ArticleBMC genomics2014
NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- StrucNS reveals interaction-weighted network topology as the driving predictor of absolute stability of natural and de novo proteins.Bioinformatics (Oxford, England) · 2026Article
- Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model.ACS catalysis · 2026Article
- An in silico approach for screening non-synonymous SNPs in Mycobacterium tuberculosis PPE68 protein and impact on structure.Scientific reports · 2025Article
- DDGWizard: Integration of feature calculation resources for analysis and prediction of changes in protein thermostability upon point mutations.PLoS computational biology · 2025Article
- OrgNet: orientation-gnostic protein stability assessment using convolutional neural networks.Bioinformatics (Oxford, England) · 2025Article
- Development of a β-glucosidase improved for glucose retroinhibition for cellulosic ethanol production: an integrated bioinformatics and genetic engineering approach.Biotechnology for biofuels and bioproducts · 2025Article
- LICEDB: light industrial core enzyme database for industrial applications and AI enzyme design.Database : the journal of biological databases and curation · 2025Article
- Osmolyte-induced protein stability changes explained by graph theory.Computational and structural biotechnology journal · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Review
- Simulated complexes formed from a set of postsynaptic proteins suggest a localised effect of a hypomorphic Shank mutation.BMC neuroscience · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- HomozygousHuman mutation · 2024Article
- Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability.BMC bioinformatics · 2023Article
- Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.Acta biochimica et biophysica Sinica · 2023Review
- Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures.Theranostics · 2023Article
- LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries.Computational and structural biotechnology journal · 2023Article
- BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.Protein science : a publication of the Protein Society · 2022Article
- Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation.PLoS computational biology · 2022Article
- Recent advancements in enzyme-mediated crosslinkable hydrogels:APL bioengineering · 2021Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes.
resultsHere we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/.
conclusionsNeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.
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