Successes and challenges in simulating the folding of large proteins

Abstract

Computational simulations of protein folding can be used to interpret experimental folding results, to design new folding experiments, and to test the effects of mutations and small molecules on folding. However, whereas major experimental and computational progress has been made in understanding how small proteins fold, research on larger, multidomain proteins, which comprise the majority of proteins, is less advanced. Specifically, large proteins often fold via long-lived partially folded intermediates, whose structures, potentially toxic oligomerization, and interactions with cellular chaperones remain poorly understood. Molecular dynamics based folding simulations that rely on knowledge of the native structure can provide critical, detailed information on folding free energy landscapes, intermediates, and pathways. Further, increases in computational power and methodological advances have made folding simulations of large proteins practical and valuable. Here, using serpins that inhibit proteases as an example, we review native-centric methods for simulating the folding of large proteins. These synergistic approaches range from Gō and related structurebased models that can predict the effects of the native structure on folding to all-atom-based methods that include side-chain chemistry and can predict how disease-associated mutations may impact folding. The application of these computational approaches to serpins and other large proteins highlights the successes and limitations of current computational methods and underscores how computational results can be used to inform experiments. These powerful simulation approaches in combination with experiments can provide unique insights into how large proteins fold and misfold, expanding our ability to predict and manipulate protein folding.

All-Atom Simulation of the HET-s Prion Replication

“…However, whenever a reasonable proxy of the RC is available, then the BF variational scheme enables to keep the systematic errors of rMD to a minimum. Protein folding pathways obtained with the BF approach have been found to agree very well with the results of both plain MD simulations [13] and kinetic experiments [17,18], arguably reflecting the fact that a good RC for these is available [19], supported also by energy landscape theory arguments [20]. Unfortunately, the BF approach may be flawed by uncontrolled systematic errors when applied to study processes in which the RC is poorly known.…”

The Molecular Mechanism of Domain Swapping of the C-Terminal Domain of the SARS-Coronavirus Main Protease

“…Structured proteins fold on a biologically relevant timescale because of a funnel shaped energy landscape in which interactions not present in the native or folded state of the protein (nonnative interactions) stabilize structure far less than native interactions do ( 38 ). Thus, protein models that encode only the native structure of the protein, termed structure-based models (SBMs), can be used to simulate proteins and have been successfully used to understand the barriers to and the mechanisms of protein folding ( 39 , 40 , 41 , 42 ) and domain swapping ( 43 , 44 , 45 , 46 ). We performed MD simulations of an SBM ( 39 , 47 , 48 ) of M pro C to investigate both protein folding and domain swapping.…”

All-atom simulation of the HET-s prion replication

“…Protein folding pathways obtained with the BF approach have been found to agree very well with the results of both plain MD simulations [16] and kinetic experiments [17,18], arguably reflecting the fact that a good CV for protein folding is available [19], as suggested by energy landscape theory arguments [20]. Unfortunately, the BF approach may be flawed by uncontrolled systematic errors whenever it is applied to study processes in which the reaction coordinate is poorly known.…”