COVID-3D is an online resource that provides a bridge between the wealth of genomic information being collected on SARS-CoV-2 and their structural and functional consequences, to provide biological insights and help guide therapeutic development efforts.
Tools
To facilitate the further development and evaluation of methods to predict these changes, we have developed ThermoMutDB, a manually curated database containing >14,669 experimental data of thermodynamic parameters for wild type and mutant proteins.
A novel predictive tool that uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be active against one or multiple cancer cell lines.
Calculation and visualisation of all molecular interactions between atoms of molecules of interest, including proteins, nucleic acids, carbohydrates as well as small molecules.
Prediction of small molecule pharmacokinetic and toxicity properties using graph-based signatures.
Quantification of the extent of localized purifying selection in protein-coding sequences. The missense tolerance ratio (MTR) summarizes available human standing variation data within genes to encapsulate population level genetic variation.
A novel graph-based signature approach for the quantitative prediction of the effects of missense mutations on protein stability.
An integrated computational approach for quantitative prediction of the effects of missense mutations on protein stability.
Analysis and visualisation of protein dynamics using normal mode analysis. Quantitative prediction of the effects of missense mutations on protein dynamics and stability.
Fast and accurate evaluation of the effects of single and multiple point mutations on protein folding, stability, flexibility and conformation by combining protein dynamics and machine learning.
Predicting the effects of mutations in membrane proteins on stability, and whether they are likely to be benign or pathogenic.
A scalable and effective machine learning model for accurately assessing changes in protein-protein binding affinity caused by single and multiple missense mutations
A novel graph-based signature approach for the quantitative prediction of the effects of missense mutations on protein-protein binding affinity.
An optimised novel graph-based signature approach for the quantitative prediction of the effects of missense mutations on protein-protein binding affinity.
Quantitative prediction of the effects of missense mutations on antibody-antigen binding affinity to guide rational antibody engineering.
Predicting the effects of introducing multiple point mutations on antibody-antigen binding affinity.
A novel graph-based signature approach for the quantitative prediction of the effects of missense mutations on protein-DNA binding affinity.
Quantitative prediction of the effects of missense mutations on protein-nucleic acid binding affinities using graph-based signatures.
Quantitative prediction of the effects of missense mutations on affinities of small molecules for proteins using graph-based signatures.
A graph-based signature approach to rapidly identify compounds likely to be active against Mycobacterium
This manually curated, literature-derived database of the effects of over 1,000 mutations protein-ligand binding affinity together with the three-dimensional structures of the complex.
Accurate prediction of the risk of ccRCC associated with all possible VHL missense mutations.
A relational database to facilitate rational protein engineering of Botulinum for therapeutic use.