Abstract:Ensembles of protein structures are increasingly used to represent the conformational variation of a protein as determined by experiment and/or by molecular simulations, as well as uncertainties that may be associated with structure determinations or predictions. Making the best use of such information requires the ability to quantitatively compare entire ensembles. For this reason, we recently introduced the Ensemblator (Clark et al., Protein Sci 2015; 24:1528), a novel approach to compare user-defined groups… Show more
“…We used the Ensemblator program (Brereton & Karplus, 2018), which aligns independently determined 3D structures and identifies regions of structural conservation or plasticity, to visualize how a sequence that is strictly conserved is capable of binding such a wide variety of sequences. By overlaying the protomers from five published crystal and NMR structures of free LC8, we observed that the β-strand that directly binds to partners is highly variable (Fig 5B; Fan et al, 2002; Benison et al, 2007; Hall et al, 2008) and has the highest root mean squared deviation (RMSD) values between structures.…”
Section: Resultsmentioning
confidence: 99%
“…Alignment of LC8 structures was done using the Ensemblator (Brereton & Karplus, 2018) program, and the RMSD for residues in free LC8 structures (listed above) was calculated using the built-in local alignment tool. This tool works by aligning each dipeptide within the protein and calculating the RMSD for the next amino acid within the protein sequence.…”
Hub proteins participate in cellular regulation by dynamic binding of multiple proteins within interaction networks. The hub protein LC8 reversibly interacts with more than 100 partners through a flexible pocket at its dimer interface. To explore the diversity of the LC8 partner pool, we screened for LC8 binding partners using a proteomic phage display library composed of peptides from the human proteome, which had no bias toward a known LC8 motif. Of the identified hits, we validated binding of 29 peptides using isothermal titration calorimetry. Of the 29 peptides, 19 were entirely novel, and all had the canonical TQT motif anchor. A striking observation is that numerous peptides containing the TQT anchor do not bind LC8, indicating that residues outside of the anchor facilitate LC8 interactions. Using both LC8-binding and nonbinding peptides containing the motif anchor, we developed the “LC8Pred” algorithm that identifies critical residues flanking the anchor and parses random sequences to predict LC8-binding motifs with ∼78% accuracy. Our findings significantly expand the scope of the LC8 hub interactome.
“…We used the Ensemblator program (Brereton & Karplus, 2018), which aligns independently determined 3D structures and identifies regions of structural conservation or plasticity, to visualize how a sequence that is strictly conserved is capable of binding such a wide variety of sequences. By overlaying the protomers from five published crystal and NMR structures of free LC8, we observed that the β-strand that directly binds to partners is highly variable (Fig 5B; Fan et al, 2002; Benison et al, 2007; Hall et al, 2008) and has the highest root mean squared deviation (RMSD) values between structures.…”
Section: Resultsmentioning
confidence: 99%
“…Alignment of LC8 structures was done using the Ensemblator (Brereton & Karplus, 2018) program, and the RMSD for residues in free LC8 structures (listed above) was calculated using the built-in local alignment tool. This tool works by aligning each dipeptide within the protein and calculating the RMSD for the next amino acid within the protein sequence.…”
Hub proteins participate in cellular regulation by dynamic binding of multiple proteins within interaction networks. The hub protein LC8 reversibly interacts with more than 100 partners through a flexible pocket at its dimer interface. To explore the diversity of the LC8 partner pool, we screened for LC8 binding partners using a proteomic phage display library composed of peptides from the human proteome, which had no bias toward a known LC8 motif. Of the identified hits, we validated binding of 29 peptides using isothermal titration calorimetry. Of the 29 peptides, 19 were entirely novel, and all had the canonical TQT motif anchor. A striking observation is that numerous peptides containing the TQT anchor do not bind LC8, indicating that residues outside of the anchor facilitate LC8 interactions. Using both LC8-binding and nonbinding peptides containing the motif anchor, we developed the “LC8Pred” algorithm that identifies critical residues flanking the anchor and parses random sequences to predict LC8-binding motifs with ∼78% accuracy. Our findings significantly expand the scope of the LC8 hub interactome.
“…To evaluate the relative accuracy and precision of NOE-driven structure determination approaches using different input assignments and NOE datasets (HCNH or HCNH+HCCH), we performed a single joint Ensemblator 25 , 26 analysis of the 12 resulting aLP structural ensembles (six each using Rosetta or CYANA 27 ) and a set of 51 X-ray structures from the CoDNaS 28 database ( see Methods). A dimensionality-reduced visualization of the relationships between the models (Fig.…”
Section: Resultsmentioning
confidence: 99%
“… Fig. 4 Ensemblator 25 , 26 analysis of α-lytic protease NMR and X-ray ensembles. a t-SNE dimensionality reduction results showing the relationships of the aLP models.…”
Section: Resultsmentioning
confidence: 99%
“…Analysis of the ensembles was performed using the Ensemblator 25 , 26 software for atom- and residue-level global and local comparisons. The Ensemblator first iteratively overlays pairs of structures and finally defines a “common core” of atoms that are consistently within a specified cutoff distance.…”
Automated methods for NMR structure determination of proteins are continuously becoming more robust. However, current methods addressing larger, more complex targets rely on analyzing 6–10 complementary spectra, suggesting the need for alternative approaches. Here, we describe 4D-CHAINS/autoNOE-Rosetta, a complete pipeline for NOE-driven structure determination of medium- to larger-sized proteins. The 4D-CHAINS algorithm analyzes two 4D spectra recorded using a single, fully protonated protein sample in an iterative ansatz where common NOEs between different spin systems supplement conventional through-bond connectivities to establish assignments of sidechain and backbone resonances at high levels of completeness and with a minimum error rate. The 4D-CHAINS assignments are then used to guide automated assignment of long-range NOEs and structure refinement in autoNOE-Rosetta. Our results on four targets ranging in size from 15.5 to 27.3 kDa illustrate that the structures of proteins can be determined accurately and in an unsupervised manner in a matter of days.
The importance of β‐turns to protein folding has motivated extensive efforts to stabilize the motif with non‐canonical backbone connectivity. Prior work has focused almost exclusively on turns between strands in a β‐sheet (i. e., hairpins). Turns in other structural contexts are also common in nature and have distinct conformational preferences; however, design principles for their mimicry remain poorly understood. Here, we report strategies that stabilize non‐hairpin β‐turns through systematic evaluation of the impacts of backbone alteration on the high‐resolution folded structure and folded stability of a helix‐loop‐helix prototype protein. Several well‐established hairpin turn mimetics are shown detrimental to folded stability and/or hydrophobic core packing, while less‐explored modification schemes that reinforce alternate turn types lead to improved stability and more faithful structural mimicry. Collectively, these results have implications in control over protein folding through chemical modification as well as the design of protein mimetics.
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