Enzymatic hydrolysis of polyethylene terephthalate (PET) has been the subject of extensive previous research that can be grouped into two categories, viz. enzymatic surface modification of polyester fibers and management of PET waste by enzymatic hydrolysis. Different enzymes with rather specific properties are required for these two processes. Enzymatic surface modification is possible with several hydrolases, such as lipases, carboxylesterases, cutinases, and proteases. These enzymes should be designated as PET surface–modifying enzymes and should not degrade the building blocks of PET but should hydrolyze the surface polymer chain so that the intensity of PET is not weakened. Conversely, management of PET waste requires substantial degradation of the building blocks of PET; therefore, only a limited number of cutinases have been recognized as PET hydrolases since the first PET hydrolase was discovered by Müller et al. ( Macromol Rapid Commun 26:1400–1405, 2005 ). Here, we introduce current knowledge on enzymatic degradation of PET with a focus on the key class of enzymes, PET hydrolases, pertaining to the definition of enzymatic requirements for PET hydrolysis, structural analyses of PET hydrolases, and the reaction mechanisms. This review gives a deep insight into the structural basis and dynamics of PET hydrolases based on the recent progress in X-ray crystallography. Based on the knowledge accumulated to date, we discuss the potential for PET hydrolysis applications, such as in designing waste stream management. Electronic supplementary material The online version of this article (10.1007/s00253-019-09717-y) contains supplementary material, which is available to authorized users.

Polyethylene terephthalate (PET) hydrolase is a challenging target as PET is a commonly used plastic that is extremely resistant to enzymatic attack. Since the discovery of a PET hydrolase from Thermobif ida f usca in 2005, novel PET hydrolases and their availability toward waste PET have been investigated. At present, at least four thermophilic cutinases are known as PET hydrolases that could be used for the management of amorphous PET waste, such as packaging materials. Heat-labile PETase from Ideonella sakaiensis and its homologues from mesophilic bacteria exist in the environment. However, PET can be efficiently hydrolyzed with thermophilic hydrolases. This Review focuses on the current state of PET hydrolases and the potential of their application. Contrary to an amorphous PET, the enzymatic hydrolysis of crystalline PET (particularly PET bottles) remains to be fully elucidated. It cannot be assured whether the biorecycling of general PET would be put into practice in the near future, but the plan is getting closer to the goal. PET hydrolases can be versatile polyesterases as they can hydrolyze not only PET but also other polyesters. Additionally, the thermostability of PET hydrolases is advantageous to their application in terms of reaction speed and durability.

The recent accumulation of large amounts of 3D structural data warrants a sensitive and automatic method to compare and classify these structures. We developed a web server for comparing protein 3D structures using the program Matras (http://biunit.aist-nara.ac.jp/matras). An advantage of Matras is its structure similarity score, which is defined as the log-odds of the probabilities, similar to Dayhoff's substitution model of amino acids. This score is designed to detect evolutionarily related (homologous) structural similarities. Our web server has three main services. The first one is a pairwise 3D alignment, which is simply align two structures. A user can assign structures by either inputting PDB codes or by uploading PDB format files in the local machine. The second service is a multiple 3D alignment, which compares several protein structures. This program employs the progressive alignment algorithm, in which pairwise 3D alignments are assembled in the proper order. The third service is a 3D library search, which compares one query structure against a large number of library structures. We hope this server provides useful tools for insights into protein 3D structures.

Detection of pockets on protein surfaces is an important step toward finding the binding sites of small molecules. In a previous study, we defined a pocket as a space into which a small spherical probe can enter, but a large probe cannot. The radius of the large probes corresponds to the shallowness of pockets. We showed that each type of binding molecule has a characteristic shallowness distribution. In this study, we introduced fundamental changes to our previous algorithm by using a 3D grid representation of proteins and probes, and the theory of mathematical morphology. We invented an efficient algorithm for calculating deep and shallow pockets (multiscale pockets) simultaneously, using several different sizes of spherical probes (multiscale probes). We implemented our algorithm as a new program, ghecom (grid-based HECOMi finder). The statistics of calculated pockets for the structural dataset showed that our program had a higher performance of detecting binding pockets, than four other popular pocket-finding programs proposed previously. The ghecom also calculates the shallowness of binding ligands, R(inaccess) (minimum radius of inaccessible spherical probes) that can be obtained from the multiscale molecular volume. We showed that each part of the binding molecule had a bias toward a specific range of shallowness. These findings will be useful for predicting the types of molecules that will be most likely to bind putative binding pockets, as well as the configurations of binding molecules. The program ghecom is available through the Web server (http://biunit.naist.jp/ghecom).

The prevalence of infection with hepatitis A virus (HAV), HBV, HCV, HDV, and HEV was evaluated in 249 apparently healthy individuals, including 122 inhabitants in Ulaanbaatar, the capital city of Mongolia, and 127 age-and sex-matched members of nomadic tribes who lived around the capital city. Overall, hepatitis B surface antigen (HBsAg) was detected in 24 subjects (10%), of whom 22 (92%) had detectable HBV DNA. Surprisingly, HDV RNA was detectable in 20 (83%) of the 24 HBsAg-positive subjects. HCV-associated antibodies were detected in 41 (16%) and HCV RNA was detected in 36 (14%) subjects, none of whom was coinfected with HBV, indicating that HBV/HCV carriers account for one-fourth of this population. Antibodies to HAV and HEV were detected in 249 (100%) and 28 (11%) subjects, respectively. Of 22 HBV DNA-positive subjects, genotype D was detected in 21 subjects and genotype F was detected in 1 subject. All 20 HDV isolates recovered from HDV RNA-positive subjects segregated into genotype I, but these differed by 2.1 to 11.4% from each other in the 522-to 526-nucleotide sequence. Of 36 HCV RNA-positive samples, 35 (97%) were genotype 1b and 1 was genotype 2a. Reflecting an extremely high prevalence of hepatitis virus infections, there were no appreciable differences in the prevalence of hepatitis virus markers between the two studied populations with distinct living place and lifestyle. A nationwide epidemiological survey of hepatitis viruses should be conducted in an effort to prevent de novo infection with hepatitis viruses in Mongolia.

Determining a one-to-one atom correspondence between two chemical compounds is important to measure molecular similarities and to find compounds with similar biological activities. This calculation can be formalized as the maximum common substructure (MCS) problem, which is well-studied and has been shown to be NP-complete. Although many rigorous and heuristic algorithms have been developed, none of these algorithms is sufficiently fast and accurate. We developed a new program, called "kcombu" using a build-up algorithm, which is a type of the greedy heuristic algorithms. The program can search connected and disconnected MCSs as well as topologically constrained disconnected MCS (TD-MCS), which is introduced in this study. To evaluate the performance of our program, we prepared two correct standards: the exact correspondences generated by the maximum clique algorithms and the 3D correspondences obtained from superimposed 3D structure of the molecules in a complex 3D structure with the same protein. For the five sets of molecules taken from the protein structure database, the agreement value between the build-up and the exact correspondences for the connected MCS is sufficiently high, but the computation time of the build-up algorithm is much smaller than that of the exact algorithm. The comparison between the build-up and the 3D correspondences shows that the TD-MCS has the best agreement value among the other types of MCS. Additionally, we observed a strong correlation between the molecular similarity and the agreement with the correct and 3D correspondences; more similar molecule pairs are more correctly matched. Molecular pairs with more than 40% Tanimoto similarities can be correctly matched for more than half of the atoms with the 3D correspondences.

Currently the protein mutant database (PMD) contains over 81 000 mutants, including artificial as well as natural mutants of various proteins extracted from about 10 000 articles. We recently developed a powerful viewing and retrieving system (http://pmd.ddbj.nig.ac.jp), which is integrated with the sequence and tertiary structure databases. The system has the following features: (i) mutated sequences are displayed after being automatically generated from the information described in the entry together with the sequence data of wild-type proteins integrated. This is a convenient feature because it allows one to see the position of altered amino acids (shown in a different color) in the entire sequence of a wild-type protein; (ii) for those proteins whose 3D structures have been experimentally determined, a 3D structure is displayed to show mutation sites in a different color; (iii) a sequence homology search against PMD can be carried out with any query sequence; (iv) a summary of mutations of homologous sequences can be displayed, which shows all the mutations at a certain site of a protein, recorded throughout the PMD.

Recently, electron microscopy measurement of single particles has enabled us to reconstruct a low-resolution 3D density map of large biomolecular complexes. If structures of the complex subunits can be solved by x-ray crystallography at atomic resolution, fitting these models into the 3D density map can generate an atomic resolution model of the entire large complex. The fitting of multiple subunits, however, generally requires large computational costs; therefore, development of an efficient algorithm is required. We developed a fast fitting program, “gmfit”, which employs a Gaussian mixture model (GMM) to represent approximated shapes of the 3D density map and the atomic models. A GMM is a distribution function composed by adding together several 3D Gaussian density functions. Because our model analytically provides an integral of a product of two distribution functions, it enables us to quickly calculate the fitness of the density map and the atomic models. Using the integral, two types of potential energy function are introduced: the attraction potential energy between a 3D density map and each subunit, and the repulsion potential energy between subunits. The restraint energy for symmetry is also employed to build symmetrical origomeric complexes. To find the optimal configuration of subunits, we randomly generated initial configurations of subunit models, and performed a steepest-descent method using forces and torques of the three potential energies. Comparison between an original density map and its GMM showed that the required number of Gaussian distribution functions for a given accuracy depended on both resolution and molecular size. We then performed test fitting calculations for simulated low-resolution density maps of atomic models of homodimer, trimer, and hexamer, using different search parameters. The results indicated that our method was able to rebuild atomic models of a complex even for maps of 30 Å resolution if sufficient numbers (eight or more) of Gaussian distribution functions were employed for each subunit, and the symmetric restraints were assigned for complexes with more than three subunits. As a more realistic test, we tried to build an atomic model of the GroEL/ES complex by fitting 21-subunit atomic models into the 3D density map obtained by cryoelectron microscopy using the C7 symmetric restraints. A model with low root mean-square deviations (14.7 Å) was obtained as the lowest-energy model, showing that our fitting method was reasonably accurate. Inclusion of other restraints from biological and biochemical experiments could further enhance the accuracy.