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While great progress has been made, only 10% of the nearly 1,000 integral, α-helical, multi-span membrane protein families are represented by at least one experimentally determined structure in the PDB. Previously, we developed the algorithm BCL::MP-Fold, which samples the large conformational space of membrane proteins de novo by assembling predicted secondary structure elements guided by knowledge-based potentials. Here, we present a case study of rhodopsin fold determination by integrating sparse and/or low-resolution restraints from multiple experimental techniques including electron microscopy, electron paramagnetic resonance spectroscopy, and nuclear magnetic resonance spectroscopy. Simultaneous incorporation of orthogonal experimental restraints not only significantly improved the sampling accuracy but also allowed identification of the correct fold, which is demonstrated by a protein size-normalized transmembrane root-mean-square deviation as low as 1.2 Å. The protocol developed in this case study can be used for the determination of unknown membrane protein folds when limited experimental restraints are available.
Copyright © 2018 Elsevier Ltd. All rights reserved.
Integrins are transmembrane cell-extracellular matrix adhesion receptors that impact many cellular functions. A subgroup of integrins contain an inserted (I) domain within the α-subunits (αI) that mediate ligand recognition where function is contingent on binding a divalent cation at the metal ion dependent adhesion site (MIDAS). Ca is reported to promote α1I but inhibit α2I ligand binding. We co-crystallized individual I-domains with MIDAS-bound Ca and report structures at 1.4 and 2.15 Å resolution, respectively. Both structures are in the "closed" ligand binding conformation where Ca induces minimal global structural changes. Comparisons with Mg-bound structures reveal Mg and Ca bind α1I in a manner sufficient to promote ligand binding. In contrast, Ca is displaced in the α2I domain MIDAS by 1.4 Å relative to Mg and unable to directly coordinate all MIDAS residues. We identified an E152-R192 salt bridge hypothesized to limit the flexibility of the α2I MIDAS, thus, reducing Ca binding. A α2I E152A construct resulted in a 10,000-fold increase in Mg and Ca binding affinity while increasing binding to collagen ligands 20%. These data indicate the E152-R192 salt bridge is a key distinction in the molecular mechanism of differential ion binding of these two I domains.
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
Pre-mRNA processing protein 40 (Prp40) is a nuclear protein that has a role in pre-mRNA splicing. Prp40 possesses two leucine-rich nuclear export signals, but little is known about the function of Prp40 in the export process. Another protein that has a role in protein export is centrin, a member of the EF-hand superfamily of Ca-binding proteins. Prp40 was found to be a centrin target by yeast-two-hybrid screening using both Homo sapiens centrin 2 (Hscen2) and Chlamydomonas reinhardtii centrin (Crcen). We identified a centrin-binding site within H. sapiens Prp40 homolog A (HsPrp40A), which contains a hydrophobic triad WLL that is known to be important in the interaction with centrin. This centrin-binding site is highly conserved within the first nuclear export signal consensus sequence identified in Saccharomyces cerevisiae Prp40. Here, we examine the interaction of HsPrp40A peptide (HsPrp40Ap) with both Hscen2 and Crcen by isothermal titration calorimetry. We employed the thermodynamic parameterization to estimate the polar and apolar surface area of the interface. In addition, we have defined the molecular mechanism of thermally induced unfolding and dissociation of the Crcen-HsPrp40Ap complex using two-dimensional infrared correlation spectroscopy. These complementary techniques showed for the first time, to our knowledge, that HsPrp40Ap interacts with centrin in vitro, supporting a coupled functional role for these proteins in pre-mRNA splicing.
Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Cyclooxygenase-2 catalyses the biosynthesis of prostaglandins from arachidonic acid but also the biosynthesis of prostaglandin glycerol esters (PG-Gs) from 2-arachidonoylglycerol. Previous studies identified PG-Gs as signalling molecules involved in inflammation. Thus, the glyceryl ester of prostaglandin E, PGE-G, mobilizes Ca and activates protein kinase C and ERK, suggesting the involvement of a G protein-coupled receptor (GPCR). To identify the endogenous receptor for PGE-G, we performed a subtractive screening approach where mRNA from PGE-G response-positive and -negative cell lines was subjected to transcriptome-wide RNA sequencing analysis. We found several GPCRs that are only expressed in the PGE-G responder cell lines. Using a set of functional readouts in heterologous and endogenous expression systems, we identified the UDP receptor P2Y as the specific target of PGE-G. We show that PGE-G and UDP are both agonists at P2Y, but they activate the receptor with extremely different EC values of ~1 pM and ~50 nM, respectively. The identification of the PGE-G/P2Y pair uncovers the signalling mode of PG-Gs as previously under-appreciated products of cyclooxygenase-2.
Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their potential inaccuracies.
Passive macromolecular diffusion through nuclear pore complexes (NPCs) is thought to decrease dramatically beyond a 30-60-kD size threshold. Using thousands of independent time-resolved fluorescence microscopy measurements in vivo, we show that the NPC lacks such a firm size threshold; instead, it forms a soft barrier to passive diffusion that intensifies gradually with increasing molecular mass in both the wild-type and mutant strains with various subsets of phenylalanine-glycine (FG) domains and different levels of baseline passive permeability. Brownian dynamics simulations replicate these findings and indicate that the soft barrier results from the highly dynamic FG repeat domains and the diffusing macromolecules mutually constraining and competing for available volume in the interior of the NPC, setting up entropic repulsion forces. We found that FG domains with exceptionally high net charge and low hydropathy near the cytoplasmic end of the central channel contribute more strongly to obstruction of passive diffusion than to facilitated transport, revealing a compartmentalized functional arrangement within the NPC.
© 2016 Timney et al.
There is a compelling and growing need to accurately predict the impact of amino acid mutations on protein stability for problems in personalized medicine and other applications. Here the ability of 10 computational tools to accurately predict mutation-induced perturbation of folding stability (ΔΔG) for membrane proteins of known structure was assessed. All methods for predicting ΔΔG values performed significantly worse when applied to membrane proteins than when applied to soluble proteins, yielding estimated concordance, Pearson, and Spearman correlation coefficients of <0.4 for membrane proteins. Rosetta and PROVEAN showed a modest ability to classify mutations as destabilizing (ΔΔG < -0.5 kcal/mol), with a 7 in 10 chance of correctly discriminating a randomly chosen destabilizing variant from a randomly chosen stabilizing variant. However, even this performance is significantly worse than for soluble proteins. This study highlights the need for further development of reliable and reproducible methods for predicting thermodynamic folding stability in membrane proteins.
It is demonstrated that time-dependent density functional theory (DFT) calculations can accurately predict changes in near-UV electronic circular dichroism (ECD) spectra of DNA as the structure is altered from the linear (free) B-DNA form to the supercoiled N-DNA form found in nucleosome core particles. At the DFT/B3LYP level of theory, the ECD signal response is reduced by a factor of 6.7 in going from the B-DNA to the N-DNA form, and it is illustrated how more than 90% of the individual base-pair dimers contribute to this strong hypochromic effect. Of the several inter-base pair parameters, an increase in twist angles is identified as to strongly contribute to a reduced ellipticity. The present work provides first evidence that first-principles calculations can elucidate changes in DNA dichroism due to the supramolecular organization of the nucleoprotein particle and associates these changes with the local structural features of nucleosomal DNA.
Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm. Multi-specificity design (MSD), on the other hand, involves considering the stability of multiple protein states simultaneously. We have developed a novel MSD algorithm, which we refer to as REstrained CONvergence in multi-specificity design (RECON). The algorithm allows each state to adopt its own sequence throughout the design process rather than enforcing a single sequence on all states. Convergence to a single sequence is encouraged through an incrementally increasing convergence restraint for corresponding positions. Compared to MSD algorithms that enforce (constrain) an identical sequence on all states the energy landscape is simplified, which accelerates the search drastically. As a result, RECON can readily be used in simulations with a flexible protein backbone. We have benchmarked RECON on two design tasks. First, we designed antibodies derived from a common germline gene against their diverse targets to assess recovery of the germline, polyspecific sequence. Second, we design "promiscuous", polyspecific proteins against all binding partners and measure recovery of the native sequence. We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.