ArticleProtein science : a publication of the Protein Society2023
LambdaPP: Fast and accessible protein-specific phenotype predictions.
Article in Protein science : a publication of the Protein Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Proteins need extra attention: improving the predictive power of protein language models on mutational datasets with hint tokens.NAR genomics and bioinformatics · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- Evaluation of Three Mutations in Codon 385 of Glucose-6-Phosphate Dehydrogenase via Biochemical and In Silico Analysis.International journal of molecular sciences · 2024Article
- SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Biodiversity of microorganisms in the Baltic Sea: the power of novel methods in the identification of marine microbes.FEMS microbiology reviews · 2024Review
- DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024Article
- Protein embeddings predict binding residues in disordered regions.Scientific reports · 2024Article
- Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31.Scientific reports · 2024Article
- Insights into the inner workings of transformer models for protein function prediction.Bioinformatics (Oxford, England) · 2024Article
- DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.Nucleic acids research · 2024Article
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
- LambdaPP: Fast and accessible protein-specific phenotype predictions.Protein science : a publication of the Protein Society · 2023Article
- The promises of large language models for protein design and modeling.Frontiers in bioinformatics · 2023Article
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Authors and funding
13 authors.
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Abstract
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP-leveraging ColabFold and computed in minutes-is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. LambdaPP is freely available for everyone to use under embed.predictprotein.org, the interactive results for the case study can be found under https://embed.predictprotein.org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP.
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