ArticlePlant biotechnology journal2022
PPVED: A machine learning tool for predicting the effect of single amino acid substitution on protein function in plants.
Article in Plant biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 24 citations in OpenAlex.
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- Pan-genome analysis reveals hidden diversity and selection signatures of auxin response factors (ARFs) associated with breeding in barley.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- SiLRL1, a bHLH transcription factor from foxtail millet, promotes carotenoid accumulation and improves drought tolerance.BMC plant biology · 2025Article
- Article
- Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- A chromosome-level genome assembly ofFrontiers in plant science · 2024Article
- Identification and haplotype analysis of SiCHLI: a gene for yellow-green seedling as morphological marker to accelerate foxtail millet (Setaria italica) hybrid breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- ABA-inducible DEEPER ROOTING 1 improves adaptation of maize to water deficiency.Plant biotechnology journal · 2022Article
- PPVED: A machine learning tool for predicting the effect of single amino acid substitution on protein function in plants.Plant biotechnology journal · 2022Article
- A genome-wide association study of folates in sweet corn kernels.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single amino acid substitution (SAAS) produces the most common variant of protein function change under physiological conditions. As the number of SAAS events in plants has increased exponentially, an effective prediction tool is required to help identify and distinguish functional SAASs from the whole genome as either potentially causal traits or as variants. Here, we constructed a plant SAAS database that stores 12 865 SAASs in 6172 proteins and developed a tool called Plant Protein Variation Effect Detector (PPVED) that predicts the effect of SAASs on protein function in plants. PPVED achieved an 87% predictive accuracy when applied to plant SAASs, an accuracy that was much higher than those from six human database software: SIFT, PROVEAN, PANTHER-PSEP, PhD-SNP, PolyPhen-2, and MutPred2. The predictive effect of six SAASs from three proteins in Arabidopsis and maize was validated with wet lab experiments, of which five substitution sites were accurately predicted. PPVED could facilitate the identification and characterization of genetic variants that explain observed phenotype variations in plants, contributing to solutions for challenges in functional genomics and systems biology. PPVED can be accessed under a CC-BY (4.0) license via http://www.ppved.org.cn.
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