ArticleBriefings in bioinformatics2022
Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 26 citations in OpenAlex.
- Computational identification of rare pathogenic genomic variants in esophageal cancer markers: Transcript-level analysis, sequence-based insights, and structural-functional impacts of non-synonymous SNPs.Biochemistry and biophysics reports · 2026Article
- Employing AlphaFold and TED to explore the structural impacts of stress-associated mutations in rice.Bioinformatics advances · 2026Article
- One-step labeling with a fluorinated porphyrin-based probe enablesInnovation (Cambridge (Mass.)) · 2025Article
- PyVADesign: a python-based cloning tool for one-step generation of large mutant libraries.Bioinformatics (Oxford, England) · 2025Article
- Advanced Strategies in Phage Research: Innovations, Applications, and Challenges.Microorganisms · 2025Review
- Decoding the functional impact of the cancer genome through protein-protein interactions.Nature reviews. Cancer · 2025Review
- Challenges in bridging the gap between protein structure prediction and functional interpretation.Proteins · 2025Article
- Molecular Dynamics Simulation of Kir6.2 Variants Reveals Potential Association with Diabetes Mellitus.Molecules (Basel, Switzerland) · 2024Article
- Artificial Intelligence-based database for prediction of protein structure and their alterations in ocular diseases.Database : the journal of biological databases and curation · 2023Article
- N4-hydroxycytidine, the active compound of Molnupiravir, promotes SARS-CoV-2 mutagenesis and escape from a neutralizing nanobody.iScience · 2023Article
- Novel machine learning approaches revolutionize protein knowledge.Trends in biochemical sciences · 2023Review
- The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors.Current opinion in structural biology · 2023Review
- AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor.Chemical science · 2023Article
- Exploring AlphaFold2's Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein.International journal of molecular sciences · 2023Article
- Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond.Biomolecules · 2022Article
- Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions.Molecules (Basel, Switzerland) · 2022Review
- Integrating Precision Medicine into the Standard of Care for Male Infertility: What Will it Take?European urology · 2022Article
- GCAT: A network model of mutational influences between amino acid positions in PSD95Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.
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Registered trials
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