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 650M model has 33 layers, 20 attention heads, a hidden space dimension of 1280, and contains 650M 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 650M 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_650M_converted.nemo, version 1.0

Training & Evaluation:

Training Dataset:

Links:

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 Apache 2.0

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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 650M model](https://huggingface.co/facebook/esm2_t36_650M_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_650M_converted.nemo, version 1.0
\n\n## Training & Evaluation:\n\n### Training Dataset:\n\n**Links:** \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 Apache 2.0","displayName","ESM-2nv 650M",["D",1737486122554],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/clara/models/esm2nv650m","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Clara.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[254,255,256,257,258,259],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_222":282,"_261":283,"_262":284,"_264":265},{"_222":278,"_261":279,"_262":280,"_264":265},{"_222":274,"_261":275,"_262":276,"_264":265},{"_222":270,"_261":271,"_262":272,"_264":265},{"_222":267,"_261":267,"_262":268,"_264":265},{"_222":260,"_261":260,"_262":263,"_264":265},"NSPECT-1OVA-WHO6","value","filter",{"_266":265,"_261":260},"category","label","field","NeMo",{"_266":269,"_261":267},"framework","Healthcare","indus_healthcare",{"_266":273,"_261":271},"industry","Drug Discovery","uscs_drug_discovery",{"_266":277,"_261":275},"application","DL","soln_dl",{"_266":281,"_261":279},"technology","Clara","pltfm_clara",{"_266":285,"_261":283},"platform",{"_132":1459,"_134":1460},"ngcApps",[1412],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1394":1395,"_1396":1397,"_1398":1399,"_1400":1401,"_1402":297,"_159":321,"_1403":1404,"_1392":60,"_1405":109,"_1406":109,"_1407":1408,"_1409":109,"_1410":1411},"amplitude",{"_1390":1391,"_1392":60,"_993":-7,"_1393":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1384":1385,"_1386":1387,"_1388":1389},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1268":239,"_1269":1270,"_1271":1272,"_1273":1274},"announcements",[],"consentState",{"_359":1258,"_1259":109,"_1260":35,"_1261":35,"_1262":1263},"consentConfig",{"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360},"showContactAdmin",1782973861934,[],[],[335,336,337,338,339],{"_49":63,"_51":74,"_53":127,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":127,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":127,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":340,"_59":60},{"_49":50,"_51":52,"_53":127,"_55":-5,"_57":-5,"_59":60},4,"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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. Usually used to maintain an anonymised user session by the server.","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","ApplicationGatewayAffinityCORS","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure all requests from a user during the session are sent to the same server.","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.Supports Amplitude product analytics.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","AMP_TLDTEST","c_code","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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