Model Overview

Description:

ESM-2nv is a protein language model that provides numerical embeddings for each amino acid in a protein sequence. It is developed using the BioNeMo Framework. The embeddings from its encoder can be used as features for predictive models. The ESM-2nv 3B model has 36 layers, 40 attention heads, a hidden space dimension of 2560, and contains 3B parameters. This model is ready for commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case [1]; see link to Hugging Face Model Card for ESM-2 3B model.

References:

[1] Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y. and dos Santos Costa, A., 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637), pp.1123-1130.\n[2] \"UniProt: the universal protein knowledgebase in 2021.\" Nucleic acids research 49, no. D1 (2021): D480-D489.\n[3] Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.\n

Model Architecture:

Architecture Type: BERT
\nNetwork Architecture: ESM-2

Input:

Input Type(s): Text (Protein Sequences)
\nInput Parameters: 1D
\nOther Properties Related to Input: Protein sequence represented as a string of canonical amino acids, of maximum length 1022. Longer sequences are automatically truncated to this length.

Output:

Output Type(s): Text (Protein Sequences)
\nOutput Parameters: 1D
\nOther Properties Related to Output: Numeric vector with one float-point value corresponding to an embedding for each amino acid in the input protein sequence. Maximum output length is 1022 embeddings - one embedding vector per amino acid.

Software Integration:

Runtime Engine(s):

BioNeMo, NeMo 1.2

Supported Hardware Microarchitecture Compatibility:

Ampere
Hopper
Volta

Preferred/Supported Operating System(s):

Linux

Model Version(s):

esm2nv_3B_converted.nemo, version 1.0

Training & Evaluation:

Training Dataset:

Link:

UniRef50
UniRef90

Data Collection Method by dataset

Human

Properties: UniRef50 (release 04/2021) was used for training [2]. The representative sequence for each UniRef50 cluster was selected, resulting in 49,874,565 protein sequences. The sequences were randomly split with 249,372 sequences in validation and 49,625,193 in training. All training sequences that matched a validation sequence with 50% sequence identity were removed from the train set, resulting in 49,425,807 train sequences. A sampling dataset of UniRef90 sequences was created based on any UniRef90 representatives and cluster members that had complete sequences available from UniRef90 or UniRef100, and filtered to UniRef90 sequences for clusters that corresponded to the UniRef50 train set. The UniRef90 dataset was combined with the filtered UniRef50 training dataset to create the sampling fasta file. A mapping file was created to enable rapid replacement of UniRef50 sequences with a sequence sampled uniformly from the corresponding records in the sampling fasta file during each training update. The UniRef50 training fasta was sorted in the order of occurrence of records in column 1 of the mapping file. The UniRef90+UniRef50 sampling fasta file was sorted in the order of occurrence of records in column 2 of the mapping file. Protein sequences longer than 1024 amino acids were cropped to 1022 from sequence start [3].

Unlike ESM-2 pre-training data, the curated pre-training dataset provided with ESM-2nv release contains hits for de novo proteins, since sequences in UniRef100, UniRef90, and UniRef50 with high sequence similarity to a non-public 81 de novo proteins [1] are not filtered.

Evaluation Dataset:

Link: FLIP – secondary structure, conservation,subcellular localization, meltome, GB1 activity
\nData Collection Method by dataset

Hybrid: Human & Automated

Labeling Method by dataset

Hybrid: Human & Automated

Properties:\nThe FLIP datasets evaluate the performance of the model on five specific downstream tasks for proteins. It provides pre-defined splits for fine-tuning a pretrained model using task-specific train and validation examples, and subsequently evaluating it on a task-specific test split.

The secondary structure FLIP dataset contains experimental secondary structures, with 9712 proteins for model finetuning, 1080 proteins for validation, and 648 proteins for testing.

Conservation dataset contains conservation scores of the residues of protein sequences with 9392 proteins for training, 555 proteins for validation, and 519 proteins for testing.

Subcellular localization dataset contains protein subcellular locations with 9503 proteins for training, 1678 proteins for validation, and 2768 proteins for testing.

Meltome dataset contains experimental melting temperatures for proteins, with 22335 proteins for training, 2482 proteins for validation, and 3134 proteins for testing.

The GB1 activity dataset contains experimental binding affinities of GB1 protein variants with variation at four sites (V39, D40, G41 and V54) measured in a binding assay, with 6289 proteins for training, 699 proteins for validation, and 1745 proteins for testing.

Inference:

Engine: BioNeMo, NeMo
\nTest Hardware:

Ampere
Hopper
Volta

Accuracy Benchmarks

The accuracy of ESM2-nv was measured using the Fitness Landscape Inference for Proteins (FLIP) benchmarks {cite:p}dallago2021flip, which involve training a downstream task on top of the ESM2-nv model and measuring some characteristic:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

FLIP type	Dataset	Dataset Split for Measurement	Metric	650m	3b
Secondary Structure	sequences.fasta	test	accuracy (%)	85.2	85.7
GB1	two_vs_rest.fasta	test	rmse (score)	1.29	1.28
Residue Conservation	sequences.fasta	test	accuracy (%)	33.2	34.5
Protein Melting Point (Meltome)	mixed_split.fasta	test	rmse (°C)	7.19	6.68

License

ESM-2nv is as provided under the {{model_license_slug}}.

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\n\n### Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case [1]; see link to [Hugging Face Model Card for ESM-2 3B model](https://huggingface.co/facebook/esm2_t36_3B_UR50D).\n\n### References:\n\n[1] Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y. and dos Santos Costa, A., 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 379(6637), pp.1123-1130.\n[2] \"UniProt: the universal protein knowledgebase in 2021.\" Nucleic acids research 49, no. D1 (2021): D480-D489.\n[3] Devlin, J., Chang, M.W., Lee, K. and Toutanova, K., 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.\n
\n\n### Model Architecture:\n\n**Architecture Type:** BERT
\n**Network Architecture:** ESM-2
\n\n### Input:\n\n**Input Type(s):** Text (Protein Sequences)
\n**Input Parameters:** 1D
\n**Other Properties Related to Input:** Protein sequence represented as a string of canonical amino acids, of maximum length 1022. Longer sequences are automatically truncated to this length.
\n\n### Output:\n\n**Output Type(s):** Text (Protein Sequences)
\n**Output Parameters:** 1D
\n**Other Properties Related to Output:** Numeric vector with one float-point value corresponding to an embedding for each amino acid in the input protein sequence. Maximum output length is 1022 embeddings - one embedding vector per amino acid.
\n\n### Software Integration:\n\n**Runtime Engine(s):**\n* BioNeMo, NeMo 1.2
\n\n**Supported Hardware Microarchitecture Compatibility:**
\n* Ampere
\n* Hopper
\n* Volta
\n\n**Preferred/Supported Operating System(s):**
\n* Linux
\n\n### Model Version(s):\n\nesm2nv_3B_converted.nemo, version 1.0
\n\n## Training & Evaluation:\n\n### Training Dataset:\n\n**Link:** \n* [UniRef50](https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref50)
\n* [UniRef90](https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref90)
\n\n**Data Collection Method by dataset**
\n* Human
\n\n**Properties:** UniRef50 (release 04/2021) was used for training [2]. The representative sequence for each UniRef50 cluster was selected, resulting in 49,874,565 protein sequences. The sequences were randomly split with 249,372 sequences in validation and 49,625,193 in training. All training sequences that matched a validation sequence with 50% sequence identity were removed from the train set, resulting in 49,425,807 train sequences. A sampling dataset of UniRef90 sequences was created based on any UniRef90 representatives and cluster members that had complete sequences available from UniRef90 or UniRef100, and filtered to UniRef90 sequences for clusters that corresponded to the UniRef50 train set. The UniRef90 dataset was combined with the filtered UniRef50 training dataset to create the sampling fasta file. A mapping file was created to enable rapid replacement of UniRef50 sequences with a sequence sampled uniformly from the corresponding records in the sampling fasta file during each training update. The UniRef50 training fasta was sorted in the order of occurrence of records in column 1 of the mapping file. The UniRef90+UniRef50 sampling fasta file was sorted in the order of occurrence of records in column 2 of the mapping file. Protein sequences longer than 1024 amino acids were cropped to 1022 from sequence start [3].
\n\nUnlike ESM-2 pre-training data, the curated pre-training dataset provided with ESM-2nv release contains hits for de novo proteins, since sequences in UniRef100, UniRef90, and UniRef50 with high sequence similarity to a non-public 81 de novo proteins [1] are not filtered.
\n\n### Evaluation Dataset:\n\n**Link:** [FLIP – secondary structure, conservation,subcellular localization, meltome, GB1 activity](http://data.bioembeddings.com/public/FLIP/fasta/)
\n**Data Collection Method by dataset**
\n* Hybrid: Human & Automated
\n\n**Labeling Method by dataset**
\n* Hybrid: Human & Automated
\n\n**Properties:**\nThe FLIP datasets evaluate the performance of the model on five specific downstream tasks for proteins. It provides pre-defined splits for fine-tuning a pretrained model using task-specific train and validation examples, and subsequently evaluating it on a task-specific test split.\n\nThe secondary structure FLIP dataset contains experimental secondary structures, with 9712 proteins for model finetuning, 1080 proteins for validation, and 648 proteins for testing.\n\nConservation dataset contains conservation scores of the residues of protein sequences with 9392 proteins for training, 555 proteins for validation, and 519 proteins for testing.\n\nSubcellular localization dataset contains protein subcellular locations with 9503 proteins for training, 1678 proteins for validation, and 2768 proteins for testing.\n\nMeltome dataset contains experimental melting temperatures for proteins, with 22335 proteins for training, 2482 proteins for validation, and 3134 proteins for testing.\n\nThe GB1 activity dataset contains experimental binding affinities of GB1 protein variants with variation at four sites (V39, D40, G41 and V54) measured in a binding assay, with 6289 proteins for training, 699 proteins for validation, and 1745 proteins for testing.
\n\n### Inference:\n\n**Engine:** BioNeMo, NeMo
\n**Test Hardware:**
\n* Ampere
\n* Hopper
\n* Volta
\n\n#### Accuracy Benchmarks\n\nThe accuracy of ESM2-nv was measured using the Fitness Landscape Inference for Proteins (FLIP) benchmarks {cite:p}`dallago2021flip`, which involve training a downstream task on top of the ESM2-nv model and measuring some characteristic:\n\n| FLIP type | Dataset | Dataset Split for Measurement | Metric | 650m | 3b |\n|---------------------------------|-------------------|-------------------------------|---------------------|-------|------|\n| Secondary Structure | sequences.fasta | test | accuracy (%) | 85.2 | 85.7 |\n| GB1 | two_vs_rest.fasta | test | rmse (score) | 1.29 | 1.28 |\n| Residue Conservation | sequences.fasta | test | accuracy (%) | 33.2 | 34.5 |\n| Protein Melting Point (Meltome) | mixed_split.fasta | test | rmse (°C) | 7.19 | 6.68 |\n\n### License\n\nESM-2nv is as provided under the {{model_license_slug}}.","displayName","ESM-2nv 3B",["D",1737486128136],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/clara/models/esm2nv3b","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Clara.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[255,256,257,258,259,260],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_224":283,"_262":284,"_263":285,"_265":266},{"_224":279,"_262":280,"_263":281,"_265":266},{"_224":275,"_262":276,"_263":277,"_265":266},{"_224":271,"_262":272,"_263":273,"_265":266},{"_224":268,"_262":268,"_263":269,"_265":266},{"_224":261,"_262":261,"_263":264,"_265":266},"NSPECT-KO70-P9IR","value","filter",{"_267":266,"_262":261},"category","label","field","NeMo",{"_267":270,"_262":268},"framework","Healthcare","indus_healthcare",{"_267":274,"_262":272},"industry","Drug Discovery","uscs_drug_discovery",{"_267":278,"_262":276},"application","DL","soln_dl",{"_267":282,"_262":280},"technology","Clara","pltfm_clara",{"_267":286,"_262":284},"platform",{"_132":1262,"_134":1263},"ngcApps",[1215],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1197":1198,"_1199":1200,"_1201":1202,"_1203":1204,"_1205":298,"_163":324,"_1206":75,"_1207":60,"_1208":109,"_1209":109,"_1210":1211,"_1212":109,"_1213":1214},"amplitude",{"_1194":1195,"_836":-7,"_1196":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1188":1189,"_1190":1191,"_1192":1193},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788816141120,"1.15.0","defaultSearchSuggestions",{"_1076":191,"_1077":1078,"_1079":1080,"_1081":1082},"announcements",[],"consentState",{"_362":1066,"_1067":109,"_1068":35,"_1069":35,"_1070":1071},"consentConfig",{"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357,"_358":359,"_360":361,"_362":363},"showContactAdmin",1788816141121,[],[],[338,339,340,341,342,343],{"_49":64,"_51":74,"_53":127,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":127,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":69,"_53":127,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":127,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":127,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":127,"_55":-5,"_57":-5,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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If you do not allow these cookies, then some or all of these services may not function properly.",[593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617],[564,565,566,567,568],{"_403":478,"_224":478,"_190":191,"_376":588},{"_403":452,"_224":452,"_190":191,"_376":583},{"_403":578,"_224":578,"_190":191,"_376":579},{"_403":573,"_224":573,"_190":191,"_376":574},{"_403":569,"_224":569,"_190":191,"_376":570},"nvidia.partners",[571],{"_80":572,"_409":569,"_410":35,"_190":446},"edge","app.hushly.com",[575],{"_80":576,"_409":573,"_410":35,"_190":577},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[580],{"_80":581,"_409":578,"_410":35,"_190":582},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704],[656,657,658,659],{"_403":671,"_224":671,"_190":191,"_376":672},{"_403":666,"_224":666,"_190":191,"_376":667},{"_403":465,"_224":465,"_190":191,"_376":663},{"_403":578,"_224":578,"_190":191,"_376":660},[661],{"_80":662,"_409":578,"_410":109,"_190":582},"_gh_sess",[664],{"_80":665,"_409":465,"_410":109,"_190":472},"YSC","bizible.com",[668],{"_80":669,"_409":666,"_410":35,"_190":670},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[673],{"_80":674,"_409":671,"_410":35,"_190":675},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_80":746,"_409":524,"_410":35,"_190":747},{"_80":731,"_409":745,"_410":109,"_190":732},{"_80":714,"_409":527,"_410":35,"_190":639},{"_80":714,"_409":627,"_410":35,"_190":639},{"_80":743,"_409":524,"_410":35,"_190":744},{"_80":731,"_409":530,"_410":109,"_190":732},{"_80":741,"_409":524,"_410":35,"_190":742},{"_80":731,"_409":527,"_410":109,"_190":732},{"_80":739,"_409":626,"_410":35,"_190":740},{"_80":731,"_409":648,"_410":109,"_190":732},{"_80":711,"_409":527,"_410":35,"_190":713},{"_80":737,"_409":524,"_410":109,"_190":738},{"_80":735,"_409":524,"_410":35,"_190":736},{"_80":733,"_409":524,"_410":109,"_190":734},{"_80":731,"_409":622,"_410":109,"_190":732},{"_80":729,"_409":524,"_410":35,"_190":730},{"_80":727,"_409":524,"_410":35,"_190":728},{"_80":725,"_409":524,"_410":109,"_190":726},{"_80":723,"_409":631,"_410":35,"_190":724},{"_80":711,"_409":622,"_410":35,"_190":713},{"_80":721,"_409":524,"_410":35,"_190":722},{"_80":719,"_409":524,"_410":109,"_190":720},{"_80":717,"_409":524,"_410":35,"_190":718},{"_80":715,"_409":524,"_410":35,"_190":716},{"_80":714,"_409":524,"_410":35,"_190":639},{"_80":711,"_409":712,"_410":35,"_190":713},{"_80":709,"_409":524,"_410":35,"_190":710},{"_80":707,"_409":524,"_410":109,"_190":708},{"_80":705,"_409":524,"_410":35,"_190":706},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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