A-Alpha Bio
A-Alpha Bio is a Seattle biotechnology company, founded in 2017 out of the University of Washington's Institute for Protein Design and Center for Synthetic Biology, that measures, predicts and engineers protein-protein interactions. Its experimental platform AlphaSeq reprograms yeast mating to quantify millions of protein-protein binding affinities in a single experiment, and its computational platform AlphaBind is a domain-specific deep-learning model trained on hundreds of millions of those affinity measurements to predict and optimize antibody-antigen binding from sequence. In July 2026 the company launched Atlas, a web platform and data ecosystem that publishes ML-ready protein interaction "Data Blocks" for licensing, custom on-demand data generation, and a quarterly-release Atlas Consortium whose founding members include GSK, Boltz, Cradle and Dyno Therapeutics. Atlas is backed by a public HTTP API — the Data Product API at api.atlas.aalphabio.com — which serves dataset discovery, dataset metadata and structured Data Cards anonymously, and gates CSV data, CSV schemas and structure (.cif) files behind a bearer token issued through AWS Cognito sign-in.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles A-Alpha Bio the way a machine reads it — 12 machine-readable artifacts across 1 API, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — A-Alpha Bio scores 39.2/100 (thin), with a separate agent-readiness read of 36/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-08-17 · rubric v0.11.0
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How we profile A-Alpha Bio
Each block below is one kind of artifact we hold for A-Alpha Bio. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 1
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
A-Alpha Bio Datasets API
The Datasets API from A-Alpha Bio — 9 operation(s) for datasets.
Open Collections 2
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
API Collection
OPEN COLLECTIONA Alpha Bio Datasets API
OPEN COLLECTIONMCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
a-alpha-bio-mcp.yml
MCP SERVERJSON Schema 3
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
Examples 3
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for A-Alpha Bio — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Agent Surfaces 3
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 5
Pagination, idempotency, versioning, errors, and events
Build 3
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 8
The organization behind the API
Scroll within the panel for all 8 ·
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/a-alpha-bio · machine-readable index on apis.io
This is an independent, third-party profile of A-Alpha Bio, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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