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The unique viviparous Pacific Beetle cockroaches provide nutrition to their embryo by secreting milk proteins Lili-Mip, which is a lipid-binding glycoprotein that crystallizes in vivo. The resolved in vivo crystal structure of variably glycosylated Lili-Mip shows a classical Lipocalin fold with an eight-stranded antiparallel beta-barrel enclosing a fatty acid. The availability of physiologically unaltered glycoprotein structure makes Lili-Mip a very attractive model system to investigate the role of glycans on protein structure, dynamics, and function. Towards that end, we have employed all-atom molecular dynamics simulations on various glycosylated stages of a bound and free Lili-Mip protein and characterized the impact of glycans and the bound lipid on the dynamics of this glycoconjugate. Our work provides important molecular-level mechanistic insights into the role of glycans in the nutrient storage function of the Lili-Mip protein. Our analyses show that the glycans locally stabilize spatially proximal residues and regulate the low amplitude opening motions of the residues at the entrance of the binding pocket. Glycans, which are located at the portal end of the barrel, also restrict the distal barrel depth and allosterically modulate the lipid dynamics in the barrel. A simple but effective distance-based network analysis of the protein also reveals the role of glycans in the subtle rewiring of residues crucial for determining the barrel depth and lipid orientation.

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into “structurally similar” clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, Aβ42, and a C-terminal fragment ofα-synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered sub-states within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information and provide interpretable visualizations of the conformational heterogeneity within each ensemble and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs.SignificanceGrouping heterogeneous conformations of IDPs into “structurally similar” clusters facilitates a clearer understanding of the properties of IDP conformational ensembles and provides insights into ”structural ensemble: function” relationships. In this work, we provide a unique approach for clustering IDP ensembles efficiently using a non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), to create clusters with structurally similar IDP conformations. We show how this can be used for meaningful biophysical analyses such as understanding the binding mechanisms of IDPs such asα-synuclein and Amyloidβ42 with small drug molecules.Graphical Abstract

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., Mapping free energy landscapes of complex multi-funneled metamorphic proteins and weakly-funneled intrinsically disordered proteins (IDPs) remains challenging. Here authors present a parallel-tempering method that takes advantage of accelerated water dynamics for efficient and accurate conformational sampling across a wide variety of proteins.

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.

The intrinsically disordered proteins and protein regions (IDPs/IDPRs) do not have unique structures, but are known to be functionally important and their conformational flexibility and structural plasticity have engendered a paradigmatic shift in the classical sequence–structure–function maxim. Fundamental understanding in this field has significantly evolved since the discovery of this class of proteins about 25 years ago. Though the IDPRs of transmembrane proteins (TMP-IDPRs) comply with the broad definition of typical IDPs and IDPRs found in water-soluble globular proteins, much less is explored and known about them. In this review, we assimilate the key emerging biophysical principles from the limited studies on TMP-IDPRs and provide several context-specific biological examples to highlight the ubiquitous nature of TMP-IDPRs and their functional importance in cellular functions. Besides providing a spectrum of insights from sequence to structural disorder and functions, we also review the challenges and methodological advances in studying the structure–function relationship of TMP-IDPRs. We also lay stress upon the importance of an integrative framework, where ensemble-averaged (and mostly low-resolution) data from multiple experiments can be faithfully integrated with modelling techniques such as advanced sampling, coarse-graining, and free energy minimization methods for a high-fidelity characterization of TMP-IDPRs. We close the review by providing futuristic perspective with suggestions on how we could use the ideas and methods from the exciting field of protein engineering in conjunction with integrative modelling framework to advance the IDPR field and harness the sequence–disorder–function paradigm towards functional design of 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.

Human immunodeficiency virus-1 (HIV-1) protease is an important drug target for acquired immune deficiency syndrome therapy. Nearly 10 small molecule drugs have been approved by the Food and Drug Administration (FDA). However, prolonged use of these drugs produced protease mutants that are not susceptible to many of these drugs. The mutated proteases, however, continue to cleave the substrate peptides and thus remain largely functional. This poses a major challenge for the treatment strategies. Thus, it has become imperative to understand how these mutations induce drug resistance while maintaining the enzymatic activity of this protein. Here, we perform a comprehensive study of the wild type (WT) and clinically relevant mutated protease bound to a series of FDA-approved drugs and substrates of varying sequences to unravel the mechanism of unhindered activity of the drug-resistant protease variants. Our results from large molecular dynamics simulations suggest that while binding of the substrate to WT and protease mutants involves multiple H-bonding interactions between substrate subsites and the protease's main chain atoms, the drug binds primarily through the hydrophobic interactions with the side chains of protease's active site and flap residues. This implies that any side chain variations caused by mutations in protease could greatly modulate the binding affinity of inhibitors, but not of the substrates. The significantly weaker free energy of binding of the drugs could also be attributed to the limited number of interaction subsites present in the inhibitor structures compared to the substrates. These findings in combination with the identified protease flap and active site residues that contribute to ligand recognition and strong binding can help in the design of future resistance-evading HIV-1 protease inhibitors.