Molecular descriptors are essential to not only quantitative structure-activity relationship (QSAR) models but also machine learning–based material, chemical, and biological data analysis. Here, we propose persistent spectral–based machine learning (PerSpect ML) models for drug design. Different from all previous spectral models, a filtration process is introduced to generate a sequence of spectral models at various different scales. PerSpect attributes are defined as the function of spectral variables over the filtration value. Molecular descriptors obtained from PerSpect attributes are combined with machine learning models for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases including PDBbind-2007, PDBbind-2013, and PDBbind-2016, are better than all existing models, as far as we know. The proposed PerSpect theory provides a powerful feature engineering framework. PerSpect ML models demonstrate great potential to significantly improve the performance of learning models in molecular data analysis.

pH-sensitive and biodegradable charge-transfer nanocomplex for second near-infrared photoacoustic tumor imaging

Double-stranded RNA (dsRNA) structures form triplexes and RNA-protein complexes through binding to single-stranded RNA (ssRNA) regions and proteins, respectively, for diverse biological functions. Hence, targeting dsRNAs through major-groove triplex formation is a promising strategy for the development of chemical probes and potential therapeutics. Short (e.g., 6–10 mer) chemically-modified Peptide Nucleic Acids (PNAs) have been developed that bind to dsRNAs sequence specifically at physiological conditions. For example, a PNA incorporating a modified base thio-pseudoisocytosine (L) has an enhanced recognition of a G–C pair in an RNA duplex through major-groove L·G–C base triple formation at physiological pH, with reduced pH dependence as observed for C+·G–C base triple formation. Currently, an unmodified T base is often incorporated into PNAs to recognize a Watson–Crick A–U pair through major-groove T·A–U base triple formation. A substitution of the 5-methyl group in T by hydrogen and halogen atoms (F, Cl, Br, and I) causes a decrease of the pKa of N3 nitrogen atom, which may result in improved hydrogen bonding in addition to enhanced base stacking interactions. Here, we synthesized a series of PNAs incorporating uracil and halouracils, followed by binding studies by non-denaturing polyacrylamide gel electrophoresis, circular dichroism, and thermal melting. Our results suggest that replacing T with uracil and halouracils may enhance the recognition of an A–U pair by PNA·RNA2 triplex formation in a sequence-dependent manner, underscoring the importance of local stacking interactions. Incorporating bromouracils and chlorouracils into a PNA results in a significantly reduced pH dependence of triplex formation even for PNAs containing C bases, likely due to an upshift of the apparent pKa of N3 atoms of C bases. Thus, halogenation and other chemical modifications may be utilized to enhance hydrogen bonding of the adjacent base triples and thus triplex formation. Furthermore, our experimental and computational modelling data suggest that PNA·RNA2 triplexes may be stabilized by incorporating a BrUL step but not an LBrU step, in dsRNA-binding PNAs.

In this paper, we systematically review weighted persistent homology (WPH) models and their applications in biomolecular data analysis. Essentially, the weight value, which reflects physical, chemical and biological properties, can be assigned to vertices (atom centers), edges (bonds), or higher order simplexes (cluster of atoms), depending on the biomolecular structure, function, and dynamics properties. Further, we propose the first localized weighted persistent homology (LWPH). Inspired by the great success of element specific persistent homology (ESPH), we do not treat biomolecules as an inseparable system like all previous weighted models, instead we decompose them into a series of local domains, which may be overlapped with each other. The general persistent homology or weighted persistent homology analysis is then applied on each of these local domains. In this way, functional properties, that are embedded in local structures, can be revealed. Our model has been applied to systematically studying DNA structures. It has been found that our LWPH based features can be used to successfully discriminate the A-, B-, and Z-types of DNA. More importantly, our LWPH based PCA model can identify two configurational states of DNA structure in ion liquid environment, which can be revealed only by the complicated helical coordinate system. The great consistence with the helical-coordinate model demonstrates that our model captures local structure variations so well that it is comparable with geometric models. Moreover, geometric measurements are usually defined in very local regions. For instance, the helical-coordinate system is limited to one or two basepairs. However, our LWPH can quantitatively characterize structure information in local regions or domains with arbitrary sizes and shapes, where traditional geometrical measurements fail.

contaminants owing to their chemical affi nity with these contaminants, large surface area, porous structure, and other remarkable physical properties. [ 1 ] Due to their superior hydrophobic and oleophilic characteristics, carbon aerogels can effectively absorb a variety of oils and organic solvents without water penetration. However, most of these carbon aerogels are synthesized from carbonaceous precursors, such as carbon-containing polymers, [ 2 ] carbon-containing fi bers, [ 3 ] carbon nanofi bers, [ 4 ] carbon nanotubes, [ 5 ] and graphene. [ 6 ] Problems of these expensive precursors and complex and energyconsuming process in the production of carbon aerogels restrict the practical use of carbon aerogels on a large-scale. In this regard, the utilization of renewable biomass as precursors, together with simple and low-energy treatment to fabricate sustainable carbon aerogels is strongly desired. [ 7 ] In this study, we employ nanofi brillated cellulose (NFC) as a sustainable and scalable precursor for producing carbon aerogels. The reason to choose NFC as the precursor is based on its unique structures and properties. First, cellulose is a good carbon source. Cellulose-containing woods have been used as the precursor of activated coals for many years. Second, comparing with traditional cellulosic pulps in the micrometer-scale, NFC produced by disintegration of pulps has much smaller diameters in nanometer-scale (typically <10 nm) as well as advantageous mechanical strength due to the extended chain crystals of cellulose and large specifi c surface areas. [ 8 ] Further, NFC is able to form porous aerogels with a microscopic network and a large specifi c surface area. Although the NFC aerogels are expected to use for absorbing or separating of oils and organic solvents from water, the separation effi ciency is low because of the strong hydrophilicity of cellulose. [ 9 ] To improve the absorbent performance, the surface of NFC aerogels was chemically modifi ed to be hydrophobic and oleophilic with TiO 2 coating [ 10 ] or vapor phase silanization. [ 11 ] However, these modifi ed NFC aerogels still showed a weak absorbent performance (<45 times of their own weight). Jiang and Hsieh synthesized a functionalized NFC aerogel by chemical vapor deposition of (triethoxyl(octyl) silane) which was able to absorb 139-356 times organic solvents or oils by weight. [ 12 ] Unfortunately, their NFC aerogel also absorbed water when it was immersed into water, Sustainable carbon aerogels with low density (≈7.8 mg cm −3 ), high porosity, high resiliency, excellent hydrophobicity, and oleophilic characteristics are synthesized by employing nanofi brillated cellulose as the precursor. The as-prepared carbon aerogels show a remarkable capacity for the absorption of a variety of oils and organic solvents with weight gains ranging from 7422 to 22356. Under extreme conditions (e.g., at severe temperatures and in corrosive liquids), these carbon aerogels still demonstrate a superior absorption performance. Furthermore, a device ...