🧬 OVERALL BIG PICTURE

This lecture is about how to link genotype (DNA/RNA) to phenotype (protein function) in order to:

• Screen huge mutant libraries
• Select proteins with desired properties (binding, stability, activity)

Key challenge: 👉 You must keep the gene physically linked to the protein it produces

🧪 PART 1 — BASIC TRANSLATION (IMPORTANT FOUNDATION)

❗ Does tRNA recognize stop codons?

No.

• tRNA recognizes only codons for amino acids
• At stop codons:
  • No tRNA exists
  • Instead → release factors bind
  • They hydrolyze the peptide, releasing it from the ribosome

👉 This is critical because:

• It breaks genotype–phenotype linkage

❗ What happens at stop codon?

• Ribosome reaches stop codon
• Release factor enters A-site
• Peptide is released

👉 Result:

• ❌ Protein is no longer attached to mRNA or ribosome
• ❌ You lose link between genotype and phenotype

🧫 PART 2 — IN VITRO PROTEIN SYNTHESIS

❗ Can proteins have PTMs (post-translational modifications)?

Yes — but only if:

• The required enzymes are present in the system

Example:

• If you include kinases → phosphorylation possible
• If not → no PTMs

👉 So PTMs are optional and controlled

🧬 PART 3 — POLYSOME DISPLAY (EARLY SYSTEM)

How it works:

1. DNA library → transcribed to mRNA
2. mRNA → translated by ribosomes
3. Protein comes out
4. You select proteins based on function (binding etc.)
5. Recover mRNA → reverse transcribe → PCR → repeat

❗ Problem:

• Not covalent
• At stop codon:
  • Ribosome releases protein
  • ❌ genotype–phenotype link lost

🧬 PART 4 — RIBOSOME DISPLAY

Key improvement:

👉 Remove stop codon

What happens now?

• Ribosome reaches end of mRNA
• No stop codon → cannot release protein
• Ribosome stalls
• Protein stays attached to ribosome + mRNA

👉 This keeps: ✔ genotype (mRNA) ✔ phenotype (protein) ✔ ribosome (bridge)

❗ Hairpin structure (important)

• Added at mRNA end
• Prevents ribosome from falling off

❗ Ribosome display = proteome?

Not exactly.

• It is a selection system
• It can screen large libraries (~10¹²–10¹³ variants)

👉 So: ✔ It samples proteome-like diversity ❌ It is not the full natural proteome

❗ Effect of Mg²⁺ and temperature

Low Mg²⁺ + high temp:

• Ribosome dissociates
• ❌ system breaks
• ❌ genotype–phenotype lost

High Mg²⁺ + low temp:

• Ribosome stable
• ✔ system works

👉 Because ribosome is:

• Non-covalent complex

🧬 PART 5 — RNA–PEPTIDE FUSION (PUROMYCIN SYSTEM)

❗ Covalent system (big improvement)

Key idea:

Use Puromycin

How it works:

1. Attach puromycin to mRNA (ligation)
2. Ribosome translates protein
3. At end:
   • Puromycin enters A-site
   • Mimics tRNA
   • Forms covalent bond with peptide

👉 Result: ✔ Protein permanently linked to mRNA

❗ Why better?

• Independent of:
  • Mg²⁺
  • Temperature

👉 Much more stable than ribosome display

❗ Limitation:

• Puromycin ligation is not 100% efficient

🧬 PART 6 — mRNA INSTABILITY

❗ Why is mRNA unstable?

• Single-stranded
• Easily degraded by nucleases (RNases)

👉 Even tiny contamination → degradation

❗ DNA vs mRNA stability

DNA is more stable because:

• Double-stranded
• Protected structure
• Less reactive chemistry

👉 This motivates: ➡️ DNA-based display systems

🧬 PART 7 — DNA DISPLAY SYSTEMS

❗ Lac repressor idea

• Protein fused to Lac repressor
• Lac repressor binds plasmid DNA

👉 Intended:

• Link protein to its gene

❗ Problem:

• Binding is non-covalent
• Leads to cross-binding (trans-reactions)

👉 Genotype–phenotype mismatch

🧬 PART 8 — COVALENT DNA DISPLAY (RepA system)

How it works:

• DNA library encodes:
  • Protein of interest
  • RepA protein
• RepA:
  • Binds DNA site
  • Forms covalent bond with DNA

👉 Result: ✔ Protein covalently linked to its gene

❗ Problem:

• Still possible:
  • RepA binds wrong DNA
  • → trans-reactions

🧪 PART 9 — COMPARTMENTALIZATION (VERY IMPORTANT)

Key idea:

👉 Instead of linking physically → isolate

❗ Small droplets (emulsions)

• Water-in-oil droplets
• Each droplet contains:

👉 Ideally:

• 1 gene only

❗ Why "only 1 gene per droplet"?

• Prevent mixing
• Ensure:
  • Protein acts on its own gene

👉 This maintains genotype–phenotype link indirectly

❗ Why some droplets empty?

Statistical distribution:

• Many droplets → no gene
• Some → 1 gene
• Rare → multiple

🧬 PART 10 — METHYLTRANSFERASE SELECTION SYSTEM

How it works:

1. Each droplet:
   • Contains gene
   • Produces enzyme
2. If enzyme active:
   • Adds methyl group to DNA
3. After breaking emulsion:
   • Add restriction enzyme

👉 Result:

• Unmethylated DNA → cut
• Methylated DNA → survives

❗ Why some DNA methylated, some not?

• Depends on enzyme activity:
  • Active mutant → methylated
  • Inactive mutant → not

🧬 PART 11 — MODERN BEAD-BASED SYSTEM

❗ Biotin + magnetic beads

• DNA linked to bead via:
  • biotin–streptavidin interaction

👉 Purpose: ✔ physically isolate each gene

Workflow (clarifying your question):

1. Gene attached to bead
2. Protein expressed
3. Protein captured on bead (via antibody)
4. Bead transferred to new droplet with substrate
5. Enzyme acts:
   • substrate → product
6. Detect product:
   • e.g., fluorescence
7. Sort beads (e.g. FACS)

👉 Final: ✔ genotype + phenotype remain linked

❗ Do we retrieve gene at the end?

Yes.

👉 Always:

• PCR amplify selected genes
• Repeat selection cycles

🧬 PART 12 — SELECTION FOR STABILITY (PROTEOLYSIS)

Key principle:

• Folded protein → resistant
• Unfolded protein → degraded

❗ Alpha helix question:

• Folded helix → cannot fit into protease
• Unfolded → flexible → degraded

👉 Used to select: ✔ stable proteins

🧬 PART 13 — ENZYME ACTIVITY SELECTION

❗ General idea:

Link enzyme to its substrate

❗ Acidic vs basic helices

• Acidic helix → negative charge
• Basic helix → positive charge

👉 They bind: ✔ electrostatic interaction

❗ Substrate linking

• Substrate attached to enzyme using linkers
• Example:
  • disulfide bonds
  • chemical linkers

❗ If enzyme is active:

• It modifies substrate
• Enables detection

🧬 PART 14 — NUCLEASE EXAMPLE

❗ Oligo sensitive to nuclease?

Means:

• DNA substrate can be cut by nuclease
• If enzyme active → cleavage occurs

Selection:

• Cleaved DNA → released
• Non-cleaved → stays bound

🧬 PART 15 — CALMODULIN SYSTEM

Uses:

• Calmodulin
• Substrate binds via:
  • calmodulin-binding peptide

👉 Enables: ✔ enzyme-substrate proximity

🧬 PART 16 — DNA POLYMERASE SYSTEM

❗ Why DNA polymerase?

• Detect activity via:
  • incorporation of labeled nucleotides

Example:

• biotin-labeled nucleotides

👉 Active enzyme: ✔ incorporates label → detectable

❗ 5′ maleimidyl group

• Chemical linker
• Reacts with:
  • thiol groups (cysteine)

👉 Used to: ✔ attach substrate to protein

⚠️ FINAL IMPORTANT CONCEPT — LIMITATION

❗ Single turnover problem

Many systems detect:

• only one reaction event

But real interest:

• catalytic efficiency (kcat)

👉 Solution: ✔ compartmentalization (droplets)

• allows multiple turnovers

🧠 FINAL SUMMARY

Systems compared:

🔑 CORE TAKEAWAY

All methods solve the same problem:

👉 How do you keep a protein linked to its gene while selecting for function?

Different strategies:

• Physical linkage (ribosome)
• Covalent linkage (puromycin, RepA)
• Isolation (droplets)

🧬 1. “tRNA will recognize stop codon?” (clarification extension)

You already saw:

• ❌ No tRNA recognizes stop codons
• ✔ Instead → release factors

👉 Subtle but important:

• Stop codons are “sense gaps” in the genetic code
• This ensures:
  • Translation must terminate
  • Prevents random amino acid insertion

🧬 2. “Reach stop codon → lose peptide → lose genotype & phenotype?”

Yes — but let’s refine:

What exactly is lost?

• The physical linkage, not the information itself

👉 Why this matters:

• You cannot trace which gene produced which protein
• This breaks selection systems

🧬 3. “Ribosome display = proteome?” (deeper nuance)

You asked earlier — here is the precise interpretation:

• It is not a natural proteome
• It is a synthetic, massively diverse library

👉 Important distinction:

• Proteome = what cells actually express
• Ribosome display = what you engineer and screen

👉 Key advantage:

• Can exceed natural diversity (~10¹²–10¹³ variants vs biological limits)

🧬 4. “Covalent system binds mRNA not protein?”

Clarify carefully:

In RNA–peptide fusion:

• Puromycin binds: ✔ the protein (peptide chain)
• And it is already attached to: ✔ the mRNA

👉 So final structure:

mRNA — puromycin — protein

👉 Therefore: ✔ It links protein ↔ mRNA indirectly via puromycin ✔ The bond is covalent to the protein

🧬 5. “RNA–peptide fusion works like ribosome display?”

Similar goal, different mechanism:

👉 Key difference:

• Ribosome = bridge
• Puromycin = permanent chemical bond

🧬 6. “LAC repressor linked to plasmid?”

Yes, but not ideal.

• Lac repressor binds DNA sequence
• Fusion protein attaches to Lac repressor

👉 Idea:

• Protein indirectly linked to its plasmid

❗ Why it fails:

• Binding is non-covalent
• Leads to cross-binding (trans interaction)

👉 Example problem:

• Protein A binds DNA B
• ❌ Wrong genotype–phenotype pairing

🧬 7. “in Cis vs trans (you wrote CisD)”

Important concept:

Cis interaction

• Protein interacts with its own gene ✔ Correct linkage

Trans interaction

• Protein interacts with another gene ❌ Wrong linkage

👉 Trans is the main problem in many systems

🧬 8. “Compartmentalization introduces mutations?”

Not directly — but:

Possible sources of mutations:

• PCR amplification errors
• Transcription errors
• Replication errors

👉 Compartmentalization does NOT create mutations ✔ It only isolates reactions

🧬 9. “R/M site → reduction?”

You likely mean Restriction/Modification (R/M) system

• Restriction enzyme → cuts DNA
• Methyltransferase → protects DNA

👉 Mechanism:

• Methylated DNA → protected
• Non-methylated → cut

👉 Not “reduction” — it is DNA cleavage protection

🧬 10. “Encapsulated → modified DNA?”

Yes — mechanism:

Inside droplet:

1. Gene → enzyme
2. Enzyme acts on DNA
3. DNA becomes:
   • modified (e.g., methylated)
   • or not

👉 After breaking droplets:

• You select based on DNA state

🧬 11. “Biotin + magnetic beads — purpose?”

Key components:

• Biotin (on DNA)
• Streptavidin (on bead)

👉 Very strong interaction

Purpose:

1. Attach one gene per bead
2. Keep genotype physically isolated
3. Enable:
   • easy washing
   • sorting
   • recovery

👉 Beads = mini carriers of genotype–phenotype

🧬 12. “Full workflow (important clarification)”

Let’s cleanly reconstruct it:

Step-by-step:

1. DNA (genotype) attached to bead
2. Transcription → mRNA
3. Translation → protein
4. Protein captured on same bead (via antibody/tag)

👉 Now: ✔ genotype and phenotype linked

5. Move beads to new environment
6. Add substrate
7. If enzyme active:
   • substrate → product

8. Detect product:
   • fluorescence / binding
9. Sort beads (e.g. FACS)
10. Recover DNA → PCR → next round

🧬 13. “If enzyme active it eats substrate?”

Yes, but more precisely:

• Enzyme catalyzes conversion
• Not necessarily “eat”

Example:

• nuclease → cleaves DNA
• polymerase → builds DNA
• protease → cuts protein

👉 Activity = chemical transformation

🧬 14. “Oligo sensitive to nuclease?”

Means:

• Short DNA strand can be: ✔ cleaved by nuclease

👉 If enzyme active:

• oligo is cut → detectable

🧬 15. “Base aa and acidic aa will link together?”

Yes — via electrostatics:

👉 Opposite charges: ✔ attract → form complex

🧬 16. “Substrate binds calmodulin binding peptide?”

Mechanism:

• Calmodulin binds specific peptide

👉 Used as:

• adapter system

So:

• substrate attached to peptide
• peptide binds calmodulin
• calmodulin linked to enzyme

✔ brings substrate close to enzyme

🧬 17. “Why DNA polymerase used?”

Because:

• Activity is easy to detect

👉 Polymerase:

• Adds nucleotides

If labeled nucleotides used: ✔ active enzyme → labeled product

🧬 18. “5′ maleimidyl — what is it?”

Chemical linker:

• Reacts with: ✔ thiol groups (–SH, cysteine)

👉 Purpose: ✔ attach substrate to protein or DNA

🧬 19. “Acidic helper phage?”

In phage system:

• Helper phage provides:
  • protein with acidic helix (negative)

👉 Used to:

• bind basic helix (positive) on substrate

✔ forms stable interaction

🧬 20. “Selection by proteolysis (alpha helix part)”

Clarification:

• Folded protein:
  • compact → inaccessible
  • ✔ survives
• Unfolded protein:
  • flexible → accessible
  • ❌ degraded

👉 So: ✔ Only stable proteins survive selection

🧬 FINAL TAKEAWAY FOR YOUR QUESTIONS

All your points revolve around one core principle:

👉 Maintaining correct linkage between:

• genotype (DNA/RNA)
• phenotype (protein function)

And methods differ in how they solve this:

• Ribosome → temporary bridge
• Puromycin → covalent link
• DNA systems → direct linkage
• Droplets → physical isolation

🧬 1. DISPLAY SYSTEMS — BIG OVERVIEW

Before going into details, the lecture compares three major strategies:

1. In vivo display

• Happens in living systems:
  • bacteria
  • yeast
  • phage
• Protein is displayed on cell surface

👉 Limitation:

• Library size limited by transformation efficiency

2. In vitro display

• Happens in a test tube
• No cells involved

👉 Advantage: ✔ Much larger libraries (~10¹²–10¹³)

3. Compartmentalization

• Uses tiny droplets as artificial cells

👉 Key idea: ✔ Physically isolate genotype + phenotype

🧬 2. WHY IN VITRO SYSTEMS WERE DEVELOPED

Problem with in vivo:

• Cannot transform enough variants

👉 Solution:

• Move system outside cells
• Use:
  • purified ribosomes
  • tRNAs
  • enzymes

✔ Enables massive diversity screening

🧬 3. POLYSOME DISPLAY (HISTORICAL CONTEXT)

This was the first attempt (1990s) to:

👉 Link genotype ↔ phenotype in vitro

Concept:

• Many ribosomes translate the same mRNA (polysome)
• Protein remains near its mRNA

❗ Why it was important:

• Proof of concept for in vitro selection

❗ Why it was replaced:

• Linkage unstable (non-covalent + stop codon issue)

🧬 4. LIBRARY DESIGN (IMPORTANT THEORY)

DNA library structure:

• Promoter (e.g. T7)
• Randomized region (mutants)
• Translation signals

❗ NNK codons (mentioned in file)

Used for mutagenesis:

• N = any nucleotide (A, T, G, C)
• K = G or T

👉 Why NNK?

• Covers all amino acids
• Minimizes stop codons

🧬 5. SELECTION CYCLE (CORE CONCEPT)

All display systems follow this loop:

1. Generate library
2. Express proteins
3. Select desired function
4. Recover genes
5. Amplify (PCR)
6. Repeat

👉 This is directed evolution

🧬 6. AFFINITY SELECTION (CLASSIC USE CASE)

Example:

• Find protein that binds target

Workflow:

1. Immobilize target on surface
2. Add library
3. Wash away non-binders
4. Elute binders
5. Amplify

👉 Repeated cycles increase specificity

🧬 7. PHAGE DISPLAY (RECAP FROM EARLIER LECTURES)

Uses:

• Filamentous bacteriophage

Key protein:

• Protein 3 (pIII)

Structure:

• Domain 1 & 2 → infection
• Domain 3 → structural anchoring

Key idea:

• Fuse protein of interest to pIII
• Display on phage surface

👉 Link: ✔ genotype (inside phage) ✔ phenotype (surface protein)

🧬 8. HELPER PHAGE (IMPORTANT DETAIL)

Used to:

• Provide missing viral components

👉 Enables: ✔ proper phage assembly

🧬 9. PHAGEMID SYSTEM

Hybrid system:

• plasmid + phage elements

👉 Advantages:

• easier cloning
• controlled expression

🧬 10. LIMITATION OF AFFINITY SELECTION

You mostly select: ✔ binding ability

But proteins also need:

• stability
• catalytic activity

👉 Requires alternative selection strategies

🧬 11. SELECTION FOR STABILITY (DEEPER VIEW)

Principle:

• Stable proteins resist unfolding
• Unstable proteins unfold easily

Detection via proteolysis:

• Proteases cut unfolded regions only

👉 Outcome:

• Stable proteins survive
• Unstable proteins degraded

🧬 12. FILAMENTOUS PHAGE ENGINEERING FOR STABILITY

Strategy:

Insert protein between domains of pIII

What happens:

• Stable protein: ✔ protects phage structure ✔ remains infectious
• Unstable protein: ❌ degraded ❌ phage loses infectivity

👉 Selection: ✔ Infectivity = stability marker

🧬 13. ENZYME ACTIVITY SELECTION (GENERAL THEORY)

Challenge:

• Need to detect function, not just binding

Requirement:

👉 Substrate must be:

• physically linked to enzyme

Why?

• Ensures: ✔ enzyme acts on its own substrate ✔ genotype–phenotype linkage preserved

🧬 14. LINKER DESIGN (IMPORTANT CONCEPT)

Linkers connect:

• enzyme
• substrate

Requirements:

• flexible enough for reaction
• stable enough to maintain connection

Types:

• peptide linkers
• chemical linkers
• disulfide bonds

🧬 15. SINGLE TURNOVER LIMITATION (VERY IMPORTANT)

Problem:

• Many systems detect: ✔ only one catalytic event

Why this is bad:

• Real enzymes differ in:
  • turnover rate (kcat)

👉 You want: ✔ fast enzymes ❌ not just “works once”

🧬 16. WHY COMPARTMENTALIZATION SOLVES THIS

Inside droplets:

• enzyme + substrate trapped together

👉 Active enzyme: ✔ converts many substrate molecules

Result:

• stronger signal
• allows selection for: ✔ efficiency ✔ kinetics

🧬 17. EMULSION TECHNOLOGY (CORE IDEA)

Water-in-oil droplets

• Tiny “artificial cells”

Contents:

• one gene
• transcription system
• translation system
• substrate

Effect:

✔ isolates reactions ✔ prevents cross-talk

🧬 18. STATISTICAL LOADING (IMPORTANT DETAIL)

You cannot guarantee:

• exactly 1 gene per droplet

👉 Instead:

• control concentration so:
  • most droplets empty
  • some contain 1 gene

✔ This minimizes mixing errors

🧬 19. FACS (Fluorescence-Activated Cell Sorting)

Used to: ✔ sort droplets or beads

Principle:

• detect fluorescence
• separate based on signal

In this context:

• fluorescence = enzyme activity

🧬 20. INDUSTRIAL APPLICATION PERSPECTIVE

The lecture emphasizes:

👉 Goal is NOT just theory

But to:

• engineer enzymes for:
  • detergents
  • drugs
  • biotechnology

Examples:

• lipases (washing powder)
• antibodies
• polymerases

🧬 21. EVOLUTION ENGINEERING CONCEPT

All methods mimic:

👉 Natural evolution in fast-forward

Steps:

1. Mutation (library creation)
2. Selection
3. Amplification

✔ repeated cycles → optimized proteins

🧠 FINAL CONSOLIDATED UNDERSTANDING

All topics you didn’t mention mainly support this framework:

Core problem:

👉 How to evolve proteins in the lab?

Key requirements:

• Large diversity
• Link genotype ↔ phenotype
• Detect desired function

Three major solutions:

1. Display systems (phage, ribosome, mRNA)
2. Covalent linkage (puromycin, DNA systems)
3. Physical isolation (compartmentalization)

Three major selection targets:

• Binding (affinity)
• Stability (proteolysis)
• Activity (substrate conversion)