Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Quinoline Group Modified Carbon Nanotubes for the Detection of Zinc Ions

Carbon nanotubes (CNTs) were covalently modified by fluorescence ligand (glycine-N-8-quinolylamide) and formed a hybrid material which could be used as a selective probe for metal ions detection. The anchoring to the surface of the CNTs was carried out by the reaction between the precursor and the carboxyl groups available on the surface of the support. Fourier transform infrared spectroscopy (FTIR) and Thermogravimetric analysis (TGA) unambiguously proved the existence of covalent bonds between CNTs and functional ligands. Fluorescence characterization shows that the obtained organic–inorganic hybrid composite is highly selective and sensitive (0.2 μM) to Zn(II) detection

Multiscale Coarse-Graining of the Protein Energy Landscape

A variety of coarse-grained (CG) models exists for simulation of proteins. An outstanding problem is the construction of a CG model with physically accurate conformational energetics rivaling all-atom force fields. In the present work, atomistic simulations of peptide folding and aggregation equilibria are force-matched using multiscale coarse-graining to develop and test a CG interaction potential of general utility for the simulation of proteins of arbitrary sequence. The reduced representation relies on multiple interaction sites to maintain the anisotropic packing and polarity of individual sidechains. CG energy landscapes computed from replica exchange simulations of the folding of Trpzip, Trp-cage and adenylate kinase resemble those of other reduced representations; non-native structures are observed with energies similar to those of the native state. The artifactual stabilization of misfolded states implies that non-native interactions play a deciding role in deviations from ideal funnel-like cooperative folding. The role of surface tension, backbone hydrogen bonding and the smooth pairwise CG landscape is discussed. Ab initio folding aside, the improved treatment of sidechain rotamers results in stability of the native state in constant temperature simulations of Trpzip, Trp-cage, and the open to closed conformational transition of adenylate kinase, illustrating the potential value of the CG force field for simulating protein complexes and transitions between well-defined structural states

Mechanical and Assembly Units of Viral Capsids Identified via Quasi-Rigid Domain Decomposition

Key steps in a viral life-cycle, such as self-assembly of a protective protein container or in some cases also subsequent maturation events, are governed by the interplay of physico-chemical mechanisms involving various spatial and temporal scales. These salient aspects of a viral life cycle are hence well described and rationalised from a mesoscopic perspective. Accordingly, various experimental and computational efforts have been directed towards identifying the fundamental building blocks that are instrumental for the mechanical response, or constitute the assembly units, of a few specific viral shells. Motivated by these earlier studies we introduce and apply a general and efficient computational scheme for identifying the stable domains of a given viral capsid. The method is based on elastic network models and quasi-rigid domain decomposition. It is first applied to a heterogeneous set of well-characterized viruses (CCMV, MS2, STNV, STMV) for which the known mechanical or assembly domains are correctly identified. The validated method is next applied to other viral particles such as L-A, Pariacoto and polyoma viruses, whose fundamental functional domains are still unknown or debated and for which we formulate verifiable predictions. The numerical code implementing the domain decomposition strategy is made freely available

Addition of {M2S2O2}(2+), M = Mo, W, to A-alpha-[PW9O34](9-). Synthesis and structural characterizations in the solid state and in solution

Bereau V, Cadot E, Bögge H, Müller A, Secheresse F. Addition of {M2S2O2}(2+), M = Mo, W, to A-alpha-[PW9O34](9-). Synthesis and structural characterizations in the solid state and in solution. INORGANIC CHEMISTRY. 1999;38(25):5803-5808.The [P2W18M6S6O74(H2O)(6)](12-) anions, M = Mo, W, were obtained through the stereospecific addition of the dithiocation [M2O2S2](2+) to the trivacant A-alpha-[PW9O34](9-). K-12[P2W18Mo6S6O74(H2O)(6)]. 26H(2)O has been isolated as crystals and has been characterized by X-ray diffraction (orthorhombic Pmn2(I) with a = 31.530(6) Angstrom, b = 19.703(4) Angstrom, c = 18.761(4) Angstrom, Z = 4). The structure of the anion consists of a sandwich-like arrangement of two alpha-[PW9O34](9-) subunits bridged by three [Mo2O2S2(H2O)(2)] cores. The X-ray diffraction structural analysis showed that one [Mo2O2S2] bridging unit was rotated through an angle of 180 degrees with respect to the other two. Among the six water molecules attached to the Mo centers, four are directed toward the inner cavity while the two remaining ones are directed out of the cavity. According to the X-ray data, W-183 NMR characterizations of both compounds show the lowering of the local symmetry of the alpha-[PW9O34](9-) subunit from C-3v to C-s. For [P2W24S6O74(H2O)(6)](12-) two additional deshielded resonances were observed characteristic of the distribution of the three [W2O2S2(H2O)(2)] bridging units. Infrared data are also given

Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings

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 structureactivity relationships because their activities typically have multi-scale and multi-parameter dependencies. Here, we used a small library of 163 peptides 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 chemical 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 chemical space of short functional self-assembling peptides exemplified for therapeutic viral gene delivery