Computational Structural Biology: Successes, Future Directions, and Challenges.

Ruth Nussinov,Amarda Shehu,Hyunbum Jang,Chung-Jung Tsai

doi:10.3390/molecules24030637

Abstract

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous ‘big data’ integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells’ actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

Highlights

Steps have been taken toward the modeling of cells, gearing to blend molecular, cryo-electron microscopy, cryo-electron tomography, cellular, and systems/human scales [12,13,14,15,16] and to facilitate in situ structural biology studies on a proteomic scale [17]
In line with the aim of this Special Issue, here, we focus on computational structural biology
Computational structural biology aims to model and exploit the structural landscape to understand protein function and dysfunction by harnessing the active and inactive states and considering the shape of the free-energy landscape, identifying the conformations at its minima, the metastable states, and the barriers which need to be crossed to switch between the states [118,119,120,121,122]

Summary

Introduction

Transcriptomic, and proteomic data, as well as structural foot printing, computational biology has made great strides toward a more reliable multiscale biological modeling [3]. Compelling advances have been made in modeling protein and RNA structures and in mapping chromatin and its dynamics at high resolution [45,46,47,48,49,50,51,52] These advances are compelling since, despite the high-throughput data, understanding cell signaling networks is listed among the top unanswered questions of modern science. It is convenient for scientists to consider biological molecules in terms of their sequences Such a simplification bypasses the challenge of reliably modeling their structures on a large scale under diverse conditions and accounting for their function-related fluctuations. Our discussion initiates with computational biology and moves on to computational structural biology, formulating what we perceive could be its directions in the future

The Breadth of Computational Biology

The Quest to Understand the Molecular Mechanisms

Challenges in Computational Structural Biology

Some Emerging Principles in Computational Structural Biology

Areas that May Take the Center Stage

Conclusions

Methods