Selective autophagy receptors (SARs) are central to cellular homeostatic and organellar recycling pathways. Over the last two decades, more than 30 SARs have been discovered and validated using a variety of experimental approaches ranging from cell biology to biochemistry, including high-throughput imaging and screening methods. Yet, the extent of selective autophagy pathways operating under various cellular contexts, for example, under basal and starvation conditions, remains unresolved. Currently, our knowledge of all known SARs and their associated cargo components is fragmentary and limited by experimental data with varying degrees of resolution. Here, we use classical predictive and modeling approaches to integrate high-quality autophagosome content profiling data with disparate datasets. We identify a global set of potential SARs and their associated cargo components active under basal autophagy, starvation-induced, and proteasome-inhibition conditions. We provide a detailed account of cellular components, biochemical pathways, and molecular processes that are degraded via autophagy. Our analysis yields a catalog of new potential SARs that satisfy the characteristics of bonafide, well-characterized SARs. We categorize them by the subcellular compartments they emerge from and classify them based on their likely mode of action. Our structural modeling validates a large subset of predicted interactions with the human ATG8 family of proteins and shows characteristic, conserved LC3-interacting region (LIR)–LIR docking site (LDS) and ubiquitin-interacting motif (UIM)–UIM docking site (UDS) binding modes. Our analysis also revealed the most abundant cargo molecules targeted by these new SARs. Our findings expand the repertoire of SARs and provide unprecedented details into the global autophagic state of HeLa cells. Taken together, our findings provide motivation for the design of new experiments, testing the role of these novel factors in selective autophagy.

Gene therapy via retroviral vectors holds great promise for treating a variety of serious diseases. It requires the use of additives to boost infectivity. Amyloid-like peptide nanofibers (PNFs) were shown to efficiently enhance retroviral gene transfer. However, the underlying mode of action of these peptides remains largely unknown. Data-mining is an efficient method to systematically study structure–function relationship and unveil patterns in a database. This data-mining study elucidates the multi-scale structure–property–activity relationship of transduction enhancing peptides for retroviral gene transfer. In contrast to previous reports, we find that not the amyloid fibrils themselves, but rather µm-sized β-sheet rich aggregates enhance infectivity. Specifically, microscopic aggregation of β-sheet rich amyloid structures with a hydrophobic surface pattern and positive surface charge are identified as key material properties. We validate the reliability of the amphiphilic sequence pattern and the general applicability of the key properties by rationally creating new active sequences and identifying short amyloidal peptides from various pathogenic and functional origin. Data-mining—even for small datasets—enables the development of new efficient retroviral transduction enhancers and provides important insights into the diverse bioactivity of the functional material class of amyloids.

This paper considers the possibility of technology licensing via fixed-fee, royalty or two-part tariff and tacit collusion between firms that produce homogeneous goods under asymmetric cost structures and compete in quantities. In contrast to Lin (1996), all forms of licensing facilitate (obstruct) collusion, if the initial cost difference between the firms is relatively less (more). Technology will always be licensed, and the optimal form of licensing is either fixed-fee or royalty or two-part tariff, but collusion may or may not be possible post-licensing. Welfare decreases after licensing if the firms collude only after licensing but not collude under no-licensing.

Amyloid-like nanofibers from self-assembling peptides can promote viral gene transfer for therapeutic applications. Traditionally, new sequences are discovered either from screening large libraries or by creating derivatives of known active peptides. However, the discovery of de novo peptides, which are sequence-wise not related to any known active peptides, is limited by the difficulty to rationally predict structure-activity relationships because their activities typically have multi-scale and multi-parameter dependencies. Here, we used a small library of 163 peptides as a training set to predict de novo sequences for viral infectivity enhancement using a machine learning (ML) approach based on natural language processing. Specifically, we trained an ML model using continuous vector representations of the peptides, which were previously shown to retain relevant information embedded in the sequences. We used the trained ML model to sample the sequence space of peptides with 6 amino acids to identify promising candidates. These 6-mers were then further screened for charge and aggregation propensity. The resulting 16 new 6-mers were tested and found to be active with a 25% hit rate. Strikingly, these de novo sequences are the shortest active peptides for infectivity enhancement reported so far and show no sequence relation to the training set. Moreover, by screening the sequence space, we discovered the first hydrophobic peptide fibrils with a moderately negative surface charge that can enhance infectivity. Hence, this ML strategy is a time- and cost-efficient way for expanding the sequence space of short functional self-assembling peptides exemplified for therapeutic viral gene delivery.

A peptide's amino acid sequence affects its taste, but how? A rigorous structure-property connection is challenging to determine because of both the exponentially growing peptide sequence space and the scarcity of experimental measurements compared to the size of that space. By sensory methods, many peptides have been identified as tasting bitter or umami. Baselines have been determined but relate only single amino acid characteristics, in particular hydrophobicity in bitter peptides and negative charges for umami. In this work, we refine this picture by extracting sequential amino acid patterns. Our method coarse-grains the peptide sequence space to facilitate the systematic identification of common residue patterns. We identify optimal patterns for both bitter and umami peptides: one hydrophobic followed by four polar residues and two negative followed by three polar residues, respectively. We find systematic improvements compared to both random and the baselines mentioned above. Our method complements quantitative structure-activity relationship methods by leveraging sequential information to help locate taste-specific characteristics in peptides and proteins.

This article was originally published online on June 29, 2021 with an error in affiliation 3. All affiliations are correct as they appear above. All online and printed versions of the article were corrected on June 30, 2021. AIP Publishing apologizes for this error.

Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity. To discover easily interpretable equations that explain the data well, we use the recently proposed sure-independence screening and sparsifying operator (SISSO), an artificial-intelligence technique that combines symbolic regression with compressed sensing. Our study is based on a large in silico dataset of 0.4 × 106 small molecules extracted from coarse-grained simulations. We rationalize the equation suggested by SISSO via an analysis of the inhomogeneous solubility-diffusion model in several asymptotic acidity regimes. We further extend our analysis to the dependence on lipid-membrane composition. Lipid-tail unsaturation plays a key role but surprisingly contributes stepwise rather than proportionally. Our results are in line with previously observed changes in permeability, suggesting the distinction between liquid-disordered and liquid-ordered permeation. Together, compressed sensing with analytically derived asymptotes establish and validate an accurate, broadly applicable, and interpretable equation for passive permeability across both drug and lipid-tail chemistry.

Small solutes have been shown to alter the lateral organization of cell membranes and reconstituted phospholipid bilayers; however, the mechanisms by which these changes happen are still largely unknown. Traditionally, both experiment and simulation studies have been restricted to testing only a few compounds at a time, failing to identify general molecular descriptors or chemical properties that would allow extrapolating beyond the subset of considered solutes. In this work, we probe the competing energetics of inserting a solute in different membrane environments by means of the potential of mean force. We show that these calculations can be used as a computationally efficient proxy to establish whether a solute will stabilize or destabilize domain phase separation. Combined with umbrella-sampling simulations and coarse-grained molecular dynamics simulations, we are able to screen solutes across a wide range of chemistries and polarities. Our results indicate that for the system under consideration, preferential partitioning and therefore effectiveness in altering membrane phase separation are strictly linked to the location of insertion in the bilayer (i.e., midplane or interface). Our approach represents a fast and simple tool for obtaining structural and thermodynamic insight into the partitioning of small molecules between lipid domains and its relation to phase separation, ultimately providing a platform for identifying the key determinants of this process.

There is an urgent need for biomaterials that support tissue healing, particularly neuronal regeneration. In a medium throughput screen novel self-assembling peptide (SAP) sequences that form fibrils and stimulated nerve fiber growth of peripheral nervous system (PNS)-derived neurons are identified. Based on the peptide sequences and fibril morphologies and by applying rational data-mining, important structural parameters stimulating neuronal activity are elucidated. Three SAPs (SAP1e, SAP2e, and SAP5c) enhance adhesion and growth of PNS neurons. These SAPs form 2D and 3D matrices that serve as bioactive scaffolds stimulating cell adhesion and growth. The newly discovered SAPs also support the growth of CNS neurons and glia cells. Subsequently, the potential of SAPs to enhance PNS regeneration in vivo is analyzed. For this, the facial nerve driving whisker movement in mice is injured. Notably, SAPs persist for up to 3 weeks in the injury site indicating highly adhesive properties and stability. After SAP administration, more motor neurons incorporating markers for successive regeneration are observed. Recovery of whisker movement is elevated in SAP-injected mice. In summary, short peptides that form fibrils are identified and the adhesion, growth, and regeneration of neurons have been efficiently enhanced without the necessity to attach hormones or growth factors.

We study the asymptotic properties of the steady state mass distribution for a class of collision kernels in an aggregation-shattering model in the limit of small shattering probabilities. It is shown that the exponents characterizing the large and small mass asymptotic behavior of the mass distribution depend on whether the collision kernel is local (the aggregation mass flux is essentially generated by collisions between particles of similar masses) or nonlocal (collision between particles of widely different masses give the main contribution to the mass flux). We show that the nonlocal regime is further divided into two subregimes corresponding to weak and strong nonlocality. We also observe that at the boundaries between the local and nonlocal regimes, the mass distribution acquires logarithmic corrections to scaling and calculate these corrections. Exact solutions for special kernels and numerical simulations are used to validate some nonrigorous steps used in the analysis. Our results show that for local kernels, the scaling solutions carry a constant flux of mass due to aggregation, whereas for the nonlocal case there is a correction to the constant flux exponent. Our results suggest that for general scale-invariant kernels, the universality classes of mass distributions are labeled by two parameters: the homogeneity degree of the kernel and one further number measuring the degree of the nonlocality of the kernel.

Irreversible aggregation is an archetypal example of a system driven far from equilibrium by sources and sinks of a conserved quantity (mass). The source is a steady input of monomers and the evaporation of colliding particles with a small probability is the sink. Using exact and heuristic analyses, we find a universal regime and two distinct non-universal regimes distinguished by the relative importance of mergers between small and large particles. At the boundary between the regimes we find an analogue of the logarithmic correction conjectured by Kraichnan for two-dimensional turbulence.

We study the behavior of holographic entanglement entropy (HEE) for imbalanced holographic superconductors. We employ a numerical approach to consider the robust case of fully back-reacted gravity system. The hairy black hole solution is found by using our numerical scheme. Then it is used to compute the HEE for the superconducting case. The cases we study show that in presence of a mismatch between two chemical potentials, below the critical temperature, superconducting phase has a lower HEE in comparison to the AdS-Reissner- Nordström black hole phase. Interestingly, the effects of chemical imbalance are different in the contexts of black hole and superconducting phases. For black hole, HEE increases with increasing imbalance parameter while it behaves oppositely for the superconducting phase. The implications of these results are discussed. © 2014 The Author(s).

We study the phase diagram of spatially inhomogeneous Fulde-Ferrell-Larkin- Ovchinnikov (FFLO) superconducting state using the Ginzburg-Landau (GL) free energy, derived from the microscopic Hamiltonian of the system, and notice that it has a very clear Lifshitz tricritical point. We find the specific heat jumps abruptly near the first-order line in the emergent phase diagram which is very similar to the recent experimental observation in layered organic superconductor. Comparison with experimental data allows us to obtain quantitative relations between the parameters of phenomenological free energy. The region of the phase diagram where the specific heat jumps can be probed by doing a dynamical analysis of the free energy. © 2013 Elsevier B.V.

Superconductor with a spatially-modulated order parameter is known as Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superconductor. Using the time-dependent Ginzburg-Landau (TDGL) formalism we have theoretically studied the temporal behaviour of the equal-time correlation function, or the structure factor, of a FFLO superconductor following a sudden quench from the unpaired, or normal, state to the FFLO state. We find that quenching into the ordered FFLO phase can reveal the existence of a line in the mean-field phase diagram which cannot be accessed by static properties. © 2013 American Institute of Physics.

We propose a mean-field, phenomenological Ginzburg-Landau free energy functional with two competing order parameters for a two-component, spin-polarized Fermi gas. This free energy supports a tricritical point which is different from the conventional one and this change offers a correct understanding of the experimental phase diagram of imbalanced Fermi systems (Shin et al. (2008) [17]). The specific heat also happens to be different than in standard theory. © 2012 Elsevier B.V. All rights reserved.