Marriage of High-Throughput Gradient Surface Generation With Statistical Learning for the Rational Design of Functionalized Biomaterials.

Functional biomaterial is already an important aspect in modern therapeutics; yet, the design of novel multi-functional biomaterial is still a challenging task nowadays. When several biofunctional components are present, the complexity that arises from their combinations and interactions will lead to tedious trial-and-error screening. In this work, a novel strategy of biomaterial rational design through the marriage of gradient surface generation with statistical learning is presented. Not only can parameter combinations be screened in a high-throughput fashion, but also the optimal conditions beyond the experimentally tested range can be extrapolated from the models. The power of the strategy is demonstrated in rationally designing an unprecedented ternary functionalized surface for orthopedic implant, with optimal osteogenic, angiogenic, and neurogenic activities, and its optimality and the best osteointegration promotion are confirmed in vitro and in vivo, respectively. The presented strategy is expected to open up new possibilities in the rational design of biomaterials.

Reactivity of Unsupported Transition Metal-Aluminyl Complexes: A Nucleophilic TM-Al Bond.

Despite the long history of research in transition metal (TM) complexes, the study of TM-aluminyl complexes is still in its early stage of development. It is expected that the presence of an electropositive Al donor atom would open up new possibilities in TM complex reactivity, and indeed TM-aluminyl has shown an early sign of success in small-molecule activation. On the other hand, the existing reports on TM-aluminyl reactivity are often explained to readers with different understanding on individual cases, and a general picture of TM-aluminyl reactivity is still not available. In this work, we have attempted to provide a systematic picture to explain some early explorations in this field, specifically a series of recently reported heteroallene insertion reactions involving unsupported TM-aluminyl complexes. Through density functional theory calculations of a number of TM-aluminyl complexes, covering both Au and Cu centers, we found that their reactivity against heteroallenes (including CO2 and carbodiimides) is mostly based on the strong nucleophilicity of the TM-Al σ-bond.

Elucidation of the key role of Pt···Pt interactions in the directional self-assembly of platinum(II) complexes.

Here, we report the use of an amphiphilic Pt(II) complex, K[Pt{(O3SCH2CH2CH2)2bzimpy}Cl] (PtB), as a model to elucidate the key role of Pt···Pt interactions in directing self-assembly by combining temperature-dependent ultraviolet-visible (UV-Vis) spectroscopy, stopped-flow kinetic experiments, quantum mechanics (QM) calculations, and molecular dynamics (MD) simulations. Interestingly, we found that the self-assembly mechanism of PtB in aqueous solution follows a nucleation-free isodesmic model, as revealed by the temperature-dependent UV-Vis experiments. In contrast, a cooperative growth is found for the self-assembly of PtB in acetone­water (7:1, vol/vol) solution, which is further verified by the stopped-flow experiments, which clearly indicates the existence of a nucleation phase in the acetone­water (7:1, vol/vol) solution. To reveal the underlying reasons and driving forces for these self-assembly processes, we performed QM calculations and show that the Pt···Pt interactions arising from the interaction between the pz and dz2 orbitals play a crucial role in determining the formation of ordered self-assembled structures. In subsequent oligomer MD simulations, we demonstrate that this directional Pt···Pt interaction can indeed facilitate the formation of linear structures packed in a helix-like fashion. Our results suggest that the self-assembly of PtB in acetone­water (7:1, vol/vol) solution is predominantly driven by the directional noncovalent Pt···Pt interaction, leading to the cooperative growth and the formation of fibrous nanostructures. On the contrary, the self-assembly in aqueous solution forms spherical nanostructures of PtB, which is primarily due to the predominant contribution from the less directional hydrophobic interactions over the directional Pt···Pt and π−π interactions that result in an isodesmic growth.

RPnet: a reverse-projection-based neural network for coarse-graining metastable conformational states for protein dynamics.

The Markov State Model (MSM) is a powerful tool for modeling long timescale dynamics based on numerous short molecular dynamics (MD) simulation trajectories, which makes it a useful tool for elucidating the conformational changes of biological macromolecules. By partitioning the phase space into discretized states and estimating the probabilities of inter-state transitions based on short MD trajectories, one can construct a kinetic network model that could be used to extrapolate long-timescale kinetics if the Markovian condition is met. However, meeting the Markovian condition often requires hundreds or even thousands of states (microstates), which greatly hinders the comprehension of the conformational dynamics of complex biomolecules. Kinetic lumping algorithms can coarse grain numerous microstates into a handful of metastable states (macrostates), which would greatly facilitate the elucidation of biological mechanisms. In this work, we have developed a reverse-projection-based neural network (RPnet) to lump microstates into macrostates, by making use of a physics-based loss function that is based on the projection operator framework of conformational dynamics. By recognizing that microstate and macrostate transition modes can be related through a projection process, we have developed a reverse-projection scheme to directly compare the microstate and macrostate dynamics. Based on this reverse-projection scheme, we designed a loss function that allows the effective assessment of the quality of a given kinetic lumping. We then make use of a neural network to efficiently minimize this loss function to obtain an optimized set of macrostates. We have demonstrated the power of our RPnet in analyzing the dynamics of a numerical 2D potential, alanine dipeptide, and the clamp opening of an RNA polymerase. In all these systems, we have illustrated that our method could yield comparable or better results than competing methods in terms of state partitioning and reproduction of slow dynamics. We expect that our RPnet holds promise in analyzing the conformational dynamics of biological macromolecules.

Accurate Analysis of Anisotropic Carrier Mobility and Structure-property Relationships in Organic BOXD Crystalline Materials.

Charge mobility is an essential factor of organic crystalline materials. Although many investigators have made important progress, the exact relationship between the crystal structure and carrier mobility remains to be clarified. Fortunately, a series of bis-1,3,4-oxadiazole derivatives have been successfully prepared and reported. They have similar main molecular fragments but different crystal packing modes, which provide an ideal research objective for studying the effect of molecular packing on charge mobility in organic photoelectric conversion systems. In this work, the charge mobilities of these molecules are systematically evaluated from the perspective of first-principles calculation, and the effect of a molecular overlap on orbital overlap integral and final charge carrier mobility is fully discussed. It can be seen that the small intermolecular distance (less than 6 Å) is the decisive factor to achieve high electron mobility in π stacking, and better mobility can be obtained by increasing the hole migration distance appropriately. A larger dihedral angle of anisotropy is an important point limiting the charge mobility in the herringbone arrangement. It is hoped that the correlation results between the crystal structure and mobility can assist the experimental study and provide an effective way to improve the photoelectric conversion efficiency of the organic semiconductor devices and multiple basis for multiscale material system characterization and material information.

Microsecond timescale MD simulations at the transition state of PmHMGR predict remote allosteric residues.

Understanding the mechanisms of enzymatic catalysis requires a detailed understanding of the complex interplay of structure and dynamics of large systems that is a challenge for both experimental and computational approaches. More importantly, the computational demands of QM/MM simulations mean that the dynamics of the reaction can only be considered on a timescale of nanoseconds even though the conformational changes needed to reach the catalytically active state happen on a much slower timescale. Here we demonstrate an alternative approach that uses transition state force fields (TSFFs) derived by the quantum-guided molecular mechanics (Q2MM) method that provides a consistent treatment of the entire system at the classical molecular mechanics level and allows simulations at the microsecond timescale. Application of this approach to the second hydride transfer transition state of HMG-CoA reductase from Pseudomonas mevalonii (PmHMGR) identified three remote residues, R396, E399 and L407, (15-27 Å away from the active site) that have a remote dynamic effect on enzyme activity. The predictions were subsequently validated experimentally via site-directed mutagenesis. These results show that microsecond timescale MD simulations of transition states are possible and can predict rather than just rationalize remote allosteric residues.

Superatomic Ligand-Field Splitting in Ligated Gold Nanoclusters.

Gold nanoclusters are attractive because of their electronic and optical properties. Many theoretical models have been proposed to explain their electronic structures through an electron-counting approach. However, subtle features may not be well explained by electron-counting rules. In this work, we have discovered a unique example of ligand-controlled skeletal bonding in two recently reported gold nanoclusters with very similar compositions and geometries. We have shown that the superatomic orbitals of the common kernel of the two clusters undergo different ligand-field splitting because of the different ligand-field strengths in the two clusters. Such a difference is clearly revealed by constructing the Jellium orbitals via an orbital alignment process, and a subsequent localization of the Jellium orbitals allows us to obtain localized bonding models. Finally, on the basis of localized bonding models, we predict the existence of a ligated gold cluster with a [Au32]4+ kernel.

Principal interacting spin orbital: understanding the fragment interactions in open-shell systems.

Due to the recent rise in the interest and research efforts on first-row transition metal catalysis and other radical-related reactions, open-shell systems play a much more important role in modern chemistry. However, the development of bonding analysis tools for open-shell systems is still lagging behind. In this work, we present the principal interacting spin orbital (PISO) analysis, which is an analysis framework developed based on our previously reported principal interacting orbital (PIO) analysis. We will demonstrate the power of our framework to analyse different kinds of open-shell systems, ranging from simple organic radicals to much more complicated coordination complexes, from which we can see how different kinds of odd-electron bonds could be identified. We will also illustrate its advantage when used in the analysis of chemical reactions, through which we can observe subtle patterns that could be helpful for tuning or rational design of related reactions.

A deep learning framework to predict binding preference of RNA constituents on protein surface.

Protein-RNA interaction plays important roles in post-transcriptional regulation. However, the task of predicting these interactions given a protein structure is difficult. Here we show that, by leveraging a deep learning model NucleicNet, attributes such as binding preference of RNA backbone constituents and different bases can be predicted from local physicochemical characteristics of protein structure surface. On a diverse set of challenging RNA-binding proteins, including Fem-3-binding-factor 2, Argonaute 2 and Ribonuclease III, NucleicNet can accurately recover interaction modes discovered by structural biology experiments. Furthermore, we show that, without seeing any in vitro or in vivo assay data, NucleicNet can still achieve consistency with experiments, including RNAcompete, Immunoprecipitation Assay, and siRNA Knockdown Benchmark. NucleicNet can thus serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins.

Inter-ligand delocalisations in transition metal complexes containing multiple non-innocent ligands.

Systematic bonding analysis has been carried out for transition metal complexes containing more than one redox-active/non-innocent ligand. These complexes include tetralithio spiroaromatic palladole, dicupra[10]annulene, highly reduced nickel cyclooctadiene complex and tris(dithiolene)vanadium(iv). Our detailed bonding analysis reveals a common inter-ligand delocalisation pattern in these cases.

Intrinsic Cleavage of RNA Polymerase II Adopts a Nucleobase-independent Mechanism Assisted by Transcript Phosphate.

RNA polymerase II (Pol II) utilises the same active site for polymerization and intrinsic cleavage. Pol II proofreads the nascent transcript by its intrinsic nuclease activity to maintain high transcriptional fidelity critical for cell growth and viability. The detailed catalytic mechanism of intrinsic cleavage remains unknown. Here, we combined ab initio quantum mechanics/molecular mechanics studies and biochemical cleavage assays to show that Pol II utilises downstream phosphate oxygen to activate the attacking nucleophile in hydrolysis, while the newly formed 3'-end is protonated through active-site water without a defined general acid. Experimentally, alteration of downstream phosphate oxygen either by 2'-5' sugar linkage or stereo-specific thio-substitution of phosphate oxygen drastically reduced cleavage rate. We showed by N7-modification that guanine nucleobase does not directly involve as acid-base catalyst. Our proposed mechanism provides important insights into the understanding of intrinsic transcriptional cleavage reaction, an essential step of transcriptional fidelity control.

TAPS: A traveling-salesman based automated path searching method for functional conformational changes of biological macromolecules.

Locating the minimum free energy paths (MFEPs) between two conformational states is among the most important tasks of biomolecular simulations. For example, knowledge of the MFEP is critical for focusing the effort of unbiased simulations that are used for the construction of Markov state models to the biologically relevant regions of the system. Typically, existing path searching methods perform local sampling around the path nodes in a pre-selected collective variable (CV) space to allow a gradual downhill evolution of the path toward the MFEP. Despite the wide application of such a strategy, the gradual path evolution and the non-trivial a priori choice of CVs are also limiting its overall efficiency and automation. Here we demonstrate that non-local perpendicular sampling can be pursued to accelerate the search, provided that all nodes are reordered thereafter via a traveling-salesman scheme. Moreover, path-CVs can be computed on-the-fly and used as a coordinate system, minimizing the necessary prior knowledge about the system. Our traveling-salesman based automated path searching method achieves a 5-8 times speedup over the string method with swarms-of-trajectories for two peptide systems in vacuum and solution, making it a promising method for obtaining initial pathways when investigating functional conformational changes between a pair of structures.

Remote Bonding in Clusters [Pd3Ge18R6]2-: Modular Bonding Model for Large Clusters via Principal Interacting Orbital Analysis.

Main group cluster compounds have attracted increasing attention in the past decades. Despite recent developments in their synthesis, the description of their electronic structures is usually limited to simply applying Wade's rule originally developed for borane compounds. This traditional approach is once again challenged by two recently reported group 14 metalloid clusters in the form of [Pd3Ge18R6]2-. In this work, we put forward a modular bonding model for these two clusters, via principal interacting orbital (PIO) analysis. The site preference for six substituents has also been analyzed.

An efficient Bayesian kinetic lumping algorithm to identify metastable conformational states via Gibbs sampling.

Markov State Model (MSM) has become a popular approach to study the conformational dynamics of complex biological systems in recent years. Built upon a large number of short molecular dynamics simulation trajectories, MSM is able to predict the long time scale dynamics of complex systems. However, to achieve Markovianity, an MSM often contains hundreds or thousands of states (microstates), hindering human interpretation of the underlying system mechanism. One way to reduce the number of states is to lump kinetically similar states together and thus coarse-grain the microstates into macrostates. In this work, we introduce a probabilistic lumping algorithm, the Gibbs lumping algorithm, to assign a probability to any given kinetic lumping using the Bayesian inference. In our algorithm, the transitions among kinetically distinct macrostates are modeled by Poisson processes, which will well reflect the separation of time scales in the underlying free energy landscape of biomolecules. Furthermore, to facilitate the search for the optimal kinetic lumping (i.e., the lumped model with the highest probability), a Gibbs sampling algorithm is introduced. To demonstrate the power of our new method, we apply it to three systems: a 2D potential, alanine dipeptide, and a WW protein domain. In comparison with six other popular lumping algorithms, we show that our method can persistently produce the lumped macrostate model with the highest probability as well as the largest metastability. We anticipate that our Gibbs lumping algorithm holds great promise to be widely applied to investigate conformational changes in biological macromolecules.

Unravelling Chemical Interactions with Principal Interacting Orbital Analysis.

Decomposing chemical interactions into bonds and other higher order interactions is a common practice to analyse chemical structures, and gave birth to many chemical concepts, despite the fact that the decomposition itself might be subjective in nature. Fragment molecular orbitals (FMOs) offer a more rigorous alternative to such intuition, but might be less interpretable due to extensive delocalisation in FMOs. Inspired by the Principal Component Analysis in statistics, we hereby present a novel framework, Principal Interacting Orbital (PIO) analysis, that can very quickly identify the "dominant interacting orbitals" that are semi-localised and easily interpretable, while still maintaining mathematical rigor. Many chemical concepts that are often taken for granted, but could not be easily inferred from other computational techniques like FMO analysis, can now be visualised as PIOs. We have also illustrated, through various examples covering both organic and inorganic chemistry, how PIO analysis could help us pinpoint subtle features that might play determining roles in bonding and reactions.

Isolation and enantiostability of the B-chiral bis(salicylato)borate anions [B R (Sal)2] and [B S (Sal)2].

The chiral spiroborate anions [B S (Sal)2] and [B R (Sal)2], (R and S subscripts indicate boron stereochemistry) have been isolated as 1 : 1 quininium and 1 : 2 sparteinium salts, [HQuin][B S (Sal)2] and [H2Spa][B R (Sal)2]2 respectively, by either cation metathesis or a simple one-pot synthesis involving reaction of boric and salicylic acids with the alkaloid base. Circular dichroism (CD) spectroscopy shows that the B-based chirality is stable in polar aprotic media, such as DMF or DMSO, though labile in protic solutions. Enantiopure salts with achiral counter-cations such as [NBu4][B R (Sal)2] may then be prepared by exchange, so these B-chiral anions may have use in metathesis-based resolutions. Due to a site disorder the anion in [H2Spa][B R (Sal)2]2 is limited to 70% ee, however an enantiopure analogue [H2Spa][B R (5-Cl-Sal)2]2 is readily formed using 5-chlorosalicylic acid. This also indicates a wide family of stable enantiopure B-chiral anions may be isolated by this approach.

Path lumping: An efficient algorithm to identify metastable path channels for conformational dynamics of multi-body systems.

Constructing Markov state models from large-scale molecular dynamics simulation trajectories is a promising approach to dissect the kinetic mechanisms of complex chemical and biological processes. Combined with transition path theory, Markov state models can be applied to identify all pathways connecting any conformational states of interest. However, the identified pathways can be too complex to comprehend, especially for multi-body processes where numerous parallel pathways with comparable flux probability often coexist. Here, we have developed a path lumping method to group these parallel pathways into metastable path channels for analysis. We define the similarity between two pathways as the intercrossing flux between them and then apply the spectral clustering algorithm to lump these pathways into groups. We demonstrate the power of our method by applying it to two systems: a 2D-potential consisting of four metastable energy channels and the hydrophobic collapse process of two hydrophobic molecules. In both cases, our algorithm successfully reveals the metastable path channels. We expect this path lumping algorithm to be a promising tool for revealing unprecedented insights into the kinetic mechanisms of complex multi-body processes.

Structure and bonding of [Pd2Sn18]4-: an interesting example of the mutual delocalisation phenomenon.

In this article, we have presented a computational analysis on the structure and bonding of [Pd2Sn18]4- and illustrated that it serves as an interesting example of OMO-UMO mutual delocalisation with two identical [PdE9]2- fragments. We have also illustrated the alternative roles that could be played by an [ME9]2-/[E9]2- fragment, a simple L-type donor and a lone-pair acceptor.

Understanding the core of RNA interference: The dynamic aspects of Argonaute-mediated processes.

At the core of RNA interference, the Argonaute proteins (Ago) load and utilize small guide nucleic acids to silence mRNAs or cleave foreign nucleic acids in a sequence specific manner. In recent years, based on extensive structural studies of Ago and its interaction with the nucleic acids, considerable progress has been made to reveal the dynamic aspects of various Ago-mediated processes. Here we review these novel insights into the guide-strand loading, duplex unwinding, and effects of seed mismatch, with a focus on two representative Agos, the human Ago 2 (hAgo2) and the bacterial Thermus thermophilus Ago (TtAgo). In particular, comprehensive molecular simulation studies revealed that although sharing similar overall structures, the two Agos have vastly different conformational landscapes and guide-strand loading mechanisms because of the distinct rigidity of their L1-PAZ hinge. Given the central role of the PAZ motions in regulating the exposure of the nucleic acid binding channel, these findings exemplify the importance of protein motions in distinguishing the overlapping, yet distinct, mechanisms of Ago-mediated processes in different organisms.

Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular dynamics trajectories.

We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc.