Modeling the Interplay of Conformational and Electronic Structure in Conjugated Polyelectrolytes

Conjugated polyelectrolytes (CPEs) combine ionic, electronic, and optical functionality with the mechanical and thermodynamic properties of semiflexible, amphiphilic polyelectrolytes. Critical to CPE design is the coupling between macromolecular conformations, ionic interactions, and electronic transport, the combination of which spans electronic to mesoscopic length scales, rendering coherent theoretical analysis challenging. Here, we utilize a recently developed anisotropic CG model in combination with a phenomenological tight-binding Hamiltonian to explore the interplay of single-chain conformational and electronic structure in CPEs. Accessible single chain conformations are explored as a function of solvent conditions and chain stiffness, reproducing a rich landscape of rod-like, racquet, pearl necklace, and helical conformations observed in previous works. The electronic structure of each conformational archtype is further analyzed, incorporating through-bond coupling, through-space coupling, and electrostatic contributions to the Hamiltonian. Electrostatics is observed to influence electronic structure primarily by modifying the accessible conformational space, and only minimally by direct modulation of on-site energies. Electron transport in CPEs is most efficient in helical and racquet conformations, which is attributed to the flattening of dihedrals and through-space coupling within collapsed conformations. Relatedly, kink formation within racquets does not significantly deteriorate electronic conjugation within CPEs - an insight critical to understanding transport within locally ordered aggregates. These conclusions provide unprecedented computational insight into structure function relationships defining emerging classes of CPEs.

Специфика статистики упругих и деформационно-прочностных свойств высокопрочных волокон полипропилена

Regularities of statistical distributions of a complex of mechanical properties, including the module of elasticity (E), strength () and strain at break (b), high-strength industrial oriented polypropylene (PP) fibers have been analyzed using the Weibull and Gauss models based on large a wide array of measurements (50 identical samples in each series). The values of the statistical Weibull modulus (m) - a parameter characterizing the scatter of the measured values of the data arrays of E,  and b – have been estimated for the PP samples of two types: single fibers (monofilaments) and multifilament fibers consisting from several hundred single fibers. For the PP multifilament fibers, a more correct description of the distributions of E,  and b has been received both in the framework of the normal distribution (Gaussian distribution) and in the framework of the Weibull distribution in comparison with the description of such distributions for the PP monofilaments. The influence of the polymer chain conformation on the regularities of the statistical distributions of E,  and b for the high-strength oriented polymeric materials with different chemical chain structures and the correctness of their descriptions in the framework of the Gauss and Weibull models have been analyzed. For this purpose, the values of m calculated in this work for PP with a helical chain conformation have been compared with the values of m determined by us earlier for ultra-high molecular weight polyethylene and polyamide-6 with the chain conformations in the form of an in-plane trans-zigzag.

Mechanistic insights into the enhancement or inhibition of phase separation by polyubiquitin chains of different lengths or linkages

Ubiquitin-binding shuttle UBQLN2 mediates crosstalk between proteasomal degradation and autophagy, likely via interactions with K48- and K63-linked polyubiquitin chains, respectively. UBQLN2 is recruited to stress granules in cells and undergoes liquid-liquid phase separation (LLPS) in vitro. However, interactions with ubiquitin or multivalent K48-linked chains eliminate LLPS. Here, we found that, although some polyubiquitin chain types (K11-Ub4 and K48-Ub4) did generally inhibit UBQLN2 LLPS, others (K63-Ub4, M1-Ub4 and a designed tetrameric ubiquitin construct) significantly enhanced LLPS. Using nuclear magnetic resonance (NMR) spectroscopy and complementary biophysical techniques, we demonstrated that these opposing effects stem from differences in chain conformations, but not in affinities between chains and UBQLN2. Chains with extended conformations and increased accessibility to the ubiquitin binding surface significantly promoted UBQLN2 LLPS by enabling a switch between homotypically to partially heterotypically-driven phase separation. Our study provides mechanistic insights into how the structural and conformational properties of polyubiquitin chains contribute to heterotypic phase separation with ubiquitin-binding shuttles and adaptors.

Synthesis, Characterization and Structure Properties of Biobased Hybrid Copolymers Consisting of Polydiene and Polypeptide Segments

Novel hybrid materials of the PB-b-P(o-Bn-L-Tyr) and PI-b-P(o-Bn-L-Tyr) type (where PB: 1,4/1,2-poly(butadiene), PI:3,4/1,2/1,4-poly(isoprene) and P(o-Bn-L-Tyr):poly(ortho-benzyl-L-tyrosine)) were synthesized through anionic and ring-opening polymerization under high-vacuum techniques. All final materials were molecularly characterized through infrared spectroscopy (IR) and proton and carbon nuclear magnetic resonance (1H-NMR, 13C-NMR) in order to confirm the successful synthesis and the polydiene microstructure content. The stereochemical behavior of secondary structures (α-helices and β-sheets) of the polypeptide segments combined with the different polydiene microstructures was also studied. The influence of the α-helices and β-sheets, as well as the polydiene chain conformations on the thermal properties (glass transition temperatures, thermal stability, α- and β-relaxation) of the present biobased hybrid copolymers, was investigated through differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and dielectric spectroscopy (DS). The obtained morphologies in thin films for all the synthesized materials via atomic force microscopy (AFM) indicated the formation of polypeptide fibrils in the polydiene matrix.

Light Chain Stabilization: A Therapeutic Approach to Ameliorate AL Amyloidosis

Non-native immunoglobulin light chain conformations, including aggregates, appear to cause light chain amyloidosis pathology. Despite significant progress in pharmacological eradication of the neoplastic plasma cells that secrete these light chains, in many patients impaired organ function remains. The impairment is apparently due to a subset of resistant plasma cells that continue to secrete misfolding-prone light chains. These light chains are susceptible to the proteolytic cleavage that may enable light chain aggregation. We propose that small molecules that preferentially bind to the natively folded state of full-length light chains could act as pharmacological kinetic stabilizers, protecting light chains against unfolding, proteolysis and aggregation. Although the sequence of the pathological light chain is unique to each patient, fortunately light chains have highly conserved residues that form binding sites for small molecule kinetic stabilizers. We envision that such stabilizers could complement existing and emerging therapies to benefit light chain amyloidosis patients.

Improved antibody structure prediction by deep learning of side chain conformations

Antibody engineering is becoming increasingly popular in the medical field for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering our method particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of our model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences.