Improving protein solubility and activity by introducing small peptide tags designed with machine learning models.
Ontology highlight
ABSTRACT:
SUBMITTER: Han X
PROVIDER: S-EPMC7334598 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization ΔHvap of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting ΔHvap of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of ΔHvap of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database.
| S-EPMC8747575 | biostudies-literature
Project description:Antimicrobial peptides (AMPs) provide a promising strategy against infections involving multidrug-resistant pathogens. In previous studies, we designed a short 12 amino acid AMP DP7, using a machine-learning method based on an amino acid activity contribution matrix. DP7 shows broad-spectrum antimicrobial activities both in vitro and in vivo. Here, we aim to investigate the efficacy of DP7 against multidrug resistant Staphylococcus aureus (S. aureus) and reveal the potential mechanisms. First, by measuring the killing kinetics of DP7 against S. aureus and comparing these results with antibiotics with different antimicrobial mechanisms, we hypothesize that DP7, in addition to its known ability to induce cell wall cation damage, can also exert a full killing effect. With FITC-conjugated or biotin-labeled DP7, we tracked DP7's attachment, membrane permeation and subsequent intracellular distribution in S. aureus. These results indicated that the possible targets of DP7 were within the bacterial cells. Transcriptome sequencing of S. aureus exposed to DP7 identified 333 differentially expressed genes (DEGs) influenced by DP7, involving nucleic acid metabolism, amino acid biosynthesis, cell wall destruction and pathogenesis, respectively, indicating the comprehensive killing efficacy of DP7. In addition, the genome sequencing results of the induced DP7 resistant strain S. aureus DP7-R revealed two-point mutations in the mprF and guaA gene. Moreover, in a murine model for MRSA blood stream infection, intravenously treating mice with DP7 showed a good protective effect on mice. In conclusion, DP7 is an effective bactericide for S. aureus, which deserves further study for clinical application and drug development.
| S-EPMC6546875 | biostudies-literature
Project description:MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections to cancer. This impact on the phenotype leads to a great interest in establishing the miRNAs of an organism. Experimental methods are complicated which led to the development of computational methods for pre-miRNA detection. Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences. Positive training data for model establishment, for the most part, stems from miRBase, the miRNA registry. The quality of the entries in miRBase has been questioned, though. This unknown quality led to the development of filtering strategies in attempts to produce high quality positive datasets which can lead to a scarcity of positive data. To analyze the quality of filtered data we developed a machine learning model and found it is well able to establish data quality based on intrinsic measures. Additionally, we analyzed which features describing pre-miRNAs could discriminate between low and high quality data. Both models are applicable to data from miRBase and can be used for establishing high quality positive data. This will facilitate the development of better miRNA detection tools which will make the prediction of miRNAs in disease states more accurate. Finally, we applied both models to all miRBase data and provide the list of high quality hairpins.
| S-EPMC6042829 | biostudies-other
Project description:Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.
| S-EPMC7666209 | biostudies-literature
Project description:Constructive machine learning aims to create examples from its learned domain which are likely to exhibit similar properties. Here, a recurrent neural network was trained with the chemical structures of known cell-migration modulators. This machine learning model was used to generate new molecules that mimic the training compounds. Two top-scoring designs were synthesized, and tested for functional activity in a phenotypic spheroid cell migration assay. These computationally generated small molecules significantly increased the migration of medulloblastoma cells. The results further corroborate the applicability of constructive machine learning to the de novo design of druglike molecules with desired properties.
| S-EPMC6807213 | biostudies-literature
Project description:BACKGROUND AND PURPOSE:MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND METHODS:We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models. RESULTS:Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%). CONCLUSIONS:Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
| S-EPMC6474354 | biostudies-other
Project description:Recombinant proteins are an attractive choice as a safe alternative to traditional live attenuated vaccines. However, most small-size proteins are poorly immunogenic, and adjuvants, whose mode of action remain to be fully clarified, are needed for increasing their immunogenicity. Here, we report the effects of short solubility controlling peptide tags (SCP-tags) on the immunogenicity of DENV3 envelope protein domain 3 (3ED3; 103 residues, 11.46 kDa) in ICR and Swiss albino model mice. The attachment of a 4-Ile SCP-tag (C4I-tag) increased the hydrodynamic radius of 3ED3 from 2.2 ± 0.09 to 111 ± 146 nm as assessed by dynamic light scattering in phosphate buffered saline at 37°C, indicating that the C4I-tag oligomerized 3ED3. Immunization at 30 ?g/dose showed that the untagged 3ED3 was not or poorly immunogenic, whereas the C4I-tag increased its immunogenicity by up to 39-fold as assessed by the IgG level measured using ELISA. Moreover, the increased antibody level was sustained for over 6 months after immunization and a high number of effector and central memory T cells were generated. These observations provide solid and quantitative evidence for the hypothesis that subvisible aggregates with hydrodynamic radii of 100 nm can increase immunogenicity and that SCP-tag can establish a long-term, target-specific immune response in a way adequate for the development of a peptide/protein-based DENV vaccine.
| S-EPMC7089932 | biostudies-literature
Project description:Enzyme-based tags attached to a protein-of-interest (POI) that react with a small molecule, rendering the conjugate fluorescent, are very useful for studying the POI in living cells. These tags are typically based on endogenous enzymes, so protein engineering is required to ensure that the small-molecule probe does not react with the endogenous enzyme in the cell of interest. Here we demonstrate that de novo-designed enzymes can be used as tags to attach to POIs. The inherent bioorthogonality of the de novo-designed enzyme-small-molecule probe reaction circumvents the need for protein engineering, since these enzyme activities are not present in living organisms. Herein, we transform a family of de novo-designed retroaldolases into variable-molecular-weight tags exhibiting fluorescence imaging, reporter, and electrophoresis applications that are regulated by tailored, reactive small-molecule fluorophores.
| S-EPMC4183642 | biostudies-literature
Project description:Quantum computing and artificial intelligence, combined together, may revolutionize future technologies. A significant school of thought regarding artificial intelligence is based on generative models. Here, we propose a general quantum algorithm for machine learning based on a quantum generative model. We prove that our proposed model is more capable of representing probability distributions compared with classical generative models and has exponential speedup in learning and inference at least for some instances if a quantum computer cannot be efficiently simulated classically. Our result opens a new direction for quantum machine learning and offers a remarkable example where a quantum algorithm shows exponential improvement over classical algorithms in an important application field.
| S-EPMC6286170 | biostudies-other
Project description:BACKGROUND:Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities. METHODS:Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity - the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest. RESULTS:The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models. CONCLUSIONS:The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.
| S-EPMC6794897 | biostudies-other