A5064016167- Computational Drug Discovery Methods
- Analytical Chemistry and Chromatography
- Receptor Mechanisms and Signaling
- Cell Image Analysis Techniques
- Biochemical Analysis and Sensing Techniques
- Estrogen and related hormone effects
- Regulation of Appetite and Obesity
- Advanced Proteomics Techniques and Applications
- HIV Research and Treatment
- Mosquito-borne diseases and control
- HIV/AIDS drug development and treatment
- melanin and skin pigmentation
- Metabolomics and Mass Spectrometry Studies
- Single-cell and spatial transcriptomics
- Neural Networks and Applications
- Genetics, Bioinformatics, and Biomedical Research
- Image Processing Techniques and Applications
- vaccines and immunoinformatics approaches
- Meat and Animal Product Quality
- Protein Structure and Dynamics
- Advanced Fluorescence Microscopy Techniques
- Influenza Virus Research Studies
- Malaria Research and Control
- Gene expression and cancer classification
- SARS-CoV-2 and COVID-19 Research
Uppsala University
2014-2024
Science for Life Laboratory
2021-2024
University of Gothenburg
2001
Abstract We have developed an alignment‐independent method for classification of G‐protein coupled receptors (GPCRs) according to the principal chemical properties their amino acid sequences. The relies on a multivariate approach where primary sequences are translated into vectors based physicochemical acids and transformation data uniform matrix by applying modified autocross‐covariance transform. application component analysis set 929 class A GPCRs showed clear separation major classes...
Lipophilicity is a major determinant of ADMET properties and overall suitability drug candidates. We have developed large-scale models to predict water–octanol distribution coefficient (logD) for chemical compounds, aiding discovery projects. Using ACD/logD data 1.6 million compounds from the ChEMBL database, are created evaluated by support-vector machine with linear kernel using conformal prediction methodology, outputting intervals at specified confidence level. The resulting model shows...
We have evaluated the proteochemometrics approach in analysis of interactions a diverse set or organic ligands with subtypes serotonin, dopamine, histamine, and adrenergic receptors. As used herein, exploits affinity data for series amines binding to wild-type amine G protein-coupled receptors, correlating it descriptions cross-description derived from primary amino acid sequences receptors computed structures compounds. show that after appropriate preprocessing, statistically valid models...
Abstract Motivation: Proteochemometrics is a novel technology for the analysis of interactions series proteins with ligands. We have here customized it large datasets and evaluated modeling interaction psychoactive organic amines all five known families amine G protein-coupled receptors (GPCRs). Results: The model exploited data binding 22 compounds to 31 GPCRs, correlating chemical descriptions cross-descriptions affinity using strategy. A highly valid (q 2 = 0.76) was obtained which...
Abstract Background A major obstacle in treatment of HIV is the ability virus to mutate rapidly into drug-resistant variants. method for predicting susceptibility mutated strains antiviral agents would provide substantial clinical benefit as well facilitate development new candidate drugs. Therefore, we used proteochemometrics model protease inhibitors current use, utilizing descriptions physico-chemical properties proteases and 3D structural property inhibitors. The were correlated data 828...
Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment these diseases. However, current interact with a broad variety interfere multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that predict inhibitor-kinase interactions from the chemical properties kinase macromolecules might aid design more...
Abstract Profiling drug leads by means of in silico and vitro assays as well omics is widely used discovery for safety efficacy predictions. In this study, we evaluate the performance machine learning models trained on data from gene expression phenotypic profiling assays, with chemical structure descriptors, prediction various mechanisms action target proteins. Models several hundred actions targets were using 1484 compounds characterized both L1000 profiles, cell painting assay. The...
Abstract Background The emergence and continued global spread of the current COVID-19 pandemic has highlighted need for methods to identify novel or repurposed therapeutic drugs in a fast effective way. Despite availability discovery antiviral drugs, majority tend focus on effects such given virus, its constituent proteins, enzymatic activity, often neglecting consequences host cells. This may lead partial assessment efficacy tested anti-viral compounds, as potential toxicity impacting...
A unified proteochemometric (PCM) model for the prediction of ability drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) was created made publicly available under Bioclipse Decision Support open source system at www.cyp450model.org. In regards modeling we represented chemical compounds by molecular signature descriptors CYP-isoforms alignment-independent description composition transition amino acid properties their...
A series of 45 peptide inhibitors was designed, synthesized, and evaluated against the NS2B–NS3 proteases four subtypes dengue virus, DEN-1–4. The design based on proteochemometric models for Michaelis (Km) cleavage rate constants (kcat) protease substrates. This led first to octapeptides showing submicromolar or low micromolar inhibitory activities proteases. Stepwise removal cationic substrate non-prime side residues variations in prime sequence resulted finally an uncharged tetrapeptide,...
Environmental chemicals are commonly studied one at a time, and there is need to advance our understanding of the effect exposure their combinations. Here we apply high-content microscopy imaging cells stained with multiplexed dyes (Cell Painting) profile effects Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), Dibutyltin dilaurate (DBTDL) on four human cell lines; both individually in all We show that morphological features can be used multivariate data analysis discern between...
Semantic web technologies are finding their way into the life sciences. Ontologies and semantic markup have already been used for more than a decade in molecular sciences, but not found widespread use yet. The technology Resource Description Framework (RDF) related methods show to be sufficiently versatile change that situation.The work presented here focuses on linking RDF approaches existing chemometrics fields, including cheminformatics, QSAR modeling proteochemometrics. Applications link...
Abstract Background Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect can be defined as effects on controlled from distant sites in the proteins, e.g. changes protein conformation mobility, whereas occur close proximity protein's amino acids ligand. Molecular is traditionally studied using three-dimensional methods, but with such techniques it difficult to predict caused mutational located far away ligand-binding site. We recently...
Abstract Background Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need 3D structure information. Several reported proteochemometric models ligand-receptor interactions have already yielded significant insights into various forms bio-molecular interactions. The are multivariate regression predict binding affinity for particular combination features ligand and protein. Although offered interesting...
Several viruses hijack various forms of endocytosis in order to infect host cells. Here, we report the discovery a molecule with antiviral properties that named virapinib, which limits viral entry by macropinocytosis. The identification virapinib derives from chemical screen using high-throughput microscopy, where identified entities capable preventing infection pseudotype virus expressing spike (S) protein SARS-CoV-2. Subsequent experiments confirmed capacity inhibit SARS-CoV-2, as well...
We have created quantitative structure−activity relationship (QSAR) models describing the interaction of a series 54 organic compounds with four melanocortin (MC) receptor subtypes, MC1, MC3, MC4, and MC5. In addition to traditional QSAR analysis, we applied our recently developed proteo-chemometrics approach. Proteo-chemometrics is based on combined analysis receptors ligands, wherein descriptions proteins, so-called ligand−protein cross-terms are correlated activities. The were...
Proteochemometrics was applied in the analysis of binding organic compounds to wild-type and chimeric melanocortin receptors. Thirteen receptors were designed based on statistical molecular design; each chimera contained parts from three MC<sub>1,3-5</sub> The affinities 18 determined for these four data 14 correlated physicochemical structural descriptors compounds, binary receptor sequences, cross-terms derived ligand obtain a proteochemometric model (correlation performed using partial...
Abstract G‐Protein‐coupled receptors (GPCRs) are among the most important drug targets. Because of a shortage 3D crystal structures, design for GPCRs has been ligand‐based. We propose novel, rough set‐based proteochemometric approach to study receptor and ligand recognition. The is validated on three datasets containing GPCRs. In proteochemometrics, properties ligands used in conjunction modeled predict binding affinity. set (RS) rule‐based models presented herein consist minimal decision...
Background Reverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse (RT) inhibitors (NRTIs) combination with non-nucleoside RT inhibitor or protease inhibitor. Unfortunately, HIV capable of escaping the by mutating into drug-resistant variants. Computational models that correlate susceptibilities to virus genotype and molecular properties might facilitate selection improved...
The main therapeutic targets in HIV are its protease and reverse transcriptase. A major problem treatment of is the ability virus to develop drug resistance by accumulating mutations targets. Acquiring detailed understanding molecular mechanisms for interactions drugs with mutated variants mandatory be able design inhibitors that can evade resistance. Here we have used proteochemometric modeling simultaneously analyze 21 72 unique variants. Inhibition data (pKi) were correlated descriptions...
The purinergic 12 receptor (P2Y12) is a major drug target for anticoagulant therapies, but little known about the regions involved in ligand binding and activation of this receptor. We generated four randomized P2Y12 libraries investigated their characteristics. was expressed Saccharomyces cerevisiae model system. Four were with amino acids at positions 181, 256, 265 280. Mutant variants screened functional activity yeast using natural ADP. Activation results quantitative structure-activity...
Aromatase, the rate-limiting enzyme that catalyzes conversion of androgen to estrogen, plays an essential role in development estrogen-dependent breast cancer. Side effects due aromatase inhibitors (AIs) necessitate pursuit novel inhibitor candidates with high selectivity, lower toxicity and increased potency. Designing a therapeutic agent against could be achieved computationally by means ligand-based structure-based methods. For over decade, we have utilized both approaches design...