that the molecularly imprinted nanofilm on-chip was able to detect as little as 0.00035 mg mL-1 of hemoglobin. In addition, the experimental results of hemoglobin detection on the chip well-fitted with the Langmuir adsorption isotherm model with high correlation coefficient (0.99) and association and dissociation coefficients (39.1 mL mg-1 and 0.03 mg mL-1) suggesting a monolayer binding characteristic. Assessments on selectivity, reusability and storage stability indicated that the presented chip is an alternative approach to current hemoglobin-targeted assays in low-resource regions, as well as antibody-based detection procedures in the field. In the future, this molecularly imprinted nanofilm on-chip can easily be integrated with portable plasmonic detectors, improving its access to these regions, as well as it can be tailored to detect other proteins and biomarkers.
| S-EPMC6165033 | biostudies-literature
Project description:Infrared spectroscopy of liquid biopsies is a time- and cost-effective approach that may advance biomedical diagnostics. However, the molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the method's applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease.
| S-EPMC8361728 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-07-04 | GSE236408 | GEO
Project description:BackgroundBreast cancer screening is currently predominantly based on mammography, tainted with the occurrence of both false positivity and false negativity, urging for innovative strategies, as effective detection of early-stage breast cancer bears the potential to reduce mortality. Here we report the results of a prospective pilot study on breast cancer detection using blood plasma analyzed by Fourier-transform infrared (FTIR) spectroscopy - a rapid, cost-effective technique with minimal sample volume requirements and potential to aid biomedical diagnostics. FTIR has the capacity to probe health phenotypes via the investigation of the full repertoire of molecular species within a sample at once, within a single measurement in a high-throughput manner. In this study, we take advantage of cross-molecular fingerprinting to probe for breast cancer detection.MethodsWe compare two groups: 26 patients diagnosed with breast cancer to a same-sized group of age-matched healthy, asymptomatic female participants. Training with support-vector machines (SVM), we derive classification models that we test in a repeated 10-fold cross-validation over 10 times. In addition, we investigate spectral information responsible for BC identification using statistical significance testing.ResultsOur models to detect breast cancer achieve an average overall performance of 0.79 in terms of area under the curve (AUC) of the receiver operating characteristic (ROC). In addition, we uncover a relationship between the effect size of the measured infrared fingerprints and the tumor progression.ConclusionThis pilot study provides the foundation for further extending and evaluating blood-based infrared probing approach as a possible cross-molecular fingerprinting modality to tackle breast cancer detection and thus possibly contribute to the future of cancer screening.
| S-EPMC8638519 | biostudies-literature