Frustration-induced protein intrinsic disorder

Accurate prediction of protein folding mechanisms by simple structure-based statistical mechanical models

Recent breakthroughs in highly accurate protein structure prediction using deep neural networks have made considerable progress in solving the structure prediction component of the 'protein folding problem'. However, predicting detailed mechanisms of how proteins fold into specific native structures remains challenging, especially for multidomain proteins constituting most of the proteomes. Here, we develop a simple structure-based statistical mechanical model that introduces nonlocal interactions driving the folding of multidomain proteins. Our model successfully predicts protein folding processes consistent with experiments, without the limitations of protein size and shape. Furthermore, slight modifications of the model allow prediction of disulfide-oxidative and disulfide-intact protein folding. These predictions depict details of the folding processes beyond reproducing experimental results and provide a rationale for the folding mechanisms. Thus, our physics-based models enable accurate prediction of protein folding mechanisms with low computational complexity, paving the way for solving the folding process component of the 'protein folding problem'.

The Wako-Saitô-Muñoz-Eaton Model for Predicting Protein Folding and Dynamics

Despite the recent advances in the prediction of protein structures by deep neutral networks, the elucidation of protein-folding mechanisms remains challenging. A promising theory for describing protein folding is a coarse-grained statistical mechanical model called the Wako-Saitô-Muñoz-Eaton (WSME) model. The model can calculate the free-energy landscapes of proteins based on a three-dimensional structure with low computational complexity, thereby providing a comprehensive understanding of the folding pathways and the structure and stability of the intermediates and transition states involved in the folding reaction. In this review, we summarize previous and recent studies on protein folding and dynamics performed using the WSME model and discuss future challenges and prospects. The WSME model successfully predicted the folding mechanisms of small single-domain proteins and the effects of amino-acid substitutions on protein stability and folding in a manner that was consistent with experimental results. Furthermore, extended versions of the WSME model were applied to predict the folding mechanisms of multi-domain proteins and the conformational changes associated with protein function. Thus, the WSME model may contribute significantly to solving the protein-folding problem and is expected to be useful for predicting protein folding, stability, and dynamics in basic research and in industrial and medical applications.

Biological physics by high-speed atomic force microscopy

While many fields have contributed to biological physics, nanotechnology offers a new scale of observation. High-speed atomic force microscopy (HS-AFM) provides nanometre structural information and dynamics with subsecond resolution of biological systems. Moreover, HS-AFM allows us to measure piconewton forces within microseconds giving access to unexplored, fast biophysical processes. Thus, HS-AFM provides a tool to nourish biological physics through the observation of emergent physical phenomena in biological systems. In this review, we present an overview of the contribution of HS-AFM, both in imaging and force spectroscopy modes, to the field of biological physics. We focus on examples in which HS-AFM observations on membrane remodelling, molecular motors or the unfolding of proteins have stimulated the development of novel theories or the emergence of new concepts. We finally provide expected applications and developments of HS-AFM that we believe will continue contributing to our understanding of nature, by serving to the dialogue between biology and physics. This article is part of a discussion meeting issue 'Dynamic in situ microscopy relating structure and function'.

Frustration, function and folding

Natural protein molecules are exceptional polymers. Encoded in apparently random strings of amino-acids, these objects perform clear physical tasks that are rare to find by simple chance. Accurate folding, specific binding, powerful catalysis, are examples of basic chemical activities that the great majority of polypeptides do not display, and are thought to be the outcome of the natural history of proteins. Function, a concept genuine to Biology, is at the core of evolution and often conflicts with the physical constraints. Locating the frustration between discrepant goals in a recurrent system leads to fundamental insights about the chances and necessities that shape the encoding of biological information.

Digested disorder: Quarterly intrinsic disorder digest (January/February/March, 2013)

The current literature on intrinsically disordered proteins is blooming. A simple PubMed search for “intrinsically disordered protein OR natively unfolded protein” returns about 1,800 hits (as of June 17, 2013), with many papers published quite recently. To keep interested readers up to speed with this literature, we are starting a “Digested Disorder” project, which will encompass a series of reader’s digest type of publications aiming at the objective representation of the research papers and reviews on intrinsically disordered proteins. The only two criteria for inclusion in this digest are the publication date (a paper should be published within the covered time frame) and topic (a paper should be dedicated to any aspect of protein intrinsic disorder). The current digest covers papers published during the period of January, February and March of 2013. The papers are grouped hierarchically by topics they cover, and for each of the included paper a short description is given on its major findings.