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6 results for “language model embeddings”
Embeddings from protein language models predict conservation and variant effects
<p>For this work, we used protein language model representations (embeddings) to predict sequence conservation without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthew Correlation Coefficient – MCC - for ProtT5 embeddings of 0.596±0.006 vs. 0.608±0.006 for ConSeq).</p> <p><strong><em>ConSurf10k</em>- Dataset for the development of ProtT5cons:</strong> The method (ProtT5cons) predicting residue conservation used <em>ConSurf-DB </em>(Ben Chorin et al. 2020). This resource provided sequences and conservation for 89,673 proteins. For all, experimental high-resolution three-dimensional (3D) structures were available in the Protein Data Bank (PDB) (Berman et al. 2000). As standard-of-truth for the conservation prediction, we used the values from ConSurf-DB generated using HMMER (Mistry et al. 2013), CD-HIT (Fu et al. 2012), and MAFFT-LINSi (Katoh and Standley 2013) to align proteins in the PDB (Burley et al. 2019). For proteins from families with over 50 proteins in the resulting MSA, an evolutionary rate at each residue position is computed and used along with the MSA to reconstruct a phylogenetic tree. The ConSurf-DB conservation scores ranged from 1 (most variable) to 9 (most conserved). The PISCES server (Wang and Dunbrack 2003) was used to redundancy reduce the data set such that no pair of proteins had more than 25% pairwise sequence identity. We removed proteins with resolutions >2.5Å, those shorter than 40 residues, and those longer than 10,000 residues. The resulting data set (ConSurf10k) with 10,507 proteins (or domains) was randomly partitioned into training (9,392 sequences), cross-training/validation (555) and test (519) sets.</p> <p>Uploaded data:</p> <ul> <li>ConSuf10k_PDBid_seq_cons.fasta: fasta file with PDBid, sequence and conservation annotation</li> <li>consurf10k_test_ids.txt: txt file with id's of test set</li> <li>consurf10k_train_ids.txt: txt file with id's of train set</li> <li>consurf10k_val_ids.txt: txt file with id's of cross-validation set</li> </ul>
Protein language model embeddings and predictions for the fly proteome (FlyBase)
<p>Residue and sequence embeddings of the fly (drosophila melanogaster) proteome (FlyBase for organism drosophila melanogaster, downloaded on 2022.03.01) computed using bio_embeddings (bioembeddings.com) using the ProtT5 embedder at full precision (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3). To open the embeddings file, please see <a href="https://github.com/sacdallago/bio_embeddings/blob/develop/notebooks/open_embedding_file.ipynb">this notebook</a>. The embeddings will be indexed by numbers according to the mapping file (mapping_file.csv) in this dataset. All following results will share the same mapping (for instance, to access the variation prediction results, by accessing index "0", you will query results for the sequence "FBpp0304622").</p> <p>Additionally:</p> <p>- Sequence-level predictions of subcellular localization in 10 classes using LA (https://www.biorxiv.org/content/10.1101/2021.04.25.441334v1)</p> <p>- Residue-level three state secondary structure prediction (alpha, sheet or other) using models reported in the ProtTrans paper (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3)</p> <p>- Residue-level prediction of conservation (in 9 states) and of variation effect (from 0 [no-effect] to 1 [effect]) using VESPAl (https://doi.org/10.1007/s00439-021-02411-y)</p> <p> </p> <p>Files included:</p> <p>- dmel-all-translation-r6.44.fasta --> FASTA-formatted sequences of drosophila melanogaster from FlyBase</p> <p>- mapping_file.csv --> A CSV file mapping the identifiers used in the following files (from 0 to 30737) to the identifiers in the FlyBase fasta file (dmel-all-translation-r6.44.fasta).</p> <p>- DSSP3_fly_ProtT5Sec.fasta --> Secondary structure predictions in three states for each residue of each protein in dmel-all-translation-r6.44.fasta. "H" stands for Helix; "E" stands for Sheet; "C" stands for Other.</p> <p>- subcell_fly_LA_ProtT5.csv --> Subcellular location (10 states) and memrane-boundness (2 states) for each protein in dmel-all-translation-r6.44.fasta</p> <p>- embeddings_file.h5 --> per-residue embeddings of sequences in dmel-all-translation-r6.44.fasta. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length Lx1024, with L being the length of the protein sequence. Datasets are indexed using integers. The original sequence identifier (from the FASTA header) can be accessed through the "original_id" attribute. See https://docs.bioembeddings.com/v0.2.0/notebooks/open_embedding_file.html for information on how to open the file.</p> <p>- reduced_embeddings_file.h5 --> per-sequence embeddings of sequences in dmel-all-translation-r6.44.fasta (obtained by mean-pooling the residue-embeddings along the length dimension of the protein sequence). Each dataset in the .h5 file represents a protein sequence and contains a vector of size 1024 (meaning, each sequence has the same dimension).</p> <p>- conspred_probs.h5 --> per-sequence conservation probability (softmax) prediction of sequences in dmel-all-translation-r6.44.fasta in 9 classes. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length 9xL, with L being the length of the protein sequence, and 9 being the predicted conservation class (index 0 = very variable; index 8 = very conserved)</p> <p>- vespal_SAVeffect_fly.zip --> zipped .h5 file of per-sequence variation predictions of sequences in dmel-all-translation-r6.44.fasta on a scale from 0 (neutral) to 1 (effect). -1 indicates WT substitution. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length 20xL, with L being the length of the protein sequence, and 20 being the predicted variation score for each residue substitution (AAs in the following order: "<strong>ALGVSREDTIPKFQNYMHWC</strong>" . Meaning that index 0 = substitution of the residue to "A", index = 1 substitution to residue "L", aso.)</p>
Trained Models from "General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings"
<p>Trained models from the paper:</p> <p>Lukas Galke, Isabell Cuber, Christoph Meyer, Henrik Ferdinand Noelscher, Angelina Sonderecker, and Ansgar Scherp: <strong>General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings</strong>, in: <em>International Joint Conference on Neural Networks (IJCNN), </em>2022.</p> <ul> <li>File seq2mat_hybrid_bidirectional_sbertlike-100p-bsz512 holds the model from pretraining</li> <li>File ws2020_transformer_final_models holds the fine-tuned models for each task of the GLUE benchmark</li> </ul>
Protein language model embeddings and predictions of the human proteome
<p>Residue and sequence embeddings of the human proteome (SwissProt for organism Human, downloaded on 2021.06.09) computed using bio_embeddings (bioembeddings.com) using the ProtT5 embedder at full precision (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3).</p> <p>Additionally:</p> <p>- Sequence-level predictions of subcellular localization in 10 classes using LA (https://www.biorxiv.org/content/10.1101/2021.04.25.441334v1)</p> <p>- Residue-level three state secondary structure prediction (alpha, sheet or other) using models reported in the ProtTrans paper (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3)</p> <p> </p> <p>Files included:</p> <p>- human.fasta --> FASTA-formatted sequences of human from SwissProt</p> <p>- DSSP3_human_ProtT5Sec.fasta --> Secondary structure predictions in three states for each residue of each protein in human.fasta. "H" stands for Helix; "E" stands for Sheet; "C" stands for Other.</p> <p>- subcell_human_LA_ProtT5.csv --> Subcellular location (10 states) and memrane-boundness (2 states) for each protein in human.fasta</p> <p>- embeddings_file.h5 --> per-residue embeddings of sequences in human.fasta. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length Lx1024, with L being the length of the protein sequence. Datasets are indexed using integers. The original sequence identifier (from the FASTA header) can be accessed through the "original_id" attribute. See https://docs.bioembeddings.com/v0.2.0/notebooks/open_embedding_file.html for information on how to open the file</p> <p>- reduced_embeddings_file.h5 --> per-sequence embeddings of sequences in human.fasta (obtained by mean-pooling the residue-embeddings along the length dimension of the protein sequence). Each dataset in the .h5 file represents a protein sequence and contains a vector of size 1024 (meaning, each sequence has the same dimension).</p>
Topologies, Checkpoints, and Configurations for the paper "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models"
<p>This repository consists of the <strong>files</strong> related to the <strong>paper</strong> "GVI-RL: Graph-Invariant RL for Attack Paths Discovery using Vulnerabilities Embedded with Large Language Models". In particular, this repository contains tensorboard logs, topologies, checkpoints, seeds, and results to ensure reproducibility of the results of the paper.</p> <p>The results included are related to the training/validation and hyper-parameters optimization of the outcome multi-label classifier, the GVI-RL agent, and the world model.<br>The data folder contains also the topologies used in the study, the vulnerabilities data used to generate them and the dataset for multi-label classification.</p> <p>The README.md file describes the folders' structure.</p>
RNA large language models embeddings on benchmark datasets
<p>This repository contains pre-computed embeddings for several RNA sequences, using most recent Large Language Models (LLM) pre-trained on RNA sequences. </p> <p>compressed files for each combination of RNA-LLM models and benchmarking RNA datasets. </p>
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