RNA-Puzzles Round V: blind predictions of 23 RNA structures

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, RNA structures are predicted by modeling groups before publication of the experimental structures. We report a large-scale set of predictions by 18 groups for 23 RNA-Puzzles: 4 RNA elements, 2 Aptamers, 4 Viral elements, 5 Ribozymes and 8 Riboswitches. We describe automatic assessment protocols for comparisons between prediction and experiment. Our analyses reveal some critical steps to be overcome to achieve good accuracy in modeling RNA structures: identification of helix-forming pairs and of non-Watson-Crick modules, correct coaxial stacking between helices and avoidance of entanglements. Three of the top four modeling groups in this round also ranked among the top four in the CASP15 contest.

Human transporter de-oligomerization regulates copper uptake into cells

Copper is an essential element involved in various biochemical processes, such as mitochondrial energy production and antioxidant defense, but improper regulation can lead to cellular toxicity and disease. Copper Transporter 1 (CTR1) plays a key role in copper uptake and maintaining cellular copper homeostasis. Although CTR1 endocytosis was previously thought to reduce copper uptake when levels are high, it was unclear how rapid regulation is achieved. Using single-molecule localization microscopy and single-molecule neighbor density assays, we discovered that excess copper induces monomerization of the wild-type trimeric CTR1 prior to endocytosis, a response blocked in the endocytosis-deficient CTR1 (M150L) mutant. This monomerization rapidly halts copper uptake and prevents copper overload. These findings reveal changes in protein oligomerization as a new paradigm of metal transport regulation, linking CTR1's structural changes to its endocytosis and copper homeostasis.

Islet amyloid polypeptide fibril catalyzes amyloid-β aggregation by promoting fibril nucleation rather than direct axial growth

Aberrant aggregation of amyloid-β (Aβ) and islet amyloid polypeptide (IAPP) into amyloid fibrils underlies the pathogenesis of Alzheimer's disease (AD) and type 2 diabetes (T2D), respectively. T2D significantly increases AD risk, with evidence suggesting that IAPP and Aβ co-aggregation and cross-seeding might contribute to the cross-talk between two diseases. Experimentally, preformed IAPP fibril seeds can accelerate Aβ aggregation, though the cross-seeding mechanism remains elusive. Here, we computationally demonstrated that Aβ monomer preferred to bind to the elongation ends of preformed IAPP fibrils. However, due to sequence mismatch, the Aβ monomer could not directly grow onto IAPP fibrils by forming multiple stable β-sheets with the exposed IAPP peptides. Conversely, in our control simulations of self-seeding, the Aβ monomer could axially grow on the Aβ fibril, forming parallel in-register β-sheets. Additionally, we showed that the IAPP fibril could catalyze Aβ fibril nucleation by promoting the formation of parallel in-register β-sheets in the C-terminus between bound Aβ peptides. This study enhances our understanding of the molecular interplay between Aβ and IAPP, shedding light on the cross-seeding mechanisms potentially linking T2D and AD. Our findings also underscore the importance of clearing IAPP deposits in T2D patients to mitigate AD risk.

Optogenetically engineered Septin-7 enhances immune cell infiltration of tumor spheroids

Chimeric antigen receptor T cell therapies have achieved great success in eradicating some liquid tumors, whereas the preclinical results in treating solid tumors have proven less decisive. One of the principal challenges in solid tumor treatment is the physical barrier composed of a dense extracellular matrix, which prevents immune cells from penetrating the tissue to attack intratumoral cancer cells. Here, we improve immune cell infiltration into solid tumors by manipulating septin-7 functions in cells. Using protein allosteric design, we reprogram the three-dimensional structure of septin-7 and insert a blue light-responsive light-oxygen-voltage-sensing domain 2 (LOV2), creating a light-controllable septin-7-LOV2 hybrid protein. Blue light inhibits septin-7 function in live cells, inducing extended cell protrusions and cell polarization, enhancing cell transmigration efficiency through confining spaces. We genetically edited human natural killer cell line (NK92) and mouse primary CD8+ T-cells expressing the engineered protein, and we demonstrated improved penetration and cytotoxicity against various tumor spheroid models. Our proposed strategy to enhance immune cell infiltration is compatible with other methodologies and therefore, could be used in combination to further improve cell-based immunotherapies against solid tumors.

The temperature dependence of the Hildebrand solubility parameters of selected hydrocarbon polymers and hydrocarbon solvents: a molecular dynamics investigation

CONTEXT

To determine the miscibility of liquids at high temperatures using the concept of Hildebrand solubility parameter δ , the current practice is to examine the difference in δ between two liquids at room temperature, assuming that δ is not sensitive to temperature. However, such an assumption may not be valid for certain polymer blends and solutions. Therefore, a knowledge of the δ values of the liquids of interest at high temperatures is desirable. The determination of δ at high temperatures, especially for high-molecular-weight polymers, is impossible, as polymers have vapor pressures of zero. To this end, molecular dynamics (MD) simulations provide a practical means for determining δ over a wide range of temperatures. In this work, we study the temperature dependence of δ of five hydrocarbon polymers: polyethylene (PE), isotactic and atactic polypropylene (i-PP and a-PP), polyisobutylene (PIB), and polyisoprene (PI) in five hydrocarbon solvents: n-pentane, n-hexane, n-dodecane, isobutene, and cyclohexane. The polymers are modeled as monodisperse chains with 100 repeat units. The average δ values of PE, i-PP, a-PP, PIB, and PI at 300 K are determined as 18.6, 14.9, 14.6, 14.3, and 16.4 MPa1/2, respectively, in a good agreement with experimental data. The δ values of these polymers at various temperatures are also determined. The temperature dependence of δ is fitted to two linear equations, one above and the other below the polymer's glass transition temperature Tg. The δ values are more sensitive to temperature at T ≥ Tg. The Tg values of the polymers, determined based upon their specific volumes and δ values agree with the experiment qualitatively. The determination of the temperature dependence of δ has a great potential for industrial applications, such as determining miscibility, developing polymeric organogelators as flocculants and oil spill treating agents, and identifying potential solvents and ideal processing temperatures.

METHODS

The MD simulations are performed using the GROMACS 2022.3 package with optimized potential for liquid simulations-all atom (OPLS-AA) force field parameters. All polymers are built as extended chains using CHARMM-GUI Polymer Builder.

A Piecewise Design Approach to Engineering a Miniature ACE2 Mimic to Bind SARS-CoV-2

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues its global spread, the exploration of novel therapeutic and diagnostic strategies is still needed. The virus enters host cells by binding the angiotensin-converting enzyme 2 (ACE2) receptor through the spike protein. Here, we develop an engineered, small, stable, and catalytically inactive version of ACE2, termed miniature ACE2 (mACE2), designed to bind the spike protein with high affinity. Employing a magnetic nanoparticle-based assay, we harnessed the strong binding affinity of mACE2 to develop a sensitive and specific platform for the detection or neutralization of SARS-CoV-2. Our findings highlight the potential of engineered mACE2 as a valuable tool in the fight against SARS-CoV-2. The success of developing such a small reagent based on a piecewise molecular design serves as a proof-of-concept approach for the rapid deployment of such agents to diagnose and fight other viral diseases.

Computer aided therapeutic tripeptide design, in alleviating the pathogenic proclivities of nocuous α-synuclein fibrils

Parkinson's disorder (PD) exacerbates neuronal degeneration of motor nerves, thereby effectuating uncoordinated movements and tremors. Aberrant alpha-synuclein (α-syn) is culpable of triggering PD, wherein cytotoxic amyloid aggregates of α-syn get deposited in motor neurons to instigate neuro-degeneration. Amyloid aggregates, typically rich in beta sheets are cardinal targets to mitigate their neurotoxic effects. In this analysis, owing to their interaction specificity, we formulated an efficacious tripeptide out of the aggregation-prone region of α-syn protein. With the help of a proficient computational pipeline, systematic peptide shortening and an adept molecular simulation platform, we formulated a tripeptide, VAV from α-syn structure based hexapeptide KISVRV. Indeed, the VAV tripeptide was able to effectively mitigate the α-syn amyloid fibrils' dynamic rate of beta-sheet formation. Additional trajectory analyses of the VAV- α-syn complex indicated that, upon its dynamic interaction, VAV efficiently altered the distinct pathogenic structural dynamics of α-syn, further advocating its potential in alleviating aberrant α-syn's amyloidogenic proclivities. Consistent findings from various computational analyses have led us to surmise that VAV could potentially re-alter the pathogenic conformational orientation of α-syn, essential to mitigate its cytotoxicity. Hence, VAV tripeptide could be an efficacious therapeutic candidate to efficiently ameliorate aberrant α-syn amyloid mediated neurotoxicity, eventually attenuating the nocuous effects of PD.Communicated by Ramaswamy H. Sarma.

Allosteric regulation of kinase activity in living cells

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities, making it difficult to specifically target one kinase, and allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss the methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or 'sensors,' are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

Allosteric regulation of kinase activity in living cells

The dysregulation of protein kinases is associated with multiple diseases due to the kinases' involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities making it difficult to specifically target one kinase, allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or "sensors" are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

Defensin-based therapeutic peptide design in attenuating V30M TTR-induced Familial Amyloid Polyneuropathy

In the present study, we aimed to formulate an effective therapeutic candidate against V30M mutant transthyretin (TTR) protein to hinder its pathogenic misfolding. Nicotiana alata Defensin 1 (NaD1) Antimicrobial Peptide (AMP) was availed due to its tendency to aggregate, which may compete for aggregation-prone regions of pathogenic TTR protein. Based on NaD1's potential to bind to V30M TTR, we proposed NaD1-derived tetra peptides: CKTE and SKIL to be initial therapeutic candidates. Based on their association with mutant TTR protein, CKTE tetra peptide showed considerable interaction and curative potential as compared to SKIL tetra peptide. Further analyses from discrete molecular dynamics simulation corroborate CKTE tetra peptide's effectiveness as a 'beta-sheet breaker' against V30M TTR. Various post-simulation trajectory analyses suggested that CKTE tetra peptide alters the structural dynamics of pathogenic V30M TTR protein, thereby potentially attenuating its beta-sheets and impeding its aggregation. Normal mode analysis simulation corroborated that V30M TTR conformation is altered upon its interaction with CKTE peptide. Moreover, simulated thermal denaturation findings suggested that CKTE-V30M TTR complex is more susceptible to simulated denaturation, relative to pathogenic V30M TTR; further substantiating CKTE peptide's potential to alter V30M TTR's pathogenic conformation. Moreover, the residual frustration analysis augmented CKTE tetra peptide's proclivity in reorienting the conformation of V30M TTR. Therefore, we predicted that the tetra peptide, CKTE could be a promising therapeutic candidate in mitigating the amyloidogenic detrimental effects of V30M TTR-mediated familial amyloid polyneuropathy (FAP).

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03646-4.

Big versus small: The impact of aggregate size in disease

Protein aggregation results in an array of different size soluble oligomers and larger insoluble fibrils. Insoluble fibrils were originally thought to cause neuronal cell deaths in neurodegenerative diseases due to their prevalence in tissue samples and disease models. Despite recent studies demonstrating the toxicity associated with soluble oligomers, many therapeutic strategies still focus on fibrils or consider all types of aggregates as one group. Oligomers and fibrils require different modeling and therapeutic strategies, targeting the toxic species is crucial for successful study and therapeutic development. Here, we review the role of different-size aggregates in disease, and how factors contributing to aggregation (mutations, metals, post-translational modifications, and lipid interactions) may promote oligomers opposed to fibrils. We review two different computational modeling strategies (molecular dynamics and kinetic modeling) and how they are used to model both oligomers and fibrils. Finally, we outline the current therapeutic strategies targeting aggregating proteins and their strengths and weaknesses for targeting oligomers versus fibrils. Altogether, we aim to highlight the importance of distinguishing the difference between oligomers and fibrils and determining which species is toxic when modeling and creating therapeutics for protein aggregation in disease.

A noncommutative combinatorial protein logic circuit controls cell orientation in nanoenvironments

Single-protein-based devices that integrate signal sensing with logical operations to generate functional outputs offer exceptional promise for monitoring and modulating biological systems. Engineering such intelligent nanoscale computing agents is challenging, as it requires the integration of sensor domains into a functional protein via intricate allosteric networks. We incorporate a rapamycin-sensitive sensor (uniRapR) and a blue light-responsive LOV2 domain into human Src kinase, creating a protein device that functions as a noncommutative combinatorial logic circuit. In our design, rapamycin activates Src kinase, causing protein localization to focal adhesions, whereas blue light exerts the reverse effect that inactivates Src translocation. Focal adhesion maturation induced by Src activation reduces cell migration dynamics and shifts cell orientation to align along collagen nanolane fibers. Using this protein device, we reversibly control cell orientation by applying the appropriate input signals, a framework that may be useful in tissue engineering and regenerative medicine.

Structural and dynamic insights into α-synuclein dimer conformations

Parkinson disease is associated with the aggregation of the protein α-synuclein. While α-synuclein can exist in multiple oligomeric states, the dimer has been a subject of extensive debates. Here, using an array of biophysical approaches, we demonstrate that α-synuclein in vitro exhibits primarily a monomer-dimer equilibrium in nanomolar concentrations and up to a few micromolars. We then use spatial information from hetero-isotopic cross-linking mass spectrometry experiments as restrains in discrete molecular dynamics simulations to obtain the ensemble structure of dimeric species. Out of eight structural sub-populations of dimers, we identify one that is compact, stable, abundant, and exhibits partially exposed β-sheet structures. This compact dimer is the only one where the hydroxyls of tyrosine 39 are in proximity that may promote dityrosine covalent linkage upon hydroxyl radicalization, which is implicated in α-synuclein amyloid fibrils. We propose that this α-synuclein dimer features etiological relevance to Parkinson disease.

An engineered N-acyltransferase-LOV2 domain fusion protein enables light-inducible allosteric control of enzymatic activity

Transferases are ubiquitous across all known life. While much work has been done to understand and describe these essential enzymes, there have been minimal efforts to exert tight and reversible control over their activity for various biotechnological applications. Here, we apply a rational, computation-guided methodology to design and test a transferase-class enzyme allosterically regulated by light-oxygen-voltage 2 sensing domain. We utilize computational techniques to determine the intrinsic allosteric networks within N-acyltransferase (Orf11/∗Dbv8) and identify potential allosteric sites on the protein's surface. We insert light-oxygen-voltage 2 sensing domain at the predicted allosteric site, exerting reversible control over enzymatic activity. We demonstrate blue-light regulation of N-acyltransferase (Orf11/∗Dbv8) function. Our study for the first time demonstrates optogenetic regulation of a transferase-class enzyme as a proof-of-concept for controllable transferase design. This successful design opens the door for many future applications in metabolic engineering and cellular programming.

Combining Experimental Restraints and RNA 3D Structure Prediction in RNA Nanotechnology

Precise RNA tertiary structure prediction can aid in the design of RNA nanoparticles. However, most existing RNA tertiary structure prediction methods are limited to small RNAs with relatively simple secondary structures. Large RNA molecules usually have complex secondary structures, including multibranched loops and pseudoknots, allowing for highly flexible RNA geometries and multiple stable states. Various experiments and bioinformatics analyses can often provide information about the distance between atoms (or residues) in RNA, which can be used to guide the prediction of RNA tertiary structure. In this chapter, we will introduce a platform, iFoldNMR, that can incorporate non-exchangeable imino protons resonance data from NMR as restraints for RNA 3D structure prediction. We also introduce an algorithm, DVASS, which optimizes distance restraints for better RNA 3D structure prediction.

Adaptation-proof SARS-CoV-2 vaccine design

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface spike glycoprotein - a major antibody target - is critical for virus entry via engagement of human angiotensin-converting enzyme 2 (ACE2) receptor. Despite successes with existing vaccines and therapies that primarily target the receptor binding domain (RBD) of the spike protein, the susceptibility of RBD to mutations provides escape routes for the SARS-CoV-2 from neutralizing antibodies. On the other hand, structural conservation in the spike protein can be targeted to reduce escape mutations and achieve broad protection. Here, we designed candidate stable immunogens that mimic surface features of selected conserved regions of spike protein through 'epitope grafting,' in which we present the target epitope topology on diverse heterologous scaffolds that can structurally accommodate the spike epitopes. Structural characterization of the epitope-scaffolds showed stark agreement with our computational models and target epitopes. The sera from mice immunized with engineered designs display epitope-scaffolds and spike binding activity. We also demonstrated the utility of the designed epitope-scaffolds in diagnostic applications. Taken all together, our study provides important methodology for targeting the conserved, non-RBD structural motifs of spike protein for SARS-CoV-2 epitope vaccine design and demonstrates the potential utility of 'epitope grafting' in rational vaccine design.

Adaptation-proof SARS-CoV-2 vaccine design

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface spike glycoprotein - a major antibody target - is critical for virus entry via engagement of human angiotensin-converting enzyme 2 (ACE2) receptor. Despite successes with existing vaccines and therapies that primarily target the receptor binding domain (RBD) of the spike protein, the susceptibility of RBD to mutations provides escape routes for the SARS-CoV-2 from neutralizing antibodies. On the other hand, structural conservation in the spike protein can be targeted to reduce escape mutations and achieve broad protection. Here, we designed candidate stable immunogens that mimic surface features of selected conserved regions of spike protein through 'epitope grafting,' in which we present the target epitope topology on diverse heterologous scaffolds that can structurally accommodate the spike epitopes. Structural characterization of the epitope-scaffolds showed stark agreement with our computational models and target epitopes. The sera from mice immunized with engineered designs display epitope-scaffolds and spike binding activity. We also demonstrated the utility of the designed epitope-scaffolds in diagnostic applications. Taken all together, our study provides important methodology for targeting the conserved, non-RBD structural motifs of spike protein for SARS-CoV-2 epitope vaccine design and demonstrates the potential utility of 'epitope grafting' in rational vaccine design.

A review on Huntington protein Insight into protein aggregation and therapeutic interventions

Huntington disease (HD) is a distressing, innate neurodegenerative disease that descends from CAG repeat expansion in the huntingtin gene causing behavioral changes, motor dysfunction, and dementia in children and adults. Mutation in huntingtin (HTT) protein has been suggested to cause neuron loss in the cortex and striatum through various mechanisms including abnormal regulation of transcription, proteasomal dysfunction, post-translational modification, and other events, regulating toxicity. Pathogenesis of HD involves cleavage of the huntingtin protein followed by the neuronal accumulation of its aggregated form. Several research groups made possible efforts to reduce huntingtin gene expression, protein accumulation, and protein aggregation using inhibitors and molecular chaperones as developing drugs against HD. Herein, we review the mechanism proposed towards the formation of HTT protein aggregation and the impact of therapeutic strategies for the treatment of HD.

Advances in optimizing enzyme electrostatic preorganization

Utilizing electric fields to catalyze chemical reactions is not a new idea, but in enzymology it undergoes a renaissance, inspired by Warhsel's concept of electrostatic preorganization. According to this concept, the source of the immense catalytic efficiency of enzymes is the intramolecular electric field that permanently favors the reaction transition state over the reactants. Within enzyme design, computational efforts have fallen short in designing enzymes with natural-like efficacy. The outcome could improve if long-range electrostatics (often omitted in current protocols) would be optimized. Here, we highlight the major developments in methods for analyzing and designing electric fields generated by the protein scaffolds, in order to both better understand how natural enzymes function, and aid artificial enzyme design.

Two-input protein logic gate for computation in living cells

Advances in protein design have brought us within reach of developing a nanoscale programming language, in which molecules serve as operands and their conformational states function as logic gates with precise input and output behaviors. Combining these nanoscale computing agents into larger molecules and molecular complexes will allow us to write and execute "code". Here, in an important step toward this goal, we report an engineered, single protein design that is allosterically regulated to function as a 'two-input logic OR gate'. Our system is based on chemo- and optogenetic regulation of focal adhesion kinase. In the engineered FAK, all of FAK domain architecture is retained and key intramolecular interactions between the kinase and the FERM domains are externally controlled through a rapamycin-inducible uniRapR module in the kinase domain and a light-inducible LOV2 module in the FERM domain. Orthogonal regulation of protein function was possible using the chemo- and optogenetic switches. We demonstrate that dynamic FAK activation profoundly increased cell multiaxial complexity in the fibrous extracellular matrix microenvironment and decreased cell motility. This work provides proof-of-principle for fine multimodal control of protein function and paves the way for construction of complex nanoscale computing agents.

Machine Learning to Predict Diels-Alder Reaction Barriers from the Reactant State Electron Density

Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry that countless variants, with or without catalysts, have been studied, and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous and can be used effectively for the prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels-Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels-Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels-Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes in k_cat. We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments.

Out-of-Equilibrium Biophysical Chemistry: The Case for Multidimensional, Integrated Single-Molecule Approaches

Out-of-equilibrium processes are ubiquitous across living organisms and all structural hierarchies of life. At the molecular scale, out-of-equilibrium processes (for example, enzyme catalysis, gene regulation, and motor protein functions) cause biological macromolecules to sample an ensemble of conformations over a wide range of time scales. Quantifying and conceptualizing the structure–dynamics to function relationship is challenging because continuously evolving multidimensional energy landscapes are necessary to describe nonequilibrium biological processes in biological macromolecules. In this perspective, we explore the challenges associated with state-of-the-art experimental techniques to understanding biological macromolecular function. We argue that it is time to revisit how we probe and model functional out-of-equilibrium biomolecular dynamics. We suggest that developing integrated single-molecule multiparametric force–fluorescence instruments and using advanced molecular dynamics simulations to study out-of-equilibrium biomolecules will provide a path towards understanding the principles of and mechanisms behind the structure–dynamics to function paradigm in biological macromolecules.

Probing allosteric regulations with coevolution-driven molecular simulations

Protein-mediated allosteric regulations are essential in biology, but their quantitative characterization continues to posit formidable challenges for both experiments and computations. Here, we combine coevolutionary information, multiscale molecular simulations, and free-energy methods to interrogate and quantify the allosteric regulation of functional changes in protein complexes. We apply this approach to investigate the regulation of adenylyl cyclase (AC) by stimulatory and inhibitory G proteins—a prototypical allosteric system that has long escaped from in-depth molecular characterization. We reveal a surprisingly simple ON/OFF regulation of AC functional dynamics through multiple pathways of information transfer. The binding of G proteins reshapes the free-energy landscape of AC following the classical population-shift paradigm. The model agrees with structural and biochemical data and reveals previously unknown experimentally consistent intermediates. Our approach showcases a general strategy to explore uncharted functional space in complex biomolecular regulations.

Structure of prion β-oligomers as determined by short-distance crosslinking constraint-guided discrete molecular dynamics simulations

The conversion of the native monomeric cellular prion protein (PrP^C ) into an aggregated pathological β-oligomeric form (PrP^β ) and an infectious form (PrP^Sc ) is the central element in the development of prion diseases. The structure of the aggregates and the molecular mechanisms of the conformational changes involved in the conversion are still unknown. We applied mass spectrometry combined with chemical crosslinking, hydrogen/deuterium exchange, limited proteolysis, and surface modification for the differential characterization of the native and the urea+acid-converted prion β-oligomer structures to obtain insights into the mechanisms of conversion and aggregation. For the determination of the structure of the monomer and the dimer unit of the β-oligomer, we applied a recently-developed approach for de novo protein structure determination which is based on the incorporation of zero-length and short-distance crosslinking data as intra- and inter-protein constraints in discrete molecular dynamics simulations (CL-DMD). Based on all of the structural-proteomics experimental data and the computationally predicted structures of the monomer units, we propose the potential mode of assembly of the β-oligomer. The proposed β-oligomer assembly provides a clue on the β-sheet nucleation site, and how template-based conversion of the native prion molecule occurs, growth of the prion aggregates, and maturation into fibrils may occur.

The structural heterogeneity of α-synuclein is governed by several distinct subpopulations with interconversion times slower than milliseconds

α-Synuclein plays an important role in synaptic functions by interacting with synaptic vesicle membrane, while its oligomers and fibrils are associated with several neurodegenerative diseases. The specific monomer structures that promote its membrane binding and self-association remain elusive due to its transient nature as an intrinsically disordered protein. Here, we use inter-dye distance distributions from bulk time-resolved Förster resonance energy transfer as restraints in discrete molecular dynamics simulations to map the conformational space of the α-synuclein monomer. We further confirm the generated conformational ensemble in orthogonal experiments utilizing far-UV circular dichroism and cross-linking mass spectrometry. Single-molecule protein-induced fluorescence enhancement measurements show that within this conformational ensemble, some of the conformations of α-synuclein are surprisingly stable, exhibiting conformational transitions slower than milliseconds. Our comprehensive analysis of the conformational ensemble reveals essential structural properties and potential conformations that promote its various functions in membrane interaction or oligomer and fibril formation.

Contrasting Effects of Inhibitors Li+ and Be2+ on Catalytic Cycle of Glycogen Synthase Kinase-3β

Ionic lithium shows rare effectiveness for treating bipolar disorder and is a potential drug for neurodegenerative diseases. Unfortunately, lithium suffers from significant drawbacks, mainly a narrow therapeutic window. Among the targets of lithium, glycogen synthase kinase 3β (GSK-3β) may be responsible for its therapeutic effects. The development of alternative, selective inhibitors of this kinase could prevent lithium side effects, but such efforts have met little success so far. An atomistic understanding of Li⁺ inhibition and the GSK-3β phosphorylation reaction would therefore facilitate the development of new drugs. In this study, we use extensive sampling of catalytic states with our mixed quantum-classical dynamics method QM/DMD and binding affinities from a competitive metal affinity (CMA) approach to expand the atomistic picture of Li⁺ GSK-3β inhibition. We compare Li⁺ action with Be²⁺ and find our results in agreement with in vitro kinetics studies. Ultimately, our simulations show that Li⁺ inhibition is driven by decreasing the phosphorylation reaction rate, rather than reducing catalytic turnover through tight binding to different GSK-3β states like Be²⁺ inhibition. The effect of these metals derive from electrostatic differences and especially their smaller atomic radii compared to the native Mg²⁺ and thus provide insight for the development of GSK-3β inhibitors based on other paradigms.

SAMase of Bacteriophage T3 Inactivates Escherichia coli's Methionine S-Adenosyltransferase by Forming Heteropolymers

S-Adenosylmethionine lyase (SAMase) of bacteriophage T3 degrades the intracellular SAM pools of the host Escherichia coli cells, thereby inactivating a crucial metabolite involved in a plethora of cellular functions, including DNA methylation. SAMase is the first viral protein expressed upon infection, and its activity prevents methylation of the T3 genome. Maintenance of the phage genome in a fully unmethylated state has a profound effect on the infection strategy. It allows T3 to shift from a lytic infection under normal growth conditions to a transient lysogenic infection under glucose starvation. Using single-particle cryoelectron microscopy (cryo-EM) and biochemical assays, we demonstrate that SAMase performs its function by not only degrading SAM but also by interacting with and efficiently inhibiting the host's methionine S-adenosyltransferase (MAT), the enzyme that produces SAM. Specifically, SAMase triggers open-ended head-to-tail assembly of E. coli MAT into an unusual linear filamentous structure in which adjacent MAT tetramers are joined by two SAMase dimers. Molecular dynamics simulations together with normal mode analyses suggest that the entrapment of MAT tetramers within filaments leads to an allosteric inhibition of MAT activity due to a shift to low-frequency, high-amplitude active-site-deforming modes. The amplification of uncorrelated motions between active-site residues weakens MAT's substrate binding affinity, providing a possible explanation for the observed loss of function. We propose that the dual function of SAMase as an enzyme that degrades SAM and as an inhibitor of MAT activity has emerged to achieve an efficient depletion of the intracellular SAM pools. IMPORTANCE Self-assembly of enzymes into filamentous structures in response to specific metabolic cues has recently emerged as a widespread strategy of metabolic regulation. In many instances, filamentation of metabolic enzymes occurs in response to starvation and leads to functional inactivation. Here, we report that bacteriophage T3 modulates the metabolism of the host E. coli cells by recruiting a similar strategy: silencing a central metabolic enzyme by subjecting it to phage-mediated polymerization. This observation points to an intriguing possibility that virus-induced polymerization of the host metabolic enzymes is a common mechanism implemented by viruses to metabolically reprogram and subdue infected cells.

Titr-DMD-A Rapid, Coarse-Grained Quasi-All-Atom Constant pH Molecular Dynamics Framework

The pH-dependence of enzyme fold stability and catalytic activity is a fundamentally dynamic, structural property which is difficult to study. The challenges and expense of investigating dynamic, atomic scale behavior experimentally means that computational methods, particularly constant pH molecular dynamics (CpHMD), are well situated tools for this. However, these methods often struggle with affordable sampling of sufficiently long time scales while also obtaining accurate pKa prediction and verifying the structures they generate. We introduce Titr-DMD, an affordable CpHMD method that combines the quasi-all-atom coarse-grained discrete molecular dynamics (DMD) method for conformational sampling with Propka for pKa prediction, to circumvent these issues. The combination enables rapid sampling on limited computational resources, while simulations are still performed on the atomic scale. We benchmark the method on a set of proteins with experimentally attested pKa and on the pH triggered conformational change in a staphylococcal nuclease mutant, a rare experimental study of such behavior. Our results show Titr-DMD to be an effective and inexpensive method to study pH-coupled protein dynamics.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Unraveling protein's structural dynamics: from configurational dynamics to ensemble switching guides functional mesoscale assemblies

Evidence regarding protein structure and function manifest the imperative role that dynamics play in proteins, underlining reconsideration of the unanimated sequence-to-structure-to-function paradigm. Structural dynamics portray a heterogeneous energy landscape described by conformational ensembles where each structural representation can be responsible for unique functions or enable macromolecular assemblies. Using the human p27/Cdk2/Cyclin A ternary complex as an example, we highlight the vital role of intramolecular and intermolecular dynamics for target recognition, binding, and inhibition as a critical modulator of cell division. Rapidly sampling configurations is critical for the population of different conformational ensembles encoding functional roles. To garner this knowledge, we present how the integration of (sub)ensemble and single-molecule fluorescence spectroscopy with molecular dynamic simulations can characterize structural dynamics linking the heterogeneous ensembles to function. The incorporation of dynamics into the sequence-to-structure-to-function paradigm promises to assist in tackling various challenges, including understanding the formation and regulation of mesoscale assemblies inside cells.

Pathogen-specific antimicrobials engineered de novo through membrane-protein biomimicry

Precision antimicrobials aim to kill pathogens without damaging commensal bacteria in the host, and thus to cure disease without antibiotic-associated dysbiosis. Here, we report the de novo design of a synthetic host defence peptide that targets a specific pathogen by mimicking key molecular features of the pathogen’s channel-forming membrane proteins. By exploiting physical and structural vulnerabilities within the pathogen’s cellular envelope, we designed a peptide sequence that undergoes instructed tryptophan-zippered assembly within the mycolic-acid rich outer membrane of Mycobacterium tuberculosis (Mtb) to specifically kill the pathogen without collateral toxicity towards lung commensal bacteria or host tissue. These ‘mycomembrane-templated’ assemblies elicit rapid mycobactericidal activity, and enhance the potency of antibiotics by improving their otherwise poor diffusion across the rigid Mtb envelope with respect to agents that exploit transmembrane protein channels for antimycobacterial activity. This biomimetic strategy may aid the design of other narrow-spectrum antimicrobial peptides.

Prefusion spike protein stabilization through computational mutagenesis

A novel severe acute respiratory syndrome (SARS)‐like coronavirus (SARS‐CoV‐2) has emerged as a human pathogen, causing global pandemic and resulting in over 400 000 deaths worldwide. The surface spike protein of SARS‐CoV‐2 mediates the process of coronavirus entry into human cells by binding angiotensin‐converting enzyme 2 (ACE2). Due to the critical role in viral‐host interaction and the exposure of spike protein, it has been a focus of most vaccines' developments. However, the structural and biochemical studies of the spike protein are challenging because it is thermodynamically metastable. Here, we develop a new pipeline that automatically identifies mutants that thermodynamically stabilize the spike protein. Our pipeline integrates bioinformatics analysis of conserved residues, motion dynamics from molecular dynamics simulations, and other structural analysis to identify residues that significantly contribute to the thermodynamic stability of the spike protein. We then utilize our previously developed protein design tool, Eris, to predict thermodynamically stabilizing mutations in proteins. We validate the ability of our pipeline to identify protein stabilization mutants through known prefusion spike protein mutants. We finally utilize the pipeline to identify new prefusion spike protein stabilization mutants.

RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

Various miRNAs compensate the role of miR-122 on HCV replication

One of the determinants for tissue tropism of hepatitis C virus (HCV) is miR-122, a liver-specific microRNA. Recently, it has been reported that interaction of miR-122 to HCV RNA induces a conformational change of the 5'UTR internal ribosome entry site (IRES) structure to form stem-loop II structure (SLII) and hijack of translating 80S ribosome through the binding of SLIII to 40S subunit, which leads to efficient translation. On the other hand, low levels of HCV-RNA replication have also been detected in some non-hepatic cells; however, the details of extrahepatic replication remain unknown. These observations suggest the possibility that miRNAs other than miR-122 can support efficient replication of HCV-RNA in non-hepatic cells. Here, we identified a number of such miRNAs and show that they could be divided into two groups: those that bind HCV-RNA at two locations (miR-122 binding sites I and II), in a manner similar to miR-122 (miR-122-like), and those that target a single site that bridges sites I and II and masking both G28 and C29 in the 5'UTR (non-miR-122-like). Although the enhancing activity of these non-hepatic miRNAs were lower than those of miR-122, substantial expression was detected in various normal tissues. Furthermore, structural modeling indicated that both miR-122-like and non-miR-122-like miRNAs not only can facilitate the formation of an HCV IRES SLII but also can stabilize IRES 3D structure in order to facilitate binding of SLIII to the ribosome. Together, these results suggest that HCV facilitates miR-122-independent replication in non-hepatic cells through recruitment of miRNAs other than miR-122. And our findings can provide a more detailed mechanism of miR-122-dependent enhancement of HCV-RNA translation by focusing on IRES tertiary structure.

Experimentally-driven protein structure modeling

Revolutions in natural and exact sciences started at the dawn of last century have led to the explosion of theoretical, experimental, and computational approaches to determine structures of molecules, complexes, as well as their rich conformational dynamics. Since different experimental methods produce information that is attributed to specific time and length scales, corresponding computational methods have to be tailored to these scales and experiments. These methods can be then combined and integrated in scales, hence producing a fuller picture of molecular structure and motion from the "puzzle pieces" offered by various experiments. Here, we describe a number of computational approaches to utilize experimental data to glance into structure of proteins and understand their dynamics. We will also discuss the limitations and the resolution of the constraints-based modeling approaches. SIGNIFICANCE: Experimentally-driven computational structure modeling and determination is a rapidly evolving alternative to traditional approaches for molecular structure determination. These new hybrid experimental-computational approaches are proving to be a powerful microscope to glance into the structural features of intrinsically or partially disordered proteins, dynamics of molecules and complexes. In this review, we describe various approaches in the field of experimentally-driven computational structure modeling.

Temporally Coarse-Grained All-Atom Molecular Dynamics Achieved via Stochastic Padé Approximants

A Padé approximant scheme for realizing the discrete-time evolution of the state of a many-atom system is introduced. This temporal coarse-graining scheme accounts for the underlying Newtonian physics and avoids the need for construction of spatially coarse-grained variables. Newtonian physics is incorporated through short molecular dynamics simulations at the beginning of each of the large coarse-grained timesteps. The balance between stochastic and coherent dynamics expressed by many-atom systems is captured via incorporation of the Ito formula into a Padé approximant for the time dependence of individual atom positions over large timesteps. Since the time for a many-atom system to express a characteristic ensemble of atomic velocity fluctuations is typically short relative to the characteristic time of large-scale atomic displacements, a computationally efficient and accurate temporal coarse-graining of the atom-resolved Newtonian dynamics is formulated, denoted all-atom Padé-Ito molecular dynamics (APIMD). Evolution of the system over a time step much longer than that required for standard molecular dynamics (MD) is achieved via incorporation of information from the short MD simulations into a Padé approximant extrapolation in time. The extrapolated atomic configuration is subjected to energy minimization and, when needed, thermal equilibration so as to avoid occasional unphysical close encounters deriving from the Padé approximant extrapolation and to represent configurations appropriate for the temperature of interest. APIMD is implemented and tested via comparison with traditional MD simulations of five phenomena: (1) pertussis toxin subunit deformation, (2) structural transition in a T = 1 capsid-like structure of HPV16 L1 protein, (3) coalescence of argon nanodroplets, and structural transitions in dialanine in (4) vacuum, and (5) water. Accuracy of APIMD is demonstrated using semimicroscopic descriptors (rmsd, radius of gyration, residue-residue contact maps, and densities) and the free energy. Significant computational acceleration relative to traditional molecular dynamics is illustrated.

Insight into the Structure of the "Unstructured" Tau Protein

Combining structural proteomics experimental data with computational methods is a powerful tool for protein structure prediction. Here, we apply a recently developed approach for de novo protein structure determination based on the incorporation of short-distance crosslinking data as constraints in discrete molecular dynamics simulations (CL-DMD), for the determination of the conformational ensemble of tau protein in solution. The predicted structures were in agreement with surface modification and long-distance crosslinking data. Tau in solution was found as an ensemble of rather compact globular conformations with distinct topology, inter-residue contacts, and a number of transient secondary-structure elements. Regions important for pathological aggregation consistently were found to contain β strands. The determined structures are compatible with the tau protein in solution being a molten globule at near-ground state with persistent residual structural features which we were able to capture by CL-DMD. The predicted structure may facilitate an understanding of the misfolding and oligomerization pathways of the tau protein.

Distinct Binding Modes of Vinculin Isoforms Underlie Their Functional Differences

Vinculin and its splice isoform metavinculin play key roles in regulating cellular morphology, motility, and force transduction. Vinculin is distinct from metavinculin in its ability to bundle filamentous actin (F-actin). To elucidate the molecular basis for these differences, we employed computational and experimental approaches. Results from these analyses suggest that the C terminus of both vinculin and metavinculin form stable interactions with the F-actin surface. However, the metavinculin tail (MVt) domain contains a 68 amino acid insert, with helix 1 (H1) sequestered into a globular subdomain, which protrudes from the F-actin surface and prevents actin bundling by sterically occluding actin filaments. Consistent with our model, deletion and selective point mutations within the MVt H1 disrupt this protruding structure, and facilitate actin bundling similar to vinculin tail (Vt) domain.

A Multiscale Model for the Self-Assembly of Coat Proteins in Bacteriophage MS2

The self-assembly of viral capsids is an essential step to the formation of infectious viruses. Elucidating the kinetic mechanisms of how a capsid or virus-like particle assembles could advance our knowledge about the viral lifecycle, as well as the general principles in self-assembly of biomaterials. However, current understanding of capsid assembly remains incomplete for many viruses due to the fact that the transient intermediates along the assembling pathways are experimentally difficult to be detected. In this paper, we constructed a new multiscale computational framework to simulate the self-assembly of virus-like particles. We applied our method to the coat proteins of bacteriophage MS2 as a specific model system. This virus-like particle of bacteriophage MS2 has a unique feature that its 90 sequence-identical dimers can be classified into two structurally various groups: one is the symmetric CC dimer, and the other is the asymmetric AB dimer. The homotypic interactions between AB dimers result in a 5-fold symmetric contact, while the heterotypic interactions between AB and CC dimers result in 6-fold symmetric contact. We found that the assembly can be described as a physical process of phase transition that is regulated by various factors such as concentration and specific stoichiometry between AB and CC dimers. Our simulations also demonstrate that heterotypic and homotypic interfaces play distinctive roles in modulating the assembling kinetics. The interaction between AB and CC dimers is much more dynamic than that between two AB dimers. We therefore suggest that the alternate growth of viral capsid through the heterotypic dimer interactions dominates the assembling pathways. This is, to the best of our knowledge, the first multiscale model to simulate the assembling process of coat proteins in bacteriophage MS2. The generality of this approach opens the door to its further applications in assembly of other viral capsids, virus-like particles, and novel drug delivery systems.

Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins

Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.

Limits in accuracy and a strategy of RNA structure prediction using experimental information

RNA structural complexity and flexibility present a challenge for computational modeling efforts. Experimental information and bioinformatics data can be used as restraints to improve the accuracy of RNA tertiary structure prediction. Regarding utilization of restraints, the fundamental questions are: (i) What is the limit in prediction accuracy that one can achieve with arbitrary number of restraints? (ii) Is there a strategy for selection of the minimal number of restraints that would result in the best structural model? We address the first question by testing the limits in prediction accuracy using native contacts as restraints. To address the second question, we develop an algorithm based on the distance variation allowed by secondary structure (DVASS), which ranks restraints according to their importance to RNA tertiary structure prediction. We find that due to kinetic traps, the greatest improvement in the structure prediction accuracy is achieved when we utilize only 40-60% of the total number of native contacts as restraints. When the restraints are sorted by DVASS algorithm, using only the first 20% ranked restraints can greatly improve the prediction accuracy. Our findings suggest that only a limited number of strategically selected distance restraints can significantly assist in RNA structure modeling.

Predicting the Most Stable Aptamer/Target Molecule Complex Configuration Using a Stochastic-Tunnelling Basin-Hopping Discrete Molecular Dynamics Method: A Novel Global Minimum Search Method for a Biomolecule Complex

This study proposed a novel global minimum search method for predicting the most stable biomolecule complex, which combines the strengths of three global minimum search methods (stochastic tunnelling, basin hopping, and discrete molecular dynamics) to efficiently improve the spatial domain search ability of the stochastic tunnelling-basin hopping (STUN-BH) method from our previous study. The epithelial cell adhesion molecule (EpCAM, PDB code: 4MZV) was used as a benchmark target molecule for the EpCAM aptamer EpA (Apt_EpA). For the most stable Apt_EpA/EpCAM complex predicted by our new method, the Apt_EpA was attached to the entangling loop fragments of the two EpCAM molecules with the most Apt_EpA residues. After the Apt_EpA/EpCAM complex had equilibrated with the water environment through a molecular dynamics simulation at 300 K for 10 ns, stable hydrogen bonds formed between the bases of Apt_EpA and EpCAM residues of the secondary structures, which included the alpha helix and beta sheet becoming less stable in the water environment. Those hydrogen bonds formed between the bases of Apt_EpA and EpCAM loop fragment residues remained stable in the water environment.

Amyloid Self-Assembly of hIAPP8-20 via the Accumulation of Helical Oligomers, α-Helix to β-Sheet Transition, and Formation of β-Barrel Intermediates

The self-assembly of human islet amyloid polypeptide (hIAPP) into β-sheet-rich nanofibrils is associated with the pathogeny of type 2 diabetes. Soluble hIAPP is intrinsically disordered with N-terminal residues 8-17 as α-helices. To understand the contribution of the N-terminal helix to the aggregation of full-length hIAPP, here the oligomerization dynamics of the hIAPP fragment 8-20 (hIAPP8-20) are investigated with combined computational and experimental approaches. hIAPP8-20 forms cross-β nanofibrils in silico from isolated helical monomers via the helical oligomers and α-helices to β-sheets transition, as confirmed by transmission electron microscopy, atomic force microscopy, circular dichroism spectroscopy, Fourier transform infrared spectroscopy, and reversed-phase high performance liquid chromatography. The computational results also suggest that the critical nucleus of aggregation corresponds to hexamers, consistent with a recent mass-spectroscopy study of hIAPP8-20 aggregation. hIAPP8-20 oligomers smaller than hexamers are helical and unstable, while the α-to-β transition starts from the hexamers. Converted β-sheet-rich oligomers first form β-barrel structures as intermediates before aggregating into cross-β nanofibrils. This study uncovers a complete picture of hIAPP8-20 peptide oligomerization, aggregation nucleation via conformational conversion, formation of β-barrel intermediates, and assembly of cross-β protofibrils, thereby shedding light on the aggregation of full-length hIAPP, a hallmark of pancreatic beta-cell degeneration.

Molecular Dynamics Insights for Screening the Ability of Polymers to Remove Pesticides from Water

The use of pesticides in agriculture is known to have environmental impacts, namely it leads to underground and spring water contamination. Thus, it turns out that nowadays general-endeavor towards the sustainability of farmer production requires novel strategies to capture pesticides from water and soils. We propose a methodology based on molecular dynamics simulations to identify polymers that are potentially featured to be applied for pesticide remediation in water and soils. We have employed cymoxanil (CYM), glufosinate ammonium (GLF), imidacloprid (IMI) and mancozeb (MAN) as pesticides, and have tested polymers with different characteristics as removing agents. Specifically, we have investigated oligomers of polypropylene (PP), poly(acrylic acid) protonated (PAAH) and deprotonated (PAA), and chitosan protonated (CTH) and deprotonated (CT). It has been found that all oligomers show a certain degree of selectivity concerning the interaction with the tested pesticides.

Conformational ensemble of native α-synuclein in solution as determined by short-distance crosslinking constraint-guided discrete molecular dynamics simulations

Combining structural proteomics experimental data with computational methods is a powerful tool for protein structure prediction. Here, we apply a recently-developed approach for de novo protein structure determination based on the incorporation of short-distance crosslinking data as constraints in discrete molecular dynamics simulations (CL-DMD) for the determination of conformational ensemble of the intrinsically disordered protein α-synuclein in the solution. The predicted structures were in agreement with hydrogen-deuterium exchange, circular dichroism, surface modification, and long-distance crosslinking data. We found that α-synuclein is present in solution as an ensemble of rather compact globular conformations with distinct topology and inter-residue contacts, which is well-represented by movements of the large loops and formation of few transient secondary structure elements. Non-amyloid component and C-terminal regions were consistently found to contain β-structure elements and hairpins.

As the population ages, neurodegenerative diseases such as Parkinson’s disease will become an increasing problem in many countries. Aggregation of the protein α-synuclein is the primary cause of Parkinson’s disease, but there is still a dearth of structural information pertaining to the native, non-aggregating form of this protein. A better understanding the structural state of the native protein may prove useful for the design of new therapeutics to combat this disease. In order to obtain more structural information on this protein, we have recently modelled the native α-synuclein protein. These models were generated using a novel approach which combines protein crosslinking and discrete molecular dynamics simulations. We have found that the α-synuclein protein can adopt several shapes, all with a similar topology, resembling a three fingered closed claw. A region of the protein important for aggregation was found to be protected from the surrounding biological environment in these conformations, and the stabilization of these structures may be a fruitful avenue for future drug research into mitigating the cause and effect of Parkinson’s disease.

Mechanistic insights into the Orai channel by molecular dynamics simulations

Highly Ca²⁺ selective channels trigger a large variety of cellular signaling processes in both excitable and non-excitable cells. Among these channels, the Orai channel is unique in its activation mechanism and its structure. It mediates Ca²⁺ influx into the cytosol with an extremely small unitary conductance over longer time-scales, ranging from minutes up to several hours. Its activation is regulated by the Ca²⁺ content of the endoplasmic reticulum (ER). Depletion of luminal [Ca²⁺]_ER is sensed by the STIM1 single transmembrane protein that directly binds and gates the Orai1 channel. Orai mediated Ca²⁺ influx increases cytosolic Ca²⁺ from 100 nM up to low micromolar range close to the pore and thereby forms Ca²⁺ microdomains. Hence, these features of the Orai channel can trigger long-term signaling processes without affecting the overall Ca²⁺ content of a single living cell. Here we focus on the architecture and dynamic conformational changes within the Orai channel. This review summarizes current achievements of molecular dynamics simulations in combination with live cell recordings to address gating and permeation of the Orai channel with molecular precision.

p62-Dependent Phase Separation of Patient-Derived KEAP1 Mutations and NRF2

Cancer-derived loss-of-function mutations in the KEAP1 tumor suppressor gene stabilize the NRF2 transcription factor, resulting in a prosurvival gene expression program that alters cellular metabolism and neutralizes oxidative stress. In a recent genotype-phenotype study, we classified 40% of KEAP1 mutations as ANCHOR mutants. By immunoprecipitation, these mutants bind more NRF2 than wild-type KEAP1 and ubiquitylate NRF2, but they are incapable of promoting NRF2 degradation. BioID-based protein interaction studies confirmed increased abundance of NRF2 within the KEAP1 ANCHOR mutant complexes, with no other statistically significant changes to the complexes. Discrete molecular dynamic simulation modeling and limited proteolysis suggest that the ANCHOR mutations stabilize residues in KEAP1 that contact NRF2. The modeling supports an intramolecular salt bridge between the R470C ANCHOR mutation and E493; mutation of the E493 residue confirmed the model, resulting in the ANCHOR phenotype. In live cells, the KEAP1 R320Q and R470C ANCHOR mutants colocalize with NRF2, p62/SQSTM1, and polyubiquitin in structured spherical droplets that rapidly fuse and dissolve. Transmission electron microscopy coupled with confocal fluorescent imaging revealed membraneless phase-separated biomolecular condensates. We present a model wherein ANCHOR mutations form p62-dependent biomolecular condensates that may represent a transitional state between impaired proteasomal degradation and autophagy.