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Camelid heavy-chain-only antibodies are a unique class of antibody that possesses only a single variable domain (termed VHH) for antigen recognition. Despite their apparent canonical mechanism of target recognition, where a single VHH domain binds a single target, an anti-caffeine VHH has been observed to possess 2:1 stoichiometry. Here, the structure of the anti-caffeine VHH/caffeine complex enabled the generation and biophysical analysis of variants that were used to better understand the role of VHH homodimerization in caffeine recognition. VHH interface mutants and caffeine analogs, which were examined to probe the mechanism of caffeine binding, suggested caffeine recognition is only possible with the VHH dimer species. Correspondingly, in the absence of caffeine, the anti-caffeine VHH was found to form a dimer with a dimerization constant comparable to that observed with VH:VL domains in conventional antibody systems, which was most stable near physiological temperature. While the VHH:VHH dimer structure (at 1.13 Å resolution) is reminiscent of conventional VH:VL heterodimers, the homodimeric VHH possesses a smaller angle of domain interaction, as well as a larger amount of apolar surface area burial. To test the general hypothesis that the short complementarity-determining region-3 (CDR3) may help drive VHH:VHH homodimerization, an anti-picloram VHH domain containing a short CDR3 was generated and characterized, which revealed it also existed as dimer species in solution. These results suggest homodimer-driven recognition may represent a more common method of VHH ligand recognition, opening opportunities for novel VHH homodimer affinity reagents and helping to guide their use in chemically induced dimerization applications.
Abstract not provided
Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design
Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain–affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. Further, the integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.
The separation and purification of xylene isomers is an industrially important but challenging process. Developing highly efficient adsorbents is crucial for the implementation of simulated moving bed technology for industrial separation of these isomers. Herein, we report a stacked one-dimensional coordination polymer {[Mn(dhbq)(H 2 O) 2 ], H 2 dhbq = 2,5-dihydroxy-1,4-benzoquinone} that exhibits an ideal molecular recognition and sieving of xylene isomers. Its distinct temperature-adsorbate–dependent adsorption behavior enables full separation of p -, m -, and o -xylene isomers in both vapor and liquid phases. The delicate stimuli-responsive swelling of the structure imparts this porous material with exceptionally high flexibility and stability, well-balanced adsorption capacity, high selectivity, and fast kinetics at conditions mimicking industrial settings. This study may offer an alternative approach for energy-efficient and adsorption-based industrial xylene separation and purification processes.
The adsorption performance of ZIF-71 towards two common volatile organic compounds, chlorobenzene and phenol, has been evaluated using a number of experimental techniques and Grand Canonical Monte Carlo (GCMC) simulations.
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Zeolites are crystalline microporous aluminosilicates that are commonly used as industrial sorbents and membranes. Solvent structuring and dynamics near zeolite crystal surfaces are thought to influence interfacial transport and molecular recognition processes, as well as fundamental aspects of their crystallization from solution. Here, in this work, we use molecular dynamics (MD) simulations to investigate the behavior of interfacial water near the exposed (010), (100), and (101) crystal faces of silicalite-1, one of the most widely studied zeolites. The MD simulations reveal that water’s translational and orientational order is strongly influenced by the distinct corrugations, pore apertures, and functional group distributions presented on each surface. Specifically, we observe two distinct hydration layers near each surface. Water molecules in the contact layer are hydrogen bonded to two or three exposed surface silanol groups. The relative populations of water molecules that are doubly and triply hydrogen bonded to the surface strongly depend on the distribution of the exposed silanols. The interactions with exposed surface silanols also influence water’s local orientational order and distribution across each surface. We also show that the structuring of the solvent near the different faces of silicalite-1 strongly impacts the interfacial dynamics. The translational and orientational relaxation dynamics of water are slowest in the contact layers and correlate with structural ordering near each face.
Abstract Successful mitosis depends on the timely establishment of correct chromosomal attachments to microtubules. The kinetochore, a modular multiprotein complex, mediates this connection by recognizing specialized chromatin containing a histone H3 variant called Cse4 in budding yeast and CENP‐A in vertebrates. Structural features of the kinetochore that enable discrimination between Cse4/CENP‐A and H3 have been identified in several species. How and when these contribute to centromere recognition and how they relate to the overall structure of the inner kinetochore are unsettled questions. More generally, this molecular recognition ensures that only one kinetochore is built on each chromatid and that this happens at the right place on the chromatin fiber. We have determined the crystal structure of a Cse4 peptide bound to the essential inner kinetochore Okp1‐Ame1 heterodimer from budding yeast. The structure and related experiments show in detail an essential point of Cse4 contact and provide information about the arrangement of the inner kinetochore.
The triggered self-assembly of surfactants into organized layers at aqueous interfaces is important for creating adaptive nanosystems and understanding selective ion extraction. While these transformations require molecular recognition, the underlying driving forces are modified by the local environment in ways that are not well understood. Herein, we investigate the role of ion binding and ion hydration using cyanosurf, which is composed of the cyanostar macrocycle, and its binding to anions that are either size-matched or mis-matched and either weakly or highly hydrated. We utilize the supra-amphiphile concept where anion binding converts cyanosurf into a charged and amphiphilic complex triggering its self-organization into monolayers at the air–water interface. Initially, cyanosurf forms aggregates at the surface of a pure water solution. When the weakly hydrated and size-matched hexafluorophosphate (PF 6 - ) and perchlorate (ClO 4 - ) anions are added, the macrocycles form distinct monolayer architectures. Surface-pressure isotherms reveal significant reorganization of the surface-active molecules upon anion binding while infrared reflection absorption spectroscopy show the ion-bound complexes are well ordered at the interface. Vibrational sum frequency generation spectroscopy shows the water molecules in the interfacial region are highly ordered in response to the charged monolayer of cyanosurf complexes. Consistent with the importance of recognition, we find the smaller mis-matched chloride does not trigger the transformation. However, the size-matched phosphate (H 2 PO 4 - ) also does not trigger monolayer formation indicating hydration inhibits its interfacial binding. These studies reveal how anion-selective recognition and hydration both control the binding and thus the switching of a responsive molecular interface.
Abstract The jamming of nanoparticle surfactants (NPSs) at liquid–liquid interface imparts attractive properties to the interfacial assemblies and enables the structuring of liquids. Herein, we report photoresponsive supramolecular microcapsules with jammed NPS assemblies at the oil‐water interface, taking advantage of host–guest molecular recognition. The permeability of the colloidal membrane can be effectively manipulated by switching the NPSs from a jammed state to an unjammed state with a photo trigger, leading to a controlled molecular diffusion and release, affording a versatile platform for the construction of next generation smart microcapsule systems.
The jamming of nanoparticle surfactants (NPSs) at liquid-liquid interface imparts attractive properties to the interfacial assemblies and enables the structuring of liquids. Herein, we report photoresponsive supramolecular microcapsules with jammed NPS assemblies at the oil-water interface, taking advantage of host-guest molecular recognition. The permeability of the colloidal membrane can be effectively manipulated by switching the NPSs from a jammed state to an unjammed state with a photo trigger, leading to a controlled molecular diffusion and release, affording a versatile platform for the construction of next generation smart microcapsule systems.
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.
Double helical conformation of polymer chains is widely observed in biomacromolecules and plays an essential role in exerting their biological functions, such as molecular recognition and information storage. It has remained challenging, however, to prepare synthetic helical polymers, and those that exist have mainly been limited to single-stranded polymers or short oligomeric double helices. In this work, we report the synthesis of covalent helical polymers, with a high molecular weight, from the achiral monomer hexahydroxytriphenylene through to spiroborate formation. Polymerization and crystallization occurred simultaneously under solvothermal conditions to form single crystals of the resulting helical covalent polymers. Characterization by single-crystal X-ray diffraction showed that each crystal consisted of pairs of mechanically entwined polymers. No strong non-covalent interactions were observed between the two helical polymers that formed a pair; instead, each strand interacted with neighbouring pairs through hydrogen bonding. Each individual crystal was made up of helical polymers of the same handedness, but the crystallization process produced a racemic conglomerate, with equal amounts of right-handed and left-handed crystals.
Subcellular compartmentalization is integral to the spatial regulation of mechanistic target of rapamycin (mTOR) signaling. However, the biological outputs associated with location-specific mTOR signaling events are poorly understood and challenging to decouple. Here, we engineered synthetic intracellular antibodies (intrabodies) that are capable of modulating mTOR signaling with genetically programmable spatial resolution. Epitope-directed phage display was exploited to generate high affinity synthetic antibody fragments (Fabs) against the FKBP12–Rapamycin binding site of mTOR (mTOR FRB ). We determined high-resolution crystal structures of two unique Fabs that discriminate distinct conformational states of mTOR FRB through recognition of its substrate recruitment interface. By leveraging these conformation-specific binders as intracellular probes, we uncovered the structural basis for an allosteric mechanism governing mTOR complex 1 (mTORC1) stability mediated by subtle structural adjustments within mTOR FRB . Furthermore, our results demonstrated that synthetic binders emulate natural substrates by employing divergent yet complementary hydrophobic residues at defined positions, underscoring the broad molecular recognition capability of mTOR FRB . Intracellular signaling studies showed differential time-dependent inhibition of S6 kinase 1 and Akt phosphorylation by genetically encoded intrabodies, thus supporting a mechanism of inhibition analogous to the natural product rapamycin. Finally, we implemented a feasible approach to selectively modulate mTOR signaling in the nucleus through spatially programmed intrabody expression. These findings establish intrabodies as versatile tools for dissecting the conformational regulation of mTORC1 and should be useful to explore how location-specific mTOR signaling influences disease progression.