HashGO: hashing gene ontology for protein function prediction

Gene ontology (GO) is a standardized and controlled vocabulary of terms that describe the molecular functions, biological roles and cellular locations of proteins. GO terms and GO hierarchy are regularly updated as the accumulated biological knowledge. More than 50,000 terms are included in GO and each protein is annotated with several or dozens of these terms. Therefore, accurately predicting the association between proteins and massive GO terms is rather challenging. To accurately predict the association between massive GO terms and proteins, we proposed a method called Hashing GO for protein function prediction (HashGO in short). HashGO firstly adopts a protein-term association matrix to store available GO annotations of proteins. Then, it tailors a graph hashing method to explore the underlying structure between GO terms and to obtain a series of hash functions to compress the high-dimensional protein-term association matrix into a low-dimensional one. Next, HashGO computes the semantic similarity between proteins based on Hamming distance on that low-dimensional matrix. After that, it predicts missing annotations of a protein based on the annotations of its semantic neighbors. Experimental results on archived GO annotations of two model species (Yeast and Human) show that HashGO not only more accurately predicts functions than other related approaches, but also runs faster than them.     

Landscape of ROD9 Island: Functional annotations and biological network of hypothetical proteins in Salmonella enterica

Salmonella, an Enterobacteria is a therapeutically important pathogen for the host. The advancement of genome sequencing of S. enterica serovar Enteritidis have identified a distinct ROD9 pathogenic island, imparting virulence. The occurrence of 17 ROD9 hypothetical proteins, necessitates subsequent bioinformatics approach for structural and functional aspects of protein-protein relations or networks in different pathogenic phenotypes express. A collective analysis using predictive bioinformatics tools that includes NCBI-BLASTp and BLAST2GO annotated the motif patterns and functional significance. The VFDB identified 10 virulence proteins at both genomic and metagenomic level. Phylogenetic analysis revealed a divergent and convergent relationship between 17 ROD9 and 41 SP-1 proteins. Here, combining a comprehensive approach from sequence based, motif recognitions, domain identification, virulence ability to structural modelling provides a precise function to ROD9 proteins biological network, for which no experimental information is available.     

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.     

ELECT++: Faster conformational search method for docking flexible molecules using molecular similarity

We have developed a program, ELECT++ (Effective LEssening of Conformations by Template molecules in C++), to speed up the conformational search for small flexible molecules using the similar property principle. We apply this principle to molecular shape and, importantly, to molecular flexibility. After molecules in a database are clustered according to flexibility and shape (FCLUST++), additional reagents are generated to screen the conformational space of molecules in each cluster (TEMPLATE++). We call these representative reagents of each cluster template reagents. Template reagents and clustered reagents produce, after reaction, template molecules and clustered molecules, respectively (tREACT++). The conformations of a template molecule are searched in the context of a macromolecular target. Acceptable conformational choices are then applied to all molecules in its cluster, thus effectively biasing conformational space to speed up conformational searches (tSEARCH++). In our incremental search method, it is necessary to calculate the root-mean-square deviations (RMSD) matrix of distances between different conformations of the same molecule to reduce the number of conformations. Instead of calculating the RMSD matrix for all molecules in a cluster, the RMSD matrix of a template molecule is chosen as a reference and applied to all the molecules in its cluster. We demonstrate that FCLUST++ clusters the primary amine reagents from the Available Chemicals Directory (ACD) successfully. The program tSEARCH++ was applied to dihydrofolate reductase with virtual molecules generated by tREACT++ using clustered primary amine reagents. The conformational search by the program tSEARCH++ was about 4.8 times faster than by SEARCH++, with an acceptable range of errors. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1834–1852, 1998     

On the HKUST-1/GO and HKUST-1/rGO Composites: The Impact of Synthesis Method on Physicochemical Properties

The chemical stability and adsorptive/catalytic properties of the most widely studied metal–organic framework (MOF), which is HKUST-1, can be improved by its combination with graphene oxide (GO) or reduced graphene oxide (rGO). The chemistry of GO or rGO surfaces has a significant impact on their interaction with MOFs. In this work, we demonstrate that GO and rGO interaction with HKUST-1 influences the morphology and textural properties but has no impact on the thermal stability of the final composites. We also show that synthesis environment, e.g., stirring, to some extent influences the formation of HKUST-1/GO and HKUST-1/rGO composites. Homogeneous samples of the sandwich-type composite can be obtained when using reduced graphene oxide decorated with copper (Cu/rGO), which, owing to the presence of Cu sites, allows the direct crystallisation of HKUST-1 and its further growth on the graphene surface. This work is the first part of our research on HKUST-1/GO and HKUST-1/rGO and deals with the influence of the type of graphene material and synthesis parameters on the composites’ physicochemical properties that were determined by using X-ray diffraction, scanning and transmission electron microscopy, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and thermogravimetric analysis.     

Quantum similarity measures under atomic shell approximation: First order density fitting using elementary Jacobi rotations

The elementary Jacobi rotations technique is proposed as a useful tool to obtain fitted electronic density functions expressed as linear combinations of atomic spherical shells, with the additional constraint that all coefficients are kept positive. Moreover, a Newton algorithm has been implemented to optimize atomic shell exponents, minimizing the quadratic error integral function between ab initio and fitted electronic density functions. Although the procedure is completely general, as an application example both techniques have been used to compute a 1S-type Gaussian basis for atoms H through Kr, fitted from a 3-21G basis set. Subsequently, molecular electronic densities are modeled in a promolecular approximation, as a simple sum of parameterized atomic contributions. This simple molecular approximation has been employed to show, in practice, its usefulness to some computational examples in the field of molecular quantum similarity measures. © 1997 John Wiley & Sons, Inc. J Comput Chem 18 : 2023–2039, 1997     

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

Structure‐based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this “ab initio” approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT‐Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein structure alignment program SPalign. The method was found to yield a consistent performance in DUD and DUD‐E docking benchmarks even if model structures were employed. It improves over docking methods (DOCK6 and AUTODOCK Vina) and has a performance comparable to or better than other binding‐homology methods (FINDsite and PoLi) with higher computational efficiency. The server is available at http://sparks-lab.org . © 2016 Wiley Periodicals, Inc.     

利用样本成分耗散物的非线性化学指纹图谱原理及相似度计算与评价

在恒温恒压条件下,以丙酮和样本中底物作为主要耗散物的不同成分的样本对非线性化学反应机理产生不同影响,从而引起反应体系电位-时间曲线形状不同变化为特征的B-Z化学振荡体系为例,就非线性化学指纹图谱原理进行了详细研究和讨论,并提出了计算非线性化学指纹图谱系统相似度的通用方法.利用系统相似度和欧氏距离、相关系数及夹角余弦对不同生产批次古汉养生精和18种其他样本的非线性化学指纹图谱的相似度进行了计算与分析.结果表明,相关系数和夹角余弦都不能用来作为评价非线性化指纹图谱相似度的指标.利用欧氏距离公式计算指纹图谱的非参数型相似度时,能正确反映指纹图谱的特征差异,但用其计算参数型相似度时,则有时不能正确反映样本非线性化学指纹图谱特征差异的相对程度.系统相似度能最真实反映样本指纹图谱之间差异程度,是4种相似度计算方法中最好的,可用于非线性化学指纹图谱相似度计算与评价.成功提出了一种经济、简便、易行和有效的鉴别样本真伪与评价其质量的科学方法.     

Principle of nonlinear chemical fingerprint by using dissipative components in samples as well as calculation and evaluation of similarity

Under the conditions of constant temperature and pressure,different influences of samples with different chemical components on the mechanism of nonlinear chemical reaction will cause different changes of the potential-time relationship curve of the nonlinear chemical reaction system.Using it as the character,and using the B-Z nonlinear chemical system to use acetone and substrates in samples as main dissipative substances qua an example,the principle of nonlinear chemical fingerprint has been researched and discussed in detail.At the same time,the general method for calculating the system similarity about nonlinear chemical fingerprint was also put forward,and similarities of nonlinear chemistry fingerprints of different batches of Guhan Yangshengjing and 18 sorts of other samples were calculated by Euclidean distance,correlation coefficient,included angle cosine and system similarity,at the same time,the various similarities were analyzed.The results showed that,both of correlation coefficient and included angle cosine are unable to be used as the criterion for quantitatively evaluating the similarity of nonlinear chemistry fingerprint;as non-parametric similarity,Euclidean distance can accurately reflect the feature differences in the fingerprints,but as parametric similarity,sometimes,Euclidean distance can not accurately reflect the relative extent of characteristic difference in the nonlinear chemical fingerprints;system similarity can most truthfully reflect the characteristic difference in the nonlinear chemical fingerprints,and is the best evaluating method among the four ones.Therefore,system similarity can be used to quantitatively calculate the similar extent between the nonlinear chemical fingerprints.An economical,simple and convenient,easy pushing and effective method for identifying and evaluating complicated samples has successfully been put forward.     

Dynamical similarity in the highly excited vibrations of HCP and DCP: The dynamical potential approach

The fixed points in the dynamical potentials of phosphaethyne (HCP) and deuterated phosphaethyne (DCP) derived in the coset space are identified and shown to govern the various quantal environments in which the vibrational states lie. The state dynamics is interpreted and classified by the classical actions and action integrals. This is closely related to the fixed point structure. Localized modes even at high excitation are identified. Most important is that the dynamical similarity between these two systems is identified which enables us to understand the DCP dynamics simply from that of HCP without repeated elaboration.     

eSHAFTS: Integrated and graphical drug design software based on 3D molecular similarity

With the development of computer technology, computer-aided drug design (CADD) has become an important means for drug research and development, and its representative method is virtual screening. Various virtual screening platforms have emerged in an endless stream and play great roles in the field of drug discovery. With the increasing number of compound molecules, virtual screening platforms face two challenges: low fluency and low visibility of software operations. In this article, we present an integrated and graphical drug design software eSHAFTS based on three-dimensional (3D) molecular similarity to overcome these shortcomings. Compared with other software, eSHAFTS has four main advantages, which are integrated molecular editing and drawing, interactive loading and analysis of proteins, multithread and multimode 3D molecular similarity calculation, and multidimensional information visualization. Experiments have verified its convenience, usability, and reliability. By using eSHAFTS, various tasks can be submitted and visualized in one desktop software without locally installing any dependent plug-ins or software. The software installation package can be downloaded for free at http://lilab.ecust.edu.cn/home/resource.html . © 2019 Wiley Periodicals, Inc.     

Communications on quantum similarity (2): A geometric discussion on holographic electron density theorem and confined quantum similarity measures

The so‐called holographic electron density theorem (HEDT) is analyzed from an algebraic perspective, and a brief analytical point of view is also given. The connection of the HEDT with quantum similarity measures (QSM) over electronic density functions (DF) is studied using GTO functions, atomic ASA DF, and promolecular ASA DF. Restricted integration of QSM over a box of finite side length is discussed for all this DF. This work emphasizes the geometric aspects of HEDT, but for the sake of completeness, some analytical insight based on a general Taylor series expansion is also given at the end. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

DeepAdd: Protein function prediction from k-mer embedding and additional features

With the application of new high throughput sequencing technology, a large number of protein sequences is becoming available. Determination of the functional characteristics of these proteins by experiments is an expensive endeavor that requires a lot of time. Furthermore, at the organismal level, such kind of experimental functional analyses can be conducted only for a very few selected model organisms. Computational function prediction methods can be used to fill this gap. The functions of proteins are classified by Gene Ontology (GO), which contains more than 40,000 classifications in three domains, Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). Additionally, since proteins have many functions, function prediction represents a multi-label and multi-class problem. We developed a new method to predict protein function from sequence. To this end, natural language model was used to generate word embedding of sequence and learn features from it by deep learning, and additional features to locate every protein. Our method uses the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and have noticeable improvement over several algorithms, such as FFPred, DeepGO, GoFDR and other methods compared on the CAFA3 datasets.     

Quantum fidelity for analyzing atoms and fragments in molecule: Application to similarity,chirality, and aromaticity

Quantum information theory is applied to formulate a new technique for dealing with molecular similarity problems. In this technique, the so‐called quantum fidelity appears to be a counterpart of the conventional similarity measure due to Carbo (Carbo, R.; Leyda, L.; Arnau, M. Int J Quantum Chem 1980, 17, 1185). We define many‐body spin‐free density matrices for atoms and fragments in molecule, and compute corresponding fidelity measures for molecular subsystems. It allows us to treat the problem from the beginning within a many‐electron setting. The approach is employed for analyzing similarity between free atoms and atoms in molecule. A new chirality index, as based on the fidelity between molecule and its mirror image, is suggested to be an approximately additive nonnegative quantity. We also examine a local aromaticity by computing the fidelity measures for benzenoid fragments in polyaromatic hydrocarbons. A detailed study of the proposed indices is reported at the ab initio or semiempirical levels. © 2010 Wiley Periodicals, Inc. Int J Quantum Chem, 2011     

In Vitro and In Vivo Human Body Odor Analysis Method Using GO:PANI/ZNRs/ZIF−8 Adsorbent Followed by GC/MS

Among various volatile organic compounds (VOCs) emitted from human skin, trans-2-nonenal, benzothiazole, hexyl salicylate, α-hexyl cinnamaldehyde, and isopropyl palmitate are key indicators associated with the degrees of aging. In our study, extraction and determination methods of human body odor are newly developed using headspace-in needle microextraction (HS-INME). The adsorbent was synthesized with graphene oxide:polyaniline/zinc nanorods/zeolitic imidazolate framework-8 (GO:PANI/ZNRs/ZIF−8). Then, a wire coated with the adsorbent was placed into the adsorption kit to be directly exposed to human skin as in vivo sampling and inserted into the needle so that it was able to be desorbed at the GC injector. The adsorption kit was made in-house with a 3D printer. For the in vitro method, the wire coated with the adsorbent was inserted into the needle and exposed to the headspace of the vial. When a cotton T-shirt containing body odor was transferred to a vial, the headspace of the vial was saturated with body odor VOCs. After volatile organic compounds were adsorbed in the dynamic mode, the needle was transferred to the injector for analysis of the volatile organic compounds by gas chromatography/mass spectrometry (GC/MS). The conditions of adsorbent fabrication and extraction for body odor compounds were optimized by response surface methodology (RSM). In conclusion, it was able to synthesize GO:PANI/ZNRs/ZIF−8 at the optimal condition and applicable to both in vivo and in vitro methods for body odor VOCs analysis.     

Multistep electrochemical reactions involving transport of intermediates between the near-electrode layer and the bulk solution: A kinetics analysis based on theory of generalized variables (theory of similarity)

A kinetic analysis of multistep electrochemical reactions involving partial diffusion transport of intermediates between the near-electrode layer and the bulk electrolyte is performed within theory of generalized variables (theory of similarity). Dimensionless complexes (similarity criteria) that characterize such reactions are obtained, and their values corresponding to the transport of intermediates are determined.Translated from Elektrokhimiya, Vol. 41, No. 1, 2005, pp. 121–125.Original Russian Text Copyright © 2005 by Protsenko, Danilov.