Side chain electrostatic interactions and pH-dependent expansion of the intrinsically disordered, highly acidic carboxyl-terminus of γ-tubulin

MEDOC: A Fast, Scalable, and Mathematically Exact Algorithm for the Site-Specific Prediction of the Protonation Degree in Large Disordered Proteins

Intrinsically disordered regions are found in most eukaryotic proteins and are enriched with positively and negatively charged residues. While it is often convenient to assume that these residues follow their model-compound pKa values, recent work has shown that local charge effects (charge regulation) can upshift or downshift side chain pKa values with major consequences for molecular function. Despite this, charge regulation is rarely considered when investigating disordered regions. The number of potential charge microstates that can be populated through acid/base regulation of a given number of ionizable residues in a sequence, N, scales as ∼2N. This exponential scaling makes the assessment of the full charge landscape of most proteins computationally intractable. To address this problem, we developed "multisite extent of deprotonation originating from context" (MEDOC) to determine the degree of protonation of a protein based on the local sequence context of each ionizable residue. We show that we can drastically reduce the number of parameters necessary to determine the full, analytical Boltzmann partition function of the charge landscape at both global and site-specific levels. Our algorithm applies the structure of the q-canonical ensemble, combined with novel strategies to rapidly obtain the minimal set of parameters, thereby circumventing the combinatorial explosion of the number of charge microstates even for proteins containing a large number of ionizable amino acids. We apply MEDOC to several sequences, including a global analysis of the distribution of pKa values across the entire DisProt database. Our results show differences in the distribution of predicted pKa values for different amino acids and good agreement with NMR-measured pKa values in proteins.

Dissecting the pH Sensitivity of Kinesin-Driven Transport

Kinesin-1 is a crucial motor protein that drives the microtubule-based movement of organelles, vital for cellular function and health. Mostly studied at pH 6.9, it moves at approximately 800 nm/s, covers about 1 μm before detaching, and hydrolyzes one ATP per 8 nm step. Given that cellular pH is dynamic and alterations in pH have significant implications for disease, understanding how kinesin-1 functions across different pH levels is crucial. To explore this, we executed single-molecule motility assays paired with precise optical trapping techniques over a pH range of 5.5-9.8. Our results show a consistent positive relationship between increasing pH and the enhanced detachment (off rate) and speed of kinesin-1. Measurements of the nucleotide-dependent off rate show that kinesin-1 exhibits the highest rate of ATPase activity at alkaline pH, while it demonstrates the optimal number of ATP turnover and cargo translocation efficiency at the acidic pH. Physiological pH of 6.9 optimally balances the biophysical activity of kinesin-1, potentially allowing it to function effectively across a range of pH levels. These insights emphasize the crucial role of pH homeostasis in cellular function, highlighting its importance for the precise regulation of motor proteins and efficient intracellular transport.

MEDOC: A fast, scalable and mathematically exact algorithm for the site-specific prediction of the protonation degree in large disordered proteins

Intrinsically disordered regions are found in most eukaryotic proteins and are enriched in positively and negatively charged residues. While it is often convenient to assume these residues follow their model-compound pKa values, recent work has shown that local charge effects (charge regulation) can upshift or downshift sidechain pKa values with major consequences for molecular function. Despite this, charge regulation is rarely considered when investigating disordered regions. The number of potential charge microstates that can be populated through acid/base regulation of a given number of ionizable residues in a sequence, N , scales as~ 2 N . This exponential scaling makes the assessment of the full charge landscape of most proteins computationally intractable. To address this problem, we developed MEDOC (Multisite Extent of Deprotonation Originating from Context) to determine the degree of protonation of a protein based on the local sequence context of each ionizable residue. We show that we can drastically reduce the number of parameters necessary to determine the full, analytic, Boltzmann partition function of the charge landscape at both global and site-specific levels. Our algorithm applies the structure of the q-canonical ensemble, combined with novel strategies to rapidly obtain the minimal set of parameters, thereby circumventing the combinatorial explosion of the number of charge microstates even for proteins containing a large number of ionizable amino acids. We apply MEDOC to several sequences, including a global analysis of the distribution of pKa values across the entire DisProt database. Our results show differences in the distribution of predicted pKa values for different amino acids, in agreement with NMR-measured distributions in proteins.

Gadd45β is critical for regulation of type I interferon signaling by facilitating G3BP-mediated stress granule formation

Stress granules (SGs) constitute a signaling hub that plays a critical role in type I interferon responses. Here, we report that growth arrest and DNA damage-inducible beta (Gadd45β) act as a positive regulator of SG-mediated interferon signaling by targeting G3BP upon RNA virus infection. Gadd45β deficiency markedly impairs SG formation and SG-mediated activation of interferon signaling in vitro. Gadd45β knockout mice are highly susceptible to RNA virus infection, and their ability to produce interferon and cytokines is severely impaired. Specifically, Gadd45β interacts with the RNA-binding domain of G3BP, leading to conformational expansion of G3BP1 via dissolution of its autoinhibitory electrostatic intramolecular interaction. The acidic loop 1- and RNA-binding properties of Gadd45β markedly increase the conformational expansion and RNA-binding affinity of the G3BP1-Gadd45β complex, thereby promoting assembly of SGs. These findings suggest a role for Gadd45β as a component and critical regulator of G3BP1-mediated SG formation, which facilitates RLR-mediated interferon signaling.

Driving forces of the complex formation between highly charged disordered proteins

Highly disordered complexes between oppositely charged intrinsically disordered proteins present a new paradigm of biomolecular interactions. Here, we investigate the driving forces of such interactions for the example of the highly positively charged linker histone H1 and its highly negatively charged chaperone, prothymosin α (ProTα). Temperature-dependent single-molecule Förster resonance energy transfer (FRET) experiments and isothermal titration calorimetry reveal ProTα-H1 binding to be enthalpically unfavorable, and salt-dependent affinity measurements suggest counterion release entropy to be an important thermodynamic driving force. Using single-molecule FRET, we also identify ternary complexes between ProTα and H1 in addition to the heterodimer at equilibrium and show how they contribute to the thermodynamics observed in ensemble experiments. Finally, we explain the observed thermodynamics quantitatively with a mean-field polyelectrolyte theory that treats counterion release explicitly. ProTα-H1 complex formation resembles the interactions between synthetic polyelectrolytes, and the underlying principles are likely to be of broad relevance for interactions between charged biomolecules in general.

Influence of gelatin type on physicochemical properties of electrospun nanofibers

This study explores the fabrication of nanofibers using different types of gelatins, including bovine, porcine, and fish gelatins. The gelatins exhibited distinct molecular weights and apparent viscosity values, leading to different entanglement behavior and nanofiber production. The electrospinning technique produced nanofibers with diameters from 47 to 274 nm. The electrospinning process induced conformational changes, reducing the overall crystallinity of the gelatin samples. However, porcine gelatin nanofibers exhibited enhanced molecular ordering. These findings highlight the potential of different gelatin types to produce nanofibers with distinct physicochemical properties. Overall, this study sheds light on the relationship between gelatin properties, electrospinning process conditions, and the resulting nanofiber characteristics, providing insights for tailored applications in various fields.

PKAD-2: New entries and expansion of functionalities of the database of experimentally measured pKa's of proteins

Almost all biological reactions are pH dependent and understanding the origin of pH dependence requires knowledge of the pKa's of ionizable groups. Here we report a new edition of PKAD, the PKAD-2, which is a database of experimentally measured pKa's of proteins, both wild type and mutant proteins. The new additions include 117 wild type and 54 mutant pKa values, resulting in total 1742 experimentally measured pKa's. The new edition of PKAD-2 includes 8 new wild type and 12 new mutant proteins, resulting in total of 220 proteins. This new edition incorporates a visual 3D image of the highlighted residue of interest within the corresponding protein or protein complex. Hydrogen bonds were identified, counted, and implemented as a search feature. Other new search features include the number of neighboring residues <4A from the heaviest atom of the side chain of a given amino acid. Here, we present PKAD-2 with the intention to continuously incorporate novel features and current data with the goal to be used as benchmark for computational methods.

pH-Dependent Compaction of the Intrinsically Disordered Poly-E Motif in Titin

The conformational sensitivity of intrinsically disordered proteins to shifts in pH due to their high degree of charged residues has been recognized for well over a decade. However, the role of the non-ionizable residues in this pH sensitivity remains poorly understood. Our lab has been investigating the pH sensitivity of the poly-E motifs of the PEVK region of the muscle protein titin, which provides an ideal model system to explore this question. Using a series of 15-amino acid peptides derived from one of the poly-E motif sequences, we have investigated the role of side-chain chemistry in the conformational flexibility of this region. Our results demonstrate that aromatic side chains and proline content are the two variables that most influence pH sensitivity. The introduction of aromatic side chains resulted in a more collapsed structure, even at pH 7, while the removal of prolines resulted in a higher degree of pH sensitivity. These results highlight the importance of considering the impact of non-ionizable residues on IDP function, especially when considering the impact of pH on conformational flexibility.

DispHred: A Server to Predict pH-Dependent Order-Disorder Transitions in Intrinsically Disordered Proteins

The natively unfolded nature of intrinsically disordered proteins (IDPs) relies on several physicochemical principles, of which the balance between a low sequence hydrophobicity and a high net charge appears to be critical. Under this premise, it is well-known that disordered proteins populate a defined region of the charge–hydropathy (C–H) space and that a linear boundary condition is sufficient to distinguish between folded and disordered proteins, an approach widely applied for the prediction of protein disorder. Nevertheless, it is evident that the C–H relation of a protein is not unalterable but can be modulated by factors extrinsic to its sequence. Here, we applied a C–H-based analysis to develop a computational approach that evaluates sequence disorder as a function of pH, assuming that both protein net charge and hydrophobicity are dependent on pH solution. On that basis, we developed DispHred, the first pH-dependent predictor of protein disorder. Despite its simplicity, DispHred displays very high accuracy in identifying pH-induced order/disorder protein transitions. DispHred might be useful for diverse applications, from the analysis of conditionally disordered segments to the synthetic design of disorder tags for biotechnological applications. Importantly, since many disorder predictors use hydrophobicity as an input, the here developed framework can be implemented in other state-of-the-art algorithms.

Determination of pKa Values in Intrinsically Disordered Proteins

Electrostatic interactions in intrinsically disordered proteins (IDPs) and regions (IDRs) can strongly influence their conformational sampling. Side chain pK_a values provide information on the electrostatic interaction energies of individual side chains and are required to accurately determine the molecular net charge and charge distribution. Nuclear magnetic resonance (NMR) spectroscopy is the premier method for measuring side chain pK_a values as it can detect the ionization states of individual side chains in an IDP or IDR simultaneously. In this section, we outline the use of NMR spectroscopy to determine side chain-specific pK_as for each of the nine aspartates, five glutamates, and one histidine contained in a highly acidic 35-residue intrinsically disordered peptide.