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alterlab-proteinmpnn

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업데이트2026년 7월 2일 11:18

Design protein sequences for a fixed backbone with ProteinMPNN (Dauparas 2022) — message-passing inverse folding that outputs sequences predicted to fold to a given structure, with fixed positions, tied/symmetric chains, amino-acid bias, and a soluble-model variant. Use when inverse-folding a backbone PDB into sequences, redesigning selected positions, imposing symmetry across chains, or generating the sequence step of a design→fold→score loop. For pocket/interface design WITH a bound ligand, metal, or nucleic acid prefer alterlab-ligandmpnn; to GENERATE a new backbone prefer alterlab-rfdiffusion; to refold and validate a design prefer alterlab-alphafold; for generative multimodal design prefer alterlab-esm. Part of the AlterLab Academic Skills suite.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

AlterLab-IEU

AlterLab-IEU/AlterLab-Academic-Skills

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

ProteinMPNN (fixed-backbone sequence design)

Overview

ProteinMPNN (Dauparas et al., Science 2022; dauparas/ProteinMPNN) solves the inverse-folding problem: given a protein backbone (a 3D structure with no or a placeholder sequence), it designs amino-acid sequences predicted to fold to that backbone. It is fast, robust, runs on CPU, and is the standard "sequence" step between backbone generation (alterlab-rfdiffusion) and structure validation (alterlab-alphafold).

When to Use This Skill

Use this skill when the user wants to:

Inverse-fold a backbone PDB into one or more candidate sequences.
Redesign only selected positions while fixing the rest (partial design).
Enforce symmetry by tying residues/chains so homo-oligomers get identical sequences.
Bias the amino-acid composition (e.g. avoid cysteines) or use the soluble model.
Produce the sequence step of a design → fold → score loop.

Does NOT Trigger

Scenario	Use instead
Design a pocket/interface with a ligand, metal, or nucleic acid present	`alterlab-ligandmpnn`
Generate a new backbone (no starting structure)	`alterlab-rfdiffusion`
Refold a designed sequence to check it (validation)	`alterlab-alphafold`
Generative multimodal (sequence+structure+function) design	`alterlab-esm`

Core Capabilities

1. Basic inverse folding

# Parse the PDB, then design sequences (dauparas/ProteinMPNN CLI — TODO(verify) flags/version).
# The parser lives in the repo's helper_scripts directory; run it by name:
python parse_multiple_chains.py --input_path=pdbs/ --output_path=parsed.jsonl
python protein_mpnn_run.py \
  --jsonl_path parsed.jsonl --out_folder out/ \
  --num_seq_per_target 8 --sampling_temp "0.1"

Lower --sampling_temp (e.g. 0.1) gives conservative, high-confidence designs; higher temperatures increase diversity. Output FASTA headers carry the model score (lower = better) and sequence recovery.

2. Fixed positions and chains

Supply a fixed-positions spec (JSONL from the helper scripts) to keep catalytic/known residues while redesigning the rest, and a chain spec to design only some chains. Verify the exact helper-script names against your checkout (TODO(verify)).

3. Symmetry / tied positions

Tie positions across chains so a homo-oligomer receives one sequence applied symmetrically — essential for symmetric alterlab-rfdiffusion outputs.

4. Design → fold → score loop

The canonical de-novo pipeline:

Generate a backbone with alterlab-rfdiffusion.
Design sequences for it here (ProteinMPNN), sampling several per backbone.
Score by refolding each with alterlab-alphafold and accepting only self-consistent designs (returns to the target backbone with high pLDDT, low PAE).

Resources

references/proteinmpnn_usage.md — install/pinning, helper-script inputs (fixed positions, tied chains, bias), the soluble model, temperature guidance, and loop integration. Loaded on demand.

Part of the AlterLab Academic Skills suite.

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The AlterLab front door and multi-agent launcher — routes a task to the right AlterLab skill(s) when the user invokes the suite without naming one, and for a multi-stage goal (or on the keyword 'alterflow', aliases 'alterresearch' / 'ultralab') it CLARIFIES the goal with a few questions, SELECTS the skills the task needs, and runs a dynamic multi-agent workflow composing them (via alterlab-workflow-orchestration, alterlab-research-pipeline, or alterlab-ssci-orchestrator). Triggers on 'use AlterLab skills', 'which AlterLab skill for X', 'is there an AlterLab skill for…', a multi-stage research goal, 'alterflow …', or any generic AlterLab request where the user does not know skill names. It always asks clarifying questions before executing a multi-step run. Use when someone references AlterLab generically, describes a multi-stage goal, or fires the alterflow keyword; when the user already names a specific skill, defer to that skill directly. Part of the AlterLab Academic Skills suite.

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alterlab-meta-analysis

AlterLab-IEU/AlterLab-Academic-Skills

Runs quantitative meta-analysis — computes effect sizes (Hedges' g / standardized mean difference, log odds/risk ratios) with their variances, pools them under fixed-effect and random-effects models, quantifies heterogeneity (I-squared, tau-squared, Cochran's Q), draws forest and funnel plots, and tests publication bias (Egger's regression, trim-and-fill) — using statsmodels.stats.meta_analysis in Python or the field-standard R metafor via Rscript. It enforces PRISMA reporting and the random- vs fixed-effect decision. Use when pooling effect sizes across studies, running a systematic review's quantitative synthesis, or assessing heterogeneity and publication bias. For finding and screening the literature prefer alterlab-deep-research; for a single study's statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-missing-data

AlterLab-IEU/AlterLab-Academic-Skills

Handles missing data with principled methods — forces an explicit MCAR / MAR / MNAR mechanism statement, then applies multiple imputation by chained equations (MICE) with Rubin's-rules pooling of estimates and standard errors, or full-information maximum likelihood (FIML) where a likelihood/SEM model applies. Uses statsmodels MICE / MICEData in Python or the field-standard R mice via Rscript, and warns that single (mean/regression) imputation and scikit-learn's IterativeImputer return one completed dataset without Rubin's-rules pooling, so they understate standard errors if used as multiple imputation. Use when a dataset has missing values, when choosing an imputation strategy, or when reporting how missingness was handled. For general modeling on complete data prefer alterlab-statistical-analysis; for latent-variable models with FIML prefer alterlab-sem-psychometrics. Part of the AlterLab Academic Skills suite.

2026-07-0246

alterlab-multilevel-models

AlterLab-IEU/AlterLab-Academic-Skills

Fits and reports mixed-effects / multilevel / hierarchical models for clustered, nested, longitudinal, and repeated-measures data — random intercepts and slopes, variance components and the ICC, cross-level interactions, and GLMMs (logistic/Poisson) — using statsmodels MixedLM and bambi (Bayesian on PyMC) in Python, or the field-standard R lme4 / glmmTMB / brms via Rscript. It enforces the reporting items reviews find under-reported: full fixed + random specification, centering, variance components + ICC, estimation method, assumption checks, model comparisons, and effect sizes. Use when data are grouped/nested (students in schools, repeated measures, panel/longitudinal) and the question concerns within- vs between-cluster variation. For general single-level regression prefer alterlab-statsmodels; for panel fixed effects used for causal identification prefer alterlab-causal-inference. Part of the AlterLab Academic Skills suite.

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alterlab-qualitative-analysis

AlterLab-IEU/AlterLab-Academic-Skills

Analyzes qualitative data as a dispatched pipeline module — codebook development, thematic / framework / content analysis, and inter-coder reliability computed correctly (Krippendorff's alpha as primary via the krippendorff package or a bundled stdlib nominal calculator with bootstrap CIs; Cohen's / Fleiss' kappa via statsmodels) with 95% CIs and thresholds (alpha >= .80 reliable, .667-.80 tentative). It BRANCHES by design: coefficient-based ICR for codebook / content-analytic coding, versus consensus-and-reflexivity for reflexive thematic analysis where a statistic is not the right criterion. Supports human-vs-LLM double-coding with an alpha check against a human gold standard. Use when coding interviews or open-ended text, building a codebook, or reporting intercoder reliability. For topic modeling / embeddings / supervised text classification prefer alterlab-text-as-data; for the reflexivity gate prefer alterlab-ssci-reflexivity-gate. Part of the AlterLab Academic Skills suite.

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alterlab-ssci-orchestrator

AlterLab-IEU/AlterLab-Academic-Skills

Coordinates a social-science study through the stage-gated methods pipeline — question then design-gate then measurement-gate then sampling-gate then the right analysis module then inference-gate then reporting — holding a single YAML Design Passport that each gate reads and appends, and enforcing gate order with PASS / WARN / BLOCK semantics (design-gate and inference-gate are fail-closed). It is thin: it routes and holds the artifact, it does not run analysis itself. Use when a researcher describes a whole social-science study end to end, asks to run the methods pipeline or a full methodology review, or wants the design-to-inference workflow coordinated rather than a single step. For a single stage trigger that gate directly (alterlab-ssci-design-gate, -measurement-gate, -sampling-gate, -inference-gate); for multi-agent execution mechanics see alterlab-workflow-orchestration. Part of the AlterLab Academic Skills suite.

2026-07-0246

name	alterlab-proteinmpnn
description	Design protein sequences for a fixed backbone with ProteinMPNN (Dauparas 2022) — message-passing inverse folding that outputs sequences predicted to fold to a given structure, with fixed positions, tied/symmetric chains, amino-acid bias, and a soluble-model variant. Use when inverse-folding a backbone PDB into sequences, redesigning selected positions, imposing symmetry across chains, or generating the sequence step of a design→fold→score loop. For pocket/interface design WITH a bound ligand, metal, or nucleic acid prefer alterlab-ligandmpnn; to GENERATE a new backbone prefer alterlab-rfdiffusion; to refold and validate a design prefer alterlab-alphafold; for generative multimodal design prefer alterlab-esm. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Write Edit Bash(python:) Bash(uv:)
compatibility	Runs `protein_mpnn_run.py` from `dauparas/ProteinMPNN` (PyTorch) under `uv run python`. The model is small — it runs on CPU and does not require a GPU (a GPU only speeds large batches). Network weights ship with the repo (no download/account). Input is a backbone PDB; output is a FASTA of designed sequences with scores.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

ProteinMPNN (fixed-backbone sequence design)

Overview

When to Use This Skill

Use this skill when the user wants to:

Inverse-fold a backbone PDB into one or more candidate sequences.
Redesign only selected positions while fixing the rest (partial design).
Enforce symmetry by tying residues/chains so homo-oligomers get identical sequences.
Bias the amino-acid composition (e.g. avoid cysteines) or use the soluble model.
Produce the sequence step of a design → fold → score loop.

Does NOT Trigger

Scenario	Use instead
Design a pocket/interface with a ligand, metal, or nucleic acid present	`alterlab-ligandmpnn`
Generate a new backbone (no starting structure)	`alterlab-rfdiffusion`
Refold a designed sequence to check it (validation)	`alterlab-alphafold`
Generative multimodal (sequence+structure+function) design	`alterlab-esm`

Core Capabilities

1. Basic inverse folding

# Parse the PDB, then design sequences (dauparas/ProteinMPNN CLI — TODO(verify) flags/version).
# The parser lives in the repo's helper_scripts directory; run it by name:
python parse_multiple_chains.py --input_path=pdbs/ --output_path=parsed.jsonl
python protein_mpnn_run.py \
  --jsonl_path parsed.jsonl --out_folder out/ \
  --num_seq_per_target 8 --sampling_temp "0.1"

2. Fixed positions and chains

3. Symmetry / tied positions

Tie positions across chains so a homo-oligomer receives one sequence applied symmetrically — essential for symmetric alterlab-rfdiffusion outputs.

4. Design → fold → score loop

The canonical de-novo pipeline:

Generate a backbone with alterlab-rfdiffusion.
Design sequences for it here (ProteinMPNN), sampling several per backbone.
Score by refolding each with alterlab-alphafold and accepting only self-consistent designs (returns to the target backbone with high pLDDT, low PAE).

Resources

references/proteinmpnn_usage.md — install/pinning, helper-script inputs (fixed positions, tied chains, bias), the soluble model, temperature guidance, and loop integration. Loaded on demand.

Part of the AlterLab Academic Skills suite.