CS-ROSETTA

Deciphering structural variation upon biotinylation of biotin carboxyl carrier protein domain in Streptococcus pneumoniae

Streptococcus pneumoniae is a leading cause of community-acquired pneumonia and is responsible for acute invasive and non-invasive infections. Fight against pneumococcus is currently hampered by insufficient vaccine coverage and rising antimicrobial resistance, making the research necessary on novel drug targets. High-throughput mutagenesis has shown that acetyl-CoA carboxylase (ACC) is an essential enzyme in S. pneumoniae which converts acetyl-CoA to malonyl-CoA, a key step in fatty acid biosynthesis. ACC has four subunits; Biotin carboxyl carrier protein (BCCP), Biotin carboxylase (BC), Carboxyl transferase subunit α and β. Biotinylation of S. pneumoniae BCCP (SpBCCP) is required for the activation of ACC complex. In this study, we have biophysically characterized the apo- and holo- biotinylating domain of SpBCCP80. We have performed 2D and 3D NMR experiments to analyze the changes in amino acid residues upon biotinylation of SpBCCP80. Further, we used NMR backbone chemical shift assignment data for bioinformatical analyses to determine the secondary and tertiary structure of proteins. We observed major changes in AMKVM motif and thumb region of SpBCCP80 upon biotinylation. Overall, this work provides structural insight into the apo- to holo- conversion of SpBCCP80 which can be further used as a drug target against S. pneumoniae.

[Bioinformatics analysis and validation of the interaction between PML protein and TAB1 protein]

OBJECTIVE

To analyze the interaction between PML protein and TAB1 protein using bioinformatic approaches and experimentally verify the results.

METHODS

Using Rosetta software, a 3D model of TAB1 protein was constructed through a comparative modeling approach; the secondary structure of PML protein was retrieved in the PDB database and its crystal structure and 3D structure were resolved. Zdock 3.0.2 software was used to perform protein-protein docking of PML and TAB1, and the best conformation was extracted for molecular structure analysis of the docking model. The interaction between the two proteins was detected using immunoprecipitation in α-MMC-treated M1 inflammatory macrophages.

RESULTS

When 6IMQ of PML was used as the docking site, PML protein formed 3 salt bridges, 6 hydrogen bonds and 6 hydrophobic interactions with TAB1 proteins; when 5YUF of PML was used as the docking site, PML protein formed 1 hydrogen bond, 3 electrostatic interactions and 9 hydrophobic interactions with TAB1 proteins, and both of the docking modes formed good molecular docking and interactions. In the M1 inflammatory macrophages treated with α-MMC for 4 h, positive protein bands of PML and TAB1 were detected in the cell lysates in PML-IP group.

CONCLUSION

PML protein can interact strongly with TAB1 protein.

Pseudomonas aeruginosa AlgF is a protein-protein interaction mediator required for acetylation of the alginate exopolysaccharide

Enzymatic modifications of bacterial exopolysaccharides enhance immune evasion and persistence during infection. In the Gram-negative opportunistic pathogen Pseudomonas aeruginosa, acetylation of alginate reduces opsonic killing by phagocytes and improves reactive oxygen species scavenging. Although it is well known that alginate acetylation in P. aeruginosa requires AlgI, AlgJ, AlgF, and AlgX, how these proteins coordinate polymer modification at a molecular level remains unclear. Here, we describe the structural characterization of AlgF and its protein interaction network. We characterize direct interactions between AlgF and both AlgJ and AlgX in vitro and demonstrate an association between AlgF and AlgX, as well as AlgJ and AlgI, in P. aeruginosa. We determine that AlgF does not exhibit acetylesterase activity and is unable to bind to polymannuronate in vitro. Therefore, we propose that AlgF functions to mediate protein-protein interactions between alginate acetylation enzymes, forming the periplasmic AlgJFXK (AlgJ-AlgF-AlgX-AlgK) acetylation and export complex required for robust biofilm formation.

NMR-Chemical-Shift-Driven Protocol Reveals the Cofactor-Bound, Complete Structure of Dynamic Intermediates of the Catalytic Cycle of Oncogenic KRAS G12C Protein and the Significance of the Mg2+ Ion

In this work, catalytically significant states of the oncogenic G12C variant of KRAS, those of Mg2+-free and Mg2+-bound GDP-loaded forms, have been determined using CS-Rosetta software and NMR-data-driven molecular dynamics simulations. There are several Mg2+-bound G12C KRAS/GDP structures deposited in the Protein Data Bank (PDB), so this system was used as a reference, while the structure of the Mg2+-free but GDP-bound state of the RAS cycle has not been determined previously. Due to the high flexibility of the Switch-I and Switch-II regions, which also happen to be the catalytically most significant segments, only chemical shift information could be collected for the most important regions of both systems. CS-Rosetta was used to derive an "NMR ensemble" based on the measured chemical shifts, which, however, did not contain the nonprotein components of the complex. We developed a torsional restraint set for backbone torsions based on the CS-Rosetta ensembles for MD simulations, overriding the force-field-based parametrization in the presence of the reinserted cofactors. This protocol (csdMD) resulted in complete models for both systems that also retained the structural features and heterogeneity defined by the measured chemical shifts and allowed a detailed comparison of the Mg2+-bound and Mg2+-free states of G12C KRAS/GDP.

Functional regulation of an intrinsically disordered protein via a conformationally excited state

A longstanding goal in the field of intrinsically disordered proteins (IDPs) is to characterize their structural heterogeneity and pinpoint the role of this heterogeneity in IDP function. Here, we use multinuclear chemical exchange saturation (CEST) nuclear magnetic resonance to determine the structure of a thermally accessible globally folded excited state in equilibrium with the intrinsically disordered native ensemble of a bacterial transcriptional regulator CytR. We further provide evidence from double resonance CEST experiments that the excited state, which structurally resembles the DNA-bound form of cytidine repressor (CytR), recognizes DNA by means of a "folding-before-binding" conformational selection pathway. The disorder-to-order regulatory switch in DNA recognition by natively disordered CytR therefore operates through a dynamical variant of the lock-and-key mechanism where the structurally complementary conformation is transiently accessed via thermal fluctuations.

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.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

Improvements and new functionalities of UNRES server for coarse-grained modeling of protein structure, dynamics, and interactions

In this paper we report the improvements and extensions of the UNRES server (https://unres-server.chem.ug.edu.pl) for physics-based simulations with the coarse-grained UNRES model of polypeptide chains. The improvements include the replacement of the old code with the recently optimized one and adding the recent scale-consistent variant of the UNRES force field, which performs better in the modeling of proteins with the β and the α+β structures. The scope of applications of the package was extended to data-assisted simulations with restraints from nuclear magnetic resonance (NMR) and chemical crosslink mass-spectroscopy (XL-MS) measurements. NMR restraints can be input in the NMR Exchange Format (NEF), which has become a standard. Ambiguous NMR restraints are handled without expert intervention owing to a specially designed penalty function. The server can be used to run smaller jobs directly or to prepare input data to run larger production jobs by using standalone installations of UNRES.

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.

Canine Adenovirus 1 Isolation Bioinformatics Analysis of the Fiber

Canine adenovirus type 1 (CAdV-1) is a double-stranded DNA virus, which is the causative agent of fox encephalitis. The Fiber protein is one of the structural proteins in CAdV-1, which mediates virion binding to the coxsackievirus and adenovirus receptor on host cells. The suspected virus was cultured in the MDCK cells, and it was determined through the cytopathic effects, sequencing and electron microscopy. The informatics analysis of the Fiber was done using online bioinformatics servers. The CAdV-1-JL2021 strain was isolated successfully, and were most similar to the CAdV-1 strain circulating in Italy. The occurrence of negative selection and recombination were found in the CAdV-1-JL2021 and CAdV-2-AC_000020.1. Host cell membrane was its subcellular localization. The CAdV-1-JL2021 Fiber (ON164651) had 6 glycosylation sites and 107 phosphorylation sites, exerted adhesion receptor-mediated virion attachment to host cell, which was the same as CAdV-2-AC_000020.1 Fiber. The Fiber tertiary structure of the CAdV-1-JL2021 and CAdV-2-AC_000020.1 was different, but they had the same coxsackievirus and adenovirus receptor. “VATTSPTLTFAYPLIKNNNH” were predicted to be the potential CAdV-1 B cell linear epitope. The MHC-I binding peptide “KLGVKPTTY” were both presented in the CAdV-1-JL2021 and CAdV-2-AC_000020.1 Fiber and it is useful to design the canine adenovirus vaccine.

Epitope-focused immunogen design based on the ebolavirus glycoprotein HR2-MPER region

The three human pathogenic ebolaviruses: Zaire (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) virus, cause severe disease with high fatality rates. Epitopes of ebolavirus glycoprotein (GP) recognized by antibodies with binding breadth for all three ebolaviruses are of major interest for rational vaccine design. In particular, the heptad repeat 2 -membrane-proximal external region (HR2-MPER) epitope is relatively conserved between EBOV, BDBV, and SUDV GP and targeted by human broadly-neutralizing antibodies. To study whether this epitope can serve as an immunogen for the elicitation of broadly-reactive antibody responses, protein design in Rosetta was employed to transplant the HR2-MPER epitope identified from a co-crystal structure with the known broadly-reactive monoclonal antibody (mAb) BDBV223 onto smaller scaffold proteins. From computational analysis, selected immunogen designs were produced as recombinant proteins and functionally validated, leading to the identification of a sterile alpha motif (SAM) domain displaying the BDBV-HR2-MPER epitope near its C terminus as a promising candidate. The immunogen was fused to one component of a self-assembling, two-component nanoparticle and tested for immunogenicity in rabbits. Robust titers of cross-reactive serum antibodies to BDBV and EBOV GPs and moderate titers to SUDV GP were induced following immunization. To confirm the structural composition of the immunogens, solution NMR studies were conducted and revealed structural flexibility in the C-terminal residues of the epitope. Overall, our study represents the first report on an epitope-focused immunogen design based on the structurally challenging BDBV-HR2-MPER epitope.

An engineered construct of cFLIP provides insight into DED1 structure and interactions

Cellular FLICE-like inhibitory protein (cFLIP) is a member of the Death Domain superfamily with pivotal roles in many cellular processes and disease states, including cancer and autoimmune disorders. In the context of the death-inducing signaling complex (DISC), cFLIP isoforms regulate extrinsic apoptosis by controlling procaspase-8 activation. The function of cFLIP is mediated through a series of protein-protein interactions, engaging the two N-terminal death effector domains (DEDs). Here, we solve the structure of an engineered DED1 domain of cFLIP using solution nuclear magnetic resonance (NMR) and we define the interaction with FADD and calmodulin, protein-protein interactions that regulate the function of cFLIP in the DISC. cFLIP DED1 assumes a canonical DED fold characterized by six α helices and is able to bind calmodulin and FADD through two separate interfaces. Our results clearly demonstrate the role of DED1 in the cFLIP/FADD association and contribute to the understanding of the assembly of DISC filaments.

DnaJC7 binds natively folded structural elements in tau to inhibit amyloid formation

Molecular chaperones, including Hsp70/J-domain protein (JDP) families, play central roles in binding substrates to prevent their aggregation. How JDPs select different conformations of substrates remains poorly understood. Here, we report an interaction between the JDP DnaJC7 and tau that efficiently suppresses tau aggregation in vitro and in cells. DnaJC7 binds preferentially to natively folded wild-type tau, but disease-associated mutants in tau reduce chaperone binding affinity. We identify that DnaJC7 uses a single TPR domain to recognize a β-turn structural element in tau that contains the ²⁷⁵VQIINK²⁸⁰ amyloid motif. Wild-type tau, but not mutant, β-turn structural elements can block full-length tau binding to DnaJC7. These data suggest DnaJC7 preferentially binds and stabilizes natively folded conformations of tau to prevent tau conversion into amyloids. Our work identifies a novel mechanism of tau aggregation regulation that can be exploited as both a diagnostic and a therapeutic intervention.

Current directions in combining simulation-based macromolecular modeling approaches with deep learning

Introduction: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as drugs. Success rates are limited for any of these tasks, however. Recently, deep neural network-based methods have greatly enhanced the accuracy of predictions of protein structure from sequence, generating excitement about the potential impact of deep learning.Areas covered: This review introduces biologists to deep neural network architecture, surveys recent successes of deep learning in structure prediction, and discusses emerging deep learning-based approaches for structure-function analysis and design. Particular focus is given to the interplay between simulation-based and neural network-based approaches.Expert opinion: As deep learning grows integral to macromolecular modeling, simulation- and neural network-based approaches must grow more tightly interconnected. Modular software architecture must emerge allowing both types of tools to be combined with maximal versatility. Open sharing of code under permissive licenses will be essential. Although experiments will remain the gold standard for reliable information to guide drug discovery, we may soon see successful drug development projects based on high-accuracy predictions from algorithms that combine simulation with deep learning - the ultimate validation of this combination's power.

Modeling of Hidden Structures Using Sparse Chemical Shift Data from NMR Relaxation Dispersion

NMR relaxation dispersion measurements report on conformational changes occurring on the μs-ms timescale. Chemical shift information derived from relaxation dispersion can be used to generate structural models of weakly populated alternative conformational states. Current methods to obtain such models rely on determining the signs of chemical shift changes between the conformational states, which are difficult to obtain in many situations. Here, we use a "sample and select" method to generate relevant structural models of alternative conformations of the C-terminal-associated region of Escherichia coli dihydrofolate reductase (DHFR), using only unsigned chemical shift changes for backbone amides and carbonyls (¹H, ¹⁵N, and ¹³C'). We find that CS-Rosetta sampling with unsigned chemical shift changes generates a diversity of structures that are sufficient to characterize a minor conformational state of the C-terminal region of DHFR. The excited state differs from the ground state by a change in secondary structure, consistent with previous predictions from chemical shift hypersurfaces and validated by the x-ray structure of a partially humanized mutant of E. coli DHFR (N23PP/G51PEKN). The results demonstrate that the combination of fragment modeling with sparse chemical shift data can determine the structure of an alternative conformation of DHFR sampled on the μs-ms timescale. Such methods will be useful for characterizing alternative states, which can potentially be used for in silico drug screening, as well as contributing to understanding the role of minor states in biology and molecular evolution.

Combining Experimental Data and Computational Methods for the Non-Computer Specialist

Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.

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.

Better together: Elements of successful scientific software development in a distributed collaborative community

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Structural proteomics, electron cryo-microscopy and structural modeling approaches in bacteria-human protein interactions

A central challenge in infection medicine is to determine the structure and function of host-pathogen protein-protein interactions to understand how these interactions facilitate bacterial adhesion, dissemination and survival. In this review, we focus on proteomics, electron cryo-microscopy and structural modeling to showcase instances where affinity-purification (AP) and cross-linking (XL) mass spectrometry (MS) has advanced our understanding of host-pathogen interactions. We highlight cases where XL-MS in combination with structural modeling has provided insight into the quaternary structure of interspecies protein complexes. We further exemplify how electron cryo-tomography has been used to visualize bacterial-human interactions during attachment and infection. Lastly, we discuss how AP-MS, XL-MS and electron cryo-microscopy and -tomography together with structural modeling approaches can be used in future studies to broaden our knowledge regarding the function, dynamics and evolution of such interactions. This knowledge will be of relevance for future drug and vaccine development programs.

An order-to-disorder structural switch activates the FoxM1 transcription factor

Intrinsically disordered transcription factor transactivation domains (TADs) function through structural plasticity, adopting ordered conformations when bound to transcriptional co-regulators. Many transcription factors contain a negative regulatory domain (NRD) that suppresses recruitment of transcriptional machinery through autoregulation of the TAD. We report the solution structure of an autoinhibited NRD-TAD complex within FoxM1, a critical activator of mitotic gene expression. We observe that while both the FoxM1 NRD and TAD are primarily intrinsically disordered domains, they associate and adopt a structured conformation. We identify how Plk1 and Cdk kinases cooperate to phosphorylate FoxM1, which releases the TAD into a disordered conformation that then associates with the TAZ2 or KIX domains of the transcriptional co-activator CBP. Our results support a mechanism of FoxM1 regulation in which the TAD undergoes switching between disordered and different ordered structures.