Search

Ion channels are proteins that play a significant role in physiological processes, including neuronal excitability and signal transduction. However, the precise mechanisms by which these proteins facilitate ion diffusion through cell membranes are not well understood. This is because experimental techniques to characterize ion channel activity operate on a time scale too large to understand the role of the various protein conformations on diffusion. Meanwhile, computational approaches operate on a time scale too short to rationalize the observed behavior at the microscopic scale. In this paper, we present a continuous-time random walk model that aims to bridge the scales between the atomistic models of ion channels and the experimental measurement of their conductance. We show how diffusion slows down in complex systems by using 3D lattices that map out the pore geometry of two channels: Nav1.7 and gramicidin. We also introduce spatial and dynamic site disorder to account for system heterogeneity beyond the mean field approximation. Computed diffusion coefficients show that an increase in spatial disorder slows down diffusion kinetics, while dynamic disorder has the opposite effect. Our results imply that microscopic or phenomenological models based on the potential of mean force data overlook the functional importance of protein dynamics on ion diffusion through channels. [ABSTRACT FROM AUTHOR]

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

In the folded state, biomolecules exchange between multiple conformational states crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo-electron microscopy. Although new tools are available to detect conformational and compositional heterogeneity within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the macromolecular crystallographic information file (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable the capture of macromolecular ensembles in a human and machine-interpretable way, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to the achievements of AlphaFold with single-structure prediction.

MOTIVATION: Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered proteins (IDPs). However, much of protein function is modulated beyond the translated sequence through the introduction of post-translational modifications (PTMs). RESULTS: In this work, we have provided a curated set of side chain rotamers for the most common PTMs derived from the RCSB PDB database, including phosphorylated, methylated, and acetylated sidechains. Our rotamer libraries improve upon existing methods such as SIDEpro, Rosetta, and AlphaFold3 in predicting the experimental structures for PTMs in folded proteins. In addition, we showcase our PTM libraries in full use by generating ensembles with the Monte Carlo Side Chain Entropy (MCSCE) for folded proteins, and combining MCSCE with the Local Disordered Region Sampling algorithms within IDPConformerGenerator for proteins with intrinsically disordered regions. AVAILABILITY AND IMPLEMENTATION: The codes for dihedral angle computations and library creation are available at https://github.com/THGLab/ptm_sc.git.

Natural photosystems couple light harvesting to charge separation using a 'special pair' of chlorophyll molecules that accepts excitation energy from the antenna and initiates an electron-transfer cascade. To investigate the photophysics of special pairs independently of the complexities of native photosynthetic proteins, and as a first step toward creating synthetic photosystems for new energy conversion technologies, we designed C2-symmetric proteins that hold two chlorophyll molecules in closely juxtaposed arrangements. X-ray crystallography confirmed that one designed protein binds two chlorophylls in the same orientation as native special pairs, whereas a second designed protein positions them in a previously unseen geometry. Spectroscopy revealed that the chlorophylls are excitonically coupled, and fluorescence lifetime imaging demonstrated energy transfer. The cryo-electron microscopy structure of a designed 24-chlorophyll octahedral nanocage with a special pair on each edge closely matched the design model. The results suggest that the de novo design of artificial photosynthetic systems is within reach of current computational methods.

Photoexcitation of green fluorescent protein (GFP) triggers long-range proton transfer along a "wire" of neighboring protein residues, which, in turn, activates its characteristic green fluorescence. The GFP proton wire is one of the simplest, most well-characterized models of biological proton transfer but remains challenging to simulate due to the sensitivity of its energetics to the surrounding protein conformation and the possibility of non-classical behavior associated with the movement of lightweight protons. Using a direct dynamics variational multiconfigurational Gaussian wavepacket method to provide a fully quantum description of both electrons and nuclei, we explore the mechanism of excited state proton transfer in a high-dimensional model of the GFP chromophore cluster over the first two picoseconds following excitation. During our simulation, we observe the sequential starts of two of the three proton transfers along the wire, confirming the predictions of previous studies that the overall process starts from the end of the wire furthest from the fluorescent chromophore and proceeds in a concerted but asynchronous manner. Furthermore, by comparing the full quantum dynamics to a set of classical trajectories, we provide unambiguous evidence that tunneling plays a critical role in facilitating the leading proton transfer. [ABSTRACT FROM AUTHOR]

Imaging scaffolds composed of designed protein cages fused to designed ankyrin repeat proteins (DARPins) have enabled the structure determination of small proteins by cryogenic electron microscopy (cryo-EM). One particularly well characterized scaffold type is a symmetric tetrahedral assembly composed of 24 subunits, 12 A and 12 B, which has three cargo-binding DARPins positioned on each vertex. Here, the X-ray crystal structure of a representative tetrahedral scaffold in the apo state is reported at 3.8 Å resolution. The X-ray crystal structure complements recent cryo-EM findings on a closely related scaffold, while also suggesting potential utility for crystallographic investigations. As observed in this crystal structure, one of the three DARPins, which serve as modular adaptors for binding diverse `cargo proteins, present on each of the vertices is oriented towards a large solvent channel. The crystal lattice is unusually porous, suggesting that it may be possible to soak crystals of the scaffold with small (≤30 kDa) protein cargo ligands and subsequently determine cage-cargo structures via X-ray crystallography. The results suggest the possibility that cryo-EM scaffolds may be repurposed for structure determination by X-ray crystallography, thus extending the utility of electron-microscopy scaffold designs for alternative structural biology applications.

Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

Enzymes populate ensembles of structures necessary for catalysis that are difficult to experimentally characterize. We use time-resolved mix-and-inject serial crystallography at an x-ray free electron laser to observe catalysis in a designed mutant isocyanide hydratase (ICH) enzyme that enhances sampling of important minor conformations. The active site exists in a mixture of conformations, and formation of the thioimidate intermediate selects for catalytically competent substates. The influence of cysteine ionization on the ICH ensemble is validated by determining structures of the enzyme at multiple pH values. Large molecular dynamics simulations in crystallo and time-resolved electron density maps show that Asp17 ionizes during catalysis and causes conformational changes that propagate across the dimer, permitting water to enter the active site for intermediate hydrolysis. ICH exhibits a tight coupling between ionization of active site residues and catalysis-activated protein motions, exemplifying a mechanism of electrostatic control of enzyme dynamics.

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Cells run on initiation of protein-protein interactions, which are dynamically tuned spatially and temporally to modulate cellular events. This tuning can be physical, such as attaching the protein to a cargo or protein complex, thereby altering its diffusive properties, or modulating the distance between protein pairs, or chemical, by altering the proteins conformations (e.g., nucleotide binding state of an enzyme, post-translational modification of a protein, etc.). Because a dynamic and changing subset of proteins in the cell could be in any specific state, ensemble measurements are not ideal-to untangle which of the factors are important, and how, we need single-molecule measurements. Experimentally, until now we have not had good tools to precisely measure initiation of such protein-protein interactions at the single-molecule level. Here, we develop a new method to measure dynamics of initial protein-protein interactions, allowing measurement of how properties such as the distance between proteins, and their tethered length can modulate the rate of interactions. In addition to precise measurement distance dependent motor-MT rebinding dynamics, we demonstrate the use of a dithered optical trap to measure dynamic motor-MT interactions and further discuss the possibilities of this technique being applicable to other systems.

Muscle contraction at the macrolevel is a physiological process that is ultimately due to the interaction between myosin and actin proteins at the microlevel. The actin–myosin interaction involves slow attachment and detachment responses and a rapid temporal change in protein conformation called power-stroke. Jump-diffusion models that combine jump processes between attachment and detachment with a mechanical description of the power-stroke have been proposed in the literature. However, the current formulations of these models are not fully compatible with the principles of thermodynamics. To solve the problem of coupling continuous mechanisms with discrete chemical transitions, we rely on the mathematical formalism of Poisson random measures. First, we design an efficient stochastic formulation for existing muscle contraction partial differential equation models. Then, we write a new jump-diffusion model for actin–myosin interaction. This new model describes both the behavior of muscle contraction on multiple time scales and its compatibility with thermodynamic principles. Finally, following a classical calibration procedure, we demonstrate the ability of the model to reproduce experimental data characterizing muscle behavior on fast and slow time scales. [ABSTRACT FROM AUTHOR]

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a proteins thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

Intrinsically disordered proteins and protein regions (IDPs) are prevalent in all proteomes and are essential to cellular function. Unlike folded proteins, IDPs exist in an ensemble of dissimilar conformations. Despite this structural plasticity, intramolecular interactions create sequence-specific structural biases that determine an IDP ensemble's three-dimensional shape. Such structural biases can be key to IDP function and are often measured in vitro, but whether those biases are preserved inside the cell is unclear. Here we show that structural biases in IDP ensembles found in vitro are recapitulated inside human-derived cells. We further reveal that structural biases can change in a sequence-dependent manner due to changes in the intracellular milieu, subcellular localization, and intramolecular interactions with tethered well-folded domains. We propose that the structural sensitivity of IDP ensembles can be leveraged for biological function, can be the underlying cause of IDP-driven pathology or can be used to design disorder-based biosensors and actuators.

Amyloid-β (Aβ) forms heterogeneous oligomers, which are implicated in the pathogenesis of Alzheimers disease (AD). Many Aβ oligomers consist of β-hairpin building blocks─Aβ peptides in β-hairpin conformations. β-Hairpins of Aβ can adopt a variety of alignments, but the role that β-hairpin alignment plays in the formation and heterogeneity of Aβ oligomers is poorly understood. To explore the effect of β-hairpin alignment on the oligomerization of Aβ peptides, we designed and studied two model peptides with two different β-hairpin alignments. Peptides Aβm17-36 and Aβm17-35 mimic two different β-hairpins that Aβ can form, the Aβ17-36 and Aβ17-35 β-hairpins, respectively. These hairpins are similar in composition but differ in hairpin alignment, altering the facial arrangements of the side chains of the residues that they contain. X-ray crystallography and SDS-PAGE demonstrate that the difference in facial arrangement between these peptides leads to distinct oligomer formation. In the crystal state, Aβm17-36 forms triangular trimers that further assemble to form hexamers, while Aβm17-35 forms tetrameric β-barrels. In SDS-PAGE, Aβm17-36 assembles to form a ladder of oligomers, while Aβm17-35 either assembles to form a dimer or does not assemble at all. The differences in the behavior of Aβm17-36 and Aβm17-35 suggest β-hairpin alignment as a source of the observed heterogeneity of Aβ oligomers.

The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.

Aβ aggregation leads to the formation of both insoluble amyloid fibrils and soluble oligomers. Understanding the structures of Aβ oligomers is important for delineating the mechanism of Aβ aggregation and developing effective therapeutics. Here, we use site-directed spin labeling and electron paramagnetic resonance (EPR) spectroscopy to study Aβ42 oligomers prepared by using the protocol of Aβ-derived diffusible ligands. We obtained the EPR spectra of 37 Aβ42 oligomer samples, each spin-labeled at a unique residue position of the Aβ42 sequence. Analysis of the disordered EPR components shows that the N-terminal region has a lower local structural stability. Spin label mobility analysis reveals three structured segments at residues 9-11, 15-22, and 30-40. Intermolecular spin-spin interactions indicate a parallel in-register β-sheet structure, with residues 34-38 forming the structural core. Residues 16-21 also adopt the parallel in-register β-structure, albeit with weaker intermolecular packing. Our results suggest that there is a structural class of Aβ oligomers that adopt fibril-like conformations.

The sensitivity and responsiveness of living cells to environmental changes are enabled by dynamic protein structures, inspiring efforts to construct artificial supramolecular protein assemblies. However, despite their sophisticated structures, designed protein assemblies have yet to be incorporated into macroscale devices for real-life applications. We report a 2D crystalline protein assembly of C98/E57/E66L-rhamnulose-1-phosphate aldolase (CEERhuA) that selectively blocks or passes molecular species when exposed to a chemical trigger. CEERhuA crystals are engineered via cobalt(II) coordination bonds to undergo a coherent conformational change from a closed state (pore dimensions

Chitin is an abundant biopolymer and pathogen-associated molecular pattern that stimulates a host innate immune response. Mammals express chitin-binding and chitin-degrading proteins to remove chitin from the body. One of these proteins, Acidic Mammalian Chitinase (AMCase), is an enzyme known for its ability to function under acidic conditions in the stomach but is also active in tissues with more neutral pHs, such as the lung. Here, we used a combination of biochemical, structural, and computational modeling approaches to examine how the mouse homolog (mAMCase) can act in both acidic and neutral environments. We measured kinetic properties of mAMCase activity across a broad pH range, quantifying its unusual dual activity optima at pH 2 and 7. We also solved high-resolution crystal structures of mAMCase in complex with oligomeric GlcNAcn, the building block of chitin, where we identified extensive conformational ligand heterogeneity. Leveraging these data, we conducted molecular dynamics simulations that suggest how a key catalytic residue could be protonated via distinct mechanisms in each of the two environmental pH ranges. These results integrate structural, biochemical, and computational approaches to deliver a more complete understanding of the catalytic mechanism governing mAMCase activity at different pH. Engineering proteins with tunable pH optima may provide new opportunities to develop improved enzyme variants, including AMCase, for therapeutic purposes in chitin degradation.

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

The receptor tyrosine kinase ROR2 mediates noncanonical WNT5A signaling to orchestrate tissue morphogenetic processes, and dysfunction of the pathway causes Robinow syndrome, brachydactyly B, and metastatic diseases. The domain(s) and mechanisms required for ROR2 function, however, remain unclear. We solved the crystal structure of the extracellular cysteine-rich (CRD) and Kringle (Kr) domains of ROR2 and found that, unlike other CRDs, the ROR2 CRD lacks the signature hydrophobic pocket that binds lipids/lipid-modified proteins, such as WNTs, suggesting a novel mechanism of ligand reception. Functionally, we showed that the ROR2 CRD, but not other domains, is required and minimally sufficient to promote WNT5A signaling, and Robinow mutations in the CRD and the adjacent Kr impair ROR2 secretion and function. Moreover, using function-activating and -perturbing antibodies against the Frizzled (FZ) family of WNT receptors, we demonstrate the involvement of FZ in WNT5A-ROR signaling. Thus, ROR2 acts via its CRD to potentiate the function of a receptor super-complex that includes FZ to transduce WNT5A signals.

Down syndrome (DS) is a common genetic condition caused by trisomy of chromosome 21. Among their complex clinical features, including musculoskeletal, neurological, and cardiovascular disabilities, individuals with DS have an increased risk of developing progressive dementia and early-onset Alzheimer's disease (AD). This dementia is attributed to the increased gene dosage of the amyloid-β (Aβ) precursor protein gene, the formation of self-propagating Aβ and tau prion conformers, and the deposition of neurotoxic Aβ plaques and tau neurofibrillary tangles. Tau amyloid fibrils have previously been established to adopt many distinct conformations across different neurodegenerative conditions. Here, we report the characterization of brain samples from four DS cases spanning 36-63 years of age by spectral confocal imaging with conformation-specific dyes and cryo-electron microscopy (cryo-EM) to determine structures of isolated tau fibrils. High-resolution structures revealed paired helical filament (PHF) and straight filament (SF) conformations of tau that were identical to those determined from AD cases. The PHFs and SFs are made of two C-shaped protofilaments, each containing a cross-β/β-helix motif. Similar to filaments from AD cases, most filaments from the DS cases adopted the PHF form, while a minority (approximately 20%) formed SFs. Samples from the youngest individual with no documented dementia had sparse tau deposits. To isolate tau for cryo-EM from this challenging sample we used a novel affinity-grid method involving a graphene oxide surface derivatized with anti-tau antibodies. This method improved isolation and revealed that primarily tau PHFs and a minor population of chronic traumatic encephalopathy type II-like filaments were present in this youngest case. These findings expand the similarities between AD and DS to the molecular level, providing insight into their related pathologies and the potential for targeting common tau filament folds by small-molecule therapeutics and diagnostics.

Calmodulin transduces [Ca2+] information regulating the rhythmic Ca2+ cycling between the sarcoplasmic reticulum and cytoplasm during contraction and relaxation in cardiac and skeletal muscle. However, the structural dynamics by which calmodulin modulates the sarcoplasmic reticulum Ca2+ release channel, the ryanodine receptor, at physiologically relevant [Ca2+] is unknown. Using fluorescence lifetime FRET, we resolve different structural states of calmodulin and Ca2+-driven shifts in the conformation of calmodulin bound to ryanodine receptor. Skeletal and cardiac ryanodine receptor isoforms show different calmodulin-ryanodine receptor conformations, as well as binding and structural kinetics with 0.2-ms resolution, which reflect different functional roles of calmodulin. These FRET methods provide insight into the physiological calmodulin-ryanodine receptor structural states, revealing additional distinct structural states that complement cryo-EM models that are based on less physiological conditions. This technology will drive future studies on pathological calmodulin-ryanodine receptor interactions and dynamics with other important ryanodine receptor bound modulators.

Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

Cellular DNA is subject to damage from a multitude of sources and repair or bypass of sites of damage utilize an array of context or cell cycle dependent systems. The recognition and removal of oxidatively damaged bases is the task of DNA glycosylases from the base excision repair pathway utilizing two structural families that excise base lesions in a wide range of DNA contexts including duplex, single-stranded and bubble structures arising during transcription. The mammalian NEIL2 glycosylase of the Fpg/Nei family excises lesions from each of these DNA contexts favoring the latter two with a preference for oxidized cytosine products and abasic sites. We have determined the first liganded crystal structure of mammalian NEIL2 in complex with an abasic site analog containing DNA duplex at 2.08 Å resolution. Comparison to the unliganded structure revealed a large interdomain conformational shift upon binding the DNA substrate accompanied by local conformational changes in the C-terminal domain zinc finger and N-terminal domain void-filling loop necessary to position the enzyme on the DNA. The detailed biochemical analysis of NEIL2 with an array of oxidized base lesions indicates a significant preference for its lyase activity likely to be paramount when interpreting the biological consequences of variants.

CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, experimental structures by their nature are only models themselves-their construction involves a certain degree of subjectivity in interpreting density maps and translating them to atomic coordinates. Here, we directly utilized density maps to evaluate the predictions by employing a method for ranking the quality of protein chain predictions based on their fit into the experimental density. The fit-based ranking was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy, and occasionally even better than the reference structure in some regions of the model. Local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. Additionally, the top 118 predictions associated with 9 protein target reference structures were selected for automated refinement, in addition to the top 40 predictions for 11 RNA targets. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure. This refinement was successful despite large conformational changes often being required, showing that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryo-EM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors, and together with the lack of consensus amongst models in these regions suggests that modeling, in combination with model-fit to the density, holds the potential for identifying more flexible regions within the structure.

Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein-ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.

Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha-helical coiled coils have such symmetric, extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C2 to C8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibres can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibres can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology.

The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA-only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x-ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA-protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

Prion variants are self-perpetuating conformers of a single protein that assemble into amyloid fibers and confer unique phenotypic states. Multiple prion variants can arise, particularly in response to changing environments, and interact within an organism. These interactions are often competitive, with one variant establishing phenotypic dominance over the others. This dominance has been linked to the competition for non-prion state protein, which must be converted to the prion state via a nucleated polymerization mechanism. However, the intrinsic rates of conversion, determined by the conformation of the variant, cannot explain prion variant dominance, suggesting a more complex interaction. Using the yeast prion system [PSI+], we have determined the mechanism of dominance of the [PSI+]Strong variant over the [PSI+]Weak variant in vivo. When mixed by mating, phenotypic dominance is established in zygotes, but the two variants persist and co-exist in the lineage descended from this cell. [PSI+]Strong propagons, the heritable unit, are amplified at the expense of [PSI+]Weak propagons, through the efficient conversion of soluble Sup35 protein, as revealed by fluorescence photobleaching experiments employing variantspecific mutants of Sup35. This competition, however, is highly sensitive to the fragmentation of [PSI+]Strong amyloid fibers, with even transient inhibition of the fragmentation catalyst Hsp104 promoting amplification of [PSI+]Weak propagons. Reducing the number of [PSI+]Strong propagons prior to mating, similarly promotes [PSI+]Weak amplification and conversion of soluble Sup35, indicating that template number and conversion efficiency combine to determine dominance. Thus, prion variant dominance is not an absolute hierarchy but rather an outcome arising from the dynamic interplay between unique protein conformations and their interactions with distinct cellular proteostatic niches. [ABSTRACT FROM AUTHOR]

As the rapid development of antibiotic resistance shrinks the number of clinically available antibiotics, there is an urgent need for novel options to fill the existing antibiotic pipeline. In recent years, antimicrobial peptides have attracted increased interest due to their impressive broad-spectrum antimicrobial activity and low probability of antibiotic resistance. However, macromolecular antimicrobial peptides of plant and animal origin face obstacles in antibiotic development because of their extremely short elimination half-life and poor chemical stability. Herein, we focus on medium-sized antibacterial peptides (MAPs) of microbial origin with molecular weights below 2000 Da. The low molecular weight is not sufficient to form complex protein conformations and is also associated to a better chemical stability and easier modifications. Microbially-produced peptides are often composed of a variety of non-protein amino acids and terminal modifications, which contribute to improving the elimination half-life of compounds. Therefore, MAPs have great potential for drug discovery and are likely to become key players in the development of next-generation antibiotics. In this review, we provide a detailed exploration of the modes of action demonstrated by 45 MAPs and offer a concise summary of the structure-activity relationships observed in these MAPs. [ABSTRACT FROM AUTHOR]

The formation of amyloid fibrils is a common feature of many protein systems. It has implications in both health, as amyloid fibrils are implicated in over 30 degenerative diseases, and in the biological functions of proteins. Surfaces have long been known to affect the formation of fibrils but the specific effect depends on the details of both the surface and protein. Fully understanding the role of surfaces in fibrillization requires microscopic information on protein conformation on surfaces. In this paper replica exchange molecular dynamics simulation is used to investigate the model fibril forming protein, A β (10–40) (a 31-residue segment of the amyloid-beta protein) on surfaces of different hydrophobicity. Similar to other proteins A β (10–40) is found to adsorb strongly onto hydrophobic surfaces. It also adopts significantly different sets of conformations on hydrophobic and polar surfaces, as well as in bulk solution. On hydrophobic surfaces, it adopts partially helical structures, with the helices overlapping with beta-strand regions in the mature fibril. These may be helical intermediates on the fibril formation pathway, suggesting a mechanism for the enhanced fibril formation seen on hydrophobic surfaces. [ABSTRACT FROM AUTHOR]

Protein kinases act as central molecular switches in the control of cellular functions. Alterations in the regulation and function of protein kinases may provoke diseases including cancer. In this study we investigate the conformational states of such disease-associated kinases using the high sensitivity of the kinase conformation (KinCon) reporter system. We first track BRAF kinase activity conformational changes upon melanoma drug binding. Second, we also use the KinCon reporter technology to examine the impact of regulatory protein interactions on LKB1 kinase tumor suppressor functions. Third, we explore the conformational dynamics of RIP kinases in response to TNF pathway activation and small molecule interactions. Finally, we show that CDK4/6 interactions with regulatory proteins alter conformations which remain unaffected in the presence of clinically applied inhibitors. Apart from its predictive value, the KinCon technology helps to identify cellular factors that impact drug efficacies. The understanding of the structural dynamics of full-length protein kinases when interacting with small molecule inhibitors or regulatory proteins is crucial for designing more effective therapeutic strategies. [ABSTRACT FROM AUTHOR]

Outer membrane protein (OMP) biogenesis is essential to bacterial cell survival and virulence in Gram-negative bacteria. A robust and functionally redundant protein quality control network in the periplasm prevents the formation of toxic aggregates and facilitates proper OMP assembly at the OM. Recent work demonstrates that, despite having overlapping functions, chaperones survival factor A (SurA), seventeen-kilodalton protein (Skp), and FK506 binding protein A (FkpA) and protease chaperone DegP also contribute uniquely to OMP biogenesis. The complex 'teamwork' apparent in OMP biogenesis emerges from individual interactions between periplasmic players and unfolded OMP clients. The survival and virulence of Gram-negative bacteria require proper biogenesis and maintenance of the outer membrane (OM), which is densely packed with β-barrel OM proteins (OMPs). Before reaching the OM, precursor unfolded OMPs (uOMPs) must cross the whole cell envelope. A network of periplasmic chaperones and proteases maintains unfolded but folding-competent conformations of these membrane proteins in the aqueous periplasm while simultaneously preventing off-pathway aggregation. These periplasmic proteins utilize different strategies, including conformational heterogeneity, oligomerization, multivalency, and kinetic partitioning, to perform and regulate their functions. Redundant and unique characteristics of the individual periplasmic players synergize to create a protein quality control team capable responding to changing environmental stresses. [ABSTRACT FROM AUTHOR]

This study investigated the impact of erythritol (ERY) on structural and functional properties of whey protein isolate (WPI). FTIR and CD revealed that WPI underwent structural changes, including formation of β‐folds and random coils, upon interaction with 20 mg/mL ERY. SEM showed increased surface roughness of WPI, indicating enhanced protein exposure. Moreover, binding rate exceeded 85%, accompanied by increased surface hydrophobicity. Fluorescence spectroscopy indicated a red shift in fluorescence of WPI and tyrosine (Tyr) residues, altering polarity of Tyr environment due to ERY coordination. Additionally, ERY presence enhanced the functional properties of WPI, including foaming, freeze–thaw stability, rheology and antioxidant activity. [ABSTRACT FROM AUTHOR]

Soy protein (SP) and milk protein are nutritionally complementary proteins, while they have different postprandial kinetic manifestations. In this study, different sources of proteins (casein, whey protein and SP isolate) were compounded in different concentrations and ratios. An in vitro dynamic gastrointestinal digestive system for children was used to study the digestive behaviour. For all the concentrations assessed, a consistently high level of small peptide release was demonstrated at 2.67 g/100 mL protein concentration, while the addition of 10% SP isolate would increase the release of small peptides. Overall, the study offers novel perspectives on children's products with diverse protein sources. [ABSTRACT FROM AUTHOR]

In this study, the effects and mechanism of the addition of Alaskan pollock surimi on shrimp (Solenocera crassicornis) surimi gel were explored from the aspects of gel quality, protein structure and flavor property. The ratios of pollock surimi to shrimp meat were set as 0:1 (P0S1), 1:9 (P1S9), 2:8 (P2S8), 3:7 (P3S7), 4:6 (P4S6) and 5:5 (P5S5). The results showed that the addition of pollock surimi significantly improved the gel strength and water holding capacity of the shrimp surimi gel. In addition, from the results of protein conformation, chemical interactions, three-dimensional network structure and protein pattern, the high β-sheet/ α-helix ratio, strong intermolecular interaction and high level of protein network cross-linking were the main reasons for pollock surimi to improve the gel properties of shrimp surimi gel. However, the addition of pollock surimi had a negative impact on the color and flavor of shrimp surimi gel. Among all samples, P3S7 improved the gel properties while retaining the unique color and smell of shrimp surimi gel as much as possible, which could be a feasible and promising method to improve the gel quality of shrimp surimi products. [ABSTRACT FROM AUTHOR]

Molecular docking is a commonly employed technique in structure-based drug design that generates the binding pose and affinity between ligands and targets. In the present study, nine benzo[a]phenazin enaminone derivatives containing substituted aryl and heteroaryl amines were subjected to molecular docking (Auto Dock 4.2) studies for the inhibition of ERK2 (extracellular signal-regulated kinase 2). The in silico molecular docking study results showed that compounds D and E have minimum binding energy and have good affinity toward the active pocket. The compounds demonstrated comparable binding free energies, each at −10.5 kcal mol−1, with the ligand binding sites located at ASP109(A) and the 1-H bonds positioned within a distance range of 2.98–2.99 Å. However, these compounds showed a higher ERK2 inhibitory effect compared to purvalanol, the standard drug, which had a binding free energy of −7.5 kcal mol−1. Purvalanol bound to the ligand binding sites at Met106(A) and Asn152(A), with the 2-H bonds situated at distances of 2.99 Å and 3.17 Å, respectively. Furthermore, the RMSD value for compounds D and E stabilizes around a consistent fixed value of 1.5, indicating a stable protein conformation. Moreover, molecular dynamics (MD) studies were conducted to evaluate the stability of the docked complexes with ligands D and E. We hypothesize that these compounds may inhibit ERK2 and could potentially be used as drugs for cancer treatment. Therefore, they can be regarded as potent inhibitors of ERK2 and effective anticancer compounds for therapeutic application. [ABSTRACT FROM AUTHOR]

Advanced glycation end products (AGEs) play an important role in the pathogenesis of age-linked disorders and diabetes mellitus. The aim of this study was to assess the repurposing potential of Phloroglucinol (PHL the antispasmodic drug), as an anti-glycation agent using Fructose-BSA model. The ability of PHL to inhibit AGE formation was evaluated using AGEs formation (Intrinsic fluorescence), fructosamine adduct (NBT) and free lysine availability (TNBSA) assays. The BSA protein conformation was assessed through Thioflavin-T, Congo-Red and Circular Dichroism assays. The lysine blockade and carbonyl entrapment were explored as possible mode of action. Our data showed that PHL significantly decreased the formation of AGEs with an IC50 value of 0.3mM. The fructosamine adducts and free lysine load was found to be reduced. Additionally, the BSA conformation was preserved by PHL. Mechanistic assays did not reveal involvement of lysine blockade as underlying reason for reduction in AGEs load. This was also supported by computational data whereby PHL failed to engage any catalytic residue involved in early fructose-BSA interaction. However, it was found to entrap the carbonyl moieties. In conclusion, the PHL demonstrated anti-glycation potential, which can be attributed to its ability to entrap carbonyl intermediates. Hence, the clinically available antispasmodic drug, presents itself as a promising candidate to be repurposed as anti-glycation agent. [ABSTRACT FROM AUTHOR]

Transglutaminase (TGase)-catalyzed crosslinking has gained substantial traction as a novel strategy for reducing allergenic risk in food proteins, particularly within the realm of hypoallergenic food production. This study explored the impact of TGase crosslinking on conformational changes in a binary protein system composed of soy protein isolate (SPI) and sodium caseinate (SC) at varying mass ratios (10:0, 7:3, 5:5, 3:7 (w/w)). Specifically, the immunoglobulin E (IgE) binding capacity of soy proteins within this system was examined. Prolonged TGase crosslinking (ranging from 0 h to 15 h) resulted in a gradual reduction in IgE reactivity across all SPI-SC ratios, with the order of IgE-binding capability as follows: SPI > SPI5-SC5 > SPI7-SC3 > SPI3-SC7. These alterations in protein conformation following TGase crosslinking, as demonstrated by variable intrinsic fluorescence, altered surface hydrophobicity, increased ultraviolet absorption and reduced free sulfhydryl content, were identified as the underlying causes. Additionally, ionic bonds were found to play a significant role in maintaining the structure of the dual-protein system after crosslinking, with hydrophobic forces and hydrogen bonds serving as supplementary forces. Generally, the dual-protein system may exhibit enhanced efficacy in reducing the allergenicity of soy protein. [ABSTRACT FROM AUTHOR]

Proteins play a central role in biological processes, and understanding their conformational variability is crucial for unraveling their functional mechanisms. Recent advancements in high-throughput technologies have enhanced our knowledge of protein structures, yet predicting their multiple conformational states and motions remains challenging. This study introduces Dimensionality Analysis for protein Conformational Exploration (DANCE) for a systematic and comprehensive description of protein families conformational variability. DANCE accommodates both experimental and predicted structures. It is suitable for analysing anything from single proteins to superfamilies. Employing it, we clustered all experimentally resolved protein structures available in the Protein Data Bank into conformational collections and characterized them as sets of linear motions. The resource facilitates access and exploitation of the multiple states adopted by a protein and its homologs. Beyond descriptive analysis, we assessed classical dimensionality reduction techniques for sampling unseen states on a representative benchmark. This work improves our understanding of how proteins deform to perform their functions and opens ways to a standardised evaluation of methods designed to sample and generate protein conformations. [ABSTRACT FROM AUTHOR]

In small clinical studies, the application of transcranial photobiomodulation (PBM), which typically delivers low-intensity near-infrared (NIR) to treat the brain, has led to some remarkable results in the treatment of dementia and several neurodegenerative diseases. However, despite the extensive literature detailing the mechanisms of action underlying PBM outcomes, the specific mechanisms affecting neurodegenerative diseases are not entirely clear. While large clinical trials are warranted to validate these findings, evidence of the mechanisms can explain and thus provide credible support for PBM as a potential treatment for these diseases. Tubulin and its polymerized state of microtubules have been known to play important roles in the pathology of Alzheimer's and other neurodegenerative diseases. Thus, we investigated the effects of PBM on these cellular structures in the quest for insights into the underlying therapeutic mechanisms. In this study, we employed a Raman spectroscopic analysis of the amide I band of polymerized samples of tubulin exposed to pulsed low-intensity NIR radiation (810 nm, 10 Hz, 22.5 J/cm2 dose). Peaks in the Raman fingerprint region (300–1900 cm−1)—in particular, in the amide I band (1600–1700 cm−1)—were used to quantify the percentage of protein secondary structures. Under this band, hidden signals of C = O stretching, belonging to different structures, are superimposed, producing a complex signal as a result. An accurate decomposition of the amide I band is therefore required for the reliable analysis of the conformation of proteins, which we achieved through a straightforward method employing a Voigt profile. This approach was validated through secondary structure analyses of unexposed control samples, for which comparisons with other values available in the literature could be conducted. Subsequently, using this validated method, we present novel findings of statistically significant alterations in the secondary structures of polymerized NIR-exposed tubulin, characterized by a notable decrease in α -helix content and a concurrent increase in β -sheets compared to the control samples. This PBM-induced α -helix to β -sheet transition connects to reduced microtubule stability and the introduction of dynamism to allow for the remodeling and, consequently, refreshing of microtubule structures. This newly discovered mechanism could have implications for reducing the risks associated with brain aging, including neurodegenerative diseases like Alzheimer's disease, through the introduction of an intervention following this transition. [ABSTRACT FROM AUTHOR]

GdmCl and NaSCN are two strong chaotropic salts commonly used in protein folding and stability studies, but their microscopic mechanisms remain enigmatic. Here, by CD and NMR, we investigated their effects on conformations, stability, binding and backbone dynamics on ps-ns and µs-ms time scales of a 39-residue but well-folded WW4 domain at salt concentrations ≤200 mM. Up to 200 mM, both denaturants did not alter the tertiary packing of WW4, but GdmCl exerted more severe destabilization than NaSCN. Intriguingly, GdmCl had only weak binding to amide protons, while NaSCN showed extensive binding to both hydrophobic side chains and amide protons. Neither denaturant significantly affected the overall ps-ns backbone dynamics, but they distinctively altered µs-ms backbone dynamics. This study unveils that GdmCl and NaSCN destabilize a protein before the global unfolding occurs with differential binding properties and µs-ms backbone dynamics, implying the absence of a simple correlation between thermodynamic stability and backbone dynamics of WW4 at both ps-ns and µs-ms time scales. [ABSTRACT FROM AUTHOR]

Mass spectrometry‐based approaches to assess protein conformation have become widely utilized due to their sensitivity, low sample requirements, and broad applicability to proteins regardless of size and environment. Their wide applicability and sensitivity also make these techniques suitable for the analysis of complex mixtures of proteins, and thus, they have been applied at the cell and even the simple organism levels. These works are impressive, but they predominately employ "bottom‐up" workflows and require proteolytic digestion prior to analysis. Once digested, it is not possible to distinguish the proteoform from which any single peptide is derived and therefore, one cannot associate distal—in primary structure—concurrent post‐translational modifications (PTMs) or covalent labels, as they would be found on separate peptides. Thus, analyses via bottom‐up proteomics report the average PTM status and higher‐order structure (HOS) of all existing proteoforms. Second, these works predominately employ promiscuous reagents to probe protein HOS. While this does lead to improved conformational resolution, the formation of many products can divide the signal associated with low‐copy number proteins below signal‐to‐noise thresholds and complicate the bioinformatic analysis of these already challenging systems. In this perspective, I further detail these limitations and discuss the positives and negatives of top‐down proteomics as an alternative. [ABSTRACT FROM AUTHOR]

Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a "spectrum-structure" correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments. [ABSTRACT FROM AUTHOR]

The adsorption of a model protein such as bovine serum albumin (BSA) on a magnetic material based on montmorillonite (MtMag) was studied. Kinetic data, equilibrium isotherms, and thermodynamic calculations were performed and analyzed to obtain information about the rate-limiting step of the adsorption process and propose a possible adsorption mechanism. Equilibrium studies showed optimal adsorption around pH 4.5, the maximum adsorption capacity being around 231.9 mg g-1 at 25 °C. The latter behavior may be ascribed to the most packed protein conformation at pH conditions near the BSA isoelectric point. In addition, the BSA adsorption capacity decreased with the ionic strength (10–3−10–1 M) and increased with the temperature (15–30 °C). In all cases, the Sips model yielded the best fit to the experimental results. The thermodynamic analysis revealed an isosteric heat dependence on the coverage, which allowed ruling out the Langmuir model. The kinetic data were adequately fitted by the IPD model, which suggests that the rate-limiting step of the adsorption process is the diffusion of BSA molecules into the internal sites. [ABSTRACT FROM AUTHOR]