On the origin of macromolecular sequences.

Area of Science:

• Origin of life studies
• Biophysics
• Theoretical biology

Background:

• Current theories inadequately explain the origin of order in biological macromolecules solely through genetic selection.
• Replication and natural selection require pre-existing order, posing a paradox for purely selection-driven origins.
• The vast number of possible primeval sequences makes random selection improbable for generating observed biological order.

Purpose of the Study:

• To propose an alternative hypothesis for the origin of biological order.
• To explore how order can arise in sequences without pre-existing genetic instructions.
• To link theoretical computation models to macromolecular structures and early life evolution.

Main Methods:

• Utilizing the Turing concept of computation in discrete-state automata to model order generation.
• Proposing macromolecular models based on eutactic polymer growth mechanisms.
• Analyzing statistical evidence from known amino acid sequences.

Main Results:

• Demonstrating that computational models can generate ordered, self-repeating sequences without prior instructions.
• Suggesting that such ordered sequences could form the basis for early replication and evolution.
• Finding statistical consistency between known protein sequences and non-genetic ordering principles.

Conclusions:

• The origin of biological order likely involved pre-existing order in sequences, not solely random sequences.
• Computational principles in automata may offer a mechanism for generating initial biological order.
• This model provides a framework for understanding the interplay between non-genetic ordering and genetic evolution in macromolecules.