Tumor-Suppressor p53TAD1-60 Forms a Fuzzy Complex with Metastasis-Associated S100A4: Structural Insights and Dynamics by an NMR/MD Approach

Assignment of the disordered, proline-rich N-terminal domain of the tumour suppressor p53 protein using 1HN and 1Hα-detected NMR measurements

Protein p53 is mostly known for playing a key role in tumour suppression, and mutations in the p53 gene are amongst the most frequent genomic events accompanying oncogenic transformation. Continuous research is conducted to target disordered proteins/protein regions for cancer therapy, for which atomic level information is also necessary. The disordered N-terminal part of p53 contains the transactivation and the proline-rich domains-which besides being abundant in proline residues-contains repetitive Pro-Ala motifs. NMR assignment of such repetitive, proline-rich regions is challenging due to the lack of amide protons in the 1HN-detected approaches, as well as due to the small chemical shift dispersion. In the present study we perform the full assignment of the p531-100 region by applying a combination of 1HN- and 1Hα-detected NMR experiments. We also show the increased information content when using real-time homo- and heteronuclear decoupled acquisition schemes. On the other hand, we highlight the presence of minor proline species, and using Pro-selective experiments we determine the corresponding cis or trans conformation. Secondary chemical shifts for (Cα-Cβ) atoms indicate the disordered nature of this region, with expected helical tendency for the TAD1 region. As the role of the proline-rich domain is yet not well understood our results can contribute to further successful investigations.

The influence of random-coil chemical shifts on the assessment of structural propensities in folded proteins and IDPs

In studying secondary structural propensities of proteins by nuclear magnetic resonance (NMR) spectroscopy, secondary chemical shifts (SCSs) serve as the primary atomic scale observables. For SCS calculation, the selection of an appropriate random coil chemical shift (RCCS) dataset is a crucial step, especially when investigating intrinsically disordered proteins (IDPs). The scientific literature is abundant in such datasets, however, the effect of choosing one over all the others in a concrete application has not yet been studied thoroughly and systematically. Hereby, we review the available RCCS prediction methods and to compare them, we conduct statistical inference by means of the nonparametric sum of ranking differences and comparison of ranks to random numbers (SRD-CRRN) method. We try to find the RCCS predictors best representing the general consensus regarding secondary structural propensities. The existence and the magnitude of resulting differences on secondary structure determination under varying sample conditions (temperature, pH) are demonstrated and discussed for globular proteins and especially IDPs.

Pure shift amide detection in conventional and TROSY-type experiments of 13C,15N-labeled proteins

Large coupling networks in uniformly ¹³C,¹⁵N-labeled biomolecules induce broad multiplets that even in flexible proteins are frequently not recognized as such. The reason is that given multiplets typically consist of a large number of individual resonances that result in a single broad line, in which individual components are no longer resolved. We here introduce a real-time pure shift acquisition scheme for the detection of amide protons which is based on ¹³C-BIRD^r,X. As a result the full homo- and heteronuclear coupling network can be suppressed at low power leading to real singlets at substantially improved resolution and uncompromised sensitivity. The method is tested on a small globular and an intrinsically disordered protein (IDP) where the average spectral resolution is increased by a factor of ~ 2 and higher. Equally important, the approach works without saturation of water magnetization for solvent suppression and exchanging amide protons are not affected by saturation transfer.

The Disordered EZH2 Loop: Atomic Level Characterization by 1HN- and 1Hα-Detected NMR Approaches, Interaction with the Long Noncoding HOTAIR RNA

The 96-residue-long loop of EZH2 is proposed to play a role in the interaction with long non-coding RNAs (lncRNAs) and to contribute to EZH2 recruitment to the chromatin. However, molecular details of RNA recognition have not been described so far. Cellular studies have suggested that phosphorylation of the Thr345 residue localized in this loop influences RNA binding; however, no mechanistic explanation has been offered. To address these issues, a systematic NMR study was performed. As the ¹H^N-detected NMR approach presents many challenges under physiological conditions, our earlier developed, as well as improved, ¹H^α-detected experiments were used. As a result of the successful resonance assignment, the obtained chemical shift values indicate the highly disordered nature of the EZH2 loop, with some nascent helical tendency in the Ser407–Ser412 region. Further investigations conducted on the phosphomimetic mutant EZH2^T345D showed that the mutation has only a local effect, and that the loop remains disordered. On the other hand, the mutation influences the cis/trans Pro346 equilibrium. Interactions of both the wild-type and the phosphomimetic mutant with the lncRNA HOTAIR₁₄₀ (1–140 nt) highlight that the Thr367–Ser375 region is affected. This segment does not resemble any of the previously reported RNA-binding motifs, therefore the identified binding region is unique. As no structural changes occur in the EZH2 loop upon RNA binding, we can consider the protein–RNA interaction as a “fuzzy” complex.

Modeling the Dynamics of Protein–Protein Interfaces, How and Why?

Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.

Studying the Structures of Relaxed and Fuzzy Interactions: The Diverse World of S100 Complexes

S100 proteins are small, dimeric, Ca²⁺-binding proteins of considerable interest due to their associations with cancer and rheumatic and neurodegenerative diseases. They control the functions of numerous proteins by forming protein–protein complexes with them. Several of these complexes were found to display “fuzzy” properties. Examining these highly flexible interactions, however, is a difficult task, especially from a structural biology point of view. Here, we summarize the available in vitro techniques that can be deployed to obtain structural information about these dynamic complexes. We also review the current state of knowledge about the structures of S100 complexes, focusing on their often-asymmetric nature.

When Order Meets Disorder: Modeling and Function of the Protein Interface in Fuzzy Complexes

The degree of proteins structural organization ranges from highly structured, compact folding to intrinsic disorder, where each degree of self-organization corresponds to specific functions: well-organized structural motifs in enzymes offer a proper environment for precisely positioned functional groups to participate in catalytic reactions; at the other end of the self-organization spectrum, intrinsically disordered proteins act as binding hubs via the formation of multiple, transient and often non-specific interactions. This review focusses on cases where structurally organized proteins or domains associate with highly disordered protein chains, leading to the formation of interfaces with varying degrees of fuzziness. We present a review of the computational methods developed to provide us with information on such fuzzy interfaces, and how they integrate experimental information. The discussion focusses on two specific cases, microtubules and homologous recombination nucleoprotein filaments, where a network of intrinsically disordered tails exerts regulatory function in recruiting partner macromolecules, proteins or DNA and tuning the atomic level association. Notably, we show how computational approaches such as molecular dynamics simulations can bring new knowledge to help bridging the gap between experimental analysis, that mostly concerns ensemble properties, and the behavior of individual disordered protein chains that contribute to regulation functions.

Intrinsically disordered proteins: modes of binding with emphasis on disordered domains

Our notions of protein function have long been determined by the protein structure-function paradigm. However, the idea that protein function is dictated by a prerequisite complementarity of shapes at the binding interface is becoming increasingly challenged. Interactions involving intrinsically disordered proteins (IDPs) have indicated a significant degree of disorder present in the bound state, ranging from static disorder to complete disorder, termed 'random fuzziness'. This review assesses the anatomy of an IDP and relates how its intrinsic properties permit promiscuity and allow for the various modes of interaction. Furthermore, a mechanistic overview of the types of disordered domains is detailed, while also relating to a recent example and the kinetic and thermodynamic principles governing its formation.

Selective 1 Hα NMR Methods Reveal Functionally Relevant Proline cis/trans Isomers in Intrinsically Disordered Proteins: Characterization of Minor Forms, Effects of Phosphorylation, and Occurrence in Proteome

It is important to identify proline cis/trans isomers that appear in several regulatory mechanisms of proteins, and to characterize minor species that are present due to the conformational heterogeneity in intrinsically disordered proteins (IDPs). To obtain residue level information on these mobile systems we introduce two ¹H^α‐detected, proline selective, real‐time homodecoupled NMR experiments and analyze the proline abundant transactivation domain of p53. The measurements are sensitive enough to identify minor conformers present in 4–15 % amounts; moreover, we show the consequences of CK2 phosphorylation on the cis/trans‐proline equilibrium. Using our results and available literature data we perform a statistical analysis on how the amino acid type effects the cis/trans‐proline distribution. The methods are applicable under physiological conditions, they can contribute to find key proline isomers in proteins, and statistical analysis results may help in amino acid sequence optimization for biotechnological purposes.

The diversity of molecular interactions involving intrinsically disordered proteins: A molecular modeling perspective

Intrinsically Disordered Proteins and Regions (IDPs/IDRs) are key components of a multitude of biological processes. Conformational malleability enables IDPs/IDRs to perform very specialized functions that cannot be accomplished by globular proteins. The functional role for most of these proteins is related to the recognition of other biomolecules to regulate biological processes or as a part of signaling pathways. Depending on the extent of disorder, the number of interacting sites and the type of partner, very different architectures for the resulting assemblies are possible. More recently, molecular condensates with liquid-like properties composed of multiple copies of IDPs and nucleic acids have been proven to regulate key processes in eukaryotic cells. The structural and kinetic details of disordered biomolecular complexes are difficult to unveil experimentally due to their inherent conformational heterogeneity. Computational approaches, alone or in combination with experimental data, have emerged as unavoidable tools to understand the functional mechanisms of this elusive type of assemblies. The level of description used, all-atom or coarse-grained, strongly depends on the size of the molecular systems and on the timescale of the investigated mechanism. In this mini-review, we describe the most relevant architectures found for molecular interactions involving IDPs/IDRs and the computational strategies applied for their investigation.

How to assess the structural dynamics of transcription factors by integrating sparse NMR and EPR constraints with molecular dynamics simulations

We review recent advances in modeling structural ensembles of transcription factors from nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopic data, integrated with molecular dynamics (MD) simulations. We focus on approaches that confirm computed conformational ensembles by sparse constraints obtained from magnetic resonance. This combination enables the deduction of functional and structural protein models even if nuclear Overhauser effects (NOEs) are too scarce for conventional structure determination. We highlight recent insights into the folding-upon-DNA binding transitions of intrinsically disordered transcription factors that could be assessed using such integrative approaches.

Power of Pure Shift HαCα Correlations: A Way to Characterize Biomolecules under Physiological Conditions

Intrinsically disordered proteins (IDPs) constitute an important class of biomolecules with high flexibility. Atomic-resolution studies for these molecules are essentially limited to NMR spectroscopy, which should be performed under physiological pH and temperature to populate relevant conformational ensembles. In this context, however, fundamental problems arise with established triple resonance NMR experiments: high solvent accessibility of IDPs promotes water exchange, which disfavors classical amide ¹H-detection, while ¹³C-detection suffers from significantly reduced sensitivity. A favorable alternative, the conventional detection of nonexchangeable ¹Hα, so far resulted in broad signals with insufficient resolution and sensitivity. To overcome this, we introduce here a selective Hα,Cα-correlating pure shift detection scheme, the selective Hα,Cα-HSQC (SHACA-HSQC), using extensive hetero- and homonuclear decoupling applicable to aqueous samples (≥90% H₂O) and tested on small molecules and proteins. SHACA-HSQC spectra acquired on IDPs provide uncompromised resolution and sensitivity (up to fivefold increased S/N compared to the standard ¹H,¹³C-HSQC), as shown for resonance distinction and unambiguous assignment on the disordered transactivation domain of the tumor suppressor p53, α-synuclein, and folded ubiquitin. The detection scheme can be implemented in any ¹Hα-detected triple resonance experiment and may also form the basis for the detection of isotope-labeled markers in biological studies or compound libraries.