CONCLUSIONS: We introduce a unified Python package for the prediction of protein biophysical properties, streamlining previous tools developed by the Bio2Byte research group. This suite facilitates comprehensive assessments of protein characteristics, incorporating predictors for backbone and sidechain dynamics, local secondary structure propensities, early folding, long disorder, beta-sheet aggregation and FUS-like phase separation. Our package significantly eases the integration and execution of these tools, enhancing accessibility for both computational and experimental researchers.
METHODS: The suite is available on the Python Package Index (PyPI): https://pypi.org/project/b2bTools/ and Bioconda: https://bioconda.github.io/recipes/b2btools/README.html for Linux and macOS systems, with Docker images hosted on Biocontainers: https://quay.io/repository/biocontainers/b2btools?tab=tags&tag=latest and Docker Hub: https://hub.docker.com/u/bio2byte. Online deployments are available on Galaxy Europe: https://usegalaxy.eu/root?tool_id=b2btools_single_sequence and our online server: https://bio2byte.be/b2btools/. The source code can be found at https://bitbucket.org/bio2byte/b2btools_releases.
BACKGROUND: Supplementary information are available at Bioinformatics online.

The dynamics of the backbone and side-chains of protein are routinely studied by interpreting experimentally determined 15N spin relaxation rates. R1(15N), the longitudinal relaxation rate, reports on fast motions and encodes, together with the transverse relaxation R2, structural information about the shape of the molecule and the orientation of the amide bond vectors in the internal diffusion frame. Determining error-free 15N longitudinal relaxation rates remains a challenge for small, disordered, and medium-sized proteins. Here, we show that mono-exponential fitting is sufficient, with no statistical preference for bi-exponential fitting up to 800 MHz. A detailed comparison of the TROSY and HSQC techniques at medium and high fields showed no statistically significant differences. The least error-prone DD/CSA interference removal technique is the selective inversion of amide signals while avoiding water resonance. The exchange of amide with solvent deuterons appears to affect the rate R1 of solvent-exposed amides in all fields tested and in each DD/CSA interference removal technique in a statistically significant manner. In summary, the most accurate R1(15N) rates in proteins are achieved by selective amide inversion, without the addition of D2O. Importantly, at high magnetic fields stronger than 800 MHz, when non-mono-exponential decay is involved, it is advisable to consider elimination of the shortest delays (typically up to 0.32 s) or bi-exponential fitting.

The Hofmeister series categorizes ions based on their effects on protein stability, yet the microscopic mechanism remains a mystery. In this series, NaCl is neutral, Na2SO4 and Na2HPO4 are kosmotropic, while GdmCl and NaSCN are chaotropic. This study employs CD and NMR to investigate the effects of NaCl, Na2SO4, and Na2HPO4 on the conformation, stability, binding, and backbone dynamics (ps-ns and µs-ms time scales) of the WW4 domain with a high stability and accessible side chains at concentrations ≤ 200 mM. The results indicated that none of the three salts altered the conformation of WW4 or showed significant binding to the four aliphatic hydrophobic side chains. NaCl had no effect on its thermal stability, while Na2SO4 and Na2HPO4 enhanced the stability by ~5 °C. Interestingly, NaCl only weakly interacted with the Arg27 amide proton, whereas Na2SO4 bound to Arg27 and Phe31 amide protons with Kd of 32.7 and 41.6 mM, respectively. Na2HPO4, however, bound in a non-saturable manner to Trp9, His24, and Asn36 amide protons. While the three salts had negligible effects on ps-ns backbone dynamics, NaCl and Na2SO4 displayed no effect while Na2HPO4 significantly increased the µs-ms backbone dynamics. These findings, combined with our recent results with GdmCl and NaSCN, suggest a microscopic mechanism for the Hofmeister series. Additionally, the data revealed a lack of simple correlation between thermodynamic stability and backbone dynamics, most likely due to enthalpy-entropy compensation. Our study rationalizes the selection of chloride and phosphate as the primary anions in extracellular and intracellular spaces, as well as polyphosphate as a primitive chaperone in certain single-cell organisms.

We analyzed the impact of nine previously identified missense PKD1 variants from our studies, including c.6928G > A p.G2310R, c.8809G > A p.E2937K, c.2899 T > C p.W967R, c.6284A > G p.D2095G, c.6644G > A p.R2215Q, c.7810G > A p.D2604N, c.11249G > C p.R3750P, c.1001C > T p.T334M, and c.3101A > G p.N1034S on RNA structures and PC1 protein structure dynamics utilizing computational tools. RNA structure analysis was done using short RNA snippets of 41 nucleotides with the variant position at the 21st nucleotide, ensuring 20 bases on both sides. The secondary structures of these RNA snippets were predicted using RNAstructure. Structural changes of the mutants compared to the wild type were analyzed using the MutaRNA webserver. Molecular dynamics (MD) simulation of PC1 wild-type and mutant protein regions were performed using GROMACS 2018 (GROMOS96 54a7 force field). Findings revealed that five variants including c.8809G > A (p.E2937K), c.11249G > C (p.R3750P), c.3101A > G (p.N1034S), c.6928G > A (p.G2310R), c.6644G > A (p.R2215Q) exhibited major alterations in RNA structures and thereby their interactions with other proteins or RNAs affecting protein structure dynamics. While certain variants have minimal impact on RNA conformations, their observed alterations in MD simulations indicate impact on protein structure dynamics highlighting the importance of evaluating the functional consequences of genetic variants by considering both RNA and protein levels. The study also emphasizes that each missense variant exerts a unique impact on RNA stability, and protein structure dynamics, potentially contributing to the heterogeneous clinical manifestations and progression observed in Autosomal Dominant Polycystic Kidney Disease (ADPKD) patients offering a novel perspective in this direction. Thus, the utility of studying the structure dynamics through computational tools can help in prioritizing the variants for their functional implications, understanding the molecular mechanisms underlying variability in ADPKD presentation and developing targeted therapeutic interventions.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13205-024-04057-9.

Motional properties of proteins govern recognition, catalysis, and regulation. The dynamics of tightly interacting residues can form intramolecular dynamic networks, dependencies fine-tuned by evolution to optimize a plethora of functional aspects. The constructive interaction of residues from different proteins to assemble intermolecular dynamic networks is a similarly likely case but has escaped thorough experimental assessment due to interfering association/dissociation dynamics. Here, we use fast-MAS solid-state 15N R1ρ NMR relaxation dispersion aided by molecular-dynamics simulations to mechanistically assess the hierarchy of individual µs timescale motions arising from a crystal-crystal contact, in the absence of translational motion. In contrast to the monomer, where particular mutations entail isolated perturbations, specific intermolecular interactions couple the motional properties between distant residues in the same protein. The mechanistic insights obtained from this conceptual work may improve our understanding on how intramolecular allostery can be tuned by intermolecular interactions via assembly of dynamic networks from previously isolated elements.

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a major transcription factor that functions in maintaining redox homeostasis in cells. It mediates the transcription of cytoprotective genes in response to environmental and endogenous stresses to prevent oxidative damage. Thus, Nrf2 plays a significant role in chemoprevention. However, aberrant activation of Nrf2 has been shown to protect cancer cells from apoptosis and contribute to their chemoresistance. The interaction between Nrf2 and CBP is critical for the gene transcription activation. CBP and its homologue p300 interact with two transactivation domains in Nrf2, Neh4, and Neh5 domains through their TAZ1 and TAZ2 domains. To date, the molecular basis of this crucial interaction is not known, hindering a more detailed understanding of the regulation of Nrf2. To close this knowledge gap, we have used a set of biophysical experiments to dissect the Nrf2-CBP/p300 interactions. Structural properties of Neh4 and Neh5 and their binding with the TAZ1 and TAZ2 domains of CBP/p300 were characterized. Our results show that the Neh4 and Neh5 domains of Nrf2 are intrinsically disordered, and they both can bind the TAZ1 and TAZ2 domains of CBP/p300 with micromolar affinities. The findings provide molecular insight into the regulation of Nrf2 by CBP/p300 through multi-domain interactions.

An increasingly common viewpoint is that protein dynamics datasets reside in a nonlinear subspace of low conformational energy. Ideal data analysis tools should therefore account for such nonlinear geometry. The Riemannian geometry setting can be suitable for a variety of reasons. First, it comes with a rich mathematical structure to account for a wide range of geometries that can be modeled after an energy landscape. Second, many standard data analysis tools developed for data in Euclidean space can be generalized to Riemannian manifolds. In the context of protein dynamics, a conceptual challenge comes from the lack of guidelines for constructing a smooth Riemannian structure based on an energy landscape. In addition, computational feasibility in computing geodesics and related mappings poses a major challenge. This work considers these challenges. The first part of the paper develops a local approximation technique for computing geodesics and related mappings on Riemannian manifolds in a computationally feasible manner. The second part constructs a smooth manifold and a Riemannian structure that is based on an energy landscape for protein conformations. The resulting Riemannian geometry is tested on several data analysis tasks relevant for protein dynamics data. In particular, the geodesics with given start- and end-points approximately recover corresponding molecular dynamics trajectories for proteins that undergo relatively ordered transitions with medium-sized deformations. The Riemannian protein geometry also gives physically realistic summary statistics and retrieves the underlying dimension even for large-sized deformations within seconds on a laptop.

In this work, the terahertz time-domain spectroscopy method analyzed solutions of bovine serum albumin (BSA) in two high concentrations (50 and 334 mg/mL) at three pH values (2.5, 6.5, 8.5) and the same solvents without protein, at 25°C. The spectra of dry BSA were also recorded. For the first time, a method for determining the complex dielectric permittivity of protein molecules in aqueous solutions, without the dielectric contribution of the aqueous phase, is proposed. It is shown that the dielectric permittivity of dissolved and dry BSA (lyophilized, in the native conformation) differ significantly in the terahertz frequency range. These differences are small near 70 cm-1, but they increase greatly with decreasing frequency. It was found that the dielectric losses of protein molecules in solution are close to the dielectric losses of the aqueous environment, which in this frequency range are determined by intermolecular relaxation processes of water. Since dielectric losses are directly related to molecular dynamics, this fact shows that the intramolecular dynamics of the protein completely adjusts to the intermolecular dynamics of the aqueous environment. It also indicates that the native conformation does not determine all the fundamental characteristics of a protein molecule, in particular, it does not determine the dynamics of the protein, which significantly depends on the water environment.

Trans-activation response (TAR) RNA-binding protein (TRBP) has emerged as a key player in the RNA interference pathway, wherein it binds to different pre-microRNAs (miRNAs) and small interfering RNAs (siRNAs), each varying in sequence and/or structure. We hypothesize that TRBP displays dynamic adaptability to accommodate heterogeneity in target RNA structures. Thus, it is crucial to ascertain the role of intrinsic and RNA-induced protein dynamics in RNA recognition and binding. We have previously elucidated the role of intrinsic and RNA-induced conformational exchange in the double-stranded RNA-binding domain 1 (dsRBD1) of TRBP in shape-dependent RNA recognition. The current study delves into the intrinsic and RNA-induced conformational dynamics of the TRBP-dsRBD2 and then compares it with the dsRBD1 study carried out previously. Remarkably, the two domains exhibit differential binding affinity to a 12-bp dsRNA owing to the presence of critical residues and structural plasticity. Furthermore, we report that dsRBD2 depicts constrained conformational plasticity when compared to dsRBD1. Although, in the presence of RNA, dsRBD2 undergoes induced conformational exchange within the designated RNA-binding regions and other residues, the amplitude of the motions remains modest when compared to those observed in dsRBD1. We propose a dynamics-driven model of the two tandem domains of TRBP, substantiating their contributions to the versatility of dsRNA recognition and binding.

Time-lapse imaging of the subcellular localization and dynamic behavior of proteins is critical to understand their biological functions in cells. With the advent of various methodologies and computational tools, the precise tracking and quantification of protein spatiotemporal dynamics have become feasible. Kymograph analysis, in particular, has been extensively adopted for the quantitative assessment of proteins, vesicles, and organelle movements. However, conventional kymograph analysis, which is based on a single linear trajectory, may not comprehensively capture the complexity of proteins that alter their course during intracellular transport and activity. In this chapter, we introduced an advanced protocol for whole-cell kymograph analysis that allows for three-dimensional (3D) tracking of protein dynamics. This method was validated through the analysis of tip-focused endocytosis and exocytosis processes in growing tobacco pollen tubes by employing both the advanced whole-cell and classical kymograph methods. In addition, we enhanced this method by integrating pseudo-colored kymographs that enables the direct visualization of changes in protein fluorescence intensity with fluorescence recovery after photobleaching to advance our understanding of protein localization and dynamics. This comprehensive method offers a novel insight into the intricate dynamics of protein activity within the cellular context.