Alternative Conformations Research Articles

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy TopographicImages.

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

Repeat-mediated recombination results in Complex DNA structure of the mitochondrial genome of Trachelospermum jasminoides

BackgroundTrachelospermum jasminoides has medicinal and ornamental value and is widely distributed in China. Although the chloroplast genome has been documented, the mitochondrial genome has not yet been studied.ResultsThe mitochondrial genome of T. jasminoides was assembled and functionally annotated using Illumina and nanopore reads. The mitochondrial genome comprises a master circular molecular structure of 605,764 bp and encodes 65 genes: 39 protein-coding genes, 23 transfer RNA (tRNA) genes and 3 ribosomal RNA genes. In addition to the single circular conformation, we found many alternative conformations of the T. jasminoides mitochondrial genome mediated by 42 repetitive sequences. Six repetitive sequences (DRS01–DRS06) were supported by nanopore long reads, polymerase chain reaction (PCR) amplifications, and Sanger sequencing of the PCR products. Eleven homologous fragments were identified by comparing the mitochondrial and chloroplast genome sequences, including three complete tRNA genes. Moreover, 531 edited RNA sites were identified in the protein-coding sequences based on RNA sequencing data, with nad4 having the highest number of sites (54).ConclusionTo our knowledge, this is the first description of the mitochondrial genome of T. jasminoides. Our results demonstrate the existence of multiple conformations. These findings lay a foundation for understanding the genetics and evolutionary dynamics of Apocynaceae.

Conditioned Protein Structure Prediction

Deep-learning-based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integration of expert knowledge on protein dynamics. We introduce AFEXplorer, a generic approach that tailors AlphaFold predictions to user-defined constraints in coarse coordinate spaces by optimizing embedding features. Its effectiveness in generating functional protein conformations in accordance with predefined conditions is demonstrated through comprehensive examples. AFEXplorer serves as a versatile platform for conditioned protein structure prediction, bridging the gap between automated models and domain-specific insights. Published by the American Physical Society 2024

Structural requirements for the specific binding of CRABP2 to cyclin D3.

Cellular retinoic acid binding protein 2 (CRABP2) transports retinoic acid from the cytoplasm to the nucleus where it then transfers its cargo to retinoic acid receptor-containing complexes leading to activation of gene transcription. We demonstrate using purified proteins that CRABP2 is also a cyclin D3-specific binding protein and that the CRABP2 cyclin D3 binding site and the proposed CRABP2 nuclear localization sequence overlap. Both sequences are within the helix-loop-helix motif that forms a lid to the retinoic acid binding pocket. Mutations within this sequence that block both cyclin D3 and retinoic acid binding promote formation of a CRABP2 structure in which the retinoic acid binding pocket is occupied by an alternative lid conformation. Structural and functional analysis of CRABP2 and cyclin D3 mutants combined with AlphaFold models of the ternary CDK4/6-cyclin D3-CRABP2 complex supports the identification of an α-helical protein binding site on the cyclin D3 C-terminal cyclin box fold.

Expanding Automated Multiconformer Ligand Modeling to Macrocycles and Fragments.

Small molecule ligands exhibit a diverse range of conformations in solution. Upon binding to a target protein, this conformational diversity is generally reduced. However, ligands can retain some degree of conformational flexibility even when bound to a receptor. In the Protein Data Bank (PDB), a small number of ligands have been modeled with distinct alternative conformations that are supported by X-ray crystallography density maps. However, the vast majority of structural models are fit to a single ligand conformation, potentially ignoring the underlying conformational heterogeneity present in the sample. We previously developed qFit-ligand to sample diverse ligand conformations and to select a parsimonious ensemble consistent with the density. While this approach indicated that many ligands populate alternative conformations, limitations in our sampling procedures often resulted in non-physical conformations and could not model complex ligands like macrocycles. Here, we introduce several improvements to qFit-ligand, including the use of routines within RDKit for stochastic conformational sampling. This new sampling method greatly enriches low energy conformations of small molecules and macrocycles. We further extended qFit-ligand to identify alternative conformations in PanDDA-modified density maps from high throughput X-ray fragment screening experiments. The new version of qFit-ligand improves fit to electron density and reduces torsional strain relative to deposited single conformer models and our previous version of qFit-ligand. These advances enhance the analysis of residual conformational heterogeneity present in ligand-bound structures, which can provide important insights for the rational design of therapeutic agents.

Solution NMR backbone assignment of the N-terminal tandem Zα1-Zα2 domains of Z-DNA binding protein 1.

The detection of nucleic acids that are present in atypical conformations is a crucial trigger of the innate immune response. Human Z-DNA binding protein 1 (ZBP1) is a pattern recognition receptor that harbors two Zα domains that recognize Z-DNA and Z-RNA. ZBP1 detects this alternate nucleic acid conformation as foreign, and upon stabilization of these substrates, it triggers activation of an immune response. Here, we present the backbone chemical shift assignment of a construct encompassing the Zα1 and Zα2 domains as well as the interconnecting linker of ZBP1. These assignments can be directly transferred to the isolated Zα1 and Zα2 domains, thereby demonstrating that these domains maintain virtually identical structures in the tandem context.

Solvent organization in the ultrahigh-resolution crystal structure of crambin at room temperature.

Ultrahigh-resolution structures provide unprecedented details about protein dynamics, hydrogen bonding and solvent networks. The reported 0.70 Å, room-temperature crystal structure of crambin is the highest-resolution ambient-temperature structure of a protein achieved to date. Sufficient data were collected to enable unrestrained refinement of the protein and associated solvent networks using SHELXL. Dynamic solvent networks resulting from alternative side-chain conformations and shifts in water positions are revealed, demonstrating that polypeptide flexibility and formation of clathrate-type structures at hydrophobic surfaces are the key features endowing crambin crystals with extraordinary diffraction power.

Structure prediction of alternative protein conformations

Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins with conformations most likely to exist in the PDB. However, almost all protein structures with multiple conformations represented in the PDB have been used while training these models. Therefore, it is unclear whether alternative protein conformations can be genuinely predicted using these networks, or if they are simply reproduced from memory. Here, we train a structure prediction network, Cfold, on a conformational split of the PDB to generate alternative conformations. Cfold enables efficient exploration of the conformational landscape of monomeric protein structures. Over 50% of experimentally known nonredundant alternative protein conformations evaluated here are predicted with high accuracy (TM-score > 0.8).

Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.

RNA tertiary structure and conformational dynamics revealed by BASH MaP.

The functional effects of an RNA can arise from complex three-dimensional folds known as tertiary structures. However, predicting the tertiary structure of an RNA and whether an RNA adopts distinct tertiary conformations remains challenging. To address this, we developed BASH MaP, a single-molecule dimethyl sulfate (DMS) footprinting method and DAGGER, a computational pipeline, to identify alternative tertiary structures adopted by different molecules of RNA. BASH MaP utilizes potassium borohydride to reveal the chemical accessibility of the N7 position of guanosine, a key mediator of tertiary structures. We used BASH MaP to identify diverse conformational states and dynamics of RNA G-quadruplexes, an important RNA tertiary motif, in vitro and in cells. BASH MaP and DAGGER analysis of the fluorogenic aptamer Spinach reveals that it adopts alternative tertiary conformations which determine its fluorescence states. BASH MaP thus provides an approach for structural analysis of RNA by revealing previously undetectable tertiary structures.

Abolished frameshifting for predicted structure-stabilizing SARS-CoV-2 mutants: implications to alternative conformations and their statistical structural analyses.

The SARS-CoV-2 frameshifting element (FSE) has been intensely studied and explored as a therapeutic target for coronavirus diseases, including COVID-19. Besides the intriguing virology, this small RNA is known to adopt many length-dependent conformations, as verified by multiple experimental and computational approaches. However, the role these alternative conformations play in the frameshifting mechanism and how to quantify this structural abundance has been an ongoing challenge. Here, we show by DMS and dual-luciferase functional assays that previously predicted FSE mutants (using the RAG graph theory approach) suppress structural transitions and abolish frameshifting. Furthermore, correlated mutation analysis of DMS data by three programs (DREEM, DRACO, and DANCE-MaP) reveals important differences in their estimation of specific RNA conformations, suggesting caution in the interpretation of such complex conformational landscapes. Overall, the abolished frameshifting in three different mutants confirms that all alternative conformations play a role in the pathways of ribosomal transition.

Plasticity of the selectivity filter is essential for permeation in lysosomal TPC2 channels

Two-pore channels are pathophysiologically important Na+- and Ca2+-permeable channels expressed in lysosomes and other acidic organelles. Unlike most other ion channels, their permeability is malleable and ligand-tuned such that when gated by the signaling lipid PI(3,5)P2, they are more Na+-selective than when gated by the Ca2+ mobilizing messenger nicotinic acid adenine dinucleotide phosphate. However, the structural basis that underlies such plasticity and single-channel behavior more generally remains poorly understood. A recent Cryo-electron microscopy (cryo-EM) structure of TPC2 bound to PI(3,5)P2 in a proposed open-channel conformation provided an opportunity to address this via molecular dynamics (MD) simulation. To our surprise, simulations designed to compute conductance through this structure revealed almost no Na+ permeation events even at very high transmembrane voltages. However further MD simulations identified a spontaneous transition to a dramatically different conformation of the selectivity filter that involved expansion and a flip in the orientation of two core asparagine residues. This alternative filter conformation was remarkably stable and allowed Na+ to flow through the channel leading to a conductance estimate that was in very good agreement with direct single-channel measurements. Furthermore, this conformation was more permeable for Na+ over Ca2+. Our results have important ramifications not just for understanding the control of ion selectivity in TPC2 channels but also more broadly in terms of how ion channels discriminate ions.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures.

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Sampling globally and locally correct RNA 3D structures using Ernwin, SPQR and experimental SAXS data.

The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray scattering can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (Ernwin) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit water. We apply the procedure on four different known pdb structures up to 159 nucleotides with promising results. Additionally, we predict an all-atom structure for the Plasmodium falceparum signal recognition particle ALU RNA based on SAXS data deposited in the SASBDB, which has an alternate conformation and better fit to the SAXS data than the previously published structure based on the same data but other modelling methods.

A dataset of alternately located segments in protein crystal structures

Protein Data Bank (PDB) files list the relative spatial location of atoms in a protein structure as the final output of the process of fitting and refining to experimentally determined electron density measurements. Where experimental evidence exists for multiple conformations, atoms are modelled in alternate locations. Programs reading PDB files commonly ignore these alternate conformations by default leaving users oblivious to the presence of alternate conformations in the structures they analyze. This has led to underappreciation of their prevalence, under characterisation of their features and limited the accessibility to this high-resolution data representing structural ensembles. We have trawled PDB files to extract structural features of residues with alternately located atoms. The output includes the distance between alternate conformations and identifies the location of these segments within the protein chain and in proximity of all other atoms within a defined radius. This dataset should be of use in efforts to predict multiple structures from a single sequence and support studies investigating protein flexibility and the association with protein function.

A systematic search for RNA structural switches across the human transcriptome

RNA structural switches are key regulators of gene expression in bacteria, but their characterization in Metazoa remains limited. Here, we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3ʹ untranslated region of the RORC (RAR-related orphan receptor C) transcript. In vivo dimethyl sulfate (DMS) mutational profiling with sequencing (DMS-MaPseq), coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identify trans factors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated messenger RNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.

Structural basis of water-mediated cis Watson-Crick/Hoogsteen base-pair formation in non-CpG methylation.

Non-CpG methylation is associated with several cellular processes, especially neuronal development and cancer, while its effect on DNA structure remains unclear. We have determined the crystal structures of DNA duplexes containing -CGCCG- regions as CCG repeat motifs that comprise a non-CpG site with or without cytosine methylation. Crystal structure analyses have revealed that the mC:G base-pair can simultaneously form two alternative conformations arising from non-CpG methylation, including a unique water-mediated cis Watson-Crick/Hoogsteen, (w)cWH, and Watson-Crick (WC) geometries, with partial occupancies of 0.1 and 0.9, respectively. NMR studies showed that an alternative conformation of methylated mC:G base-pair at non-CpG step exhibits characteristics of cWH with a syn-guanosine conformation in solution. DNA duplexes complexed with the DNA binding drug echinomycin result in increased occupancy of the (w)cWH geometry in the methylated base-pair (from 0.1 to 0.3). Our structural results demonstrated that cytosine methylation at a non-CpG step leads to an anti→syntransition of its complementary guanosine residue toward the (w)cWH geometry as a partial population of WC, in both drug-bound and naked mC:G base pairs. This particular geometry is specific to non-CpG methylated dinucleotide sites in B-form DNA. Overall, the current study provides new insights into DNA conformation during epigenetic regulation.

Flipons: The discovery of Z-DNA and soft-wired genomes

Flipons: The discovery of Z-DNA and soft-wired genomes Alan Herbert, Founder and President of InsideOutBio, discusses alternative DNA conformations and understanding of their biological functions. One of the great things about science is that every time you think you really understand things and can’t imagine how they could work differently, life surprises you. The biggest mistake you can make is believing that nature works according to your best design. Take physics, for example – first, there were the four humors, then atoms, then protons and electrons, then even more particles, and then quarks – who knows what’s next? Similarly, biology is undergoing its own progression, and we are finding that there is more to life than just Watson and Crick’s DNA.

Assessing pH-Dependent Conformational Changes in the Fusion Peptide Proximal Region of the SARS-CoV-2 Spike Glycoprotein.

One of the entry mechanisms of the SARS-CoV-2 coronavirus into host cells involves endosomal acidification. It has been proposed that under acidic conditions, the fusion peptide proximal region (FPPR) of the SARS-CoV-2 spike glycoprotein acts as a pH-dependent switch, modulating immune response accessibility by influencing the positioning of the receptor binding domain (RBD). This would provide indirect coupling of RBD opening to the environmental pH. Here, we explored this possible pH-dependent conformational equilibrium of the FPPR within the SARS-CoV-2 spike glycoprotein. We analyzed hundreds of experimentally determined spike structures from the Protein Data Bank and carried out pH-replica exchange molecular dynamics to explore the extent to which the FPPR conformation depends on pH and the positioning of the RBD. A meta-analysis of experimental structures identified alternate conformations of the FPPR among structures in which this flexible regions was resolved. However, the results did not support a correlation between the FPPR conformation and either RBD position or the reported pH of the cryo-EM experiment. We calculated pKa values for titratable side chains in the FPPR region using PDB structures, but these pKa values showed large differences between alternate PDB structures that otherwise adopt the same FPPR conformation type. This hampers the comparison of pKa values in different FPPR conformations to rationalize a pH-dependent conformational change. We supplemented these PDB-based analyses with all-atom simulations and used constant-pH replica exchange molecular dynamics to estimate pKa values in the context of flexibility and explicit water. The resulting titration curves show good reproducibility between simulations, but they also suggest that the titration curves of the different FPPR conformations are the same within the error bars. In summary, we were unable to find evidence supporting the previously published hypothesis of an FPPR pH-dependent equilibrium: neither from existing experimental data nor from constant-pH MD simulations. The study underscores the complexity of the spike system and opens avenues for further exploration into the interplay between pH and SARS-CoV-2 viral entry mechanisms.

De Novo Hybrid Assembly Unveils Multi-Chromosomal Mitochondrial Genomes in Ludwigia Species, Highlighting Genomic Recombination, Gene Transfer, and RNA Editing Events.

Biological invasions have been identified as the fifth cause of biodiversity loss, and their subsequent dispersal represents a major ecological challenge. The aquatic invasive species Ludwigia grandiflora subsp. hexapetala (Lgh) and Ludwigia peploides subsp. montevidensis (Lpm) are largely distributed in aquatic environments in North America and in Europe. However, they also present worrying terrestrial forms that are able to colonize wet meadows. To comprehend the mechanisms of the terrestrial adaptation of Lgh and Lpm, it is necessary to develop their genomic resources, which are currently poorly documented. We performed de novo assembly of the mitogenomes of Lgh and Lpm through hybrid assemblies, combining short reads (SR) and/or long reads (LR) before annotating both mitogenomes. We successfully assembled the mitogenomes of Lgh and Lpm into two circular molecules each, resulting in a combined total length of 711,578 bp and 722,518 bp, respectively. Notably, both the Lgh and Lpm molecules contained plastome-origin sequences, comprising 7.8% of the mitochondrial genome length. Additionally, we identified recombinations that were mediated by large repeats, suggesting the presence of multiple alternative conformations. In conclusion, our study presents the first high-quality mitogenomes of Lpm and Lgh, which are the only ones in the Myrtales order found as two circular molecules.