Theory of protein folding: the energy landscape perspective

Sodium Ion-Induced Structural Transition on the Surface of a DNA-Interacting Protein

Protein surfaces have pivotal roles in interactions between proteins and other biological molecules. However, the structural dynamics of protein surfaces have rarely been explored and are poorly understood. Here, the surface of a single-stranded DNA (ssDNA) binding protein (SSB) with four DNA binding domains that bind ssDNA in binding site sizes of 35, 56, and 65 nucleotides per tetramer is investigated. Using oligonucleotides as probes to sense the charged surface, NaCl induces a two-state structural transition on the SSB surface even at moderate concentrations. Chelation of sodium ions with charged amino acids alters the network of hydrogen bonds and/or salt bridges on the surface. Such changes are associated with changes in the electrostatic potential landscape and interaction mode. These findings advance the understanding of the molecular mechanism underlying the enigmatic salt-induced transitions between different DNA binding site sizes of SSBs. This work demonstrates that monovalent salt is a key regulator of biomolecular interactions that not only play roles in non-specific electrostatic screening effects as usually assumed but also may configure the surface of proteins to contribute to the effective regulation of biomolecular recognition and other downstream events.

Insights into the Role of Side-Chain Team Work in nDsbDOx/Red Proteins: Mechanism of Substrate Binding

N-terminal disulfide bond oxidoreductase (nDsbDOx/Red) proteins display divergent substrate binding mechanisms depending on the conformational changes to the Phe70 cap, which is also dependent on the disulfide redox state. In nDsbDOx, the cap dynamics is complex (shows both open/closed Phe70 cap conformations), resulting in an active site that is highly flexible. So the system's active site is conformationally selective (the active site adapts before substrate binding) toward its substrate. In nDsbDRed, the cap is generally closed, resulting in induced fit-type binding (adapts after substrate approach). Recent studies predict Tyr40 and Tyr42 residues to act as internal nucleophiles (Tyr40/42O-) for disulfide association/dissociation in nDsbDOx/Red, supplementing the electron transfer channel. From this perspective, we investigate the cap dynamics and the subsequent substrate binding modes in these proteins. Our molecular dynamics simulations show that the cap opening eliminates Tyr42O- electrostatic interactions irrespective of the disulfide redox state. The active site becomes highly flexible, and the conformational selection mechanism governs. However, Tyr40O- formation does not alter the chemical environment; the cap remains mostly closed and plausibly follows the induced fit mechanism. Thus, it is apparent that mostly Tyr42O- facilitates the internal nucleophile-mediated self-preparation of nDsbDOx/Red proteins for binding.

Bidirectional Allostery Mechanism in Catch-Bond Formation of CD44 Mediated Cell Adhesion

Catch-bonds, whereby noncovalent ligand-receptor interactions are counterintuitively reinforced by tensile forces, play a major role in cell adhesion under mechanical stress. A basic prerequisite for catch-bond formation, as implicated in classic catch-bond models, is that force-induced remodeling of the ligand binding interface occurs prior to bond rupture. However, what strategy receptor proteins utilize to meet such specific kinetic control remains elusive. Here we report a bidirectional allostery mechanism of catch-bond formation based on theoretical and molecular dynamics simulation studies. Binding of ligand allosterically reduces the threshold force for unlocking of otherwise stably folded force-sensing element (i.e., forward allostery), so that a much smaller tensile force can trigger the conformational switching of receptor protein to high binding-strength state via backward allosteric coupling before bond rupture. Such bidirectional allostery fulfills the specific kinetic control required by catch-bond formation and is likely to be commonly utilized in cell adhesion. The essential thermodynamic and kinetic features of receptor proteins essential for catch-bond formation were identified.

Optimal strategy for stabilizing protein folding intermediates

To manipulate the protein concentration at a certain functional state through chemical stabilizers is crucial for protein-related studies. It not only plays a key role in protein structure analysis and protein folding kinetics, but also affects protein functionality to a large extent and thus has wide applications in medicine, food industry, etc. However, due to concerns about side effects or financial costs of stabilizers, identifying optimal strategies for enhancing protein stability with a minimal amount of stabilizers is of great importance. Here, we prove that either for the fixed terminal time (including both finite and infinite cases) or for the free one, the optimal control strategy for stabilizing the folding intermediates with a linear strategy for stabilizer addition belongs to the class of bang-bang controls. The corresponding optimal switching time is derived analytically, whose phase diagram with respect to several key parameters is explored in detail. The bang-bang control will be broken when nonlinear strategies for stabilizer addition are adopted. Moreover, the above theory is applied to the stabilization of erythropoietin by ten different kinds of chemicals, providing theoretical guidance for the selection and rational usage of stabilizers. Our current study on optimal strategies for protein stabilizers not only offers deep insights into the general picture of protein folding kinetics but also provides valuable theoretical guidance on treatments for protein-related diseases in medicine.

Exploring the molecular pathways of the activation process in PPARγ recurrent bladder cancer mutants

The intricate involvement of Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) in glucose homeostasis and adipogenesis is well-established. However, its role in cancer, particularly luminal bladder cancer, remains debated. The overexpression and activation of PPARγ are implicated in tumorigenesis. Specific gain-of-function mutations (M280I, I290M, and T475M) within the ligand-binding domain of PPARγ are associated with bladder cancer and receptor activation. The underlying molecular pathways prompted by these mutations remain unclear. We employed a dual-basin structure-based model (db-SBM) to explore the conformational dynamics between the inactive and active states of PPARγ and examined the effects of the M280I, I290M, and T475M mutations. Our findings, consistent with the existing literature, reveal heightened ligand-independent transcriptional activity in the I290M and T475M mutants. Both mutants showed enhanced stabilization of the active state compared to the wild-type receptor, with the I290M mutation promoting a specific transition route, making it a prime candidate for further study. Electrostatic analysis identified residues K303 and E488 as pivotal in the I290M activation cascade. Biophysical assays confirmed that disrupting the K303-E488 interaction reduced the thermal stabilization characteristic of the I290M mutation. Our study demonstrates the predictive capabilities of combining simulation and cheminformatics methods, validated by biochemical experiments, to gain insights into molecular activation mechanisms and identify target residues for protein modulation.

Visualizing nucleation, condensation and propagation of β-tubulin folding in chaperonin TRiC

The folding nucleus (FN) initiates protein folding and enables an efficient folding pathway. Here we directly visualize the tubulin FN consisting of a nonnative, partially assembled Rossmann fold, in the closed chamber of human chaperonin TRiC. Chaperonin TRiC interacts with non-natively folded secondary structural elements, stabilizing the nucleus for transition into its first native domain. Through progressive folding, the unfolded sequence goes through drastic spatial arrangement in the TRiC chamber to sample the conformational space, mediated by the highly dynamic CCT tails. The observed presence of individual nonnative secondary structures first in the nonnative FN and then around the incrementally folded native domains supports the hypothesis that tubulin folding in TRiC is a hierarchical process of nucleation, condensation and propagation in cooperation with TRiC subunits.

Exploring the unfolding pathways of protein families using Elastic Network Model

We explore how a protein's native structure determines its unfolding process. We examine how the local structural features, like shear, and the global structural properties, like the number of soft modes, change during unfolding. Simulations are performed using a Gaussian Network Model (GNM) with bond breaking for both thermal and force-induced unfolding scenarios. We find that unfolding starts in areas of high shear in the native structure and progressively spreads to the low shear regions. Interestingly, analysis of single domain protein families (Chymotrypsin inhibitor and Barnase) reveal that proteins with distinct unfolding pathways exhibit divergent behavior of the number of soft modes during unfolding. This suggests that the number of soft modes might be a valuable tool for understanding thermal unfolding pathways. Additionally, we found a strong link between a protein's overall structural similarity (TM-score) and its unfolding pathways, highlighting the importance of the native structure in determining how a protein unfolds.

Magnesium Regulates RNA Ring Dynamics and Folding in Subgenomic Flaviviral RNA

Mosquito-borne flaviviruses including dengue, Zika, yellow fever, and regional encephalitis produce a large amount of short subgenomic flaviviral RNAs during infection. A segment of these RNAs named as xrRNA1 features a multi-pseudoknot (PK)-associated structure, which resists the host cell enzyme (XRN1) from degrading the viral RNA. We investigate how this long-range RNA PK folds in the presence of counterions, specifically in a mix of monovalent (K+) and divalent (Mg2+) salts at physiological concentrations. In this study, we use extensive explicit solvent molecular dynamics (MD) simulations to characterize the RNA ion environment of the folded RNA conformation, as determined by the crystal structure. This allowed us to identify the precise locations of various coordinated RNA-Mg2+ interactions, including inner-sphere/chelated and outer-sphere coordinated Mg2+. Given that RNA folding involves large-scale conformational changes, making it challenging to explore through classical MD simulations, we investigate the folding mechanism of xrRNA1 using an all-atom structure-based RNA model with a hybrid implicit-explicit treatment of the ion environment via the dynamic counterion condensation model, both with and without physiological Mg2+ concentration. The study reveals potential folding pathways for this xrRNA1, which is consistent with the results obtained from optical tweezer experiments. The equilibrium and free energy simulations both capture a dynamic equilibrium between the ring-open and ring-close states of the RNA, driven by a long-range PK interaction. Free energy calculations reveal that with the addition of Mg2+ ions, the equilibrium shifts more toward the ring-close state. A detailed analysis of the free energy pathways and ion-mediated contact probability map highlights the critical role of Mg2+ in bridging G50 and A33. This Mg2+-mediated connection helps form the long-range PK which in turn controls the transition between the ring-open and ring-close states. The study underscores the critical role of Mg2+ in the RNA folding transition, highlighting specific locations of Mg2+ contributing to the stabilization of long-range PK connections likely to enhance the robustness of Xrn1 resistance of flaviviral xrRNAs.

Membrane lateral organization from potential energy disconnectivity graph

Understanding the thermodynamic and kinetic properties of biomolecules requires elucidation of their complex energy landscape. A disconnectivity graph analysis of the energy landscape provides a framework for mapping the multi-dimensional landscape onto a two-dimensional representation while preserving the key features of the energy landscape. Several studies show that the structure or shape of the disconnectity graph is directly associated with the function of protein and nucleic acid molecules. In this review, we discuss how disconnectivity analysis of the potential energy surface can be extended to lipid molecules to glean important information about membrane organization. The shape of the disconnectivity graphs can be used to predict the lateral organization of multi-component lipid bilayer. We hope that this review encourages the use of disconnectivity graphs routinely by membrane biophysicists to predict the lateral organization of lipids.

Decoding Biomolecular Condensate Dynamics: An Energy Landscape Approach

A significant fraction of eukaryotic proteins contain low-complexity sequence elements with unknown functions. Many of these sequences are prone to form biomolecular condensates with unique material and dynamic properties. Mutations in low-complexity regions often result in abnormal phase transitions into pathological solid-like states. Therefore, understanding how the low-complexity sequence patterns encode the material properties of condensates is crucial for uncovering the cellular functions and evolutionary forces behind the emergence of low-complexity regions in proteins. In this work, we employ an alphabet-free energy landscape framework of the stickers and spacers to dissect how the low complexity patterns of proteins encode the material properties of condensates. We find a broad phase diagram of material properties determined by distinct energy landscape features, showing that periodic repeat motifs promote elastic-dominated while random sequences are viscous-dominated properties. We find that a certain degree of sticker periodicity is necessary to maintain the fluidity of condensates, preventing them from forming glassy or solid-like states. Finally, we show that the energy landscape framework captures viscoelastic trends seen in the recent experiments on prion domains and makes predictions for systematic variation of protein condensate viscoelasticity via altering the periodicity and strength of sticker motifs.

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Osmolyte-induced conformational stabilization of a hydrophobic polymer

Elucidating the mechanistic role of osmolytes on conformations of hydrophobic prototypical macromolecules in principle is the stepping stone towards understanding the effect of osmolytes on proteins. Motivated by this, we use equilibrium simulations and umbrella sampling techniques to dissect the underlying mechanism of osmolyte-induced conformational stability of a hydrophobic polymer. Our results unveil a remarkable osmolyte-dependent conformational stabilization of the polymer. In an aqueous solution of 4 M choline chloride (ChCl), the polymer has an even more compact structure than in water. On the other hand, an aqueous solution of 8 M urea stabilizes the extended state of the polymer. Interestingly, the polymer adopts an intermediate hairpin conformation in a mixed osmolyte solution of 4 M ChCl and 8 M urea in water due to the interplay of ChCl and urea. Our simulations identify the relative accumulation of water and the hydrophilic part of choline or preferential binding of urea near the collapsed and the extended states, respectively. Analyses split out the enthalpic and entropic contributions to the overall free energy. This decides the stabilization of the preferred conformation in the chosen osmolyte solution. Our simulations show that in an aqueous solution of ChCl, the hairpin state is stabilized by entropy gain. In contrast, the enthalpic contribution stabilizes the hairpin state in mixed environments. However, a collapsed state is energetically not favored in the presence of urea. In brief, via employing an in silico approach, the current findings indicate the importance of osmolytes in stabilizing the conformational states of hydrophobic polymers.

Nonequilibrium relaxation of soft responsive colloids

Stimuli-responsive macromolecules display large conformational changes during their dynamics, sometimes switching between states. Such a multi-stability is useful for the development of soft functional materials. Here, we introduce a mean-field dynamical density functional theory for a model of responsive colloids to study the nonequilibrium dynamics of a colloidal dispersion in time-dependent external fields, with a focus on the coupling of translational and conformational dynamics during their relaxation. Specifically, we consider soft Gaussian particles with a bimodal size distribution between two confining walls with time-dependent (switching-on and off) external gravitational and osmotic fields. We find a rich relaxation behavior of the systems in excellent agreement with particle-based Brownian dynamics computer simulations. In particular, we find time-asymmetric relaxations of integrated observables (wall pressures, mean size, and liquid center-of-mass) for activation/deactivation of external potentials, respectively, which are tunable by the ratio of translational and conformational diffusion time scales. Our work thus paves the way for studying the nonequilibrium relaxation dynamics of complex soft matter with multiple degrees of freedom and hierarchical relaxations.

Antioxidant capacity of xylooligosaccharides generated from beechwood xylan by recombinant family GH10 Aspergillus niger xylanase A and insights into the enzyme's competitive inhibition by riceXIP

In this study, the family GH10 xylanase AnXylA10 derived from Aspergillus niger JL15 strain was expressed in Pichia pastoris X33. The recombinant xylanase, reAnXylA10 exhibited optimal activity at 40 ℃ and pH 5.0. The hydrolysates generated from beechwood xylan using reAnXylA10 primarily consisted of xylobiose (X2) to xylohexaose (X6) and demonstrated remarkable antioxidant capacity. Furthermore, the rice xylanase inhibitory protein (riceXIP) was observed to competitively inhibit reAnXylA10, exhibiting an inhibition constant (Ki) of 140.6 nM. Molecular dynamics (MD) simulations of AnXylA10-riceXIP complex revealed that the α-7 helix (Q225-S238) of riceXIP intruded into the catalytic pocket of AnXylA10, thereby obstructing substrate access to the active site. Specifically, residue K226 of riceXIP formed robust interactions with E136 and E242, the two catalytic sites of AnXylA10, predominantly through high-occupied hydrogen bonds. Based on QTAIM, electron densities for the atom pairs K226riceXIP@HZ1-E136AnXylA10@OE2 and K226riceXIP@HZ3-E242AnXylA10@OE1 were determined to be 0.04628 and 0.02914 a.u., respectively. Binding free energy of AnXylA10-riceXIP complex was -59.0±7.6 kcal/mol, significantly driven by electrostatic and van der Waals forces. Gaining insights into the interaction between xylanase and its inhibitors, and mining the inhibition mechanism in depth, will facilitate the design of innovative GH10 family xylanases that are both highly efficient and resistant to inhibitors.

Structural characterization of DNA-binding domain of essential mammalian protein TTF 1

Transcription Termination Factor 1 (TTF1) is a multifunctional mammalian protein with vital roles in various cellular processes, including Pol I-mediated transcription initiation and termination, pre-rRNA processing, chromatin remodelling, DNA damage repair, and polar replication fork arrest. It comprises two distinct functional regions; the N-terminal regulatory region (1-445 aa), and the C-terminal catalytic region (445-859 aa). The Myb domain located at the C-terminal region is a conserved DNA binding domain spanning from 550 to 732 aa (183 residues). Despite its critical role in various cellular processes, the physical structure of TTF1 remains unsolved. Attempts to purify the functional TTF1 protein have been unsuccessful till date. Therefore, we focused on characterizing the Myb domain of this essential protein. We started with predicting a 3-D model of the Myb domain using homology modelling, and ab-initio method. We then determined its stability through MD simulation in an explicit solvent. The model predicted is highly stable, which stabilizes at 200ns. To experimentally validate the computational model, we cloned and expressed the codon optimized Myb domain into a bacterial expression vector and purified the protein to homogeneity. Further, characterization of the protein shows that, Myb domain is predominantly helical (65%) and is alone sufficient to bind the Sal Box DNA. This is the first-ever study to report a complete in silico model of the Myb domain, which is physically characterized. The above study will pave the way towards solving the atomic structure of this essential mammalian protein.

Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

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

Ribosome Tunnel Environment Drives the Formation of α-Helix during Cotranslational Folding

Protein conformations in cells are not solely determined by amino acid sequences; they also depend on cellular environments. For instance, the ribosome tunnel induces its specific α-helix formation during cotranslational folding. Owing to the link between these temporally α-helix and biological functions, the mechanism of α-helix formation inside the ribosome tunnel has been previously explored. Consequently, the conformational restrictions of the tunnel were considered one of the driving forces of α-helix formation. Conversely, the ribosomal tunnel environment, including its chemical properties, appears to influence the α-helix formation. However, a comprehensive analysis of the ribosome tunnel environment's impact on the α-helix formation has not been conducted yet due to challenges in experimentally controlling it. Therefore, as a new computational approach, we proposed a ribosome environment-mimicking model (REMM) based on the radius and components of the experimentally determined ribosome tunnel structures. Using REMM, we assessed the impact of the ribosome tunnel environment on α-helix formation. Herein, we employed carbon nanotubes (CNT) as a reference model alongside REMM because CNT reproduce conformational restrictions rather than the ribosome tunnel environment. Quantitatively, the ability to reproduce the α-helix of nascent peptides in the experimental structure was compared between the CNT and REMM using enhanced all-atom molecular dynamics simulations. Consequently, the REMM more accurately reproduced the α-helix of the nascent peptides than the CNT, highlighting the significance of the ribosome tunnel environment in α-helix formation. Additionally, we analyzed the properties of the peptide inside each model to reveal the mechanism of ribosome tunnel-specific α-helix formation. Consequently, we revealed that the chemical diversities of the tunnel are essential for the formation of backbone-to-backbone hydrogen bonds in the peptides. In conclusion, the ribosome tunnel environment, with the diverse chemical properties, drives its specific α-helix formation. By proposing REMM, we newly provide the technical basis for investigating the protein conformations in various cellular environments.

Universal cold RNA phase transitions

RNA's diversity of structures and functions impacts all life forms since primordia. We use calorimetric force spectroscopy to investigate RNA folding landscapes in previously unexplored low-temperature conditions. We find that Watson-Crick RNA hairpins, the most basic secondary structure elements, undergo a glass-like transition below [Formula: see text]C where the heat capacity abruptly changes and the RNA folds into a diversity of misfolded structures. We hypothesize that an altered RNA biochemistry, determined by sequence-independent ribose-water interactions, outweighs sequence-dependent base pairing. The ubiquitous ribose-water interactions lead to universal RNA phase transitions below TG, such as maximum stability at [Formula: see text]C where water density is maximum, and cold denaturation at [Formula: see text]C. RNA cold biochemistry may have a profound impact on RNA function and evolution.

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.

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.

Energy landscapes-Past, present, and future: A perspective

Energy landscapes and the closely related cost function landscapes have been recognized in science, mathematics, and various other fields such as economics as being highly useful paradigms and tools for the description and analysis of the properties of many systems, ranging from glasses, proteins, and abstract global optimization problems to business models. A multitude of algorithms for the exploration and exploitation of such landscapes have been developed over the past five decades in the various fields of applications, where many re-inventions but also much cross-fertilization have occurred. Twenty-five years ago, trying to increase the fruitful interactions between workers in different fields led to the creation of workshops and small conferences dedicated to the study of energy landscapes in general instead of only focusing on specific applications. In this perspective, I will present some history of the development of energy landscape studies and try to provide an outlook on in what directions the field might evolve in the future and what larger challenges are going to lie ahead, both from a conceptual and a practical point of view, with the main focus on applications of energy landscapes in chemistry and physics.

Quantifying Microsecond Solution-Phase Conformational Dynamics of a DNA Hairpin at the Single-Molecule Level

Quantifying the rapid conformational dynamics of biological systems is fundamental to understanding the mechanism. However, biomolecules are complex, often containing static and dynamic heterogeneity, thus motivating the use of single-molecule methods, particularly those that can operate in solution. In this study, we measure microsecond conformational dynamics of solution-phase DNA hairpins at the single-molecule level using an anti-Brownian electrokinetic (ABEL) trap. Different conformational states were distinguished by their fluorescence lifetimes, and kinetic parameters describing transitions between these states were determined using two-dimensional fluorescence lifetime correlation (2DFLCS) analysis. Rather than combining fluorescence signals from the entire data set ensemble, long observation times of individual molecules allowed ABEL-2DFLCS to be performed on each molecule independently, yielding the underlying distribution of the system's kinetic parameters. ABEL-2DFLCS on the DNA hairpins resolved an underlying heterogeneity of fluorescence lifetimes and provided signatures of two-state exponential dynamics with rapid ( Collapse

Key Words

• conformational dynamics
• single-molecule spectroscopy
• förster correlation spectroscopy
• nucleic acid hairpin
• fluorescence resonance energy transfer

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MESH Headings Collapse

Grants

• National Institute of General Medical Sciences
• Morino Foundation for Molecular Science
• Japan Society for the Promotion of Science

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Affiliation(s)

Alexander K. Foote Department of Chemistry, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
Kunihiko Ishii Molecular Spectroscopy Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Ultrafast Spectroscopy Research Team, RIKEN Center for Advanced Photonics (RAP), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
Brendan Cullinane Department of Chemistry, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
Tahei Tahara Molecular Spectroscopy Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Ultrafast Spectroscopy Research Team, RIKEN Center for Advanced Photonics (RAP), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
Randall H. Goldsmith Department of Chemistry, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States

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Machine learning in biological physics: From biomolecular prediction to design

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is "learnable" and propose its future use in the generation of unique sequences that can fold into a target structure.

Targeting the protein folding transition state by mutation: Large scale (un)folding rate accelerations without altering native stability

Proteins are constantly undergoing folding and unfolding transitions, with rates that determine their homeostasis in vivo and modulate their biological function. The ability to optimize these rates without affecting overall native stability is hence highly desirable for protein engineering and design. The great challenge is, however, that mutations generally affect folding and unfolding rates with inversely complementary fractions of the net free energy change they inflict on the native state. Here we address this challenge by targeting the folding transition state (FTS) of chymotrypsin inhibitor 2 (CI2), a very slow and stable two-state folding protein with an FTS known to be refractory to change by mutation. We first discovered that the CI2's FTS is energetically taxed by the desolvation of several, highly conserved, charges that form a buried salt bridge network in the native structure. Based on these findings, we designed a CI2 variant that bears just four mutations and aims to selectively stabilize the FTS. This variant has >250-fold faster rates in both directions and hence identical native stability, demonstrating the success of our FTS-centric design strategy. With an optimized FTS, CI2 also becomes 250-fold more sensitive to proteolytic degradation by its natural substrate chymotrypsin, and completely loses its activity as inhibitor. These results indicate that CI2 has been selected through evolution to have a very unstable FTS in order to attain the kinetic stability needed to effectively function as protease inhibitor. Moreover, the CI2 case showcases that protein (un)folding rates can critically pivot around a few key residues-interactions, which can strongly modify the general effects of known structural factors such as domain size and fold topology. From a practical standpoint, our results suggest that future efforts should perhaps focus on identifying such critical residues-interactions in proteins as best strategy to significantly improve our ability to predict and engineer protein (un)folding rates.

Intrinsic Conformational Dynamics of Glycine and Alanine in Polarizable Molecular Dynamics Force Fields: Comparison to Spectroscopic Data

Molecular dynamics (MD) is a great tool for elucidating conformational dynamics of proteins and peptides in water at the atomistic level that often surpasses the level of detail available experimentally. Structure predictions, however, are limited by the accuracy of the underlying MD force field. This limitation is particularly stark in the case of intrinsically disordered peptides and proteins, which are characterized by solvent-accessible and disordered peptide regions and domains. Recent studies show that most additive MD force fields, including CHARMM36m, do not reproduce the intrinsic conformational distributions of guest amino acid residues x in cationic GxG peptides in water in line with experimental data. Positing that a lack of polarizability in additive MD force fields may be the culprit for the reported discrepancies, we here examine the conformational dynamics of guest glycine and alanine residues in cationic GxG peptides in water using two polarizable MD force fields, CHARMM Drude and AMOEBA. Our results indicate that while AMOEBA captures the experimental data better than CHARMM Drude, neither of the two polarizable force fields offers an improvement of the Ramachandran distributions of glycine and alanine residues in cationic GGG and GAG peptides, respectively, over CHARMM36m.

FoldPAthreader: predicting protein folding pathway using a novel folding force field model derived from known protein universe

Protein folding has become a tractable problem with the significant advances in deep learning-driven protein structure prediction. Here we propose FoldPAthreader, a protein folding pathway prediction method that uses a novel folding force field model by exploring the intrinsic relationship between protein evolution and folding from the known protein universe. Further, the folding force field is used to guide Monte Carlo conformational sampling, driving the protein chain fold into its native state by exploring potential intermediates. On 30 example targets, FoldPAthreader successfully predicts 70% of the proteins whose folding pathway is consistent with biological experimental data.

Understanding the Energy Landscape of Intrinsically Disordered Protein Ensembles

A substantial portion of various organisms' proteomes comprises intrinsically disordered proteins (IDPs) that lack a defined three-dimensional structure. These IDPs exhibit a diverse array of conformations, displaying remarkable spatiotemporal heterogeneity and exceptional conformational flexibility. Characterizing the structure or structural ensemble of IDPs presents significant conceptual and methodological challenges owing to the absence of a well-defined native structure. While databases such as the Protein Ensemble Database (PED) provide IDP ensembles obtained through a combination of experimental data and molecular modeling, the absence of reaction coordinates poses challenges in comprehensively understanding pertinent aspects of the system. In this study, we leverage the energy landscape visualization method (JCTC, 6482, 2019) to scrutinize four IDP ensembles sourced from PED. ELViM, a methodology that circumvents the need for a priori reaction coordinates, aids in analyzing the ensembles. The specific IDP ensembles investigated are as follows: two fragments of nucleoporin (NUL: 884-993 and NUS: 1313-1390), yeast sic 1 N-terminal (1-90), and the N-terminal SH3 domain of Drk (1-59). Utilizing ELViM enables the comprehensive validation of ensembles, facilitating the detection of potential inconsistencies in the sampling process. Additionally, it allows for identifying and characterizing the most prevalent conformations within an ensemble. Moreover, ELViM facilitates the comparative analysis of ensembles obtained under diverse conditions, thereby providing a powerful tool for investigating the functional mechanisms of IDPs.

Solvent constraints for biopolymer folding and evolution in extraterrestrial environments

We propose that spontaneous folding and molecular evolution of biopolymers are two universal aspects that must concur for life to happen. These aspects are fundamentally related to the chemical composition of biopolymers and crucially depend on the solvent in which they are embedded. We show that molecular information theory and energy landscape theory allow us to explore the limits that solvents impose on biopolymer existence. We consider 54 solvents, including water, alcohols, hydrocarbons, halogenated solvents, aromatic solvents, and low molecular weight substances made up of elements abundant in the universe, which may potentially take part in alternative biochemistries. We find that along with water, there are many solvents for which the liquid regime is compatible with biopolymer folding and evolution. We present a ranking of the solvents in terms of biopolymer compatibility. Many of these solvents have been found in molecular clouds or may be expected to occur in extrasolar planets.

Two operational modes of atomic force microscopy reveal similar mechanical properties for homologous regions of dystrophin and utrophin

Duchenne muscular dystrophy (DMD) is a lethal muscle disease caused by the absence of the protein dystrophin. Dystrophin is hypothesized to work as a molecular shock absorber that limits myofiber membrane damage when undergoing reversible unfolding upon muscle stretching and contraction. Utrophin is a dystrophin homologue that is under investigation as a protein replacement therapy for DMD. However, it remains uncertain whether utrophin can mechanically substitute for dystrophin. Here, we compared the mechanical properties of homologous utrophin and dystrophin fragments encoding the N terminus through spectrin repeat 3 (UtrN-R3, DysN-R3) using two operational modes of atomic force microscopy (AFM), constant speed and constant force. Our comprehensive data, including the statistics of force magnitude at which the folded domains unfold in constant speed mode and the time of unfolding statistics in constant force mode, show consistent results. We recover parameters of the energy landscape of the domains and conducted Monte Carlo simulations which corroborate the conclusions drawn from experimental data. Our results confirm that UtrN-R3 expressed in bacteria exhibits significantly lower mechanical stiffness compared to insect UtrN-R3, while the mechanical stiffness of the homologous region of dystrophin (DysN-R3) is intermediate between bacterial and insect UtrN-R3, showing greater similarity to bacterial UtrN-R3.

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.

100 Years of the Lennard-Jones Potential

It is now 100 years since Lennard-Jones published his first paper introducing the now famous potential that bears his name. It is therefore timely to reflect on the many achievements, as well as the limitations, of this potential in the theory of atomic and molecular interactions, where applications range from descriptions of intermolecular forces to molecules, clusters, and condensed matter.

A pressure-jump EPR system to monitor millisecond conformational exchange rates of spin-labeled proteins

Site-directed spin labeling electron paramagnetic resonance (SDSL-EPR) using nitroxide spin labels is a well-established technology for mapping site-specific secondary and tertiary structure and for monitoring conformational changes in proteins of any degree of complexity, including membrane proteins, with high sensitivity. SDSL-EPR also provides information on protein dynamics in the time scale of ps-µs using continuous wave lineshape analysis and spin lattice relaxation time methods. However, the functionally important time domain of µs-ms, corresponding to large-scale protein motions, is inaccessible to those methods. To extend SDSL-EPR to the longer time domain, the perturbation method of pressure-jump relaxation is implemented. Here, we describe a complete high-pressure EPR system at Q-band for both static pressure and millisecond-timescale pressure-jump measurements on spin-labeled proteins. The instrument enables pressure jumps both up and down from any holding pressure, ranging from atmospheric pressure to the maximum pressure capacity of the system components (~3500 bar). To demonstrate the utility of the system, we characterize a local folding-unfolding equilibrium of T4 lysozyme. The results illustrate the ability of the system to measure thermodynamic and kinetic parameters of protein conformational exchange on the millisecond timescale.

Helical Content Correlations and Hydration Structures of the Folding Ensemble of the B Domain of Protein A

The B domain of protein A (BdpA), a small three-helix bundle, folds on a time scale of a few microseconds with heterogeneous native and unfolded states. It is widely used as a model for understanding protein folding mechanisms. In this work, we use structure-based models (SBMs) and atomistic simulations to comprehensively investigate how BdpA folding is associated with the formation of its secondary structure. The energy landscape visualization method (ELViM) was used to characterize the pathways that connect the folded and unfolded states of BdpA as well as the sets of structures displaying specific ellipticity patterns. We show that the native state conformational diversity is due mainly to the conformational variability of helix I. Helices I, II, and III occur in a weakly correlated manner, with Spearman's rank correlation coefficients of 0.1539 (I and II), 0.1259 (I and III), and 0.2561 (II and III). These results, therefore, suggest the highest cooperativity between helices II and III. Our results allow the clustering of partially folded structures of folding of the B domain of protein A on the basis of its secondary structure, paving the way to an understanding of environmental factors in the relative stability of the basins of the folding ensemble, which are illustrated by the structural dependency of the protein hydration structures, as computed with minimum-distance distribution functions.

ELViM: Exploring Biomolecular Energy Landscapes through Multidimensional Visualization

Molecular dynamics (MD) simulations provide a powerful means of exploring the dynamic behavior of biomolecular systems at the atomic level. However, analyzing the vast data sets generated by MD simulations poses significant challenges. This article discusses the energy landscape visualization method (ELViM), a multidimensional reduction technique inspired by the energy landscape theory. ELViM transcends one-dimensional representations, offering a comprehensive analysis of the effective conformational phase space without the need for predefined reaction coordinates. We apply the ELViM to study the folding landscape of the antimicrobial peptide Polybia-MP1, showcasing its versatility in capturing complex biomolecular dynamics. Using dissimilarity matrices and a force-scheme approach, the ELViM provides intuitive visualizations, revealing structural correlations and local conformational signatures. The method is demonstrated to be adaptable, robust, and applicable to various biomolecular systems.

In silico design of misfolding resistant proteins: the role of structural similarity of a competing conformational ensemble in the optimization of frustration

Most state-of-the-art in silico design methods fail due to misfolding of designed sequences to a conformation other than the target. Thus, a method to design misfolding resistant proteins will provide a better understanding of the misfolding phenomenon and will also increase the success rate of in silico design methods. In this work, we optimize the conformational ensemble to be selected for negative design purposes based on the similarity of the conformational ensemble to the target. Five ensembles with different degrees of similarity to the target are created and destabilized and the target is stabilized while designing sequences using mean field theory and Monte Carlo simulation methods. The results suggest that the degree of similarity of the non-native conformations to the target plays a prominent role in designing misfolding resistant protein sequences. The design procedures that destabilize the conformational ensemble with moderate similarity to the target have proven to be more promising. Incorporation of either highly similar or highly dissimilar conformations to the target conformation into the non-native ensemble to be destabilized may lead to sequences with a higher misfolding propensity. This will significantly reduce the conformational space to be considered in any protein design procedure. Interestingly, the results suggest that a sequence with higher frustration in the target structure does not necessarily lead to a misfold prone sequence. A successful design method may purposefully choose a frustrated sequence in the target conformation if that sequence is even more frustrated in the competing non-native conformations.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Computational insights into dynamics and conformational stability of N-acetylmannosamine kinase mutations

The activity of UDP-GlcNAc 2-epimerase/ManNAc kinase (GNE) is essential for the biosynthesis of sialic acid, which is involved in cellular processes in health and diseases. GNE contains an N-terminal epimerase domain and a C-terminal kinase domain (N-acetylmannosamine kinase, MNK). Mutations of the GNE protein led to hypoactivity of the enzyme and cause sialurea or autosomal recessive inclusion body myopathy/Nonaka myopathy. Here, we used all-atom molecular dynamics (MD) simulations to comprehend the folding, dynamics and conformational stability of MNK variants, including the wild type (WT) and three mutants (H677R, V696M and H677R/V696M). The deleterious and destabilizing nature of MNK mutants were predicted using different prediction tools. Results predicted that mutations modulate the stability, flexibility and function of MNK. The effect of mutations on the conformational stability and dynamics of MNK was next studied through the free-energy landscape (FEL), hydrogen-bonds and secondary structure changes. The FEL results show that the mutations interfere with various conformational transitions in both WT and mutants, exposing the structural underpinnings of protein destabilization and unfolding brought on by mutation. We discover that, when compared to the other two mutations, V696M and H677R/V696M, H677R has the most harmful effects. These findings have a strong correlation with published experimental studies that demonstrate how these mutations disrupt MNK activity. Hence, this computational study describes the structural details to unravel the mutant effects at the atomistic resolution and has implications for understanding the GNE's physiological and pathological role.Communicated by Ramaswamy H. Sarma.

Structures of AT8 and PHF1 phosphomimetic tau: Insights into the posttranslational modification code of tau aggregation

The microtubule-associated protein tau aggregates into amyloid fibrils in Alzheimer's disease and other neurodegenerative diseases. In these tauopathies, tau is hyperphosphorylated, suggesting that this posttranslational modification (PTM) may induce tau aggregation. Tau is also phosphorylated in normal developing brains. To investigate how tau phosphorylation induces amyloid fibrils, here we report the atomic structures of two phosphomimetic full-length tau fibrils assembled without anionic cofactors. We mutated key Ser and Thr residues to Glu in two regions of the protein. One construct contains three Glu mutations at the epitope of the anti-phospho-tau antibody AT8 (AT8-3E tau), whereas the other construct contains four Glu mutations at the epitope of the antibody PHF1 (PHF1-4E tau). Solid-state NMR data show that both phosphomimetic tau mutants form homogeneous fibrils with a single set of chemical shifts. The AT8-3E tau rigid core extends from the R3 repeat to the C terminus, whereas the PHF1-4E tau rigid core spans R2, R3, and R4 repeats. Cryoelectron microscopy data show that AT8-3E tau forms a triangular multi-layered core, whereas PHF1-4E tau forms a triple-stranded core. Interestingly, a construct combining all seven Glu mutations exhibits the same conformation as PHF1-4E tau. Scalar-coupled NMR data additionally reveal the dynamics and shape of the fuzzy coat surrounding the rigid cores. These results demonstrate that specific PTMs induce structurally specific tau aggregates, and the phosphorylation code of tau contains redundancy.

Connecting polymer collapse and the onset of jamming

Previous studies have shown that the interiors of proteins are densely packed, reaching packing fractions that are as large as those found for static packings of individual amino-acid-shaped particles. How can the interiors of proteins take on such high packing fractions given that amino acids are connected by peptide bonds and many amino acids are hydrophobic with attractive interactions? We investigate this question by comparing the structural and mechanical properties of collapsed attractive disk-shaped bead-spring polymers to those of three reference systems: static packings of repulsive disks, of attractive disks, and of repulsive disk-shaped bead-spring polymers. We show that the attractive systems quenched to temperatures below the glass transition T≪T_{g} and static packings of both repulsive disks and bead-spring polymers possess similar interior packing fractions. Previous studies have shown that static packings of repulsive disks are isostatic at jamming onset, i.e., the number of interparticle contacts N_{c} matches the number of degrees of freedom, which strongly influences their mechanical properties. We find that repulsive polymer packings are hypostatic at jamming onset (i.e., with fewer contacts than degrees of freedom) but are effectively isostatic when including stabilizing quartic modes, which give rise to quartic scaling of the potential energy with displacements along these modes. While attractive disk and polymer packings are often considered hyperstatic with excess contacts over the isostatic number, we identify a definition for interparticle contacts for which they can also be considered as effectively isostatic. As a result, we show that the mechanical properties (e.g., scaling of the potential energy with excess contact number and low-frequency contribution to the density of vibrational modes) of weakly attractive disk and polymer packings are similar to those of isostatic repulsive disk and polymer packings. Our results demonstrate that static packings generated via attractive collapse or compression of repulsive particles possess similar structural and mechanical properties.

Structural studies of catalytic peptides using molecular dynamics simulations

Many self-assembling peptides can form amyloid like structures with different sizes and morphologies. Driven by non-covalent interactions, their aggregation can occur through distinct pathways. Additionally, they can bind metal ions to create enzyme like active sites that allow them to catalyze diverse reactions. Due to the non-crystalline nature of amyloids, it is quite challenging to elucidate their structures using experimental spectroscopic techniques. In this aspect, molecular dynamics (MD) simulations provide a useful tool to derive structures of these macromolecules in solution. They can be further validated by comparing with experimentally measured structural parameters. However, these simulations require a multi-step process starting from the selection of the initial structure to the analysis of MD trajectories. There are multiple force fields, parametrization protocols, equilibration processes, software and analysis tools available for this process. Therefore, it is complicated for non-experts to select the most relevant tools and perform these simulations effectively. In this chapter, a systematic methodology that covers all major aspects of modeling of catalytic peptides is provided in a user-friendly manner. It will be helpful for researchers in this critical area of research.

In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

Plant protein solubility: A challenge or insurmountable obstacle

Currently, there is an increasing focus on comprehending the solubility of plant-based proteins, driven by the rising demand for animal-free food formulations. The solubility of proteins plays a crucial role in impacting other functional properties of proteins and food processing. Consequently, understanding protein solubility in a deeper sense may allow a better usage of plant proteins. Herein, we discussed the definition of protein solubility from both thermodynamic and colloidal perspectives. A range of factors affecting solubility of plant proteins are generalized, including intrinsic factors (amino acids composition, hydrophobicity), and extrinsic factors (pH, ionic strength, extraction and drying methods). Current methods to enhance solubility are outlined, including microwave, high intensity ultrasound, hydrostatic pressure, glycation, pH-shifting, enzymatic hydrolysis, enzymatic cross-linking, complexation and modulation of amino acids. We base the discussion on diverse modified methods of nitrogen solubility index available to determine and analyze protein solubility followed by addressing how other indigenous components affect the solubility of plant proteins. Some nonproteinaceous constituents in proteins such as carbohydrates and polyphenols may exert positive or negative impact on protein solubility. Appropriate protein extraction and modification methods that meet consumer and manufacturers requirements concerning nutritious and eco-friendly foods with lower cost should be investigated and further explored.

Protein dynamics underlying allosteric regulation

Allostery is the mechanism by which information and control are propagated in biomolecules. It regulates ligand binding, chemical reactions, and conformational changes. An increasing level of experimental resolution and control over allosteric mechanisms promises a deeper understanding of the molecular basis for life and powerful new therapeutics. In this review, we survey the literature for an up-to-date biological and theoretical understanding of protein allostery. By delineating five ways in which the energy landscape or the kinetics of a system may change to give rise to allostery, we aim to help the reader grasp its physical origins. To illustrate this framework, we examine three systems that display these forms of allostery: allosteric inhibitors of beta-lactamases, thermosensation of TRP channels, and the role of kinetic allostery in the function of kinases. Finally, we summarize the growing power of computational tools available to investigate the different forms of allostery presented in this review.

Identification of amentoflavone as a potent SARS-CoV-2 Mpro inhibitor: a combination of computational studies and in vitro biological evaluation

Small-molecule inhibitors of SARS-CoV-2 Mpro that block the active site pocket of the viral main protease have been considered potential therapeutics for the development of drugs against SARS-CoV-2. Here, we report the identification of amentoflavone (a biflavonoid) through docking-based virtual screening of a library comprised of 231 compounds consisting of flavonoids and isoflavonoids. The docking results were further substantiated through extensive analysis of the data obtained from all-atom 150 ns MD simulation. End-state effective free energy calculations using MM-PBSA calculations further suggested that (Ra)-amentoflavone (C3'-C8''-atropisomer) may show a greater binding affinity towards the Mpro than (Sa)-amentoflavone. In vitro cytotoxicity assay established that amentoflavone showed a high CC50 value indicating much lower toxicity. Further, potent inhibition of the Mpro by amentoflavone was established by studying the effect on HEK293T cells treated with SARS-CoV-2 Mpro expressing plasmid.Communicated by Ramaswamy H. Sarma.

External field-driven property localization in liquids of responsive macromolecules

We explore theoretically the effects of external potentials on the spatial distribution of particle properties in a liquid of explicitly responsive macromolecules. In particular, we focus on the bistable particle size as a coarse-grained internal degree of freedom (DoF, or "property"), σ, that moves in a bimodal energy landscape, in order to model the response of a state-switching (big-to-small) macromolecular liquid to external stimuli. We employ a mean-field density functional theory (DFT) that provides the full inhomogeneous equilibrium distributions of a one-component model system of responsive colloids (RCs) interacting with a Gaussian pair potential. For systems confined between two parallel hard walls, we observe and rationalize a significant localization of the big particle state close to the walls, with pressures described by an exact RC wall theorem. Application of more complex external potentials, such as linear (gravitational), osmotic, and Hamaker potentials, promotes even stronger particle size segregation, in which macromolecules of different size are localized in different spatial regions. Importantly, we demonstrate how the degree of responsiveness of the particle size and its coupling to the external potential tune the position-dependent size distribution. The DFT predictions are corroborated by Brownian dynamics simulations. Our study highlights the fact that particle responsiveness can be used to localize liquid properties and therefore helps to control the property- and position-dependent function of macromolecules, e.g., in biomedical applications.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

Combining experiment and energy landscapes to explore anaerobic heme breakdown in multifunctional hemoproteins

To survive, many pathogens extract heme from their host organism and break down the porphyrin scaffold to sequester the Fe2+ ion via a heme oxygenase. Recent studies have revealed that certain pathogens can anaerobically degrade heme. Our own research has shown that one such pathway proceeds via NADH-dependent heme degradation, which has been identified in a family of hemoproteins from a range of bacteria. HemS, from Yersinia enterocolitica, is the main focus of this work, along with HmuS (Yersinia pestis), ChuS (Escherichia coli) and ShuS (Shigella dysenteriae). We combine experiments, Energy Landscape Theory, and a bioinformatic investigation to place these homologues within a wider phylogenetic context. A subset of these hemoproteins are known to bind certain DNA promoter regions, suggesting not only that they can catalytically degrade heme, but that they are also involved in transcriptional modulation responding to heme flux. Many of the bacterial species responsible for these hemoproteins (including those that produce HemS, ChuS and ShuS) are known to specifically target oxygen-depleted regions of the gastrointestinal tract. A deeper understanding of anaerobic heme breakdown processes exploited by these pathogens could therefore prove useful in the development of future strategies for disease prevention.

Probing silver nanoparticle mediated mitigation of UV-photolysis in proteins by electrical impedance analysis

The dynamic equilibrium between an array of molecular forces precisely organizes the native structure of the protein. The charge on the protein, an interconnected network continuum, is crucial in determining its secondary and tertiary structure. The photolysis of the protein by ultraviolet (UV) light occurs by generating reactive oxygen intermediates from the interaction of matter and light. Herein, we have investigated the photolysis of the protein and its prevention by the pre-treatment with silver nanoparticle (AgNP) using non-faradaic electrical impedance spectroscopy (Nf-EIS). Five microliters of protein solution are used to measure its impedimetric parameters via Nf-EIS. The photoionization process sparks off an altered surface charge continuum of the protein molecules in tandem with the genesis of solvated electrons and protons, spurring an upward shift in conductivity. The AgNP pre-treatment has reduced the damaging effects of the UV radiation, which is reflected as lesser conductivity in contrast to the photolyzed protein solution. Raman Spectroscopy and circular dichroism tests affirm the trend of Nf-EIS results. These results show that Nf-EIS can evaluate protein structure analysis utilized in quality assurance and toxicity analysis for biologics.

The influence of the hydrogen-bond network on the structure and dynamics of the RAPRKKG heptapeptide and its mutants

The structural behaviour of the RAPRKKG heptapeptide after individual or multiple mutations was inspected through molecular dynamics simulation. The nature of the mutations provided information on the flexibility of the heptapeptide and on how water molecules establish hydrogen bonds with it. The structural behaviour of the wild-type and the mutated structures were measured through the analysis of protein‒protein and protein‒solvent hydrogen bonds. The conformational behaviours of the different structures were analysed through free energy landscape analysis. The flexibility characteristics of the mutants seem to depend on the reorganization of water molecules and their static or dynamic behaviour around amino acid side chains.

Molecular rules governing the structural polymorphism of amyloid fibrils in neurodegenerative diseases

Amyloid fibrils are hallmarks of various neurodegenerative diseases. The structural polymorphism of amyloid fibrils holds significant pathological importance in diseases. This review aims to provide an in-depth overview on the complexity of amyloid fibrils' structural polymorphism and its implications in disease pathogenesis. We firstly decipher the molecular rules governing the structural polymorphism of amyloid fibrils. We then discuss pivotal factors that contribute to the assortment of fibril structural polymorphs, including post-translational modifications (PTMs), disease mutations, and interacting molecules, and elucidate the structural basis of how these determinants influence amyloid fibril polymorphism. Furthermore, we underscore the need for a comprehensive understanding of the relationship between diverse fibril polymorphs and pathological activities, as well as their potential roles in therapeutic applications.