Mapping free energy landscapes of complex multi-funneled metamorphic proteins and weakly-funneled intrinsically disordered proteins (IDPs) remains challenging. While rare-event sampling molecular dynamics simulations can be useful, they often need to either impose restraints or reweigh the generated data to match experiments. Here, we present a parallel-tempering method that takes advantage of accelerated water dynamics and allows efficient and accurate conformational sampling across a wide variety of proteins. We demonstrate the improved sampling efficiency by benchmarking against standard model systems such as alanine di-peptide, TRP-cage and β-hairpin. The method successfully scales to large metamorphic proteins such as RFA-H and to highly disordered IDPs such as Histatin-5. Across the diverse proteins, the calculated ensemble averages match well with the NMR, SAXS and other biophysical experiments without the need to reweigh. By allowing accurate sampling across different landscapes, the method opens doors for sampling free energy landscape of complex uncharted proteins.

HIV-1 protease variants resist drugs by active and non-active-site mutations. The active-site mutations, which are the primary or first set of mutations, hamper the stability of the enzyme and resist the drugs minimally. As a result, secondary mutations that not only increase protein stability for unhindered catalytic activity but also resist drugs very effectively arise. While the mechanism of drug resistance of the active-site mutations is through modulating the active-site pocket volume, the mechanism of drug resistance of the non-active-site mutations is unclear. Moreover, how these allosteric mutations, which are 8-21 Å distant, communicate to the active site for drug efflux is completely unexplored. Results from molecular dynamics simulations suggest that the primary mechanism of drug resistance of the secondary mutations involves opening of the flexible protease flaps. Results from both residue- and community-based network analyses reveal that this precise action of protease is accomplished by the presence of robust communication paths between the mutational sites and the functionally relevant regions: active site and flaps. While the communication is more direct in the wild type, it traverses across multiple intermediate residues in mutants, leading to weak signaling and unregulated motions of flaps. The global integrity of the protease network is, however, maintained through the neighboring residues, which exhibit high degrees of conservation, consistent with clinical data and mutagenesis studies.

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and “promiscuous” interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Running title: Conformational sampling of proteins with funneled, multi-funneled and weakly multi-funneled free-energy landscapes Keywords: Intrinsically disordered proteins | metamorphic proteins | conformational ensemble | integrative modeling | parallel tempering Author Contributions: AR and AS conceived and designed the research; AR performed research; JN provided computational resources; AR, JN, and AS analyzed data; and AR and AS wrote the paper * Corresponding author: Anand Srivastava, anand@iisc.ac.in Abstract: Determining the conformational ensemble for proteins with multi-funneled complex free-energy landscapes is often not possible with classical structure-biology methods that produce time and ensemble averaged data. With vastly improved force fields and advances in rare-event sampling methods, molecular dynamics (MD) simulations offer a complementary approach towards determining the collection of 3-dimensional structures that proteins can adopt. However, in general, MD simulations need to either impose restraints or reweigh the generated data to match experiments. The limitations extend beyond systems with high free-energy barriers as is the case with metamorphic proteins such as RFA-H. The predicted structures in even weakly-funneled intrinsically disordered proteins (IDPs) such as Histatin-5 (His-5) are too compact relative to experiments. Here, we employ a new computationally-efficient parallel-tempering based advanced-sampling method applicable across proteins with extremely diverse free-energy landscapes. And we show that the calculated ensemble averages match reasonably well with the NMR, SAXS and other biophysical experiments without the need to reweigh. We benchmark our method against standard model systems such as alanine di-peptide, TRP-cage and β-hairpin and demonstrate significant enhancement in the sampling efficiency. The method successfully scales to large metamorphic proteins such as RFA-H and to highly disordered IDPs such as His-5 and produces experimentally-consistent ensemble. By allowing accurate sampling across diverse landscapes, the method enables for ensemble conformational sampling of deep multi-funneled metamorphic proteins as well as highly flexible IDPs with shallow multi-funneled free-energy landscape.Significance/Authors' Summary: Generating high-resolution ensemble of intrinsically disordered proteins, particularly the highly flexible ones with high-charge and low-hydrophobicity and with shallow multi-funneled free-energy landscape, is a daunting task and often not possible since information from biophysical experiments provide time and ensemble average data at low resolutions. At the other end of the spectrum are the metamorphic proteins with multiple deep funnels and elucidating the structures of the transition intermediates between the fold topologies is a non-trivial exercise. In this work, we propose a new parallel-tempering based advancedsampling method where the Hamiltonian is designed to allow faster decay of water orientation dynamics, which in turn facilitates...

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