Automatic NOESY assignment in CS-RASREC-Rosetta

Driving forces behind phase separation of the carboxy-terminal domain of RNA polymerase II

Eukaryotic gene regulation and pre-mRNA transcription depend on the carboxy-terminal domain (CTD) of RNA polymerase (Pol) II. Due to its highly repetitive, intrinsically disordered sequence, the CTD enables clustering and phase separation of Pol II. The molecular interactions that drive CTD phase separation and Pol II clustering are unclear. Here, we show that multivalent interactions involving tyrosine impart temperature- and concentration-dependent self-coacervation of the CTD. NMR spectroscopy, molecular ensemble calculations and all-atom molecular dynamics simulations demonstrate the presence of diverse tyrosine-engaging interactions, including tyrosine-proline contacts, in condensed states of human CTD and other low-complexity proteins. We further show that the network of multivalent interactions involving tyrosine is responsible for the co-recruitment of the human Mediator complex and CTD during phase separation. Our work advances the understanding of the driving forces of CTD phase separation and thus provides the basis to better understand CTD-mediated Pol II clustering in eukaryotic gene transcription.

Recent Advances in NMR Protein Structure Prediction with ROSETTA

Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Solution structure of the inhibitor of cysteine proteases 1 from Entamoeba histolytica reveals a possible auto regulatory mechanism

The genome of Entamoeba histolytica encodes approximately 50 Cysteine Proteases (CPs) whose activity is regulated by two Inhibitors of Cysteine Proteases (ICPs), EhICP1 and EhICP2. The main difference between both EhICPs is the acquisition of a 17 N-terminal targeting signal in EhICP2 and three exposed cysteine residues in EhICP1. The three exposed cysteines in EhICP1 potentiate the formation of cross-linking species that drive heterogeneity. Here we solved the NMR structure of EhICP1 using a mutant protein without accessible cysteines. Our structural data shows that EhICP1 adopts an immunoglobulin fold composed of seven β-strands, and three solvent exposed loops that resemble the structures of EhICP2 and chagasin. EhICP1 and EhICP2 are able to inhibit the archetypical cysteine protease papain by intercalating their BC loops into the protease active site independently of the character of the residue (serine or threonine) responsible to interact with the active site of papain. EhICP1 and EhICP2 present signals of functional divergence as they clustered in different clades. Two of the three exposed cysteines in EhICP1 are located at the DE loop that intercalates into the CP substrate-binding cleft. We propose that the solvent exposed cysteines of EhICP1 play a role in regulating its inhibitory activity and that in oxidative conditions, the cysteines of EhICP1 react to form intra and intermolecular disulfide bonds that render an inactive inhibitor. EhICP2 is not subject to redox regulation, as this inhibitor does not contain a single cysteine residue. This proposed redox regulation may be related to the differential cellular localization between EhICP1 and EhICP2.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Interleukin-37 monomer is the active form for reducing innate immunity

Interleukin-37 (IL-37), a member of the IL-1 family of cytokines, is a fundamental suppressor of innate and acquired immunities. Here, we used an integrative approach that combines biophysical, biochemical, and biological studies to elucidate the unique characteristics of IL-37. Our studies reveal that single amino acid mutations at the IL-37 dimer interface that result in the stable formation of IL-37 monomers also remain monomeric at high micromolar concentrations and that these monomeric IL-37 forms comprise higher antiinflammatory activities than native IL-37 on multiple cell types. We find that, because native IL-37 forms dimers with nanomolar affinity, higher IL-37 only weakly suppresses downstream markers of inflammation whereas lower concentrations are more effective. We further show that IL-37 is a heparin binding protein that modulates this self-association and that the IL-37 dimers must block the activity of the IL-37 monomer. Specifically, native IL-37 at 2.5 nM reduces lipopolysaccharide (LPS)-induced vascular cell adhesion molecule (VCAM) protein levels by ∼50%, whereas the monomeric D73K mutant reduced VCAM by 90% at the same concentration. Compared with other members of the IL-1 family, both the N and the C termini of IL-37 are extended, and we show they are disordered in the context of the free protein. Furthermore, the presence of, at least, one of these extended termini is required for IL-37 suppressive activity. Based on these structural and biological studies, we present a model of IL-37 interactions that accounts for its mechanism in suppressing innate inflammation.

De novo design of a non-local β-sheet protein with high stability and accuracy

β-sheet proteins carry out critical functions in biology, and hence are attractive scaffolds for computational protein design. Despite this potential, de novo design of all-β-sheet proteins from first principles lags far behind the design of all-α or mixed-αβ domains owing to their non-local nature and the tendency of exposed β-strand edges to aggregate. Through study of loops connecting unpaired β-strands (β-arches), we have identified a series of structural relationships between loop geometry, side chain directionality and β-strand length that arise from hydrogen bonding and packing constraints on regular β-sheet structures. We use these rules to de novo design jellyroll structures with double-stranded β-helices formed by eight antiparallel β-strands. The nuclear magnetic resonance structure of a hyperthermostable design closely matched the computational model, demonstrating accurate control over the β-sheet structure and loop geometry. Our results open the door to the design of a broad range of non-local β-sheet protein structures.

CS-ROSETTA

Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.

Chemical shift-based methods in NMR structure determination

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

Automated NMR resonance assignments and structure determination using a minimal set of 4D spectra

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.

Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints

We critically test and validate the CS-Rosetta methodology for de novo structure prediction of α-helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase-1 (mPGES-1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X-ray structures are available, resolution-adapted structural recombination (RASREC) CS-Rosetta yields structures that are as close to the X-ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES-1 and Bacillus cereus TSPO, where only X-ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS-Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close-to-native structures were obtained with one randomly picked long-range NOEs for every 14, 31, 38, and 8 residues for full-length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES-1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS-Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812-826. © 2016 Wiley Periodicals, Inc.

Structure determination of uniformly (13)C, (15)N labeled protein using qualitative distance restraints from MAS solid-state (13)C-NMR observed paramagnetic relaxation enhancement

Magic angle spinning (MAS) solid-state nuclear magnetic resonance (NMR) is a powerful method for structure determination of insoluble biomolecules. However, structure determination by MAS solid-state NMR remains challenging because it is difficult to obtain a sufficient amount of distance restraints owing to spectral complexity. Collection of distance restraints from paramagnetic relaxation enhancement (PRE) is a promising approach to alleviate this barrier. However, the precision of distance restraints provided by PRE is limited in solid-state NMR because of incomplete averaged interactions and intermolecular PREs. In this report, the backbone structure of the B1 domain of streptococcal protein G (GB1) has been successfully determined by combining the CS-Rosetta protocol and qualitative PRE restraints. The derived structure has a Cα RMSD of 1.49 Å relative to the X-ray structure. It is noteworthy that our protocol can determine the correct structure from only three cysteine-EDTA-Mn(2+) mutants because this number of PRE sites is insufficient when using a conventional structure calculation method based on restrained molecular dynamics and simulated annealing. This study shows that qualitative PRE restraints can be employed effectively for protein structure determination from a limited conformational sampling space using a protein fragment library.

The second round of Critical Assessment of Automated Structure Determination of Proteins by NMR: CASD-NMR-2013

The second round of the community-wide initiative Critical Assessment of automated Structure Determination of Proteins by NMR (CASD-NMR-2013) comprised ten blind target datasets, consisting of unprocessed spectral data, assigned chemical shift lists and unassigned NOESY peak and RDC lists, that were made available in both curated (i.e. manually refined) or un-curated (i.e. automatically generated) form. Ten structure calculation programs, using fully automated protocols only, generated a total of 164 three-dimensional structures (entries) for the ten targets, sometimes using both curated and un-curated lists to generate multiple entries for a single target. The accuracy of the entries could be established by comparing them to the corresponding manually solved structure of each target, which was not available at the time the data were provided. Across the entire data set, 71 % of all entries submitted achieved an accuracy relative to the reference NMR structure better than 1.5 Å. Methods based on NOESY peak lists achieved even better results with up to 100% of the entries within the 1.5 Å threshold for some programs. However, some methods did not converge for some targets using un-curated NOESY peak lists. Over 90% of the entries achieved an accuracy better than the more relaxed threshold of 2.5 Å that was used in the previous CASD-NMR-2010 round. Comparisons between entries generated with un-curated versus curated peaks show only marginal improvements for the latter in those cases where both calculations converged.

Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta

We have developed a novel and robust approach for automatic and unsupervised simultaneous nuclear Overhauser effect (NOE) assignment and structure determination within the CS-Rosetta framework. Starting from unassigned peak lists and chemical shift assignments, autoNOE-Rosetta determines NOE cross-peak assignments and generates structural models. The approach tolerates incomplete and raw NOE peak lists as well as incomplete or partially incorrect chemical shift assignments, and its performance has been tested on 50 protein targets ranging from 50 to 200 residues in size. We find a significantly improved performance compared to established programs, particularly for larger proteins and for NOE data obtained on perdeuterated protein samples. X-ray crystallographic structures allowed comparison of Rosetta and conventional, PDB-deposited, NMR models in 20 of 50 test cases. The unsupervised autoNOE-Rosetta models were often of significantly higher accuracy than the corresponding expert-supervised NMR models deposited in the PDB. We also tested the method with unrefined peak lists and found that performance was nearly as good as for refined peak lists. Finally, demonstrating our method's remarkable robustness against problematic input data, we provided correct models for an incorrect PDB-deposited NMR solution structure.