Optimal Design of a Molecular Recognizer: Molecular Recognition as a Bayesian Signal Detection Problem

TL;DR

This paper models molecular recognition as a Bayesian signal detection problem, revealing optimal design strategies and phase transitions that depend on molecular structure and interaction energies, with implications for biological understanding and artificial device design.

Contribution

It introduces a signal detection framework to analyze molecular recognition, identifying phase transitions and optimal design principles based on structural and energetic parameters.

Findings

Recognition can be optimized by structural complementarity or conformational flexibility.

A phase transition exists between lock-and-key and induced-fit recognition modes.

The framework explains the prevalence of conformational changes in biological recognition.

Abstract

Numerous biological functions-such as enzymatic catalysis, the immune response system, and the DNA-protein regulatory network-rely on the ability of molecules to specifically recognize target molecules within a large pool of similar competitors in a noisy biochemical environment. Using the basic framework of signal detection theory, we treat the molecular recognition process as a signal detection problem and examine its overall performance. Thus, we evaluate the optimal properties of a molecular recognizer in the presence of competition and noise. Our analysis reveals that the optimal design undergoes a "phase transition" as the structural properties of the molecules and interaction energies between them vary. In one phase, the recognizer should be complementary in structure to its target (like a lock and a key), while in the other, conformational changes upon binding, which often accompany molecular recognition, enhance recognition quality. Using this framework, the abundance of conformational changes may be explained as a result of increasing the fitness of the recognizer. Furthermore, this analysis may be used in future design of artificial signal processing devices based on biomolecules.