Search

The identification of catalytic RNAs is typically achieved through primarily experimental means. However, only a small fraction of sequence space can be analyzed even with high-throughput techniques. Methods to extrapolate from a limited data set to predict additional ribozyme sequences, particularly in a human-interpretable fashion, could be useful both for designing new functional RNAs and for generating greater understanding about a ribozyme fitness landscape. Using information theory, we express the effects of epistasis (i.e., deviations from additivity) on a ribozyme. This representation was incorporated into a simple model of the epistatic fitness landscape, which identified potentially exploitable combinations of mutations. We used this model to theoretically predict mutants of high activity for a self-aminoacylating ribozyme, identifying potentially active triple and quadruple mutants beyond the experimental data set of single and double mutants. The predictions were validated experimentally, with nine out of nine sequences being accurately predicted to have high activity. This set of sequences included mutants that form a previously unknown evolutionary ‘bridge’ between two ribozyme families that share a common motif. Individual steps in the method could be examined, understood, and guided by a human, combining interpretability and performance in a simple model to predict ribozyme sequences by extrapolation.

We developed a method for implicit solvent molecular dynamics simulations of proteins in solvent mixtures (model with implicit solvation thermodynamics, MIST). The MIST method introduces experimental group transfer free energies to the generalized Born formulation for generating molecular trajectories without the need for developing rigorous explicit-solvent force fields for multicomponent solutions. As a test case, we studied the urea-induced denaturation of the Trp-cage miniprotein in water. We demonstrate that our method allows efficient exploration of the conformational space of the protein in only a few hundreds of nanoseconds of all-atom unbiased simulations. Furthermore, selective implementation of the transfer free energies of specific peptide groups, backbone, and side chains enables us to decouple their specific energetic contributions to the conformational changes of the protein. The approach herein developed can readily be extended to the investigation of complex matrices as well as to the characterization of protein aggregation. The MIST method is implemented in Plumed (ver. 2.8) as a separate module called SASA.

Liquid-liquid phase separation (LLPS) is a process that results in the formation of a polymer-rich liquid phase coexisting with a polymer-depleted liquid phase. LLPS plays a critical role in the cell through the formation of membrane-less organelles, but it also has a number of biotechnical and biomedical applications such as drug confinement and its targeted delivery. In this chapter, we present a computational efficient methodology that uses field-theoretic simulations (FTS) with complex Langevin (CL) sampling to characterize polymer phase behavior and delineate the LLPS phase boundaries. This approach is a powerful complement to analytical and explicit-particle simulations, and it can serve to inform experimental LLPS studies. The strength of the method lies in its ability to properly sample a large ensemble of polymers in a saturated solution while including the effect of composition fluctuations on LLPS. We describe the approaches that can be used to accurately construct phase diagrams of a variety of molecularly designed polymers and illustrate the method by generating an approximation-free phase diagram for a classical symmetric diblock polyampholyte.

Improving adhesives for wet surfaces is an ongoing challenge. While the adhesive proteins of marine mussels have inspired many synthetic wet adhesives, the mechanisms of mussel adhesion are still not fully understood. Using surface forces apparatus (SFA) measurements and replica-exchange and umbrella-sampling molecular dynamics simulations, we probed the relationships between the sequence, structure, and adhesion of mussel-inspired peptides. Experimental and computational results reveal that peptides derived from mussel foot protein 3 slow (mfp-3s) containing 3,4-dihydroxyphenylalanine (Dopa), a post-translationally modified variant of tyrosine commonly found in mussel foot proteins, form adhesive monolayers on mica. In contrast, peptides with tyrosine adsorb as weakly adhesive clusters. We further considered simulations of mfp-3s derivatives on a range of hydrophobic and hydrophilic organic and inorganic surfaces (including silica, self-assembled monolayers, and a lipid bilayer) and demonstrated that the chemical character of the target surface and proximity of cationic and hydrophobic residues to Dopa affect peptide adsorption and adhesion. Collectively, our results suggest that conversion of tyrosine to Dopa in hydrophobic, sparsely charged peptides influences peptide self-association and ultimately dictates their adhesive performance.

Using a combination of molecular dynamics simulation, dialysis experiments, and electronic circular dichroism measurements, we studied the solvation thermodynamics of proteins in two osmolyte solutions, trimethylamine

Most biological events occur on time scales that are difficult to access using conventional all-atom molecular dynamics simulations in explicit solvent. Implicit solvent techniques offer a promising solution to this problem, alleviating the computational cost associated with the simulation of large systems and accelerating the sampling compared to explicit solvent models. The substitution of water molecules by a mean field, however, introduces simplifications that may penalize accuracy and impede the prediction of certain physical properties. We demonstrate that existing implicit solvent models developed using a transfer free energy approach, while satisfactory at reproducing the folding behavior of globular proteins, fare less well in characterizing the conformational properties of intrinsically disordered proteins. We develop a new implicit solvent model that maximizes the degree of accuracy for both disordered and folded proteins. We show, by comparing the simulation outputs to experimental data, that in combination with the a99SB-disp force field, the implicit solvent model can describe both disordered (aβ40, PaaA2, and drkN SH3) and folded ((AAQAA)

Amyloid β (Aβ) protein is responsible for Alzheimer's disease, and one of its important fragments, Aβ(25-35), is found in the brain and has been shown to be neurotoxic. Tachykinin neuropeptides, including Neuromedin K (NK), Kassinin, and Substance P, have been reported to reduce Aβ(25-35)'s toxicity in cells even though they share similar primary structures with Aβ(25-35). Here, we seek to understand the molecular mechanisms of how these peptides interact with Aβ(25-35) and to shed light on why some peptides with similar primary structures are toxic and others nontoxic. We use both experimental and computational methods, including ion mobility mass spectrometry and enhanced-sampling replica-exchange molecular dynamics simulations, to study the aggregation pathways of Aβ(25-35), NK, Kassinin, Substance P, and mixtures of the latter three with Aβ(25-35). NK and Substance P were observed to remove the higher-order oligomers (i.e., hexamers and dodecamers) of Aβ(25-35), which are related to its toxicity, although Substance P did so more slowly. In contrast, Kassinin was found to promote the formation of these higher-order oligomers. This result conflicts with what is expected and is elaborated on in the text. We also observe that even though they have significant structural homology with Aβ(25-35), NK, Kassinin, and Substance P do not form hexamers with a β-sheet structure like Aβ(25-35). The hexamer structure of Aβ(25-35) has been identified as a cylindrin, and this structure has been strongly correlated to toxic species. The reasons why the three tachykinin peptides behave so differently when mixed with Aβ(25-35) are discussed.

The liquid-liquid phase separation (LLPS) of Tau has been postulated to play a role in modulating the aggregation property of Tau, a process known to be critically associated with the pathology of a broad range of neurodegenerative diseases including Alzheimer's Disease. Tau can undergo LLPS by homotypic interaction through self-coacervation (SC) or by heterotypic association through complex-coacervation (CC) between Tau and binding partners such as RNA. What is unclear is in what way the formation mechanisms for self and complex coacervation of Tau are similar or different, and the addition of a binding partner to Tau alters the properties of LLPS and Tau. A combination of in vitro experimental and computational study reveals that the primary driving force for both Tau CC and SC is electrostatic interactions between Tau-RNA or Tau-Tau macromolecules. The liquid condensates formed by the complex coacervation of Tau and RNA have distinctly higher micro-viscosity and greater thermal stability than that formed by the SC of Tau. Our study shows that subtle changes in solution conditions, including molecular crowding and the presence of binding partners, can lead to the formation of different types of Tau condensates with distinct micro-viscosity that can coexist as persistent and immiscible entities in solution. We speculate that the formation, rheological properties and stability of Tau droplets can be readily tuned by cellular factors, and that liquid condensation of Tau can alter the conformational equilibrium of Tau.

Proteins are stable over a narrow temperature range, with hot and cold denaturation occurring outside of this window, both of which adversely affect protein function. While hot unfolding is entropically driven, cold denaturation, on the other hand, results from a more favorable free energy associated with the interaction of water with apolar groups at low temperature. Because of the key role of water in this latter process, capturing cold denaturation using implicit solvent models is challenging. We propose here a novel computational approach to develop an implicit solvent model that accounts for both hot and cold denaturation in simulations involving atomistically detailed protein representations. By mining a large number of protein structures solved by nuclear magnetic resonance, we derive transfer free energy contributions for the backbone and amino acids side chains representing the transfer of these moieties between water at two different temperatures. Using Trp-cage as a model system, we show that the implicit solvent model constructed using these temperature-dependent free energies of transfer recovers the parabolic temperature dependence of protein stability, capturing both hot and cold denaturation. The resulting cold-unfolded conformations show reduced secondary structure content but preserve most of their internal hydrogen-bonding network, in contrast to the extended configurations with no hydrogen-bonding populated during heat-induced denaturation.

Aberrant protein folding leading to the formation of characteristic cross-β-sheet-rich amyloid structures is well known for its association with a variety of debilitating human diseases. Often, depending upon amino acid composition, only a small segment of a large protein participates in amyloid formation and is in fact capable of self-assembling into amyloid, independent of the rest of the protein. Therefore, such peptide fragments serve as useful model systems for understanding the process of amyloid formation. An important factor that has often been overlooked while using peptides to mimic full-length protein is the charge on the termini of these peptides. Here, we show the influence of terminal charges on the aggregation of an amyloidogenic peptide from microtubule-associated protein Tau, implicated in Alzheimer's disease and tauopathies. We found that modification of terminal charges by capping the peptide at one or both of the termini drastically modulates the fibrillation of the hexapeptide sequence paired helical filament 6 (PHF6) from repeat 3 of Tau, both with and without heparin. Without heparin, the PHF6 peptide capped at both termini and PHF6 capped only at the N-terminus self-assembled to form amyloid fibrils. With heparin, all capping variants of PHF6, except for PHF6 with both termini free, formed typical amyloid fibrils. However, the rate and extent of aggregation both with and without heparin as well as the morphology of aggregates were found to be highly dependent on the terminal charges. Our molecular dynamics simulations on PHF6 capping variants corroborated our experiments and provided critical insights into the mechanism of PHF6 self-assembly. Overall, our results emphasize the importance of terminal modifications in fibrillation of small peptide fragments and provide significant insights into the aggregation of a small Tau fragment, which is considered essential for Tau filament assembly.

Variational autoencoders are artificial neural networks with the capability to reduce highly dimensional sets of data to smaller dimensional, latent representations. In this work, these models are applied to molecular dynamics simulations of the self-assembly of coarse-grained peptides to obtain a singled-valued order parameter for amyloid aggregation. This automatically learned order parameter is constructed by time-averaging the latent parametrizations of internal coordinate representations and compared to the nematic order parameter which is commonly used to study ordering of similar systems in literature. It is found that the latent space value provides more tailored insight into the aggregation mechanism's details, correctly identifying fibril formation in instances where the nematic order parameter fails to do so. A means is provided by which the latent space value can be analyzed so that the major contributing internal coordinates are identified, allowing for a direct interpretation of the latent space order parameter in terms of the behavior of the system. The latent model is found to be an effective and convenient way of representing the data from the dynamic ensemble and provides a means of reducing the dimensionality of a system whose scale exceeds molecular systems so-far considered with similar tools. This bypasses a need for researcher speculation on what elements of a system best contribute to summarizing major transitions and suggests latent models are effective and insightful when applied to large systems with a diversity of complex behaviors.

Ensembles of amino acid side chains often dominate the interfacial interactions of intrinsically disordered proteins; however, backbone contributions are far from negligible. Using a combination of...

International audience; 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 over many years.

High pressures can be detrimental for protein stability, resulting in unfolding and loss of function. This phenomenon occurs because the unfolding transition is accompanied by a decrease in volume, which is typically attributed to the elimination of cavities that are present within the native state as a result of packing defects. We present a novel computational approach that enables the study of pressure unfolding in atomistically detailed protein models in implicit solvent. We include the effect of pressure using a transfer free energy term that allows us to decouple the effect of protein residues and bound water molecules on the volume change upon unfolding. We discuss molecular dynamics simulations results using this protocol for two model proteins, Trp-cage and staphylococcal nuclease (SNase). We find that the volume reduction of bound water is the key energetic term that drives protein denaturation under the effect of pressure, for both Trp-cage and SNase. However, we note differences in unfolding mechanisms between the smaller Trp-cage and the larger SNase protein. Indeed, the unfolding of SNase, but not Trp-cage, is seen to be further accompanied by a reduction in the volume of internal cavities. Our results indicate that, for small peptides, like Trp-cage, pressure denaturation is driven by the increase in solvent accessibility upon unfolding, and the subsequent increase in the number of bound water molecules. For larger proteins, like SNase, the cavities within the native fold act as weak spots, determining the overall resistance to pressure denaturation. Our simulations display a striking agreement with the pressure-unfolding profile experimentally obtained for SNase and represent a promising approach for a computationally efficient and accurate exploration of pressure-induced denaturation of proteins.

Using enhanced-sampling replica exchange fully atomistic molecular dynamics simulations, we show that, individually, urea and guanidinium chloride (GdmCl) denature the Trpcage protein, but remarkably, the helical segment

Mitochondrially derived peptides (MDPs) such as humanin (HN) have shown a remarkable ability to modulate neurological amyloids and apoptosis-associated proteins in cells and animal models. Recently, we found that humanin-like peptides also inhibit amyloid formation outside of neural environments in islet amyloid polypeptide (IAPP) fibrils and plaques, which are hallmarks of Type II diabetes. However, the biochemical basis for regulating amyloids through endogenous MDPs remains elusive. One hypothesis is that MDPs stabilize intermediate amyloid oligomers and discourage the formation of insoluble fibrils. To test this hypothesis, we carried out simulations and experiments to extract the dominant interactions between the S14G-HN mutant (HNG) and a diverse set of IAPP structures. Replica-exchange molecular dynamics suggests that MDPs cap the growth of amyloid oligomers. Simulations also indicate that HNG-IAPP heterodimers are 10 times more stable than IAPP homodimers, which explains the substoichiometric ability of HNG to inhibit amyloid growth. Despite this strong attraction, HNG does not denature IAPP. Instead, HNG binds IAPP near the disordered NFGAIL motif, wedging itself between amyloidogenic fragments. Shielding of NFGAIL-flanking fragments reduces the formation of parallel IAPP β-sheets and subsequent nucleation of mature amyloid fibrils. From ThT spectroscopy and electron microscopy, we found that HNG does not deconstruct mature IAPP fibrils and oligomers, consistent with the simulations and our proposed hypothesis. Taken together, this work provides new mechanistic insight into how endogenous MDPs regulate pathological amyloid growth at the molecular level and in highly substoichiometric quantities, which can be exploited through peptidomimetics in diabetes or Alzheimer's disease.

Despite the importance of amyloid formation in disease pathology, the understanding of the primary structure?activity relationship for amyloid-forming peptides remains elusive. Here we use a new neural-network based method of analysis: the classifying autoencoder (CAE). This machine learning technique uses specialized architecture of artificial neural networks to provide insight into typically opaque classification processes. The method proves to be robust to noisy and limited data sets, as well as being capable of disentangling relatively complicated rules over data sets. We demonstrate its capabilities by applying the technique to an experimental database (the Waltz database) and demonstrate the CAE?s capability to provide insight into a novel descriptor, dimeric isotropic deviation?an experimental measure of the aggregation properties of the amino acids. We measure this value for all 20 of the common amino acids and find correlation between dimeric isotropic deviation and the failure to form amyloids when hydrophobic effects are not a primary driving force in amyloid formation. These applications show the value of the new method and provide a flexible and general framework to approach problems in biochemistry using artificial neural networks.

Intrinsically disordered proteins (IDPs) are an important class of proteins that do not fold to a well-defined three-dimensional shape but rather adopt an ensemble of inter-converting conformations. This feature makes their experimental characterization challenging and invites a theoretical and computational approach to complement experimental studies. In this review, we highlight the recent progress in developing new computational and theoretical approaches to study the structure and dynamics of monomeric and order higher assemblies of IDPs, with a particular emphasis on their phase separation into protein-rich condensates.

Cyclodextrins are cyclic oligosaccharides, widely used as drug carriers, solubilizers, and excipients. Among cyclodextrins, the functionalized derivative known as hydroxypropyl-β-cyclodextrin (HPβCD) offers several advantages due to its unique structural features. Its optimal use in pharmaceutical and medical applications would benefit from a molecular-level understanding of its behavior, as can be offered by molecular dynamics simulations. Here, we propose a set of parameters for all-atom simulations of HPβCD, based on the ADD force field for sugars developed in our group, and compare it to the original CHARMM36 description. Using Kirkwood-Buff integrals of binary HPβCD-water mixtures as target experimental data, we show that the ADD-based description results in a considerably improved prediction of HPβCD self-association and interaction with water. We then use the new set of parameters to characterize the behavior of HPβCD toward the different amino acids. We observe pronounced interactions of HPβCD with both polar and nonpolar moieties, with a special preference for the aromatic rings of tyrosine, phenylalanine, and tryptophan. Interestingly, our simulations further highlight a preferential orientation of HPβCD's hydrophobic cavity toward the backbone atoms of amino acids, which, coupled with a favorable interaction of HPβCD with the peptide backbone, suggest a propensity for HPβCD to denature proteins.

The following joint Editorial was originally published in ACS Applied Materials & Interfaces (DOI: 10.1021/acsami.0c10979). We confront the terrible reality that systemic racism and discrimination impacts the daily personal and professional lives of many members of the scientific community and broader society. In the U.S., the brutal killing of George Floyd while in police custody is one of the most recent examples of the centuries of systemic violence suffered by Black Americans. This moment and its aftermath lay bare the legacies of racism and its exclusionary practices. Let us be clear: we, the Editors, Staff, and Governance Members of ACS Publications condemn the tragic deaths of Black people and stand in solidarity with Black members of the science and engineering community. Moreover, ACS condemns racism, discrimination, and harassment in all forms. We will not tolerate practices and viewpoints that exclude or demean any member of our community. Despite these good intentions, we recognize that our community has not done enough to provide an environment for Black chemists to thrive. Rep. Eddie Bernice Johnson, Chairwoman of the U.S. House Committee on Science, Space, and Technology said, “So far, we have gotten by with a STEM workforce that does not come close to representing the diversity of our nation. However, if we continue to leave behind so much of our nation’s brainpower, we cannot succeed.”(1) Indeed, the U.S. National Science Foundation notes that Blacks and other under-represented minority groups continue to be under-represented in science and engineering education and employment.(2) What is abundantly clear in this moment is that this lack of representation is a symptom of systemic racism across all levels of education and professional life. We know that supportive words are not enough. We must develop and implement a concrete plan for changing our trajectory. Publications and citations are academic currency, and while we like to think publishing a manuscript is “just about the science”, we know that is not true for everyone. We have seen the biases (largely through the lens of gender and in Western countries because of the limitations in bibliometric analyses) and applaud our colleagues at the RSC for their massive study that explored these gender barriers in the publishing pipeline(3) and their recent Inclusion and Diversity Framework.(4) At the present time, unfortunately, less is known about the effects of race and ethnicity on publishing success. A study published in PeerJ, however, found that unprofessional reviewer comments had a disproportionate effect on authors from under-represented groups.(5) As the world’s leading society publisher, we have a responsibility to aggressively combat bias in all aspects of the publishing process, including systemic under-representation of Blacks in this endeavor (no ACS journal is currently led by a Black Editor-in-Chief). Within ACS Publications, we actively track gender and geographic diversity of editors, advisors, authors, and reviewers, and we anecdotally report on race of editors. Diversity encompasses many more dimensions than these, and we acknowledge that we can do much more than we have. We affirm that diversity and inclusion strengthen the research community and its impact, and we are committed to developing, implementing, tracking, and reporting on our progress to ensure that our editors, advisors, reviewers, and authors are more diverse and that all authors receive the same fair treatment and opportunity to publish in our journals. We acknowledge that we do not have all the answers now, but we seek to hear from and listen to our community on how we can improve our journals to be more diverse and inclusive. As first steps, we commit to the taking the following actions: Gathering and making public our baseline statistics on diversity within our journals, encompassing our editors, advisors, reviewers, and authors; annually reporting on progress Training new and existing editors to recognize and interrupt bias in peer review Including diversity of journal contributors as an explicit measurement of Editor-in-Chief performance Appointing an ombudsperson to serve as a liaison between Editors and our Community Developing an actionable diversity plan for each ACS journal These are only initial plans and the start of a conversation: other ideas are beginning to germinate, and we commit to sharing them with you regularly. We invite you contribute your ideas on how we can do better via our Axial website. We are listening carefully. We encourage you to take immediate action in your own circles. In a recent editorial, JACS Associate Editor Melanie Sanford(6) offered practical steps to take now. Take a moment to find out more about these actions and how to bring them into your work and your life. We all have a responsibility to eradicate racism and discrimination in the science and engineering community; indeed, to make a real difference, we need to be antiracist. The tragic events we have seen in the Black community provide great urgency to this goal. The work will be difficult and will force us to confront hard realities about our beliefs and actions. We fully expect that you, and everyone in the community, will hold us accountable.

Osmolytes are essential for cellular function under ubiquitous osmotic stress. Trimethylamine N-oxide (TMAO) is one such osmolyte that has gained remarkable attention due to its protein-protective ability against urea. This Review aims at providing a detailed account of recent theoretical and experimental developments in characterizing the structural changes and thermodynamic stability of proteins in the presence of TMAO and urea. New vapor pressure osmometry and molecular dynamics simulation results on urea-TMAO solutions are presented, and a unified molecular mechanism of TMAO counteraction of urea-induced protein denaturation is introduced. In addition, a detailed technical assessment of molecular dynamics force fields for TMAO and for urea-TMAO solutions is presented. The force field analysis highlights how many of the commonly used force field models are in fact incompatible with solvation thermodynamics and can lead to misleading conclusions. A new optimized force field for TMAO (Shea(m)) is presented, and a recently optimized force field for TMAO-urea (Netz(m)) that best reproduces experimental data is highlighted.

Complex coacervation driven liquid-liquid phase separation (LLPS) of biopolymers has been attracting attention as a novel phase in living cells. Studies of LLPS in this context are typically of proteins harboring chemical and structural complexity, leaving unclear which properties are fundamental to complex coacervation versus protein-specific. This study focuses on the role of polyethylene glycol (PEG)—a widely used molecular crowder—in LLPS. Significantly, entropy-driven LLPS is recapitulated with charged polymers lacking hydrophobicity and sequence complexity, and its propensity dramatically enhanced by PEG. Experimental and field-theoretic simulation results are consistent with PEG driving LLPS by dehydration of polymers, and show that PEG exerts its effect without partitioning into the dense coacervate phase. It is then up to biology to impose additional variations of functional significance to the LLPS of biological systems. Liquid-liquid phase separation occurs in cells and can be induced in artificial systems, but the mechanism of the effect of molecular crowders is unclear. Here dehydration entropy-driven phase separation of model charged polymers lacking any chemical complexity or hydrophobicity is shown to be enhanced by polyethylene glycol.