Technical articles on protein engineering and AI design
Deep dives into the methods, tools, and decision frameworks behind modern protein and binder discovery from the Ranomics scientific team
5 Developability Red Flags That Kill mAb Programs
The five antibody developability red flags most commonly responsible for late-stage CMC failures, how each one shows up in the clinic, and which of them you can detect from sequence before synthesis.
NNK vs NNS vs Trimer: Picking a Codon Scheme for a VHH Library
A practical comparison of NNK, NNS, NNN, and trimer codon schemes for VHH and scFv library design, covering stop codon frequency, amino acid coverage, S. cerevisiae codon bias, and when each scheme is the right choice for a yeast display campaign.
RFdiffusion Outputs Need a Developability Check Before Wet Lab
RFdiffusion, BindCraft, and ProteinMPNN optimize for structure and binding, not for developability. A concrete triage workflow for filtering generative outputs before synthesis or yeast display.
Binder Design on a Grant Budget: Scoping a Single-Target Campaign
What to prioritize, what to cut, and what actually determines cost when a PI or postdoc is running a single-target de novo binder design campaign on a defined budget.
From an AlphaFold Model to Your First Binder: A Walkthrough for Teams Without Structural Biology Expertise
A practical, step-by-step guide for small biotech and academic teams who have an AlphaFold model of their target but no structural biologist on staff — what to check, what to decide, and how to move into a binder design campaign.
When to Use Epitope Scout vs. a Structural Biologist
A practical framing of when automated epitope scoring is enough for your binder campaign and when you actually need a human structural biologist in the loop — with a checklist for deciding on your own target.
Closing the Loop: How AI Protein Design and Display Screening Work as a Single System
Most teams treat computational design and experimental screening as separate workflows. The programs that produce the best binders treat them as one coupled system.
Protein Engineering Design in the Age of Machine Learning
Modern protein engineering design increasingly relies on machine learning, but experimental data and workflow integration remain the true bottlenecks. A guide to the six-stage design cycle.
From Computational Protein Design to Validated Binders: What Actually Works
What separates successful AI protein design campaigns from failed ones? A practical breakdown of the computational and experimental steps required to go from generative models to validated binders.
ProteinMPNN and the sequence design problem: what it does and why it matters
ProteinMPNN solves the inverse folding problem. Given a backbone, which sequences will fold into it? How it works, how it fits into de novo binder design pipelines, and the practical parameters that matter.
Hotspot-guided binder design: using structure to focus the design campaign
Hotspot residues (the subset of interface contacts that contribute most of the binding energy) dramatically improve de novo binder design campaigns when used to constrain diffusion-based generation.
RFdiffusion in Practice: What Works, What Fails, and What Most Guides Leave Out
Operational lessons from running RFdiffusion binder design campaigns. Scaffold topology biases, hotspot conditioning tradeoffs, partial diffusion for scaffold grafting, failure modes on flat targets and membrane proteins, and how to avoid redundant candidate pools.
De Novo Protein Design: How the Pipeline Works in Practice
A practitioner's guide to de novo protein binder design using RFdiffusion, BindCraft, ProteinMPNN, and structural validation. What the real bottlenecks are, what determines campaign success, and how experimental validation has replaced computation as the rate-limiting step.
How Big a Yeast Display Library Do You Need for a 10 nM Binder?
A practical walkthrough of library size math for a 10 nM affinity target on yeast display: starting material, sort gate stringency, Poisson coverage for NGS, and the KD ladder across multiple rounds.
Engineering pH-Dependent Antibodies on Yeast Surface Display: A 640-Clone Case Study
A technical walkthrough of a real pH-dependent antibody engineering campaign — 640-clone yeast display library, six FACS sorts, convergent hotspot residues, and quantitative enrichment-score ranking.
The Two-Platform Approach: Using Yeast Display for Affinity and Mammalian Display for Developability
Don't choose between yeast display and mammalian display. This guide details a two-platform biologics discovery workflow, using yeast for affinity and mammalian display to screen for developability.
Troubleshooting Low Display Levels in Yeast and Mammalian Cells: A Step-by-Step Checklist
Low or non-existent display levels are a common roadblock in yeast and mammalian display campaigns. A systematic diagnostic checklist for identifying and resolving the root cause.
Deconvoluting Polyclonal Hits: Strategies for Characterizing Enriched Library Pools
Your yeast display screen is finished, but choosing the most abundant clone from NGS data can lead to costly mistakes. A strategic framework for deconvoluting polyclonal hits using enrichment ratios and convergent evolution.
Beyond FACS: An Introduction to Magnetic-Activated Cell Sorting (MACS) for Library Pre-enrichment
While FACS is the gold standard for precision sorting, it becomes a bottleneck when screening libraries with billions of variants. MACS pre-enrichment solves the throughput problem.
Beyond Antibodies: Using Surface Display to Engineer Enzymes and Receptors
While surface display is the go-to platform for antibody discovery, applications extend far beyond. This guide covers strategies for engineering enzymes and receptors using yeast and mammalian display.
The Numbers Game: A Practical Guide to Calculating and Validating Library Diversity with NGS
The success of any surface display campaign depends on library quality. A practical framework for NGS-based library validation covering diversity metrics, uniformity assessment, and sequencing workflows.
A Technical Guide to Sorting Strategies in Surface Display
In any yeast or mammalian surface display campaign, the flow cytometer is your primary selection tool. A guide to gating strategies, antigen titration, off-rate ranking, and counter-screening.
Correctly Titrating Display Levels for Reliable Affinity Data in Yeast and Mammalian Systems
Learn how to optimize display-level titration in yeast and mammalian display systems to obtain accurate affinity data and avoid avidity effects when determining Kd.
Protein Folding Optimization in Yeast Display: Engineering Better Expression Systems
Master protein folding optimization in yeast display systems with this guide covering signal peptide engineering, chaperone co-expression, ER retention strategies, and promoter optimization.
Avidity Artifacts in Yeast Display: How to Detect and Eliminate False Positive Binders
A practical guide to detecting and eliminating avidity artifacts in yeast display screening. Covers off-rate selection, soluble competition assays, display level titration, and FACS gating strategies with specific concentrations and timescales.
Library Size Limitations in Yeast Display: Advanced Strategies for Maximizing Diversity
Discover proven strategies to maximize yeast display library diversity despite transformation limitations, including optimized protocols, Golden Gate cloning, and smart library design.
Natural, Synthetic, and AI-Designed Libraries: Choosing a Strategy for Antibody Discovery
A successful antibody discovery campaign begins with choosing the right source of diversity. Comparing natural, synthetic, and AI-designed libraries for different therapeutic goals.
Deep Mutational Scanning: A High-Throughput Approach to Mapping Protein Fitness Landscapes
Deep Mutational Scanning (DMS) combines high-diversity library generation, functional selection, and next-generation sequencing to measure the functional consequences of thousands of mutations in a single experiment.
Library Design Decisions That Determine Screening Campaign Success
How diversity calculations, codon strategy, transformation efficiency, and NGS-based QC directly determine whether your screening campaign produces leads or wastes months of work.
AI de novo design vs. library screening: when to use which approach
De novo computational design and library screening are not competing methods. A decision framework for choosing between them, and why the best programs often couple both.
In Vivo Mutagenesis for AI Training Data: Why Stochastic Diversity Outperforms Designed Libraries
How in vivo DNA mutagenesis systems like CRISPR-guided base editors and error-prone polymerases generate the large, unbiased protein variant datasets that machine learning models need. Practical comparison with synthetic library approaches for AI-driven protein engineering.
How NOT to Build a High-Quality Dataset for AI Protein Engineering: A Guide to Failure
A satirical guide exposing the most common dataset mistakes in AI protein engineering, from embracing noise to aggressive data processing, and how to avoid them.
The Impact of Post-Translational Modifications in Mammalian Protein Production and Antibody Discovery
Post-translational modifications are a fundamental layer of biological regulation that dictates a protein's function and viability as a drug. Understanding glycosylation, disulfide bonds, and chemical liabilities.
Introduction to Protein Developability: What Makes a Good Biologic Drug?
A biologic with high potency is only half the battle. Many promising candidates fail due to poor developability and manufacturability. The four pillars of protein developability explained.
Leveraging AI and Deep Mutational Scanning to Engineer Novel Enzymes
A comprehensive guide to combining deep mutational scanning with machine learning for enzyme engineering, covering variant library generation, functional selection, data analysis, and the iterative AI-DMS cycle.
A Technical Guide to Directed Evolution for Enhancing Protein Stability and Function
A comprehensive guide to directed evolution covering the diversity generation cycle, stability engineering case studies, functional optimization strategies, and the integration of rational and evolutionary approaches.
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