Summary of "Las proteínas y la historia de la vida en la Tierra | JORNADAS 8M🟣"

Overview

Dr. Claudia Álvarez Carreño framed proteins as a kind of “molecular fossil record” that can be used to reconstruct events in the history of life between the origin of a genetic system and the last universal common ancestor (LUCA).

The talk focused on how protein sequences, three‑dimensional folds, and their evolutionary relationships reveal how the first compact protein folds originated and diversified, and how modern structural prediction lets researchers scale that analysis.

Key concepts and findings

Proteins as atomic/structural objects

• Proteins are chains of amino acids that fold into stable (or metastable) three‑dimensional structures (folds). Fold stability is primarily driven by hydrophobic packing and can be altered by mutations or by environmental factors (heat, pH → denaturation).
• Folding landscapes: proteins have energy landscapes with minima; some proteins have multiple stable minima (metamorphic proteins) with distinct functional consequences.
• Most functional proteins studied are compact (folded) domains. These domains act as evolutionary/building blocks and are often recombined into larger multi‑domain proteins.

Fold diversity versus time

• Despite ~4 billion years of evolution, the number of experimentally recognized unique folds is relatively small (~7,000), raising questions about origins and constraints on fold space.
• Many different modern proteins share short sequence motifs even when their overall structures differ, suggesting fragmentary relationships between folds.

Ribosome as a central record and laboratory

• The ribosome is universal and produced all coded proteins; ribosomal proteins are therefore especially informative about deep evolutionary history.
• Analyses of ribosomal proteins revealed surprising rearrangements such as circular permutations and “imperfect” rearrangements that can be explained by duplication + recombination processes rather than simple point mutations.

Mechanisms of structural innovation

• Fragment/patch hypothesis: small peptide fragments (motifs) could have been primordial building blocks that later combined into complete folds.
• Creative destruction: larger, rapid rearrangements (e.g., duplication of a segment followed by loss or recombination of ends) can create new folds more quickly than slow point mutation accumulation. This model explains certain circular permutations and motif‑sharing across different folds.

Role of modern structural prediction

• Breakthroughs such as AlphaFold2 and the ESM Atlas have produced vast numbers of accurate predicted structures (hundreds of millions), enabling large‑scale mapping of fold space.
• By combining predicted structures with sequence data, researchers can generate network maps of relationships between folds and model minimal‑change paths between structures. These are models of plausible transitions, not necessarily the true historical paths.

Dynamics and disorder

• Proteins are dynamic; some are intrinsically disordered and play important biological roles. Classic fold catalogs (X‑ray, cryo‑EM) under‑represent dynamics.
• The relationship between disorder and early protein evolution is unresolved. Some evidence suggests ancient ribosomal proteins were β‑rich; other hypotheses posit disorder played a role in pre‑ribosomal chemistry.

Broader evolutionary and origin‑of‑life considerations

• The evolution of the genetic code and which amino acids were incorporated early remain open questions. Many amino acids were present in prebiotic contexts (e.g., meteorites), and selection plus chemical constraints likely shaped which became canonical.
• Experimental ribozyme work and laboratory evolution show selection‑like processes can act on molecular replicators; such experiments inform possible scenarios but do not uniquely reconstruct how life originated.

Practical / technical approaches and methodological steps

To infer deep protein evolutionary history, the speaker recommended a multi‑step workflow:

1. Collect homologous sequences across many organisms to detect conserved regions.
2. Build sequence similarity networks (evolutionary relationship maps) that connect sequences or folds above defined similarity thresholds.
3. Compare sequence similarity networks to structural classifications (fold catalogs) to spot unexpected links (sequence similarity without structural similarity, shared motifs across different folds).
4. Analyze ribosomal proteins and other universally conserved proteins as anchors for deep‑time inference (LUCA and pre‑LUCA periods).
5. Identify and model rearrangement mechanisms:
   • Detect circular permutations and imperfect permutations.
   • Test duplication + recombination scenarios (creative destruction) that produce apparent rearrangements.
6. Use large‑scale predicted structure databases (AlphaFold2, ESM Atlas) to:
   • Expand fold discovery beyond experimentally solved structures.
   • Cluster predicted structures to define fold units.
   • Trace minimal‑change structural paths between folds via computational modeling and intermediate sequence/structure prediction.
7. Where possible, validate or explore hypotheses experimentally (folding stability assays, mutational studies, laboratory evolution) and integrate biochemical/environmental context (e.g., folding dependence on ions, temperature).
8. Consider dynamics explicitly: incorporate intrinsic disorder analyses, conformational‑ensemble simulations, and methods that capture dynamics rather than single static structures.

Lessons, implications, and open questions

• Proteins encode a retrievable history: sequence + structure + conservation across taxa can reveal deep evolutionary events and mechanisms of fold emergence.
• The relatively small number of fold types over billions of years suggests strong structural/chemical constraints or dominant pathways of fold invention and reuse.
• Large rearrangements (duplication and recombination) are important engines of fold innovation, not just slow point mutations.
• Massive, accurate structural predictions unlock new, high‑throughput approaches to map fold space and hypothesize evolutionary transitions — but experimental validation and attention to dynamics remain essential.
• Open problems include:
  • Origin and consolidation of the genetic code.
  • Precise steps from prebiotic peptides to coded proteins.
  • The role of intrinsic disorder in early evolution.
  • How environmental and chemical contexts shaped early amino acid selection.

Speakers, contributors, and referenced sources

Main speaker and event organizers

• Dr. Claudia Álvarez Carreño — main presenter (Faculty of Sciences graduate; PhD from UNAM; postdocs in Atlanta and London).
• Event hosts/introduction: Dr. Arturo Sarucán and Dr. Lascano (Antonio Alascano / “Toño”).

Collaborators, labs and groups cited by the speaker

• Antonio Alascano (mentioned collaborator)
• Arturo Becerra (colleague)
• University College London — Claudia’s current group
• Christine Orengo’s group (UCL)
• Lauren Williams’ group (Georgia)
• Origin‑of‑life laboratory (Atlanta, Georgia) — host of postdoctoral work

Algorithms, databases and technical resources referenced

• AlphaFold2 (structural prediction algorithm)
• ESM Atlas (massive predicted‑structure resource)
• Fold databases / fold catalogs (experimental fold counts ~7,000 referenced)

Other scientists and authors cited in discussion / Q&A

• Margaret (De)hoff — likely referring to Margaret Dayhoff (early computational pioneer)
• Dr. Clara Hujova — work on alternative genetic codes
• Steve Benner — work on alternative/extended genetic codes
• Jason Dworkin — meteorite amino acid analyses
• Andrei Lupas — proponent of fragment‑first models
• Sol Spiegelman — early in vitro selection experiments (Spiegelman’s experiments)
• Leslie Orgel — work on non‑cellular natural selection and origin‑of‑life theory
• Dr. Carolina Sánchez Rocha — work on intrinsically disordered proteins
• Morán Frankel Pinter — mentioned in Q&A
• Arturo Becerra — co‑author/colleague mentioned

Audience participants and questioners (as named in subtitles)

• Omar / Sipton Omar
• Don Tomás
• Rodolfo
• Beatriz Rebollar
• Leticia de la Huam
• Ms. Mariana Elisondo
• Atl Tarbuk
• Dr. Campillo
• Alex Watson
• Pablo Orozco
• Ms. Vé Ramos

Notes on transcription and names

• The subtitles used for the talk were auto‑generated and contain likely name and spelling errors (e.g., “Dehoff” vs. Margaret Dayhoff; “Spigelman” vs. Spiegelman; “Andrelupa” vs. Andrei Lupas).
• Where possible the summary preserves the names as they appear in the subtitles while noting probable corrections.

Category

Educational

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