Abstract

Unlike the secondary structure elements that connect in protein structures, loop fragments in protein chains are often highly mobile even in generally stable proteins. The structural variability of loops is often at the center of a protein’s stability, folding, and even biological function. Loops are found to mediate important biological processes, such as signaling, protein-ligand binding, and protein-protein interactions. Modeling conformations of a loop under physiological conditions remains an open problem in computational biology. This article reviews computational research in loop modeling, highlighting progress and challenges. Important insight is obtained on potential directions for future research.

Highlights

  • All biological mechanisms in the living cell involve protein molecules

  • The analysis shows that cyclic coordinate descent (CCD) maps neighbor conformations into distant regions in the constrained conformational space, potentially allowing to sample diverse geometrically-constrained loop conformations when applied to an ensemble of randomly sampled open loop conformations

  • Just to mention a few, conformational search algorithms employed in energy-based approaches include importance sampling with local minimization of randomly generated conformations [102,103,104], global energy minimization by mapping a trajectory of local minima [105,106], Molecular Dynamics (MD) simulations [101,107,108,109], genetic algorithms [65,110,111], dynamic programming in a discretized space [112,113], biased probability Monte Carlo (MC) searches [12,114,115], MC combined with MD [116], MC Simulated Annealing

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Summary

Introduction

All biological mechanisms in the living cell involve protein molecules. Proteins are central components of cellular organization and function. The backbone dihedral angles in conformations that the protein chain assumes to carry out a biological function, known as native conformations, do not populate the entire [−π, π) but are limited to specific regions in amino-acid dependent (Ramachandran) φ, ψ maps [38]. These regions are associated with local secondary structures in native conformations, such as α-helices, β-sheets, and coils. The environment can be solvent, membrane, or crystal [72,73]

Database Methods
Energy-Based Approaches
Geometry-Based Approaches
A Robotics-Inspired Method for Sampling the Loop Closure Space
Findings
Conclusions

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