RIBOSOME: THE ENGINE OF THE LIVING VON NEUMANN S CONSTRUCTOR

Transcription

1 RIBOSOME: THE ENGINE OF THE LIVING VON NEUMANN S CONSTRUCTOR IAS 2012

2 Von Neumann s universal constructor Self-reproducing machine: constructor + tape (1948/9). Program on tape: (i) retrieve parts from sea of spares. (ii) assemble them into a duplicate ; (iii) copy tape.. (1966)

3 Von Neumann s design allows open-ended evolution Motivated by biological self-replication: Construction universality. Evolvability. Key insight (before DNA) separation of information and function. Tape is read twice: for construction and when copied. How to design fast/accurate/compact constructor? mutations Implementation by Nobili & Pesavento (1995)

4 DNA Building blocks : Dual spaces of DNA and proteins Building blocks: 4 nucleic bases = {A, T, G, C}. 20 amino acids. protein Polymer: DNA double-helix. Inert information storage ( tape ) Polymer = protein. Functional molecules ( constructor )

5 The genetic code maps DNA to protein Genetic code: maps 3-letter words in 4-letter DNA language (4 3 = 64 codons) to protein language of 20 amino acids. codon =, {A, T, G, C}. b1b 2b3 b i (codon) amino-acid. Genetic code embeds the codon-graph (Hamming graph) into space of amino-acids. G A T C 3 ϕ Genetic code charge polarity size Translation machinery, whose main component is the ribosome, facilitates the map.

6 RNA intermediates can be both tapes and machines DNA RNA protein Primordial RNA world : RNA molecules are both information carriers (DNA) and executers (proteins).

7 Ribosomes translate nucleic bases to amino acids Ribosomes are large molecular machines that synthesize proteins with mrna blueprint and trnas that carry the genetic code. protein large subunit Aminoacid trna genetic code amino-acid= (codon) small subunit Anti-codon trna mrna Goodsell, The Machinery of Life

8 Noise Ribosome needs to recognize the correct trna mrna Ribosome ϕ(c 4 ) ϕ(c 5 ) ϕ(c 1 ) ϕ(c 2 ) ϕ(c 3 ) protein ϕ(c i ) c i trnas c 1 c 2 c 3 c 4 c 5 c 6 c 7 c 8 c 9 c 10 c 11 c 12 trnas ϕ(c i ) c i Ribosome c j Accept trna Reject trna (i) binding wrong trnas: amino-acid (ii) unbinding correct trnas: amino-acid (codon) (codon) How to construct fast\accurate\small molecular decoder?

9 Noise Decoding at the ribosome is a molecular recognition problem trnas ϕ(c i ) c i Ribosome c j Accept trna Reject trna Central problem in biology and chemistry: How to evolve molecules that recognize in a noisy environment? (crowded, thermally fluctuating, weak interactions). How to estimate recognition performance ( fitness )? What are the relevant degrees-of-freedom? Dimension? Scaling? What is the role of conformational changes?

10 Ribosome sets physical limit on self-reproduction rate Large fraction of cell mass is ribosomes. For self-reproduction each ribosome should self-reproduce. Sets lower bound on self-reproduction rate. T 4 massribo 10 amino-acids R C 20 amino-acids/sec 500 sec mass ribo Fastest growing bacteria (Clostridium perfringens): T ~ 600 sec. T Problem: how ribosome accuracy affects fitness depends on (i) (ii) Basic protein properties (mutations). Biological context (environment etc.). error R W rate RC

11 20 30 nm Ribosomes are complicated machines with many d.o.f. Ribosomes are made of proteins and RNAs: ~ 10 4 nucleic bases in RNA. ~ 10 4 amino-acids in proteins. Total mass : ~ a.u. High-res structure is known (Yonath et al.). Within this known complexity: What are the relevant degrees-of-freedom? (magenta RNA, grey protein, from Goodsell, Nanotechnology ) How does this machine operate?

12 Decoding is determined by energy landscapes of correct and wrong trnas Decoding is multi-stage process. Kinetics involves large conformational changes. Steady-state decoding rates (Arrhenius law, k e G ) R C ~ R W ~ 1 e b 1 + e b 2 + e b 3 1 e b 1 + e b 2 + e δ+b 3

13 Ribosome kinetics exhibits large dimensionality reduction Effective dimension decreases by at least 3 orders of magnitude: ~ 10 4 structural parameters ~ 10 kinetic parameters (energy landscape). Generic phenomenon in biomolecules: many catalytic molecules (enzymes) can be described by a few kinetic parameters (transition state landscape). What is the origin of dimensionality reduction? Hints: - Protein function mainly involve the lowest modes of their vibrational spectra (hinges). - Sectors: Normal modes of sequence evolution (Leibler & Ranganthan).

14 Transition states reduce the dimensionality of effective parameter space

15 Theory can be tested with measured rates The codon-specific stages are Codon recognition and GTP activation. (UUU) (CUC) (Rodnina s lab, Gottingen) R C ~ R W ~ 1 e b 1 + e b 2 + e b 3 1 e b 1 + e b 2 + e δ+b 3

16 How to estimate recognition performance ( fitness )? What is the actual dimension of the problem?

17 Yonatan Savir Recognition fitness has generic features Fitness F is often obscure and context-dependent.: look for generic properties of F R C, R W = F(B, δ, b 3 ). Only requirement: biologically reasonable, F R C 0, F R W 0. B R C ~ R W ~ 1 e B + e b 3 1 e B + e δ+b 3 (e B = e b 1 + e b 2) Searching for optimum in (B, δ, b 3 ) space: (i) F δ (ii) 0 : δ approaches biophysical limit. F = 0 & F 0 B b 3 or F 0 & F = 0 : B b 3 δ F b 3 = 0 min B max δ F B = 0 b 3 Optimization is essentially 1D (2 other parameters approach limit).

18 What is the optimal energy landscape of the ribosome? For example, distortion fitness from engineering (weight d is context-dependent) : F = R C d R W 1 e B +e b 3 1D problem: optimum is along b 3. (measured: = b 3 b 2 ) d e B +e b 3+δ What is the optimal b 3 (or )? Is the ribosome optimal? Role of conformational changes?

19 Optimal design is a Max-Min strategy Weight d can vary (i) For each d normalize F. (ii) Worst case scenario : max min F δ/2 + B δ/2 + B Max-Min solution is symmetric : b 3 = 1 δ + B 2

20 Ribosome shows an energy barrier which is nearly optimal Measurements: Δ C 7 k B T, δ 12k B T, B = B b 2 1k B T. Prediction: the optimal regime is symmetric, Δ C < 0, Δ W > 0. Δ C = 1 δ + B 2 The ribosome is nearly optimal (according to Max-Min prediction).

21 Decoding is optimal for all six measured trnas Except for UUC which encodes the same amino-acid (UUC) = (UUU) phenylalanine.

22 Optimality is valid for wide range of fitness functions Ribosome optimal in wide region: F = R C d R W e d e General feature: any fitness function F(R C, R W ) ribosome δ/2 + B exhibits optimum as long as both rates are relevant. R C d R W 2 R C d (R W /R C ) F F e / R R C W e δ/2 + B

23 Theory predicts optimal regime of ribosomes for all organisms Δ C = 1 δ + B 2 Optimal region in the space of all possible landscapes, δ Δ C 0. Mutations and antibiotics tend to push away of optimality.

24 What is the role of conformational changes? Energy barrier results from binding energy and deformation energy penalty: 1 2 C Gdeform Gbind B. Therefore 1 Gdeform Gbind B. 2 G bind C G deform For any G bind Gdeform 0 1 B 5 kbt, 2 non-zero deformation is optimal for trna recognition. Energy barrier that discerns the right target from competitors.

25 Yonatan Savir Recombination machinery recognizes homologous DNA Exchange between two homologous DNAs. Essential for: Genome integrity (repair machinery). Genetic diversity (crossover, sex). Task: Detect correct, homologous DNA target among many incorrect lookalikes. DNA stretches during recombination: large deformation energy barrier.

26 Energy barriers for optimal recognition may be a general design principle of recognition systems with competition ϕ(c 5 ) ϕ(c 1 ) ϕ(c 2 ) ϕ(c 3 ) ϕ(c 4 ) ϕ(c i ) c i c 1 c 2 c 3 c 4 c 5 c 6 c 7 c 8 c 9 c 10 c 11 c 12 Recombination optimizes extension energy of dsdna. Ribosome optimizes energy barriers of decoding Relevant energy Conformational proofreading: Design principle follows from optimization of information transfer function. May explain induced fit (Koshland 1958). Why molecules deform upon binding to target. Fitness F Relevant energy Applies to any enzymatic kinetics in the presence of competition...

27 Open questions, future directions Understanding evolvable matter: What are the degrees-of-freedom underlying dimensional reduction? (Rubisco and other enzymes) Basic logic of molecular information cahnnels (e.g. utilizing conformational changes, worst case scenario). Translation machinery coevolved with proteins Physics of the state of matter called proteins (evolvable, mapped from DNA space, glassy dynamics).

28 THANKS Yonatan Savir more:

29 Self-printing? Proposed demonstration of simple robot self-replication, from advanced automation for space missions, NASA conference 1980.