Unraveling protein's structural dynamics: from configurational dynamics to ensemble switching guides functional mesoscale assemblies

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning

To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combination with various biochemical and structural features of protein residues to discriminate neutral vs. deleterious mutations. However, the power of these methods is often limited because they either assume known protein structures or treat residues independently without fully considering their interactions. To address the above limitations, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 20 network centrality scores to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively (via machine learning). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods (and optionally predictive scores from other methods) via machine learning to predict hotspot sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design.

DNA controls the dimerization of the human FoxP1 forkhead domain

Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences and gating access to genes. Even when the binding of TFs and their cofactors to DNA is reversible, indicating a reversible control of gene expression, there is little knowledge about the molecular effect DNA has on TFs. Using single-molecule multiparameter fluorescence spectroscopy, molecular dynamics simulations, and biochemical assays, we find that the monomeric form of the forkhead (FKH) domain of the human FoxP1 behaves as a disordered protein and increases its folded population when it dimerizes. Notably, DNA binding promotes a disordered FKH dimer bound to DNA, negatively controlling the stability of the dimeric FoxP1:DNA complex. The DNA-mediated reversible regulation on FKH dimers suggests that FoxP1-dependent gene suppression is unstable, and it must require the presence of other dimerization domains or cofactors to revert the negative impact exerted by the DNA.

Gauging Dynamics-driven Allostery Using a New Computational Tool: A CAP Case Study

In this study, we utilize Protein Residue Networks (PRNs), constructed using Local Spatial Pattern (LSP) alignment, to explore the dynamic behavior of Catabolite Activator Protein (CAP) upon the sequential binding of cAMP. We employed the Degree Centrality of these PRNs to investigate protein dynamics on a sub-nanosecond time scale, hypothesizing that it would reflect changes in CAP's entropy related to its thermal motions. We show that the binding of the first cAMP led to an increase in stability in the Cyclic-Nucleotide Binding Domain A (CNBD-A) and destabilization in CNBD-B, agreeing with previous reports explaining the negative cooperativity of cAMP binding in terms of an entropy-driven allostery. LSP-based PRNs also allow for the study of Betweenness Centrality, another graph-theoretical characteristic of PRNs, providing insights into global residue connectivity within CAP. Using this approach, we were able to correctly identify amino acids that were shown to be critical in mediating allosteric interactions in CAP. The agreement between our studies and previous experimental reports validates our method, particularly with respect to the reliability of Degree Centrality as a proxy for entropy related to protein thermal dynamics. Because LSP-based PRNs can be easily extended to include dynamics of small organic molecules, polynucleotides, or other allosteric proteins, the methods presented here mark a significant advancement in the field, positioning them as vital tools for a fast, cost-effective, and accurate analysis of entropy-driven allostery and identification of allosteric hotspots.

DNA facilitates heterodimerization between human transcription factors FoxP1 and FoxP2 by increasing their conformational flexibility

Transcription factors regulate gene expression by binding to DNA. They have disordered regions and specific DNA-binding domains. Binding to DNA causes structural changes, including folding and interactions with other molecules. The FoxP subfamily of transcription factors in humans is unique because they can form heterotypic interactions without DNA. However, it is unclear how they form heterodimers and how DNA binding affects their function. We used computational and experimental methods to study the structural changes in FoxP1's DNA-binding domain when it forms a heterodimer with FoxP2. We found that FoxP1 has complex and diverse conformational dynamics, transitioning between compact and extended states. Surprisingly, DNA binding increases the flexibility of FoxP1, contrary to the typical folding-upon-binding mechanism. In addition, we observed a 3-fold increase in the rate of heterodimerization after FoxP1 binds to DNA. These findings emphasize the importance of structural flexibility in promoting heterodimerization to form transcriptional complexes.

Domain tethering impacts dimerization and DNA-mediated allostery in the human transcription factor FoxP1

Transcription factors are multidomain proteins with specific DNA binding and regulatory domains. In the human FoxP subfamily (FoxP1, FoxP2, FoxP3, and FoxP4) of transcription factors, a 90 residue-long disordered region links a Leucine Zipper (ZIP)-known to form coiled-coil dimers-and a Forkhead (FKH) domain-known to form domain swapping dimers. We used replica exchange discrete molecular dynamics simulations, single-molecule fluorescence experiments, and other biophysical tools to understand how domain tethering in FoxP1 impacts dimerization at ZIP and FKH domains and how DNA binding allosterically regulates their dimerization. We found that domain tethering promotes FoxP1 dimerization but inhibits a FKH domain-swapped structure. Furthermore, our findings indicate that the linker mediates the mutual organization and dynamics of ZIP and FKH domains, forming closed and open states with and without interdomain contacts, thus highlighting the role of the linkers in multidomain proteins. Finally, we found that DNA allosterically promotes structural changes that decrease the dimerization propensity of FoxP1. We postulate that, upon DNA binding, the interdomain linker plays a crucial role in the gene regulatory function of FoxP1.

Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1-2 Proteins

The structural flexibility of proteins is crucial for their functions. Many experimental and computational approaches can probe protein dynamics across a range of time and length-scales. Integrative approaches synthesize the complementary outputs of these techniques and provide a comprehensive view of the dynamic conformational space of proteins, including the functionally relevant limiting conformational states and transition pathways between them. Here, we introduce an integrative paradigm to model the conformational states of multidomain proteins. As a model system, we use the first two tandem PDZ domains of postsynaptic density protein 95. First, we utilize available sequence information collected from genomic databases to identify potential amino acid interactions in the PDZ1-2 tandem that underlie modeling of the functionally relevant conformations maintained through evolution. This was accomplished through combination of coarse-grained structural modeling with outputs from direct coupling analysis measuring amino acid coevolution, a hybrid approach called SBM+DCA. We recapitulated five distinct, experimentally derived PDZ1-2 tandem conformations. In addition, SBM+DCA unveiled an unidentified, twisted conformation of the PDZ1-2 tandem. Finally, we implemented an integrative framework for the design of single-molecule Förster resonance energy transfer (smFRET) experiments incorporating the outputs of SBM+DCA with simulated FRET observables. This resulting FRET network is designed to mutually resolve the predicted limiting state conformations through global analysis. Using simulated FRET observables, we demonstrate that structural modeling with the newly designed FRET network is expected to outperform a previously used empirical FRET network at resolving all states simultaneously. Integrative approaches to experimental design have the potential to provide a new level of detail in characterizing the evolutionarily conserved conformational landscapes of proteins, and thus new insights into functional relevance of protein dynamics in biological function.

Metamorphic proteins under a computational microscope: Lessons from a fold-switching RfaH protein

Metamorphic proteins constitute unexpected paradigms of the protein folding problem, as their sequences encode two alternative folds, which reversibly interconvert within biologically relevant timescales to trigger different cellular responses. Once considered a rare aberration, metamorphism may be common among proteins that must respond to rapidly changing environments, exemplified by NusG-like proteins, the only transcription factors present in every domain of life. RfaH, a specialized paralog of bacterial NusG, undergoes an all-α to all-β domain switch to activate expression of virulence and conjugation genes in many animal and plant pathogens and is the quintessential example of a metamorphic protein. The dramatic nature of RfaH structural transformation and the richness of its evolutionary history makes for an excellent model for studying how metamorphic proteins switch folds. Here, we summarize the structural and functional evidence that sparked the discovery of RfaH as a metamorphic protein, the experimental and computational approaches that enabled the description of the molecular mechanism and refolding pathways of its structural interconversion, and the ongoing efforts to find signatures and general properties to ultimately describe the protein metamorphome.

Fuzzy supertertiary interactions within PSD-95 enable ligand binding

The scaffold protein PSD-95 links postsynaptic receptors to sites of presynaptic neurotransmitter release. Flexible linkers between folded domains in PSD-95 enable a dynamic supertertiary structure. Interdomain interactions within the PSG supramodule, formed by PDZ3, SH3, and Guanylate Kinase domains, regulate PSD-95 activity. Here we combined discrete molecular dynamics and single molecule Förster resonance energy transfer (FRET) to characterize the PSG supramodule, with time resolution spanning picoseconds to seconds. We used a FRET network to measure distances in full-length PSD-95 and model the conformational ensemble. We found that PDZ3 samples two conformational basins, which we confirmed with disulfide mapping. To understand effects on activity, we measured binding of the synaptic adhesion protein neuroligin. We found that PSD-95 bound neuroligin well at physiological pH while truncated PDZ3 bound poorly. Our hybrid structural models reveal how the supertertiary context of PDZ3 enables recognition of this critical synaptic ligand.

An overview on the exploring the interaction of inorganic nanoparticles with microtubules for the advancement of cancer therapeutics

Targeting microtubules (MTs), dynamic and stable proteins in cells, by different ligands have been reported to be a potential strategy to combat cancer cells. Inorganic nanoparticles (NPs) have been widely used as anticancer, antibacterial, and free radical scavenging agents, where the come in contact with biological macromolecules. The interaction between the NPs and biological macromolecules like MTs frequently occurs through different mechanisms. A prerequisite for a detailed exploration of MT structures and functions for biomedical applications like cancer therapy is to investigate profoundly the mechanisms involved in MT-NP interactions, for which the full explanation and characterization of the parameters that are responsible for the formation of a NP-protein complex are crucial. Therefore, in view of the fact that the goal of the rational NP-based future drug design and new therapies is to rely on the information of the structural details and protein-NPs binding mechanisms to manipulate the process of developing new potential drugs, a comprehensive investigation of the essence of the molecular recognition/interaction is also of considerable importance. In the present review, first, the microtubule (MT) structure and its binding sites upon interaction with MT stabilizing agents (MSAs) and MT destabilizing agents (MDAs) are introduced and rationalized. Next, MT targeting in cancer therapy and interaction of NPs with MTs are discussed. Furthermore, interaction of NPs with proteins and the manipulation of protein corona (PC), experimental techniques, and direct interaction of NPs with MTs, are discussed, and finally the challenges and future perspective of the field are introduced. We envision this review can provide useful information on the manipulation of the MT lattice for the progress of cancer nanomedicine.

Effects of 4-vinylcyclohexene diepoxide on the cell cycle, apoptosis, and steroid hormone secretion of goat ovarian granulosa cells

4-Vinylcyclohexene diepoxide (VCD) is a potentially hazardous industrial chemical that may enter a goat's body in various ways during industrial breeding. Ovarian granulosa cells (GCs) play a critical role in supporting follicle development and hormone synthesis. However, there are few studies on the effect of VCD on goat ovarian GCs. In this study, goat ovarian GCs were isolated and treated with VCD. The results showed that treatment with VCD increased the proportion of S phase and G2/M cells, but decreased the proportion of G1 phase. VCD treatment significantly inhibited the expression of cyclin A and cyclin-dependent kinase 2 (CDK2). But the expression levels of p21 and p27 were increased. VCD could induce an apparent increase in the proportion of apoptosis and the level of cleaved caspase 3. Treatment with VCD significantly reduced the progesterone and estrogen concentration in the medium in which goat ovarian GCs were cultured. Correspondingly, the expression level of steroidogenic acute regulatory protein (STAR) was significantly downregulated. Treatment with 0.25 and 0.5 mM VCD, the protein expression level of insulin-like growth factor 1 receptor (IGF1R) and Akt were significantly decreased. Moreover, treatment with 0.25 mM VCD significantly inhibited the phosphorylation of Akt. In conclusion, VCD exposure had cytotoxic effects such as decreased cell viability, disordered cell cycle, increased apoptosis, and interference with steroid hormone synthesis on goat GCs. These cytotoxic effects of VCD on goat GCs may be due to the downregulation of IGF1R and the inhibition of IGF1R/Akt signaling pathway.

Multi-parameter photon-by-photon hidden Markov modeling

Single molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modeling (H²MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on the sub-millisecond timescale. However, dye photophysical transitions intertwined with FRET dynamics may cause artifacts. Here, we introduce multi-parameter H²MM (mpH²MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH²MM to decouple FRET dynamics caused by conformational changes from photophysical transitions in confocal-based smFRET measurements of a DNA hairpin, the maltose binding protein, MalE, and the type-III secretion system effector, YopO, from Yersinia species, all exhibiting conformational dynamics ranging from the sub-second to microsecond timescales. Overall, we show that using mpH²MM facilitates the identification and quantification of biomolecular sub-populations and their origin.

In this work, the authors demonstrate the application of multi-parameter photon-by-photon hidden Markov modeling (mpH²MM) on alternating laser excitation (ALEX)-based smFRET measurements. The utility of mpH²MM in identifying and quantifying dynamic biomolecular sub-populations is demonstrated in three different systems.

Site-specific labeling and functional efficiencies of human fibroblast growth Factor-1 with a range of fluorescent dyes in the flexible N-Terminal region and a rigid β-turn region

Human fibroblast growth factor 1 (hFGF1) binding to its receptor and heparin play critical roles in cell proliferation, angiogenesis and wound healing but is also implicated in cancer. Fluorescence imaging is a powerful approach to study such protein interactions, but it is not always obvious if the site chosen will be efficiently labeled, often relying on trial-and-error. To provide a more systematic approach towards an efficient site-specific labeling strategy, we labeled two structurally distinct regions of the protein - the flexible N-terminus and a rigid loop. Several dyes were chosen to cover the visible region and to investigate how the structure of the dye affects the labeling efficiency. Flexibility in either the protein labeling site or the dye structure was found to result in high labeling efficiency, but flexibility in both resulted in a significant decrease in labeling efficiency. Conversely, too much rigidity in both can result in dye-protein interactions that can aggregate the protein. Importantly, site-specifically labeling hFGF1 in these regions maintained biological activity. These results could be applicable to other proteins by considering the flexibility of both the protein labeling site and the dye structure.

Mechanism of activation and the rewired network: New drug design concepts

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.

Human FoxP Transcription Factors as Tractable Models of the Evolution and Functional Outcomes of Three-Dimensional Domain Swapping

The association of two or more proteins to adopt a quaternary complex is one of the most widespread mechanisms by which protein function is modulated. In this scenario, three-dimensional domain swapping (3D-DS) constitutes one plausible pathway for the evolution of protein oligomerization that exploits readily available intramolecular contacts to be established in an intermolecular fashion. However, analysis of the oligomerization kinetics and thermodynamics of most extant 3D-DS proteins shows its dependence on protein unfolding, obscuring the elucidation of the emergence of 3D-DS during evolution, its occurrence under physiological conditions, and its biological relevance. Here, we describe the human FoxP subfamily of transcription factors as a feasible model to study the evolution of 3D-DS, due to their significantly faster dissociation and dimerization kinetics and lower dissociation constants in comparison to most 3D-DS models. Through the biophysical and functional characterization of FoxP proteins, relevant structural aspects highlighting the evolutionary adaptations of these proteins to enable efficient 3D-DS have been ascertained. Most biophysical studies on FoxP suggest that the dynamics of the polypeptide chain are crucial to decrease the energy barrier of 3D-DS, enabling its fast oligomerization under physiological conditions. Moreover, comparison of biophysical parameters between human FoxP proteins in the context of their minute sequence differences suggests differential evolutionary strategies to favor homoassociation and presages the possibility of heteroassociations, with direct impacts in their gene regulation function.

Out-of-Equilibrium Biophysical Chemistry: The Case for Multidimensional, Integrated Single-Molecule Approaches

Out-of-equilibrium processes are ubiquitous across living organisms and all structural hierarchies of life. At the molecular scale, out-of-equilibrium processes (for example, enzyme catalysis, gene regulation, and motor protein functions) cause biological macromolecules to sample an ensemble of conformations over a wide range of time scales. Quantifying and conceptualizing the structure–dynamics to function relationship is challenging because continuously evolving multidimensional energy landscapes are necessary to describe nonequilibrium biological processes in biological macromolecules. In this perspective, we explore the challenges associated with state-of-the-art experimental techniques to understanding biological macromolecular function. We argue that it is time to revisit how we probe and model functional out-of-equilibrium biomolecular dynamics. We suggest that developing integrated single-molecule multiparametric force–fluorescence instruments and using advanced molecular dynamics simulations to study out-of-equilibrium biomolecules will provide a path towards understanding the principles of and mechanisms behind the structure–dynamics to function paradigm in biological macromolecules.