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Abstract This study presents the exploration of sequence‐defined polyurethanes (PUs) as a new class of heteropolymers capable of precise conformational control. Utilizing molecular dynamics simulations, the folding behavior of polyurethane chains is investigated of varying lengths (11, 20, and 50 monomers) in both vacuum and aqueous environments. The simulations reveal that the heterogeneous chains systematically refold to approach the designed target structures better than non‐designed chains or chains with artificially disrupted hydrogen‐bond networks. The subsequent synthesis of an optimized 11‐mer sequence (P1) is achieved through solid‐phase chemistry, with thorough characterization via NMR, MS, and SEC confirming the accuracy of the predicted sequence and its controlled chain length. Solubility tests showed favorable results across multiple solvents, highlighting the versatility of the designed polymer. This research underscores the potential of sequence‐defined polyurethanes to emulate the structural and functional attributes of biological macromolecules, opening new pathways for their application in catalysis, drug delivery, and advanced material design. The findings illustrate a promising direction for the development of synthetic polymers with tailored properties, emphasizing the transformative impact of sequence control in polymer chemistry. 

This study presents an enhanced protein design algorithm that aims to emulate natural heterogeneity of protein sequences. Initial analysis revealed that natural proteins exhibit a permutation composition lower than the theoretical maximum, suggesting a selective utilization of the 20-letter amino acid alphabet. By not constraining the amino acid composition of the protein sequence but instead allowing random reshuffling of the composition, the resulting design algorithm generates sequences that maintain lower permutation compositions in equilibrium, aligning closely with natural proteins. Folding free energy computations demonstrated that the designed sequences refold to their native structures with high precision, except for proteins with large disordered regions. In addition, direct coupling analysis showed a strong correlation between predicted and actual protein contacts, with accuracy exceeding 82% for a large number of top pairs (>4L). The algorithm also resolved biases in previous designs, ensuring a more accurate representation of protein interactions. Overall, it not only mimics the natural heterogeneity of proteins but also ensures correct folding, marking a significant advancement in protein design and engineering. 

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

The complex nature and structure of biomolecules and nanoparticles and their interactions make it challenging to achieve a deeper understanding of the dynamics at the nano–bio interface of enzymes and plasmonic nanoparticles subjected to light excitation. In this study, circular dichroism (CD) and Raman spectroscopic experiments and molecular dynamics (MD) simulations were used to investigate the potential changes at the nano–bio interface upon plasmonic excitation. Our data showed that photothermal and thermal heating induced distinct changes in the secondary structure of a model nanobioconjugate composed of lipase fromCandida antarcticafraction B (CALB) and gold nanoparticles (AuNPs). The use of a green laser led to a substantial decrease in the α-helix content of the lipase from 66% to 13% and an increase in the β-sheet content from 5% to 31% compared to the initial conformation of the nanobioconjugate. In contrast, the differences under similar thermal heating conditions were only 55% and 11%, respectively. This study revealed important differences related to the enzyme secondary structure, enzyme–nanoparticle interactions, and the stability of the enzyme catalytic triad (Ser105-Asp187-His224), influenced by the instantaneous local temperature increase generated from photothermal heating compared to the slower rate of thermal heating of the bulk. These results provide valuable insights into the interactions between biomolecules and plasmonic nanoparticles induced by photothermal heating, advancing plasmonic biocatalysis and related fields. 

Abstract Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins’ most remarkable feature is their modularity. The large amount of information required to specify each protein’s function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution. 

The last years have witnessed remarkable advances in our understanding of the emergence and consequences of topological constraints in biological and soft matter. Examples are abundant in relation to (bio)polymeric systems and range from the characterization of knots in single polymers and proteins to that of whole chromosomes and polymer melts. At the same time, considerable advances have been made in the description of the interplay between topological and physical properties in complex fluids, with the development of techniques that now allow researchers to control the formation of and interaction between defects in diverse classes of liquid crystals. Thanks to technological progress and the integration of experiments with increasingly sophisticated numerical simulations, topological biological and soft matter is a vibrant area of research attracting scientists from a broad range of disciplines. However, owing to the high degree of specialization of modern science, many results have remained confined to their own particular fields, with different jargon making it difficult for researchers to share ideas and work together towards a comprehensive view of the diverse phenomena at play. Compelled by these motivations, here we present a comprehensive overview of topological effects in systems ranging from DNA and genome organization to entangled proteins, polymeric materials, liquid crystals, and theoretical physics, with the intention of reducing the barriers between different fields of soft matter and biophysics. Particular care has been taken in providing a coherent formal introduction to the topological properties of polymers and of continuum materials and in highlighting the underlying common aspects concerning the emergence, characterization, and effects of topological objects in different systems. The second half of the review is dedicated to the presentation of the latest results in selected problems, specifically, the effects of topological constraints on the viscoelastic properties of polymeric materials; their relation with genome organization; a discussion on the emergence and possible effects of knots and other entanglements in proteins; the emergence and effects of topological defects and solitons in complex fluids. This review is dedicated to the memory of Marek Cieplak.