The protein folding problem

Annealed fractional Lévy-Itō diffusion models for protein generation

Protein generation has numerous applications in designing therapeutic antibodies and creating new drugs. Still, it is a demanding task due to the inherent complexities of protein structures and the limitations of current generative models. Proteins possess intricate geometry, and sampling their conformational space is challenging due to its high dimensionality. This paper introduces novel Markovian and non-Markovian generative diffusion models based on fractional stochastic differential equations and the Lévy distribution, allowing for a more effective exploration of the conformational space. The approach is applied to a dataset of 40 , 000 proteins and evaluated in terms of Fréchet distance, fidelity, and diversity, outperforming the state-of-the-art by 25.4%, 35.8%, and 11.8%, respectively.

A Perspective on Protein Structure Prediction Using Quantum Computers

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

Bioinspired stormwater control measure for the enhanced removal of truly dissolved polycyclic aromatic hydrocarbons and heavy metals from urban runoff

Stormwater harvesting (SWH) addresses the UN's Sustainable Development Goals (SDGs). Conventional stormwater control measures (SCMs) effectively remove particulate and colloidal contaminants from urban runoff; however, they fail to retain dissolved contaminants, particularly substances of concern like polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs), thereby hindering the SWH applicability. Here, inspired by protein folding in nature, we reported a novel biomimetic SCM for the efficient removal of dissolved PAHs and HMs from urban runoff. Lab-scale tests were conducted together with a more mechanistic investigation on how the contaminants were removed. By integrating hydrophobic organic chains with low-cost hydrophilic flocculant matrixes, our biomimetic flocculants achieved a 1.4-9.5 times removal of all detected dissolved PAHs and HMs, while enhancing the removal of a wide-spectrum of particulate and colloidal contaminants, compared to existing SCMs. Ecotoxicity, as indicated by newborn Daphnia magna as experimental organisms, was reduced from "acute toxicity" of the original runoff sample (toxic unit of ∼2.6) to "non-toxicity" (toxic unit < 0.4) of the treated water. The improved performance is attributed to the protein-folding-like features of the bioinspired flocculants providing: (i) stronger binding to PAHs (via hydrophobic association) and HMs (via coordination), and (ii) the ability of spontaneous aggregation. The bio-inspired approach in this work holds strong promise as an alternative or supplementary component in SCM systems, and is expected to contribute to sustainable water management practices in relation to SDGs.

Systematic Investigation of the Trafficking of Glycoproteins on the Cell Surface

Glycoproteins located on the cell surface play a pivotal role in nearly every extracellular activity. N-glycosylation is one of the most common and important protein modifications in eukaryotic cells, and it often regulates protein folding and trafficking. Glycosylation of cell-surface proteins undergoes meticulous regulation by various enzymes in the endoplasmic reticulum (ER) and the Golgi, ensuring their proper folding and trafficking to the cell surface. However, the impacts of protein N-glycosylation, N-glycan maturity, and protein folding status on the trafficking of cell-surface glycoproteins remain to be explored. In this work, we comprehensively and site-specifically studied the trafficking of cell-surface glycoproteins in human cells. Integrating metabolic labeling, bioorthogonal chemistry, and multiplexed proteomics, we investigated 706 N-glycosylation sites on 396 cell-surface glycoproteins in monocytes, either by inhibiting protein N-glycosylation, disturbing N-glycan maturation, or perturbing protein folding in the ER. The current results reveal their distinct impacts on the trafficking of surface glycoproteins. The inhibition of protein N-glycosylation dramatically suppresses the trafficking of many cell-surface glycoproteins. The N-glycan immaturity has more substantial effects on proteins with high N-glycosylation site densities, while the perturbation of protein folding in the ER exerts a more pronounced impact on surface glycoproteins with larger sizes. Furthermore, for N-glycosylated proteins, their trafficking to the cell surface is related to the secondary structures and adjacent amino acid residues of glycosylation sites. Systematic analysis of surface glycoprotein trafficking advances our understanding of the mechanisms underlying protein secretion and surface presentation.

Sequence-dependent and -independent information in a combined random energy model for protein folding and coding

Random energy models (REMs) provide a simple description of the energy landscapes that guide protein folding and evolution. The requirement of a large energy gap between the native structure and unfolded conformations, considered necessary for cooperative, protein-like, folding behavior, indicates that proteins differ markedly from random heteropolymers. It has been suggested, therefore, that natural selection might have acted to choose nonrandom amino acid sequences satisfying this particular condition, implying that a large fraction of possible, unselected random sequences, would not fold to any structure. From an informational perspective, however, this scenario could indicate that protein structures, regarded as messages to be transmitted through a communication channel, would not be efficiently encoded in amino acid sequences, regarded as the communication channel for this transmission, since a large fraction of possible channel states would not be used. Here, we use a combined REM for conformations and sequences, with previously estimated parameters for natural proteins, to explore an alternative possibility in which the appropriate shape of the landscape results mainly from the deviation from randomness of possible native structures instead of sequences. We observe that this situation emerges naturally if the distribution of conformational energies happens to arise from two independent contributions corresponding to sequence-dependent and -independent terms. This construction is consistent with the hypothesis of a protein burial folding code, with native structures being determined by a modest amount of sequence-dependent atomic burial information with sequence-independent constraints imposed by unspecific hydrogen bond formation. More generally, an appropriate combination of sequence-dependent and -independent information accommodates the possibility of an efficient structural encoding with the main physical requirement for folding, providing possible insight not only on the folding process but also on several aspects sequence evolution such as neutral networks, conformational coverage, and de novo gene emergence.

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

Mechanism of DNA origami folding elucidated by mesoscopic simulations

Many experimental and computational efforts have sought to understand DNA origami folding, but the time and length scales of this process pose significant challenges. Here, we present a mesoscopic model that uses a switchable force field to capture the behavior of single- and double-stranded DNA motifs and transitions between them, allowing us to simulate the folding of DNA origami up to several kilobases in size. Brownian dynamics simulations of small structures reveal a hierarchical folding process involving zipping into a partially folded precursor followed by crystallization into the final structure. We elucidate the effects of various design choices on folding order and kinetics. Larger structures are found to exhibit heterogeneous staple incorporation kinetics and frequent trapping in metastable states, as opposed to more accessible structures which exhibit first-order kinetics and virtually defect-free folding. This model opens an avenue to better understand and design DNA nanostructures for improved yield and folding performance.

Trimethylamine-N-oxide depletes urea in a peptide solvation shell

Trimethylamine-N-oxide (TMAO) and urea are metabolites that are used by some marine animals to maintain their cell volume in a saline environment. Urea is a well-known denaturant, and TMAO is a protective osmolyte that counteracts urea-induced protein denaturation. TMAO also has a general protein-protective effect, for example, it counters pressure-induced protein denaturation in deep-sea fish. These opposing effects on protein stability have been linked to the spatial relationship of TMAO, urea, and protein molecules. It is generally accepted that urea-induced denaturation proceeds through the accumulation of urea at the protein surface and their subsequent interaction. In contrast, it has been suggested that TMAO's protein-stabilizing effects stem from its exclusion from the protein surface, and its ability to deplete urea from protein surfaces; however, these spatial relationships are uncertain. We used neutron diffraction, coupled with structural refinement modeling, to study the spatial associations of TMAO and urea with the tripeptide derivative glycine-proline-glycinamide in aqueous urea, aqueous TMAO, and aqueous urea-TMAO (in the mole ratio 1:2 TMAO:urea). We found that TMAO depleted urea from the peptide's surface and that while TMAO was not excluded from the tripeptide's surface, strong atomic interactions between the peptide and TMAO were limited to hydrogen bond donating peptide groups. We found that the repartition of urea, by TMAO, was associated with preferential TMAO-urea bonding and enhanced urea-water hydrogen bonding, thereby anchoring urea in the bulk solution and depleting urea from the peptide surface.

New insights into GATOR2-dependent interactions and its conformational changes in amino acid sensing

Eukaryotic cells coordinate growth under different environmental conditions via mechanistic target of rapamycin complex 1 (mTORC1). In the amino-acid-sensing signalling pathway, the GATOR2 complex, containing five evolutionarily conserved subunits (WDR59, Mios, WDR24, Seh1L and Sec13), is required to regulate mTORC1 activity by interacting with upstream CASTOR1 (arginine sensor) and Sestrin2 (leucine sensor and downstream GATOR1 complex). GATOR2 complex utilizes β-propellers to engage with CASTOR1, Sestrin2 and GATOR1, removal of these β-propellers results in substantial loss of mTORC1 capacity. However, structural information regarding the interface between amino acid sensors and GATOR2 remains elusive. With the recent progress of the AI-based tool AlphaFold2 (AF2) for protein structure prediction, structural models were predicted for Sentrin2-WDR24-Seh1L and CASTOR1-Mios β-propeller. Furthermore, the effectiveness of relevant residues within the interface was examined using biochemical experiments combined with molecular dynamics (MD) simulations. Notably, fluorescence resonance energy transfer (FRET) analysis detected the structural transition of GATOR2 in response to amino acid signals, and the deletion of Mios β-propeller severely impeded that change at distinct arginine levels. These findings provide structural perspectives on the association between GATOR2 and amino acid sensors and can facilitate future research on structure determination and function.

Tailoring Hierarchical Structure and Rare Earth Affinity of Compositionally Identical Polymers via Sequence Control

Macromolecule sequence, structure, and function are inherently intertwined. While well-established relationships exist in proteins, they are more challenging to define for synthetic polymer nanoparticles due to their molecular weight, sequence, and conformational dispersities. To explore the impact of sequence on nanoparticle structure, we synthesized a set of 16 compositionally identical, sequence-controlled polymers with distinct monomer patterning of dimethyl acrylamide and a bioinspired, structure-driving di(phenylalanine) acrylamide (FF). Sequence control was achieved through multiblock polymerizations, yielding unique ensembles of polymer sequences which were simulated by kinetic Monte Carlo simulations. Systematic analysis of the global (tertiary- and quaternary-like) structure in this amphiphilic copolymer series revealed the effect of multiple sequence descriptors: the number of domains, the hydropathy of terminal domains, and the patchiness (density) of FF within a domain, each of which impacted both chain collapse and the distribution of single- and multichain assemblies. Furthermore, both the conformational freedom of chain segments and local-scale, β-sheet-like interactions were sensitive to the patchiness of FF. To connect sequence, structure, and target function, we evaluated an additional series of nine sequence-controlled copolymers as sequestrants for rare earth elements (REEs) by incorporating a functional acrylic acid monomer into select polymer scaffolds. We identified key sequence variables that influence the binding affinity, capacity, and selectivity of the polymers for REEs. Collectively, these results highlight the potential of and boundaries of sequence control via multiblock polymerizations to drive primary sequence ensembles hierarchical structures, and ultimately the functionality of compositionally identical polymeric materials.

From covalent transition states in chemistry to noncovalent in biology: from β- to Φ-value analysis of protein folding

Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted's landmark paper that introduced the β-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding - 'part (b) of the protein folding problem'. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of β- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.

De novo transcriptomic analysis of Doum Palm (Hyphaene compressa) revealed an insight into its potential drought tolerance

BACKGROUND

Doum palms (Hyphaene compressa) perform a crucial starring role in the lives of Kenya's arid and semi-arid people for empowerment and sustenance. Despite the crop's potential for economic gain, there is a lack of genetic resources and detailed information about its domestication at the molecular level. Given the doum palm's vast potential as a widely distributed plant in semi-arid and arid climates and a source of many applications, coupled with the current changing climate scenario, it is essential to understand the molecular processes that provide drought resistance to this plant.

RESULTS

Assembly of the first transcriptome of doum palms subjected to water stress generated about 39.97 Gb of RNA-Seq data. The assembled transcriptome revealed 193,167 unigenes with an average length of 1655 bp, with 128,708 (66.63%) successfully annotated in seven public databases. Unigenes exhibited significant differentially expressed genes (DEGs) in well-watered and stressed-treated plants, with 45071 and 42457 accounting for up-regulated and down-regulated DEGs, respectively. GO term, KEGG, and KOG analysis showed that DEGs were functionally enriched cellular processes, metabolic processes, cellular and catalytic activity, metabolism, genetic information processing, signal transduction mechanisms, and posttranslational modification pathways. Transcription factors (TF), such as the MYB, WRKY, NAC family, FAR1, B3, bHLH, and bZIP, were the prominent TF families identified as doum palm DEGs encoding drought stress tolerance.

CONCLUSIONS

This study provides a complete understanding of DEGs involved in drought stress at the transcriptome level in doum palms. This research is, therefore, the foundation for the characterization of potential genes, leading to a clear understanding of its drought stress responses and providing resources for improved genetic modification.

The Role of Force Fields and Water Models in Protein Folding and Unfolding Dynamics

Protein folding is a fascinating, not fully understood phenomenon in biology. Molecular dynamics (MD) simulations are an invaluable tool to study conformational changes in atomistic detail, including folding and unfolding processes of proteins. However, the accuracy of the conformational ensembles derived from MD simulations inevitably relies on the quality of the underlying force field in combination with the respective water model. Here, we investigate protein folding, unfolding, and misfolding of fast-folding proteins by examining different force fields with their recommended water models, i.e., ff14SB with the TIP3P model and ff19SB with the OPC model. To this end, we generated long conventional MD simulations highlighting the perks and pitfalls of these setups. Using Markov state models, we defined kinetically independent conformational substates and emphasized their distinct characteristics, as well as their corresponding state probabilities. Surprisingly, we found substantial differences in thermodynamics and kinetics of protein folding, depending on the combination of the protein force field and water model, originating primarily from the different water models. These results emphasize the importance of carefully choosing the force field and the respective water model as they determine the accuracy of the observed dynamics of folding events. Thus, the findings support the hypothesis that the water model is at least equally important as the force field and hence needs to be considered in future studies investigating protein dynamics and folding in all areas of biophysics.

Role of the Alpha-B-Crystallin Protein in Cardiomyopathic Disease

Alpha-B-crystallin, a member of the small heat shock family of proteins, has been implicated in a variety of cardiomyopathies and in normal cardiac homeostasis. It is known to function as a molecular chaperone, particularly for desmin, but also interacts with a wide variety of additional proteins. The molecular chaperone function is also enhanced by signal-dependent phosphorylation at specific residues under stress conditions. Naturally occurring mutations in CRYAB, the gene that encodes alpha-B-crystallin, have been suggested to alter ionic intermolecular interactions that affect dimerization and chaperone function. These mutations have been associated with myofibrillar myopathy, restrictive cardiomyopathy, and hypertrophic cardiomyopathy and promote pathological hypertrophy through different mechanisms such as desmin aggregation, increased reductive stress, or activation of calcineurin-NFAT signaling. This review will discuss the known mechanisms by which alpha-B-crystallin functions in cardiac homeostasis and the pathogenesis of cardiomyopathies and provide insight into potential future areas of exploration.

Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure

Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achieving near-atomic resolutions of a wide variety of macromolecular complexes. Ushered in by AlphaFold, machine learning has powered the current generation of predictive modeling tools, which can accurately and reliably predict models for proteins and some complexes directly from the sequence alone. Although they offer new opportunities individually, there is an inherent synergy between these techniques, allowing for the construction of large, complex macromolecular models. Here, we give a brief overview of these approaches in addition to illustrating works that combine these techniques for model building. These examples provide insight into model building, assessment, and limitations when integrating predictive modeling with cryo-EM density maps. Together, these approaches offer the potential to greatly accelerate the generation of macromolecular structural insights, particularly when coupled with experimental data.

A TeZla micromixer for interrogating the early and broad folding landscape of G-quadruplex via multistage velocity descending

Biomacromolecular folding kinetics involves fast folding events and broad timescales. Current techniques face limitations in either the required time resolution or the observation window. In this study, we developed the TeZla micromixer, integrating Tesla and Zigzag microstructures with a multistage velocity descending strategy. TeZla achieves a significant short mixing dead time (40 µs) and a wide time window covering four orders of magnitude (up to 300 ms). Using this unique micromixer, we explored the folding landscape of c-Myc G4 and its noncanonical-G4 derivatives with different loop lengths or G-vacancy sites. Our findings revealed that c-Myc can bypass folding intermediates and directly adopt a G4 structure in the cation-deficient buffer. Moreover, we found that the loop length and specific G-vacancy site could affect the folding pathway and significantly slow down the folding rates. These results were also cross-validated with real-time NMR and circular dichroism. In conclusion, TeZla represents a versatile tool for studying biomolecular folding kinetics, and our findings may ultimately contribute to the design of drugs targeting G4 structures.

Machine learning and artificial intelligence in neuroscience: A primer for researchers

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields. For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues. Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.

Effects of Crowding and Cosolutes on Biomolecular Function at Extreme Environmental Conditions

The extent of the effect of cellular crowding and cosolutes on the functioning of proteins and cells is manifold and includes the stabilization of the biomolecular systems, the excluded volume effect, and the modulation of molecular dynamics. Simultaneously, it is becoming increasingly clear how important it is to take the environment into account if we are to shed light on biological function under various external conditions. Many biosystems thrive under extreme conditions, including the deep sea and subseafloor crust, and can take advantage of some of the effects of crowding. These relationships have been studied in recent years using various biophysical techniques, including neutron and X-ray scattering, calorimetry, FTIR, UV-vis and fluorescence spectroscopies. Combining knowledge of the structure and conformational dynamics of biomolecules under extreme conditions, such as temperature, high hydrostatic pressure, and high salinity, we highlight the importance of considering all results in the context of the environment. Here we discuss crowding and cosolute effects on proteins, nucleic acids, membranes, and live cells and explain how it is possible to experimentally separate crowding-induced effects from other influences. Such findings will contribute to a better understanding of the homeoviscous adaptation of organisms and the limits of life in general.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.

Exploring the molecular mechanism of cold-adaption of an alkaline protease mutant by molecular dynamics simulations and residue interaction network

Psychrophilic proteases have attracted enormous attention in past decades, due to their high catalytic activity at low temperatures in a wide range of industrial processes, especially in the detergent and leather industries. Among them, H5 is an alkaline protease mutant, which featuring psychrophilic-like behavior, but the reasons that H5 with higher activity at low temperatures are still poorly understood. Herein, the molecular dynamics (MD) simulations combined with residue interaction network (RIN) were utilized to investigate the mechanisms of the cold-adaption of mutant H5. The results demonstrated that two loops involved in the substrate binding G100-S104 and S125-S129 in H5 had higher mobility, and the distance enlargement between the two loops modulated the substrate's accessibility compared with wild type counterpart. Besides, H5 enhanced conformational flexibility by weakening salt bridges and increasing interaction with the solvent. In particular, the absence of Lys251-Asp197-Arg247 salt bridge network may contribute to the structural mobility. Based on the free energy landscape and molecular mechanics Poisson-Boltzmann surface area of the wild type and H5, it was elucidated that H5 possessed a large population of interconvertible conformations, resulting in the weaker substrate binding free energy. The calculated RIN topology parameters such as the average degree, average cluster coefficient, and average path length further verified that the mutant H5 attenuated residue-to-residue interactions. The investigation of the mechanisms by which how the residue mutation affects the stability and activity of enzymes provides a theoretical basis for the development of cold-adapted protease.

Variation of CI-2 Conformers upon Addition of Methanol to Water: An IMS-MS-Based Thermodynamic Analysis

Chymotrypsin inhibitor 2 (CI-2) is a well-studied, textbook example of a cooperative, two-state, native ↔ denatured folding transition. A recent hybrid ion mobility spectrometry (IMS)/mass spectrometry (MS) thermal denaturation study of CI-2 (the well-studied truncated 64-residue model) in water reported evidence that this two-state transition involves numerous (∼41) unique native and non-native (denatured) solution conformations. The characterization of so many, often low-abundance, states is possible because of the very high dynamic range of IMS-MS measurements of ionic species that are produced upon electrospraying CI-2 solutions from a variable temperature electrospray ionization source. A thermodynamic analysis of these states revealed large changes in enthalpy (ΔH) and entropy (ΔS) at different temperatures, and it was suggested that such variation might arise because of temperature-dependent conformational changes of the protein in response to changes in the conformational entropy and the dielectric permeability of water, which drops from a value of ε ∼ 79 at 24 °C to ∼ 60 at 82 °C. Herein, we examine how adding methanol to water influences the distributions of CI-2 conformers and their ensuing stabilities. The dielectric constant of a 60:40 water:methanol (MeOH) drops from ε ∼ 60 at 24 °C to ∼ 51 at 64 °C. Although the same set of conformers observed in water appears to be present in 60:40 water:MeOH, the abundance of each is substantially altered by the presence of methanol. Relative free energy values (ΔG) and thermodynamic values [ΔH and ΔS and heat capacities (ΔCp)] are derived from a Gibbs-Helmholtz analysis. A comparison of these data from water and water:MeOH systems allows rare insight into how variations in solvation and temperature affect many-state protein equilibria. While these studies confirm that variations in solvent dielectric constant with temperature affect the distributions of conformers that are observed, our findings suggest that other solvent differences may also affect abundances.

Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins

Intrinsic disorder is instrumental for a wide range of protein functions, and its analysis, using computational predictions from primary structures, complements secondary and tertiary structure-based approaches. In this Tutorial, we provide an overview and comparison of 23 publicly available computational tools with complementary parameters useful for intrinsic disorder prediction, partly relying on results from the Critical Assessment of protein Intrinsic Disorder prediction experiment. We consider factors such as accuracy, runtime, availability and the need for functional insights. The selected tools are available as web servers and downloadable programs, offer state-of-the-art predictions and can be used in a high-throughput manner. We provide examples and instructions for the selected tools to illustrate practical aspects related to the submission, collection and interpretation of predictions, as well as the timing and their limitations. We highlight two predictors for intrinsically disordered proteins, flDPnn as accurate and fast and IUPred as very fast and moderately accurate, while suggesting ANCHOR2 and MoRFchibi as two of the best-performing predictors for intrinsically disordered region binding. We link these tools to additional resources, including databases of predictions and web servers that integrate multiple predictive methods. Altogether, this Tutorial provides a hands-on guide to comparatively evaluating multiple predictors, submitting and collecting their own predictions, and reading and interpreting results. It is suitable for experimentalists and computational biologists interested in accurately and conveniently identifying intrinsic disorder, facilitating the functional characterization of the rapidly growing collections of protein sequences.

In silico protein dynamics in the human cytoplasm: Partial folding, misfolding, fold switching, and non-native interactions

We examine the influence of cellular interactions in all-atom models of a section of the Homo sapiens cytoplasm on the early folding events of the three-helix bundle protein B (PB). While genetically engineered PB is known to fold in dilute water box simulations in three microseconds, the three initially unfolded PB copies in our two cytoplasm models using a similar force field did not reach the native state during 30-microsecond simulations. We did however capture the formation of all three helices in a compact native-like topology. Folding in vivo is delayed because intramolecular contact formation within PB is in direct competition with intermolecular contacts between PB and surrounding macromolecules. In extreme cases, intermolecular beta-sheets are formed. Interactions with other macromolecules are also observed to promote structure formation, for example when a PB helix in our simulations is shielded from solvent by macromolecular crowding. Sticking and crowding in our models initiate sampling of helix/sheet structural plasticity of PB. Relatedly, in past in vitro experiments, similar GA domains were shown to switch between two different folds. Finally, we also observed that stickiness between PB and the cellular environment can be modulated in our simulations through the reduction in protein hydrophobicity when we reversed PB back to the wild-type sequence. This study demonstrates that even fast-folding proteins can get stuck in non-native states in the cell, making them useful models for protein-chaperone interactions and early stages of aggregate formation relevant to cellular disease.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

Appraisal of blob-Based Approaches in the Prediction of Protein Folding Times

A series of reports published in the last 3 years has illustrated that a blob-based model (BBM) can predict the folding time of proteins from their primary amino acid (aa) sequence based on three simple rules established to characterize the long-range backbone dynamics (LRBD) of racemic polypeptides. The sole use of LRBD to predict protein folding times with the BBM represents a radical departure from all other prediction methods currently applied to determine protein folding times, which rely instead on parameters such as the structure content, folding kinetics, chain length, amino acid properties, or contact topography of proteins. Furthermore, the built-in modularity of the BBM enables the parametrization and inclusion of new phenomena affecting the LRBD of polypeptides, while its conceptual simplicity makes it an interesting new mathematical tool for studying protein folding. However, its novelty implies that its relationship with many other methods used to predict protein folding times has not been well researched. Consequently, the purpose of this report is to uncover the physical phenomena encountered during protein folding that are best described by the BBM through the identification of parameters that have been recognized over the years as being strong predictors for protein folding, such as protein size, topology, structural class, and folding kinetics. This was accomplished by determining the parameters most strongly correlated with the folding times predicted by the BBM. While the BBM in its present form appears to be a good indicator of the folding times of the vast majority of the 195 proteins considered so far, this report finds that it excels for moderately large proteins that are primarily composed of locally formed structural motifs such as α-helices or for proteins that fold in multiple steps. Altogether, these observations based on the use of the BBM support the notion that proteins fold the way they do because the LRBD of polypeptides is mostly driven by the local interactions experienced between aa's within reach of one another.

Accurate prediction of protein folding mechanisms by simple structure-based statistical mechanical models

Recent breakthroughs in highly accurate protein structure prediction using deep neural networks have made considerable progress in solving the structure prediction component of the 'protein folding problem'. However, predicting detailed mechanisms of how proteins fold into specific native structures remains challenging, especially for multidomain proteins constituting most of the proteomes. Here, we develop a simple structure-based statistical mechanical model that introduces nonlocal interactions driving the folding of multidomain proteins. Our model successfully predicts protein folding processes consistent with experiments, without the limitations of protein size and shape. Furthermore, slight modifications of the model allow prediction of disulfide-oxidative and disulfide-intact protein folding. These predictions depict details of the folding processes beyond reproducing experimental results and provide a rationale for the folding mechanisms. Thus, our physics-based models enable accurate prediction of protein folding mechanisms with low computational complexity, paving the way for solving the folding process component of the 'protein folding problem'.

Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines

Antibodies and other new antibody-like formats have emerged as one of the most rapidly growing classes of biotherapeutic proteins. Understanding the structural features that drive antibody function and, consequently, their molecular recognition is critical for engineering antibodies. Here, we present the structural architecture of conventional IgG antibodies alongside other formats. We emphasize the importance of considering antibodies as conformational ensembles in solution instead of focusing on single-static structures because their functions and properties are strongly governed by their dynamic nature. Thus, in this review, we provide an overview of the unique structural and dynamic characteristics of antibodies with respect to their antigen recognition, biophysical properties, and effector functions. We highlight the numerous technical advances in antibody structure prediction and design, enabled by the vast number of experimentally determined high-quality structures recorded with cryo-EM, NMR, and X-ray crystallography. Lastly, we assess antibody and vaccine design strategies in the context of structure and dynamics.

The Hsp70 and JDP proteins: Structure-function perspective on molecular chaperone activity

Proteins are the most structurally diverse cellular biomolecules that act as molecular machines driving essential activities of all living organisms. To be functional, most of the proteins need to fold into a specific three-dimensional structure, which on one hand should be stable enough to oppose disruptive conditions and on the other hand flexible enough to allow conformational dynamics necessary for their biological functions. This compromise between stability and dynamics makes proteins susceptible to stress-induced misfolding and aggregation. Moreover, the folding process itself is intrinsically prone to conformational errors. Molecular chaperones are proteins that mitigate folding defects and maintain the structural integrity of the cellular proteome. Promiscuous Hsp70 chaperones are central to these processes and their activity depends on the interaction with obligatory J-domain protein (JDP) partners. In this review, we discuss structural aspects of Hsp70s, JDPs, and their interaction in the context of biological activities.

Einstein Model of a Graph to Characterize Protein Folded/Unfolded States

The folded structures of proteins can be accurately predicted by deep learning algorithms from their amino-acid sequences. By contrast, in spite of decades of research studies, the prediction of folding pathways and the unfolded and misfolded states of proteins, which are intimately related to diseases, remains challenging. A two-state (folded/unfolded) description of protein folding dynamics hides the complexity of the unfolded and misfolded microstates. Here, we focus on the development of simplified order parameters to decipher the complexity of disordered protein structures. First, we show that any connected, undirected, and simple graph can be associated with a linear chain of atoms in thermal equilibrium. This analogy provides an interpretation of the usual topological descriptors of a graph, namely the Kirchhoff index and Randić resistance, in terms of effective force constants of a linear chain. We derive an exact relation between the Kirchhoff index and the average shortest path length for a linear graph and define the free energies of a graph using an Einstein model. Second, we represent the three-dimensional protein structures by connected, undirected, and simple graphs. As a proof of concept, we compute the topological descriptors and the graph free energies for an all-atom molecular dynamics trajectory of folding/unfolding events of the proteins Trp-cage and HP-36 and for the ensemble of experimental NMR models of Trp-cage. The present work shows that the local, nonlocal, and global force constants and free energies of a graph are promising tools to quantify unfolded/disordered protein states and folding/unfolding dynamics. In particular, they allow the detection of transient misfolded rigid states.

Structure of amino acid sequence-reversed wtRop protein: insights from atomistic molecular dynamics simulations

This study aims to the investigation of the advantages of designing new proteins presume upon a 'bias' sequence of amino acids, based on the reversed sequence of parent proteins, such as the retro ones. The structural simplicity of wtRop offers a very attractive model system to study these aspects. The current work is based on all-atom Molecular Dynamics (MD) simulations and corresponding experimental evidence on two different types of reversed wtRop protein, one with a fully reversed sequence of amino acids (rRop) and another with a partially reversed sequence (prRop), where only the five residues of the loop region (30ASP-34GLN) were not reversed. The exploration of the structure of the two retro proteins is performed highlighting the similarities and the differences with their parent protein, by employing various measures. Two models have been studied for both reversed proteins, a dimeric and a monomeric with the former one found to be more stable than the latter. Preferable equilibrium structures that the protein molecule can attain are explored, indicating the equilibration pathway. Simulation findings indicate a disruption of the α-helical structure and the appearance of additional secondary structures for both retro proteins. Reduced structural stability compared to their parent protein (wtRop) is also found. A corruption of the hydrophobic core is observed in the dimeric models. Furthermore, the simulations findings are consistent with the experimental characterization of prRop by circular dichroism spectroscopy (CD) which highlights an unstable, highly α-helical protein.Communicated by Ramaswamy H. Sarma.

Development of a novel prediction model based on protein structure for identifying RPE65-associated inherited retinal disease (IRDs) of missense variants

Purpose

This study aimed to develop a prediction model to classify RPE65-mediated inherited retinal disease (IRDs) based on protein secondary structure and to analyze phenotype-protein structure correlations of RPE65 missense variants in a Chinese cohort.

Methods

Pathogenic or likely pathogenic missense variants of RPE65 were obtained from UniProt, ClinVar, and HGMD databases. The three-dimensional structure of RPE65 was retrieved from the Protein Data Bank (PDB) and modified with Pymol software. A novel prediction model was developed using LASSO regression and multivariate logistic regression to identify RPE65-associated IRDs. A total of 21 Chinese probands with RPE65 variants were collected to analyze phenotype-protein structure correlations of RPE65 missense variants.

Results

The study found that both pathogenic and population missense variants were associated with structural features of RPE65. Pathogenic variants were linked to sheet, β-sheet, strands, β-hairpins, Fe2+ (iron center), and active site cavity, while population variants were related to helix, loop, helices, and helix-helix interactions. The novel prediction model showed accuracy and confidence in predicting the disease type of RPE65 variants (AUC = 0.7531). The study identified 25 missense variants in Chinese patients, accounting for 72.4% of total mutations. A significant correlation was observed between clinical characteristics of RPE65-associated IRDs and changes in amino acid type, specifically for missense variants of F8 (H68Y, P419S).

Conclusion

The study developed a novel prediction model based on the protein structure of RPE65 and investigated phenotype-protein structure correlations of RPE65 missense variants in a Chinese cohort. The findings provide insights into the precise diagnosis of RPE65-mutated IRDs.

Resolving Hidden Solution Conformations of Hemoglobin Using IMS-IMS on a Cyclic Instrument

Ion mobility spectrometry-mass spectrometry (IMS-MS) experiments on a cyclic IMS instrument were used to examine heterogeneous distributions of structures found in the 15+ to 18+ charge states of the hemoglobin tetramer (Hb). The resolving power of IMS measurements is known to increase with increasing drift-region length. This effect is not significant for Hb charge states as peaks were shown to broaden with increasing drift-region length. This observation suggests that multiple structures with similar cross sections may be present. To examine this hypothesis, selections of drift time distributions were isolated and subsequently reinjected into the mobility region for additional separation. These IMS-IMS experiments demonstrate that selected regions separate further upon additional passes around the drift cell, consistent with the idea that initial resolving power was limited due to the presence of many closely related conformations. Additional variable temperature electrospray ionization (vT-ESI) experiments were conducted to study how changing the solution temperature affects solution conformations. Some features in these IMS-IMS studies were observed to change similarly with solution temperature compared to features in the single IMS distribution. Other features changed differently in the selected mobility data, indicating that solution structures that were obscured upon IMS analysis because of the complex heterogeneity of the original distribution are discernible after reducing the number of conformers that are analyzed by further IMS analysis. These results illustrate that the combination of vT-ESI with IMS-IMS is useful for resolving and exploring conformer distributions and stabilities in systems that exhibit a large degree of structural heterogeneity.

The "Beacon" Structural Model of Protein Folding: Application for Trp-Cage in Water

Protein folding is a process in which a polypeptide must undergo folding process to obtain its three-dimensional structure. Thermodynamically, it is a process of enthalpy to overcome the loss of conformational entropy in folding. Folding is primarily related to hydrophobic interactions and intramolecular hydrogen bondings. During folding, hydrophobic interactions are regarded to be the driving forces, especially in the initial structural collapse of a protein. Additionally, folding is guided by the strong interactions within proteins, such as intramolecular hydrogen bondings related to the α-helices and β-sheets of proteins. Therefore, a protein is divided into the folding key (FK) regions related to intramolecular hydrogen bondings and the non-folding key (non-FK) regions. Various conformations are expected for FK and non-FK regions. Different from non-FK regions, it is necessary for FK regions to form the specific conformations in folding, which are regarded as the necessary folding pathways (or "beacons"). Additionally, sequential folding is expected for the FK regions, and the intermediate state is found during folding. They are reflected on the local basins in the free energy landscape (FEL) of folding. To demonstrate the structural model, molecular dynamics (MD) simulations are conducted on the folding pathway of the TRP-cage in water.

New drug target identification in Vibrio vulnificus by subtractive genome analysis and their inhibitors through molecular docking and molecular dynamics simulations

Vibrio vulnificus is a rod shape, Gram-negative bacterium that causes sepsis (with a greater than 50% mortality rate), necrotizing fasciitis, gastroenteritis, skin, and soft tissue infection, wound infection, peritonitis, meningitis, pneumonia, keratitis, and arthritis. Based on pathogenicity V. vulnificus is categorized into three biotypes. Type 1 and type 3 cause diseases in humans while biotype 2 causes diseases in eel and fish. Due to indiscriminate use of antibiotics V. vulnificus has developed resistance to many antibiotics so curing is dramatically a challenge. V. vulnificus is resistant to cefazolin, streptomycin, tetracycline, aztreonam, tobramycin, cefepime, and gentamycin. Subtractive genome analysis is the most effective method for drug target identification. The method is based on the subtraction of homologous proteins from both pathogen and host. By this process set of proteins present only in the pathogen and perform essential functions in the pathogen can be identified. The entire proteome of Vibrio vulnificus strain ATCC 27562 was reduced step by step to a single protein predicted as the drug target. AlphaFold2 is one of the applications of deep learning algorithms in biomedicine and is correctly considered the game changer in the field of structural biology. Accuracy and speed are the major strength of AlphaFold2. In the PDB database, the crystal structure of the predicted drug target was not present, therefore the Colab notebook was used to predict the 3D structure by the AlphaFold2, and subsequently, the predicted model was validated. Potent inhibitors against the new target were predicted by virtual screening and molecular docking study. The most stable compound ZINC01318774 tightly attaches to the binding pocket of bisphosphoglycerate-independent phosphoglycerate mutase. The time-dependent molecular dynamics simulation revealed compound ZINC01318774 was superior as compared to the standard drug tetracycline in terms of stability. The availability of V. vulnificus strain ATCC 27562 has allowed in silico identification of drug target which will provide a base for the discovery of specific therapeutic targets against Vibrio vulnificus.

CBP60-DB: An AlphaFold-predicted plant kingdom-wide database of the CALMODULIN-BINDING PROTEIN 60 protein family with a novel structural clustering algorithm

Molecular genetic analyses in the model species Arabidopsis thaliana have demonstrated the major roles of different CALMODULIN-BINDING PROTEIN 60 (CBP60) proteins in growth, stress signaling, and immune responses. Prominently, CBP60g and SARD1 are paralogous CBP60 transcription factors that regulate numerous components of the immune system, such as cell surface and intracellular immune receptors, MAP kinases, WRKY transcription factors, and biosynthetic enzymes for immunity-activating metabolites salicylic acid (SA) and N-hydroxypipecolic acid (NHP). However, their function, regulation, and diversification in most species remain unclear. Here, we have created CBP60-DB (https://cbp60db.wlu.ca/), a structural and bioinformatic database that comprehensively characterized 1052 CBP60 gene homologs (encoding 2376 unique transcripts and 1996 unique proteins) across 62 phylogenetically diverse genomes in the plant kingdom. We have employed deep learning-predicted structural analyses using AlphaFold2 and then generated dedicated web pages for all plant CBP60 proteins. Importantly, we have generated a novel clustering visualization algorithm to interrogate kingdom-wide structural similarities for more efficient inference of conserved functions across various plant taxa. Because well-characterized CBP60 proteins in Arabidopsis are known to be transcription factors with putative calmodulin-binding domains, we have integrated external bioinformatic resources to analyze protein domains and motifs. Collectively, we present a plant kingdom-wide identification of this important protein family in a user-friendly AlphaFold-anchored database, representing a novel and significant resource for the broader plant biology community.

Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes

MOTIVATION

Despite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established.

RESULTS

We analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g. TM-score) but poorly on another (e.g. DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures.

AVAILABILITY AND IMPLEMENTATION

All scripts, models, and data used to perform the analysis in this study are freely available at https://gitlab.com/ElofssonLab/afm-benchmark.

A tile model of circuit topology for self-entangled biopolymers

Building on the theory of circuit topology for intra-chain contacts in entangled proteins, we introduce tiles as a way to rigorously model local entanglements which are held in place by molecular forces. We develop operations that combine tiles so that entangled chains can be represented by algebraic expressions. Then we use our model to show that the only knot types that such entangled chains can have are [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and connected sums of these knots. This includes all proteins knots that have thus far been identified.

Protein-folding-inspired approach for UF fouling mitigation using elevated membrane cleaning temperature and residual hydrophobic-modified flocculant after flocculation-sedimentation pre-treatment

Hydrophobic-modified flocculants have demonstrated considerable promise in the removal of emerging contaminants by flocculation. However, there is a lack of information about the impacts of dosing such flocculants on the performance of subsequent treatment unit(s) in the overall water treatment process. In this work, inspired by the ubiquitous protein folding phenomenon, an innovative approach using an elevated membrane cleaning temperature as the means to induce residual hydrophobic-modified chitosan flocculant (TRC), after flocculation-sedimentation, to reduce membrane fouling in a subsequent ultrafiltration was proposed; this was evaluated in a continuous flocculation-sedimentation-ultrafiltration (FSUF) process treating samples of the Yangtze River. The hydrophobic chains of TRC had similar temperature-dependent hydrophobicity to those of natural proteins. In the 40-day operation of the FSUF system with combined dosing of alum and TRC, a moderately elevated cleaning water temperature (45 °C) of both backwash with air-bubbling and soaking with sponge-scrubbing cleaning, significantly reduced reversible and irreversible fouling resistance by 49.8%∼61.3% and 73.9%∼83.3%, respectively, compared to the system using cleaning water at 25 °C. Material flow analysis, statistical analysis, instrumental characterizations, and computational simulations, showed that the enhanced fouling mitigation originated from three factors: the reduced contaminant accumulation onto membranes, the strengthened membrane-surface-modification role of TRC, and the weakened structure of the fouling material containing TRC, at the elevated cleaning temperature. Other measures of the performance, these being water purification, membrane stability and economic aspects, also confirmed the potential and feasibility of the proposed approach. This work has provided new insights into the role of hydrophobic-modified flocculants in membrane fouling control, in addition to emerging contaminant removal, in a FSUF surface water treatment process.

Polymer folding through active processes recreates features of genome organization

From proteins to chromosomes, polymers fold into specific conformations that control their biological function. Polymer folding has long been studied with equilibrium thermodynamics, yet intracellular organization and regulation involve energy-consuming, active processes. Signatures of activity have been measured in the context of chromatin motion, which shows spatial correlations and enhanced subdiffusion only in the presence of adenosine triphosphate. Moreover, chromatin motion varies with genomic coordinate, pointing toward a heterogeneous pattern of active processes along the sequence. How do such patterns of activity affect the conformation of a polymer such as chromatin? We address this question by combining analytical theory and simulations to study a polymer subjected to sequence-dependent correlated active forces. Our analysis shows that a local increase in activity (larger active forces) can cause the polymer backbone to bend and expand, while less active segments straighten out and condense. Our simulations further predict that modest activity differences can drive compartmentalization of the polymer consistent with the patterns observed in chromosome conformation capture experiments. Moreover, segments of the polymer that show correlated active (sub)diffusion attract each other through effective long-ranged harmonic interactions, whereas anticorrelations lead to effective repulsions. Thus, our theory offers nonequilibrium mechanisms for forming genomic compartments, which cannot be distinguished from affinity-based folding using structural data alone. As a first step toward exploring whether active mechanisms contribute to shaping genome conformations, we discuss a data-driven approach.

Disruption of Spatial Proximities among Charged Groups in Equilibrium-Denatured States of Proteins Tracked Using Protein Charge Transfer Spectra

The absorption and luminescence originating from protein charge transfer spectra (ProCharTS) depend on the proximity between multiple charged groups in a protein. This makes ProCharTS absorbance/luminescence intensity a sensitive probe for detecting changes in the protein structure, which alter the proximity among charged groups in the protein. In this work, ProCharTS absorbance of charge-rich proteins like human serum albumin (HSA), α₃C, and α₃W was used to monitor structural changes upon chemical denaturant-induced protein unfolding under equilibrium conditions. The denaturation midpoints were estimated using nonlinear regression analysis. For HSA, absorbance at 325 and 340 nm estimated the GdnHCl-induced denaturation midpoints to be 0.80 and 0.61 M, respectively. A similar analysis of α₃C and α₃W ProCharTS absorbance yielded denaturation midpoints of 0.88 and 0.86 M at 325 nm and 0.96 and 0.66 M at 340 nm, respectively. A previously reported molten globule-like state in the GdnHCl-induced HSA unfolding pathway was detected by the increase in HSA ProCharTS absorbance at 0.5 M GdnHCl. To validate the above results, protein unfolding was additionally monitored using conventional methods like circular dichroism (CD), Trp, and dansyl fluorescence. Our results suggest that disruption of charged amino acid sidechain contacts as revealed by ProCharTS occurs at lower denaturant concentrations compared to the loss of secondary/folded structure monitored by CD and fluorescence. Further, HSA ProCharTS absorbance at 315-340 nm revealed that tertiary contacts among charged residues were disrupted at lower GdnHCl concentrations compared to sequence adjacent contacts. Our data underscore the utility of ProCharTS as a novel label-free tool to track unfolding in charge-rich proteins.

Rethinking the protein folding problem from a new perspective

One of the main concerns of Anfinsen was to reveal the connection between the amino-acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, the thermodynamic hypothesis. As to the how, this remains an unsolved challenge. Consequently, the protein folding problem is examined here from a new perspective, namely, as an 'analytic whole'. Conceiving the protein folding in this way enabled us to (i) examine in detail why the force-field-based approaches have failed, among other purposes, in their ability to predict the three-dimensional structure of a protein accurately; (ii) propose how to redefine them to prevent these shortcomings, and (iii) conjecture on the origin of the state-of-the-art numerical-methods success to predict the tridimensional structure of proteins accurately.

Self-Assembly of Phenylalanine-Leucine, Leucine-Phenylalanine, and Cyclo(-leucine-phenylalanine) Dipeptides through Simulations and Experiments

For over two decades, peptide self-assembly has been the focus of attention and a great source of inspiration for biomedical and nanotechnological applications. The resulting peptide nanostructures and their properties are closely related to the information encoded within each peptide building block, their sequence, and their modes of self-organization. In this work. we assess the behavior and differences between the self-association of the aromatic-aliphatic Phe-Leu dipeptide compared to its retro-sequence Leu-Phe and cyclic Cyclo(-Leu-Phe) counterparts, using a combination of simulation and experimental methods. Detailed all-atom molecular dynamics (MD) simulations offer a quantitative prediction at the molecular level of the conformational, dynamical and structural properties of the peptides' self-assembly, while field emission scanning electron microscopy (FESEM) experiments allow microscopic observation of the self-assembled end-structures. The complementarity and qualitative agreement between the two methods not only highlights the differences between the self-assembly propensity of cyclic and linear retro-sequence peptides but also sheds light on underlying mechanisms of self-organization. The self-assembling propensity was found to follow the order: Cyclo(-Leu-Phe) > Leu-Phe > Phe-Leu.

Modeling the role of charged residues in thermophilic proteins by rotamer and dynamic cross correlation analysis

Discerning the determinants of protein thermostability is very important both from the theoretical and applied perspective. Different lines of evidence seem to indicate that a dynamical network of salt bridges/charged residues plays a fundamental role in the thermostability of enzymes. In this work, we applied measures of dynamic variance, like the Gini coefficients, Kullback-Leibler (KL) divergence and dynamic cross correlation (DCC) coefficients to compare the behavior of 3 pairs of homologous proteins from the thermophilic bacterium Thermus thermophilus and mesophilic Escherichia coli. Molecular dynamic (MD) simulations of these proteins were performed at 303 K and 363 K. In the characterization of their side chain rotamer distributions, the corresponding Gini coefficients and KL-divergence both revealed significant correlations with temperature. Similarly, a DCC analysis revealed a higher trend to de-correlate the movement of charged residues at higher temperatures in the thermophilic proteins, when compared with their mesophilic homologues. These results highlight the importance of dynamic electrostatic network interactions for the thermostability of enzymes.

Origins of life: first came evolutionary dynamics

When life arose from prebiotic molecules 3.5 billion years ago, what came first? Informational molecules (RNA, DNA), functional ones (proteins), or something else? We argue here for a different logic: rather than seeking a molecule type, we seek a dynamical process. Biology required an ability to evolve before it could choose and optimise materials. We hypothesise that the evolution process was rooted in the peptide folding process. Modelling shows how short random peptides can collapse in water and catalyse the elongation of others, powering both increased folding stability and emergent autocatalysis through a disorder-to-order process.

Predicting non-equilibrium folding behavior of polymer chains using the steepest-entropy-ascent quantum thermodynamic framework

The steepest-entropy-ascent quantum thermodynamic (SEAQT) framework is used to explore the influence of heating and cooling on polymer chain folding kinetics. The framework predicts how a chain moves from an initial non-equilibrium state to stable equilibrium along a unique thermodynamic path. The thermodynamic state is expressed by occupation probabilities corresponding to the levels of a discrete energy landscape. The landscape is generated using the Replica Exchange Wang-Landau method applied to a polymer chain represented by a sequence of hydrophobic and polar monomers with a simple hydrophobic-polar amino acid model. The chain conformation evolves as energy shifts among the levels of the energy landscape according to the principle of steepest entropy ascent. This principle is implemented via the SEAQT equation of motion. The SEAQT framework has the benefit of providing insight into structural properties under non-equilibrium conditions. Chain conformations during heating and cooling change continuously without sharp transitions in morphology. The changes are more drastic along non-equilibrium paths than along quasi-equilibrium paths. The SEAQT-predicted kinetics are fitted to rates associated with the experimental intensity profiles of cytochrome c protein folding with Rouse dynamics.

Dissecting the energetic architecture within an RNA tertiary structural motif via high-throughput thermodynamic measurements

Structured RNAs and RNA/protein complexes perform critical cellular functions. They often contain structurally conserved tertiary contact "motifs," whose occurrence simplifies the RNA folding landscape. Prior studies have focused on the conformational and energetic modularity of intact motifs. Here, we turn to the dissection of one common motif, the 11nt receptor (11ntR), using quantitative analysis of RNA on a massively parallel array to measure the binding of all single and double 11ntR mutants to GAAA and GUAA tetraloops, thereby probing the energetic architecture of the motif. While the 11ntR behaves as a motif, its cooperativity is not absolute. Instead, we uncovered a gradient from high cooperativity amongst base-paired and neighboring residues to additivity between distant residues. As expected, substitutions at residues in direct contact with the GAAA tetraloop resulted in the largest decreases to binding, and energetic penalties of mutations were substantially smaller for binding to the alternate GUAA tetraloop, which lacks tertiary contacts present with the canonical GAAA tetraloop. However, we found that the energetic consequences of base partner substitutions are not, in general, simply described by base pair type or isostericity. We also found exceptions to the previously established stability-abundance relationship for 11ntR sequence variants. These findings of "exceptions to the rule" highlight the power of systematic high-throughput approaches to uncover novel variants for future study in addition to providing an energetic map of a functional RNA.

Synergetic Effects of Alanine and Glycine in Blob-Based Methods for Predicting Protein Folding Times

The polypeptide PGlyAlaGlu was prepared with 20 mol % glycine (Gly), 36 mol % d,l-alanine (Ala), and 44 mol % d,l-glutamic acid (Glu) and labeled with the dye 1-pyrenemethylamine to yield a series of Py-PGlyAlaGlu samples. The fluorescence decays of the Py-PGlyAlaGlu samples were analyzed according to the fluorescence blob model (FBM) to obtain the number N_blob^exp of amino acids (aa's) encompassed inside the subvolume V_blob of the polypeptide probed by an excited pyrene. An N_blob^exp value of 29 (±2) was retrieved for Py-PGlyAlaGlu, which was much larger than for any of the copolypeptide PGlyGlu or PAlaGlu prepared with either Gly and Glu or Ala and Glu, respectively. The continuous increase in N_blob^exp with decreasing side chain size (SCS) from 10 aa's for PGlu to 16 aa's for PAlaGlu and 22 aa's for PGlyGlu was used earlier to define the reach of an aa and determine the groups of aa's that could interact with each other along a polypeptide backbone according to their SCS. These groups of aa's, referred to as blobs, led to the implementation of blob-based models (BBM) to predict the folding time τ_F^theo,BBM of 145 proteins, which was found to match their experimental folding time τ_F^exp with a relatively high 0.71 correlation coefficient. Nevertheless, the much higher N_blob^exp value found for Py-PGlyAlaGlu compared to all other pyrene-labeled polypeptides studied to date indicates that the reach of aa's along a polypeptide sequence is affected not only by SCS but also by synergetic effects between different aa's. Following this new insight, a revised BBM was implemented to predict τ_F^theo,BBM for 195 proteins assuming the existence or absence of synergies to control the interactions between aa's along a polypeptide sequence. Similarly good correlation coefficients of 0.71 and 0.74 were obtained for a direct 1:1 comparison of τ_F^exp and τ_F^theo,BBM for the 195 proteins without and with synergies, respectively. This result suggests that synergetic effects between different aa's have little effect on τ_F^theo,BBM predicted from BBM underlying the robustness of this methodology.