ACTIVITY is a database on DNA/RNA site sequences with known activity magnitudes, measurement systems, sequence-activity relationships under fixed experimental conditions and procedures to adapt these relationships from one measurement system to another. This database deposits information on DNA/RNA affinities to proteins and cell nuclear extracts, cutting efficiencies, gene transcription activity, mRNA translation efficiencies, mutability and other biological activities of natural sites occurring within promoters, mRNA leaders, and other regulatory regions in pro- and eukaryotic genomes, their mutant forms and synthetic analogues. Since activity magnitudes are heavily system-dependent, the current version of ACTIVITY is supplemented by three novel sub-databases: (i) SYSTEM, measurement systems; (ii) KNOWLEDGE, sequence-activity relationships under fixed experimental conditions; and (iii) CROSS_TEST, procedures adapting a relationship from one measurement system to another. These databases are useful in molecular biology, pharmacogenetics, metabolic engineering, drug design and biotechnology. The databases can be queried using SRS and are available through the Web, http://wwwmgs. bionet.nsc.ru/systems/Activity/.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC29829 | PMC |
http://dx.doi.org/10.1093/nar/29.1.284 | DOI Listing |
Foods
November 2024
Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, China.
Food-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental validation through bioinformatics methods. Here, we construct an ACE inhibitory peptide predictor (ACEiPP) using optimized amino acid descriptors (AADs) and long- and short-term memory neural networks.
View Article and Find Full Text PDFACS Cent Sci
April 2024
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Antigen processing is critical for therapeutic vaccines to generate epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often limits the quantity of epitopes released. We address this challenge using machine learning to ascribe a proteasomal degradation score to epitope sequences. Epitopes with varying scores were translocated into cells using nontoxic anthrax proteins.
View Article and Find Full Text PDFACS Appl Mater Interfaces
May 2024
State Key Laboratory of Medicinal Chemical Biology, Key Laboratory of Bioactive Materials of Ministry of Education and College of Life Sciences, Nankai University, Tianjin 300071, China.
Enzymes catalyze almost all material conversion processes within living organisms, yet their natural evolution remains unobserved. Short peptides, derived from proteins and featuring active sites, have emerged as promising building blocks for constructing bioactive supramolecular materials that mimic native proteins through self-assembly. Herein, we employ histidine-containing isomeric tetrapeptides KHFF, HKFF, KFHF, HFKF, FKHF, and FHKF to craft supramolecular self-assemblies, aiming to explore the sequence-activity landscapes of enzyme evolution.
View Article and Find Full Text PDFMolecules
December 2023
School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental ()-selective amine transaminases from (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data.
View Article and Find Full Text PDFbioRxiv
September 2023
Department of Chemistry, Massachusetts Institute of Technology; 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA.
Antigen processing is critical for producing epitope peptides that are presented by HLA molecules for T cell recognition. Therapeutic vaccines aim to harness these epitopes for priming cytotoxic T cell responses against cancer and pathogens, but insufficient processing often reduces vaccine efficacy through limiting the quantity of epitopes released. Here, we set out to improve antigen processing by harnessing protein degradation signals called degrons from the ubiquitin-proteasome system.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!