The protein folding problem

Analytical Control Strategy for Biologics. Part I: Foundations

Biologic therapeutics encompass different modalities with vastly different molecular profiles. Despite these differences, all products follow a similar approach to Pharmaceutical Development, which includes an integrated control strategy that relies on a clinical target product profile (TPP), a quality target product profile (QTPP), biophysical, biochemical and biological characterization, elucidation of critical quality attributes (CQAs), and development of an analytical control strategy. Technical and regulatory requirements for biologics development are established in numerous regulatory guidance documents issued by ICH, FDA, EMA, and other bodies. While there is substantial published knowledge on specific studies needed for development of a product, there is no specific guidance on establishing a comprehensive analytical control strategy as part of a modern integrated control strategy. This commentary is Part I of a two-part commentary series on analytical control strategy. In this part we present the foundations that are essential for developing an analytical control strategy to enable efficient lifecycle management across different biologic protein-based therapeutic modalities. In Part II, we will present a stage-appropriate roadmap to implementing an analytical control strategy from discovery research through the commercial life of the biologic.

No structure, no problem: Protein stabilization by Hero proteins and other chaperone-like IDPs

In order for a protein to function, it must fold into its proper three-dimensional structure. Otherwise, improperly folded proteins are typically prone to aggregate through a process that is detrimental to cellular health. It is widely known that a diverse group of proteins, called molecular chaperones, function to promote proper folding of other proteins and prevent aggregation. In contrast, intrinsically disordered proteins (IDPs) lack substantial tertiary structures, but nonetheless serve important functional roles. In some cases, IDPs have been observed to display remarkably chaperone-like activities, where they stabilize the activities of client proteins and prevent their aggregation. While it was previously thought that chaperone-like IDPs were mainly utilized by extremophilic organisms in their survival of extreme stress, we recently showed that a group of chaperone-like IDPs, we named heat-resistant obscure (Hero) proteins, are also widespread in non-extremophile animals, including humans and flies. Thus, we should consider the possibility that IDPs serve significant chaperone-like functions in protein stabilization relevant to physiological conditions. However, as most of our understanding of how chaperones function is based on insights from their structured domains, it is unclear how chaperone-like IDPs elicit chaperone-like effects without these structures. Here we summarize our understanding of Hero proteins to date and, based on experimental evidence, outline the features that are likely important for their protein stabilizing activities. We draw on concepts from the studies of chaperones and chaperone-like IDPs, in order to draft potential models of how chaperone-like IDPs achieve chaperone-like effects in the absence of well-defined structures.

Nonequilibrium dynamics of the helix-coil transition in polyalanine

In this work, the nonequilibrium pathways of the collapse of the helix-forming biopolymer polyalanine are investigated. To this end, the full time evolution of the helix-coil transition is simulated using molecular dynamics simulations. At the start of the transition, short 310-helices form, seemingly leading to the molecule becoming more aspherical midway through the collapse. After the completed collapse, the formation of α-helices becomes the prevalent ordering mechanism leading to helical bundles, a typical structural motif representative of the equilibrium behavior of longer chains. The dynamics of this transition is quantified in terms of the power-law scaling of two associated relaxation times as a function of chain length.

Protein structure prediction via deep learning: an in-depth review

The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionally, experimental methods like X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy have been the gold standard for determining protein structures. However, these approaches are often costly, inefficient, and time-consuming. At the same time, the number of known protein sequences far exceeds the number of experimentally determined structures, creating a gap that necessitates the use of computational approaches. Deep learning has emerged as a promising solution to address this challenge over the past decade. This review provides a comprehensive guide to applying deep learning methodologies and tools in protein structure prediction. We initially outline the databases related to the protein structure prediction, then delve into the recently developed large language models as well as state-of-the-art deep learning-based methods. The review concludes with a perspective on the future of predicting protein structure, highlighting potential challenges and opportunities.

Emerging frontiers in protein structure prediction following the AlphaFold revolution

Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated in a very short space of time through a revolution in protein structure prediction driven by deep learning, led by AlphaFold. This has provided a wealth of new structural information. Interpreting these predictions is critical to determining where and when this information is useful. But proteins are not static nor do they act alone, and structures of proteins interacting with other proteins and other biomolecules are critical to a complete understanding of their biological function at the molecular level. This review focuses on the application of state-of-the-art protein structure prediction to these advanced applications. We also suggest a set of guidelines for reporting AlphaFold predictions.

Mapping conformational landscape in protein folding: Benchmarking dimensionality reduction and clustering techniques on the Trp-Cage mini-protein

Quantitative characterization of protein conformational landscapes is a computationally challenging task due to their high dimensionality and inherent complexity. In this study, we systematically benchmark several widely used dimensionality reduction and clustering methods to analyze the conformational states of the Trp-Cage mini-protein, a model system with well-documented folding dynamics. Dimensionality reduction techniques, including Principal Component Analysis (PCA), Time-lagged Independent Component Analysis (TICA), and Variational Autoencoders (VAE), were employed to project the high-dimensional free energy landscape onto 2D spaces for visualization. Additionally, clustering methods such as K-means, hierarchical clustering, HDBSCAN, and Gaussian Mixture Models (GMM) were used to identify discrete conformational states directly in the high-dimensional space. Our findings reveal that density-based clustering approaches, particularly HDBSCAN, provide physically meaningful representations of free energy minima. While highlighting the strengths and limitations of each method, our study underscores that no single technique is universally optimal for capturing the complex folding pathways, emphasizing the necessity for careful selection and interpretation of computational methods in biomolecular simulations. These insights will contribute to refining the available tools for analyzing protein conformational landscapes, enabling a deeper understanding of folding mechanisms and intermediate states.

Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives

This review provides a comprehensive analysis of AlphaFold (AF) and its derivatives (AF2 and AF3) in protein structure prediction. These tools have revolutionized structural biology with their highly accurate predictions, driving progress in protein modeling, drug discovery, and the study of protein dynamics. Its exceptional accuracy has redefined our understanding of protein folding, which enables groundbreaking advancements in protein design, disease research and discusses future integration with experimental techniques. In addition, their achievement features, architectures, important case studies, and noteworthy effects in the field of biology and medicine were evaluated. In consideration of the fact that AF2 is a relatively recent innovation, it has already been taken into account in many studies that highlight its applications in many ways. Moreover, the limitations of AF2 that directed to the introduction of AF3 are also reported, which is a great improvement as it provides precise predictions of the structures and interactions of proteins, DNA, RNA, and ligands, thereby aiding in the understanding of the molecular level. Addressing current challenges and forecasting future developments, this work underscores the lasting significance of AF in reshaping the scientific landscape of protein research.

Accuracy of distance distributions and dynamics from single-molecule FRET

Single-molecule spectroscopy combined with Förster resonance energy transfer is widely used to quantify distance dynamics and distributions in biomolecules. Most commonly, measurements are interpreted using simple analytical relations between experimental observables and the underlying distance distributions. However, these relations make simplifying assumptions, such as a separation of timescales between interdye distance dynamics, fluorescence lifetimes, and dye reorientation, the validity of which is notoriously difficult to assess from experimental data alone. Here, we use experimentally validated long-timescale, all-atom explicit-solvent molecular dynamics simulations of a disordered peptide with explicit fluorophores for testing these assumptions, in particular the separation of the relevant timescales and the description of chain dynamics in terms of diffusion in a potential of mean force. Our results allow us to quantitatively assess the resulting errors; they indicate that, even outside the simple limiting regimes, the errors from common approximations in data analysis are generally smaller than the systematic uncertainty limiting the accuracy of Förster resonance energy transfer efficiencies. We also illustrate how the direct comparison between measured and simulated experimental data can be employed to optimize force field parameters and develop increasingly realistic simulation models.

Impacts of External Electric Fields on Structures and Alignments of Ring Molecules

Ring molecules, which lack free ends, exhibit unique chemical and physical properties, making them promising candidates for nanodevice applications. Unlike their linear counterparts with two free ends, the behavior of ring molecules in water under external electric fields (EF) is not well understood. In this research, we employ molecular dynamics (MD) simulations to explore the structural and alignment behavior of two ring molecules of different sizes─C30H60 and C60H120─in water, under 300 K, 1 bar and various EF conditions, including direct current EF (DC EF), alternating current EF (AC EF), and circular polarized EF (CP EF) at different frequencies. Our findings reveal the following: (1) both large and small rings exhibit two free energy minima. For C60H120, these correspond to collapsed and stretched configurations, while for C30H60, they represent open and closed configurations. (2) The applied EF can regulate the depth of these free energy minima. For C60H120, no EF, AC EF, and high-frequency CP EF favor the collapsed state, while DC EF and low-frequency CP EF promote the stretched configuration. In the case of C30H60, no EF and high-frequency CP EF favor the open-ring state, whereas all other EF conditions tend to close the ring. (3) Both ring molecules align with the directional EF to minimize disruption of the hydrogen-bond network, with C60H120 showing a stronger alignment effect than C30H60 due to its longer structure. (4) Under CP EF, ring molecules exhibit rotation driven by the rotating EF, but there is a lag in the angle between the EF vector and the molecule's elongation. Higher frequency CP EF shows less ability to capture and align the molecule. This research enhances our understanding of how ring molecules behave in water under external EF and provides a theoretical foundation for future engineering applications involving controlled manipulation of these molecules.

Designing single-polymer-chain nanoparticles to mimic biomolecular hydration frustration

Native folded proteins rely on sculpting the local chemical environment of their active or binding sites, as well as their shapes, to achieve functionality. In particular, proteins use hydration frustration-control over the dehydration of hydrophilic residues and the hydration of hydrophobic residues-to amplify their chemical or binding activity. Here we uncover that single-polymer-chain nanoparticles formed by random heteropolymers comprising four or more components can display similar levels of hydration frustration. We categorize these nanoparticles into three types based on whether either hydrophobic or hydrophilic residues, or both types, display frustrated states. We propose a series of physicochemical rules that determine the state of these nanoparticles. We demonstrate the generality of these rules in atomistic and simplified Monte Carlo models of single-polymer-chain nanoparticles with different backbones and residues. Our work provides insights into the design of single-chain nanoparticles, an emerging polymer modality that achieves the ease and cost of fabrication of polymeric material with the functionality of biological proteins.

Navigating the light and shadow of scientific publishing faced with machine learning and generative AI

BACKGROUND

The public release of ChatGPT in November 2022 sparked a boom and public interest in generative artificial intelligence (AI) that has led to journals and journal families hastily releasing generative AI policies, ranging from asking authors for acknowledgement or declaration to the outright banning of use.

RESULTS

Here, we briefly discuss the basics of machine learning, generative AI, and how it will affect scientific publishing. We focus especially on potential risks and benefits to the scientific community as a whole and journals specifically.

CONCLUSION

While the concerns of editors, for example about manufactured studies, are valid, some recently implemented or suggested policies will not be sustainable in the long run. The quality of generated text and code is quickly becoming so high that it will not only be impossible to detect the use of generative AI but would also mean taking a powerful tool away from researchers that can make their life easier every day.

SIGNIFICANCE

We discuss the history and current state of AI and highlight its relevance for medical publishing and pain research. We provide guidance on how to act now to increase good scientific practice in the world of ChatGPT and call for a task force focusing on improving publishing pain research with use of generative AI.

Decoding the effects of mutation on protein interactions using machine learning

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect interactions between proteins and other biomolecules, such as proteins, DNA/RNA, and ligands, which are vital for regulating numerous biological processes. Developing computational approaches with high accuracy and efficiency is critical for elucidating the mechanisms underlying various diseases, identifying potential biomarkers for early diagnosis, and developing targeted therapies. This review provides a comprehensive overview of recent advancements in predicting the impact of mutations on protein interactions across different interaction types, which are central to understanding biological processes and disease mechanisms, including cancer. We summarize recent progress in predictive approaches, including physicochemical-based, machine learning, and deep learning methods, evaluating the strengths and limitations of each. Additionally, we discuss the challenges related to the limitations of mutational data, including biases, data quality, and dataset size, and explore the difficulties in developing accurate prediction tools for mutation-induced effects on protein interactions. Finally, we discuss future directions for advancing these computational tools, highlighting the capabilities of advancing technologies, such as artificial intelligence to drive significant improvements in mutational effects prediction.

The mechanism of amyloid fibril growth from Φ-value analysis

Amyloid fibrils are highly stable misfolded protein assemblies that play an important role in several neurodegenerative and systemic diseases. Although structural information of the amyloid state is now abundant, mechanistic details about the misfolding process remain elusive. Inspired by the Φ-value analysis of protein folding, we combined experiments and molecular simulations to resolve amino-acid contacts and determine the structure of the transition-state ensemble-the rate-limiting step-for fibril elongation of PI3K-SH3 amyloid fibrils. The ensemble was validated experimentally by Tanford β analysis and computationally by free energy calculations. Although protein folding proceeds on funnel-shaped landscapes, here we find that the energy landscape for the misfolding reaction consists of a large 'golf course' region, defined by a single energy barrier and transition state, accessing a sharply funnelled region. Thus, misfolding occurs by rare, successful monomer-fibril end collisions interspersed by numerous unsuccessful binding attempts. Taken together, these insights provide a quantitative and highly resolved description of a protein misfolding reaction.

Advanced molecular modeling of proteins: Methods, breakthroughs, and future prospects

The contemporary advancements in molecular modeling of proteins have significantly enhanced our comprehension of biological processes and the functional roles of proteins on a global scale. The application of advanced methodologies, including homology modeling, molecular dynamics simulations, and quantum mechanics/molecular mechanics strategies, has empowered numerous researchers to forecast the behavior of protein macromolecules, elucidate drug-protein interactions, and develop drugs with enhanced precision. This chapter elucidates the advent of deep learning algorithms such as AlphaFold, a notable advancement that has significantly improved the precision of intricate protein structure predictions. The recent advancements have significantly enhanced the precision of protein predictions and expedited drug discovery and development processes. Integrating approaches like multi-scale modeling and hybrid methods incorporating reliable experimental data is anticipated to revolutionize and offer more significant implications for precision medicine and targeted treatments.

NMR Spectroscopy for the Validation of AlphaFold2 Structures

The introduction of AlphaFold has fundamentally changed our ability to predict the structure of proteins from their primary sequence of amino acids. As machine learning (ML) and artificial intelligence (AI) based protein prediction continues to advance, we examine the potential of hybrid techniques that combine experiment and computation that may yield more accurate structures than AI alone with significantly reduced experimental burden. We have developed heuristics comparing N-edited NOESY spectra and AlphaFold predicted structures that seek to determine whether the predicted structure reasonably describes the structure of the protein which generated the NOESY. We present a large collection of data connecting entries across the BMRB, PDB and AlphaFold Database that includes experimentally derived structures and corresponding spectra, establishing it as a means to develop and test hybrid methods utilizing AlphaFold and NMR spectra to perform structure determination. These data test the new heuristics' ability to identify inaccurate AlphaFold structures. A support vector machine was developed to test the consistency of NMR data with predicted structure and we show its application to the structure of an unsolved engineered protein, LoTOP.

Motif-driven dynamics and intermediates during unfolding of multi-domain BphC enzyme

Understanding the folding mechanisms of multi-domain proteins is crucial for gaining insights into protein folding dynamics. The BphC enzyme, a key player in the degradation of polychlorinated biphenyls consists of eight identical subunits, each containing two domains, with each domain comprising two "βαβββ" motifs. In this study, we employed high-temperature molecular dynamics simulations to systematically analyze the unfolding dynamics of a BphC subunit. Our results reveal that the unfolding process of BphC is a complex, multi-intermediate, and multi-phased event. Notably, we identified a thermodynamically stable partially unfolded intermediate. The unfolding sequences, pathways, and rates of the motifs differ significantly. Motif D unfolds first and most rapidly, while Motif C initiates unfolding before Motifs A and B but completes it slightly later. The unfolding behavior of the motifs strongly influences the domain unfolding, leading to the early initiation of Domain 2 unfolding compared to Domain 1, although at a slower rate. The motifs and domains exhibit both independence and cooperativity during the unfolding process, which we interpret through proposed cascading effects. We hypothesize that the folding mechanism of BphC begins with local folding, which propagates through cooperative interactions across structural hierarchies to achieve the folded state. These findings provide new insights into the folding and unfolding mechanisms of multi-domain proteins.

Advances and Mechanisms of RNA-Ligand Interaction Predictions

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA-ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA-ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA-ligand interaction prediction tools.

The Use of Dansyl Chloride to Probe Protein Structure and Dynamics

Dansyl labeling is a widely used approach for enhancing the detection of small molecules by UV spectroscopy and mass spectrometry. It has been successfully applied to identify and quantify a variety of biological and environmental specimens. Despite clear advantages, the dansylation reaction has found very few applications in the study of proteins. We reasoned that the mild labeling conditions, small size, and rapid reaction could be beneficial for studying protein structure and dynamics. To test this, we investigated the impact of dansylation on protein fold, stability, protein-protein, and protein-cofactor interactions. We selected two model proteins, myoglobin and alcohol dehydrogenase, for analysis using native mass spectrometry and ion mobility mass spectrometry. Our work establishes the utility of dansyl chloride as a covalent probe to study protein structure and dynamics under native conditions.

Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost

Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, an amino acid sequence is tailored to meet the required abundance of a proteome, underscoring the intricate relationship between sequence and functional demand. Yet, the specific role of amino acid sequences in determining proteome abundance remains elusive. Here we show that the amino acid sequence alone encodes over half of protein abundance variation across all domains of life, ranging from bacteria to mouse and human. With an attempt to go beyond predictions, we trained a manageable-size Transformer model to interpret latent factors predictive of protein abundances. Intuitively, the model's attention focused on the protein's structural features linked to stability and metabolic costs related to protein synthesis. To probe these relationships, we introduce MGEM (Mutation Guided by an Embedded Manifold), a methodology for guiding protein abundance through sequence modifications. We find that mutations which increase predicted abundance have significantly altered protein polarity and hydrophobicity, underscoring a connection between protein structural features and abundance. Through molecular dynamics simulations we revealed that abundance-enhancing mutations possibly contribute to protein thermostability by increasing rigidity, which occurs at a lower synthesis cost.

Beyond Misfolding: A New Paradigm for the Relationship Between Protein Folding and Aggregation

Aggregation is intricately linked to protein folding, necessitating a precise understanding of their relationship. Traditionally, aggregation has been viewed primarily as a sequential consequence of protein folding and misfolding. However, this conventional paradigm is inherently incomplete and can be deeply misleading. Remarkably, it fails to adequately explain how intrinsic and extrinsic factors, such as charges and cellular macromolecules, prevent intermolecular aggregation independently of intramolecular protein folding and structure. The pervasive inconsistencies between protein folding and aggregation call for a new framework. In all combined reactions of molecules, both intramolecular and intermolecular rate (or equilibrium) constants are mutually independent; accordingly, intrinsic and extrinsic factors independently affect both rate constants. This universal principle, when applied to protein folding and aggregation, indicates that they should be treated as two independent yet interconnected processes. Based on this principle, a new framework provides groundbreaking insights into misfolding, Anfinsen's thermodynamic hypothesis, molecular chaperones, intrinsic chaperone-like activities of cellular macromolecules, intermolecular repulsive force-driven aggregation inhibition, proteome solubility maintenance, and proteinopathies. Consequently, this paradigm shift not only refines our current understanding but also offers a more comprehensive view of how aggregation is coupled to protein folding in the complex cellular milieu.

Wetting of a Dynamically Patterned Surface Is a Time-Dependent Matter

In nature and many technological applications, aqueous solutions are in contact with patterned surfaces, which are dynamic over time scales spanning from ps to μs. For instance, in biology, exposed polar and apolar residues of biomolecules form a pattern, which fluctuates in time due to side chain and conformational motions. At metal/and oxide/water interfaces, the pattern is formed by surface topmost atoms, and fluctuations are due to, e.g., local surface polarization and rearrangements in the adsorbed water layer. All these dynamics have the potential to influence key processes such as wetting, energy relaxation, and biological function. Yet, their impact on the water H-bond network remains often elusive. Here, we leverage molecular dynamics to address this fundamental question at a self-assembled monolayer (SAM)/water interface, where ns dynamics is induced by frustrating SAM-water interactions via methylation of the terminal -OH groups of poly(ethylene glycol) (PEG) chains. We find that surface dynamics couples to the water H-bond network, inducing a response on the same ns time scale. This leads to time fluctuations of local wetting, oscillating from hydrophobic to hydrophilic environments. Our results suggest that rather than average properties, it is the local─ both in time and space─ solvation that determines the chemical-physical properties of dynamically patterned surfaces in water.

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.

Solving the protein folding problem…

The protein folding problem was, to paraphrase Churchill, 'A riddle wrapped in a mystery inside an enigma'. The riddle, in this context, was the folding code; what interactions at the amino acid level are driving the folding process? The mystery was the kinetic question (Levinthal's paradox); how does the folding process occur so quickly (typically in timescales ranging from μS to mS)? Finally, the enigma represents the computational problem of developing approaches to predict the final folded sate of a protein given only its amino acid sequence. Herein, I trace the path to solving this riddle wrapped in a mystery inside an enigma.

RNA adapts its flexibility to efficiently fold and resist unfolding

Recent studies have demonstrated that the mechanisms through which biopolymers like RNA interconvert between multiple folded structures are critical for their cellular functions. A major obstacle to elucidating these mechanisms is the lack of experimental approaches that can resolve these interconversions between functionally relevant biomolecular structures. Here, we dissect the complete set of structural rearrangements executed by an ultra-stable RNA, the UUCG stem-loop, at the single-molecule level using a nano-electronic device with microsecond time resolution. We show that the stem-loop samples at least four conformations along two folding pathways leading to two distinct folded structures, only one of which has been previously observed. By modulating its flexibility, the stem-loop can adaptively select between these pathways, enabling it to both fold rapidly and resist unfolding. This paradigm of stabilization through compensatory changes in flexibility broadens our understanding of stable RNA structures and is expected to serve as a general strategy employed by all biopolymers.

A primer for junior trainees: Recognition of RNA modifications by RNA-binding proteins

The complexity of RNA cannot be fully expressed with the canonical A, C, G, and U alphabet. To date, over 170 distinct chemical modifications to RNA have been discovered in living systems. RNA modifications can profoundly impact the cellular outcomes of messenger RNAs (mRNAs), transfer and ribosomal RNAs, and noncoding RNAs. Additionally, aberrant RNA modifications are associated with human disease. RNA modifications are a rising topic within the fields of biochemistry and molecular biology. The role of RNA modifications in gene regulation, disease pathogenesis, and therapeutic applications increasingly captures the attention of the scientific community. This review aims to provide undergraduates, junior trainees, and educators with an appreciation for the significance of RNA modifications in eukaryotic organisms, alongside the skills required to identify and analyze fundamental RNA-protein interactions. The pumilio RNA-binding protein and YT521-B homology (YTH) family of modified RNA-binding proteins serve as examples to highlight the fundamental biochemical interactions that underlie the specific recognition of both unmodified and modified ribonucleotides, respectively. By instilling these foundational, textbook concepts through practical examples, this review contributes an analytical toolkit that facilitates engagement with RNA modifications research at large.

From Geometry of Hamiltonian Dynamics to Topology of Phase Transitions: A Review

In this review work, we outline a conceptual path that, starting from the numerical investigation of the transition between weak chaos and strong chaos in Hamiltonian systems with many degrees of freedom, comes to highlight how, at the basis of equilibrium phase transitions, there must be major changes in the topology of submanifolds of the phase space of Hamiltonian systems that describe systems that exhibit phase transitions. In fact, the numerical investigation of Hamiltonian flows of a large number of degrees of freedom that undergo a thermodynamic phase transition has revealed peculiar dynamical signatures detected through the energy dependence of the largest Lyapunov exponent, that is, of the degree of chaoticity of the dynamics at the phase transition point. The geometrization of Hamiltonian flows in terms of geodesic flows on suitably defined Riemannian manifolds, used to explain the origin of deterministic chaos, combined with the investigation of the dynamical counterpart of phase transitions unveils peculiar geometrical changes of the mechanical manifolds in correspondence to the peculiar dynamical changes at the phase transition point. Then, it turns out that these peculiar geometrical changes are the effect of deeper topological changes of the configuration space hypersurfaces ∑v=VN-1(v) as well as of the manifolds {Mv=VN-1((-∞,v])}v∈R bounded by the ∑v. In other words, denoting by vc the critical value of the average potential energy density at which the phase transition takes place, the members of the family {∑v}vvc; additionally, the members of the family {Mv}v>vc are not diffeomorphic to those of {Mv}v>vc. The topological theory of the deep origin of phase transitions allows a unifying framework to tackle phase transitions that may or may not be due to a symmetry-breaking phenomenon (that is, with or without an order parameter) and to finite/small N systems.

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning

Regulatory proteins have evolved diverse repressor domains (RDs) to enable precise context-specific repression of transcription. However, our understanding of how sequence variation impacts the functional activity of RDs is limited. To address this gap, we generated a high-throughput mutational scanning dataset measuring the repressor activity of 115,000 variant sequences spanning more than 50 RDs in human cells. We identified thousands of clinical variants with loss or gain of repressor function, including TWIST1 HLH variants associated with Saethre-Chotzen syndrome and MECP2 domain variants associated with Rett syndrome. We also leveraged these data to annotate short linear interacting motifs (SLiMs) that are critical for repression in disordered RDs. Then, we designed a deep learning model called TENet ( T ranscriptional E ffector Net work) that integrates sequence, structure and biochemical representations of sequence variants to accurately predict repressor activity. We systematically tested generalization within and across domains with varying homology using the mutational scanning dataset. Finally, we employed TENet within a directed evolution sequence editing framework to tune the activity of both structured and disordered RDs and experimentally test thousands of designs. Our work highlights critical considerations for future dataset design and model training strategies to improve functional variant prioritization and precision design of synthetic regulatory proteins.

Conformational fluidity of intrinsically disordered proteins in crowded environment: a molecular dynamics simulation study

The class of intrinsically disordered proteins lacks stable three-dimensional structures. Their flexibility allows them to engage in a wide variety of interactions with other biomolecules thus making them biologically relevant and efficient. The intrinsic disorders of these proteins, which undergo binding-induced folding, allow alterations in their topologies while conserving their binding sites. Due to the lack of well-defined three-dimensional structures in the absence of their physiological partners, the folding and the conformational dynamics of these proteins remained poorly understood. Particularly, it is unclear how these proteins exist in the crowded intracellular milieu. In the present study, molecular dynamic simulations of two intrinsically unstructured proteins and two controls (folded proteins) were conducted in the presence and absence of molecular crowders to obtain an in-depth insight into their conformational flexibility. The present study revealed that polymer crowders stabilize the disordered proteins through enthalpic as well as entropic effects that are significantly more than their monomeric counterpart. Taken together, the study delves deep into crowding effects on intrinsically disordered proteins and provides insights into how molecular crowders induce a significantly diverse ensemble of dynamic scaffolds needed to carry out diverse functions.

Electric field modulated configuration and orientation of aqueous molecule chains

Understanding how external electric fields (EFs) impact the properties of aqueous molecules is crucial for various applications in chemistry, biology, and engineering. In this paper, we present a study utilizing molecular dynamics simulation to explore how direct-current (DC) and alternative-current (AC) EFs affect hydrophobic (n-triacontane) and hydrophilic (PEG-10) oligomer chains. Through a machine learning approach, we extract a 2-dimensional free energy (FE) landscape of these molecules, revealing that electric fields modulate the FE landscape to favor stretched configurations and enhance the alignment of the chain with the electric field. Our observations indicate that DC EFs have a more prominent impact on modulation compared to AC EFs and that EFs have a stronger effect on hydrophobic chains than on hydrophilic oligomers. We analyze the orientation of water dipole moments and hydrogen bonds, finding that EFs align water molecules and induce more directional hydrogen bond networks, forming 1D water structures. This favors the stretched configuration and alignment of the studied oligomers simultaneously, as it minimizes the disruption of 1D structures. This research deepens our understanding of the mechanisms by which electric fields modulate molecular properties and could guide the broader application of EFs to control other aqueous molecules, such as proteins or biomolecules.

Contact-Map-Driven Exploration of Heterogeneous Protein-Folding Paths

We have recently shown how physically realizable protein-folding pathways can be generated using directed walks in the space of inter-residue contact-maps; combined with a back-transformation to move from protein contact-maps to Cartesian coordinates, we have demonstrated how this approach can generate protein-folding trajectory ensembles without recourse to molecular dynamics. In this article, we demonstrate that this framework can be used to study a challenging protein-folding problem that is known to exhibit two different folding paths which were previously identified through molecular dynamics simulation at several different temperatures. From the viewpoint of protein-folding mechanism prediction, this particular problem is extremely challenging to address, specifically involving folding to an identical nontrivial compact native structure along distinct pathways defined by heterogeneous secondary structural elements. Here, we show how our previously reported contact-map-based protein-folding strategy can be significantly enhanced to enable accurate and robust prediction of heterogeneous folding paths by (i) introducing a novel topologically informed metric for comparing two protein contact maps, (ii) reformulating our graph-represented folding path generation, and (iii) introducing a new and more reliable structural back-mapping algorithm. These changes improve the reliability of generating structurally sound folding intermediates and dramatically decrease the number of physically irrelevant folding intermediates generated by our previous simulation strategy. Most importantly, we demonstrate how our enhanced folding algorithm can successfully identify the alternative folding mechanisms of a multifolding-pathway protein, in line with direct molecular dynamics simulations.

Origins of Life: The Protein Folding Problem all over again?

How did specific useful protein sequences arise from simpler molecules at the origin of life? This seemingly needle-in-a-haystack problem has remarkably close resemblance to the old Protein Folding Problem, for which the solution is now known from statistical physics. Based on the logic that Origins must have come only after there was an operative evolution mechanism-which selects on phenotype, not genotype-we give a perspective that proteins and their folding processes are likely to have been the primary driver of the early stages of the origin of life.

Delayed Excitations Induce Polymer Looping and Coherent Motion

We consider inhomogeneous polymers driven by energy-consuming active processes which encode temporal patterns of athermal kicks. We find that such temporal excitation programs, propagated by tension along the polymer, can effectively couple distinct polymer loci. Consequently, distant loci exhibit correlated motions that fold the polymer into specific conformations, as set by the local actions of the active processes and their distribution along the polymer. Interestingly, active kicks that are canceled out by a time-delayed echo can induce strong compaction of the active polymer.

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.

A Stochastic Landscape Approach for Protein Folding State Classification

Protein folding is a critical process that determines the functional state of proteins. Proper folding is essential for proteins to acquire their functional three-dimensional structures and execute their biological role, whereas misfolded proteins can lead to various diseases, including neurodegenerative disorders like Alzheimer's and Parkinson's. Therefore, a deeper understanding of protein folding is vital for understanding disease mechanisms and developing therapeutic strategies. This study introduces the Stochastic Landscape Classification (SLC), an innovative, automated, nonlearning algorithm that quantitatively analyzes protein folding dynamics. Focusing on collective variables (CVs) - low-dimensional representations of complex dynamical systems like molecular dynamics (MD) of macromolecules - the SLC approach segments the CVs into distinct macrostates, revealing the protein folding pathway explored by MD simulations. The segmentation is achieved by analyzing changes in CV trends and clustering these segments using a standard density-based spatial clustering of applications with noise (DBSCAN) scheme. Applied to the MD-based CV trajectories of Chignolin and Trp-Cage proteins, the SLC demonstrates apposite accuracy, validated by comparing standard classification metrics against ground-truth data. These metrics affirm the efficacy of the SLC in capturing intricate protein dynamics and offer a method to evaluate and select the most informative CVs. The practical application of this technique lies in its ability to provide a detailed, quantitative description of protein folding processes, with significant implications for understanding and manipulating protein behavior in industrial and pharmaceutical contexts.

Essential dynamics of ubiquitin in water and in a natural deep eutectic solvent

Natural deep eutectic solvents (NADESs) comprised of osmolytes are of interest as potential biomolecular (cryo)protectants. However, the way these solvents influence the structure and dynamics of biomolecules as well as the role of water remains poorly understood. We carried out principal component analysis of various secondary structure elements of ubiquitin in water and a betaine : glycerol : water (1 : 2 : ζ; ζ = 0, 1, 2, 5, 10, 20, 45) NADES, from molecular dynamics trajectories, to gain insight into the protein dynamics as it undergoes a transition from a highly viscous anhydrous to an aqueous environment. A crossover of the protein's essential dynamics at ζ ∼ 5, induced by solvent-shell coupled fluctuations, is observed, indicating that ubiquitin might (re)fold in the NADES upon water addition at ζ > ∼5. Further, in contrast to water, the anhydrous NADES preserves ubiquitin's essential modes at high temperatures explaining the protein's seemingly enhanced thermal stability.

A pangenome analysis of ESKAPE bacteriophages: the underrepresentation may impact machine learning models

Bacteriophages are the most prevalent biological entities in the biosphere. However, limitations in both medical relevance and sequencing technologies have led to a systematic underestimation of the genetic diversity within phages. This underrepresentation not only creates a significant gap in our understanding of phage roles across diverse biosystems but also introduces biases in computational models reliant on these data for training and testing. In this study, we focused on publicly available genomes of bacteriophages infecting high-priority ESKAPE pathogens to show the extent and impact of this underrepresentation. First, we demonstrate a stark underrepresentation of ESKAPE phage genomes within the public genome and protein databases. Next, a pangenome analysis of these ESKAPE phages reveals extensive sharing of core genes among phages infecting the same host. Furthermore, genome analyses and clustering highlight close nucleotide-level relationships among the ESKAPE phages, raising concerns about the limited diversity within current public databases. Lastly, we uncover a scarcity of unique lytic phages and phage proteins with antimicrobial activities against ESKAPE pathogens. This comprehensive analysis of the ESKAPE phages underscores the severity of underrepresentation and its potential implications. This lack of diversity in phage genomes may restrict the resurgence of phage therapy and cause biased outcomes in data-driven computational models due to incomplete and unbalanced biological datasets.

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.

Towards the understanding of molecular motors and its relationship with local unfolding

Molecular motors are machines essential for life since they convert chemical energy into mechanical work. However, the precise mechanism by which nucleotide binding, catalysis, or release of products is coupled to the work performed by the molecular motor is still not entirely clear. This is due, in part, to a lack of understanding of the role of force in the mechanical-structural processes involved in enzyme catalysis. From a mechanical perspective, one promising hypothesis is the Haldane-Pauling hypothesis which considers the idea that part of the enzymatic catalysis is strain-induced. It suggests that enzymes cannot be efficient catalysts if they are fully complementary to the substrates. Instead, they must exert strain on the substrate upon binding, using enzyme-substrate energy interaction (binding energy) to accelerate the reaction rate. A novel idea suggests that during catalysis, significant strain energy is built up, which is then released by a local unfolding/refolding event known as 'cracking'. Recent evidence has also shown that in catalytic reactions involving conformational changes, part of the heat released results in a center-of-mass acceleration of the enzyme, raising the possibility that the heat released by the reaction itself could affect the enzyme's integrity. Thus, it has been suggested that this released heat could promote or be linked to the cracking seen in proteins such as adenylate kinase (AK). We propose that the energy released as a consequence of ligand binding/catalysis is associated with the local unfolding/refolding events (cracking), and that this energy is capable of driving the mechanical work.

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.