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Nucleic acid (NA)–ligand interactions have crucial roles in many cellular processes and, thus, are increasingly attracting therapeutic interest in drug discovery. Molecular docking is a valuable tool for studying molecular interactions. However, because NAs differ significantly from proteins in both their physical and chemical properties, traditional docking algorithms and scoring functions for protein–ligand interactions might not be applicable to NA–ligand docking. Therefore, various sampling strategies and scoring functions for NA–ligand interactions have been developed. Here, we review the basic principles and current status of docking algorithms and scoring functions for DNA/RNA–ligand interactions. We also discuss challenges and limitations of current docking and scoring approaches.

Protein-protein docking is an important computational tool for studying protein-protein interactions. A variety of docking programs with different sampling algorithms and scoring functions as well as computational efficiencies have been subsequently developed over the last decades. Here, we have reviewed the trend and performance of current global docking programs through a comprehensive assessment of the 18 docking/scoring protocols of 14 global docking programs on the latest protein docking benchmark 4.0. The effects of docking algorithms, interaction types, and conformational changes on the docking performance were investigated and discussed. The findings are expected to provide a general guideline for the choice of an appropriate docking protocol and offer insights into the optimization and development of docking and scoring algorithms.

Inverse docking is a relatively new technique that has been used to identify potential receptor targets of small molecules. Our docking software package MDock is well suited for such an application as it is both computationally efficient, yet simultaneously shows adequate results in binding affinity predictions and enrichment tests. As a validation study, we present the first stage results of an inverse-docking study which seeks to identify potential direct targets of PRIMA-1. PRIMA-1 is well known for its ability to restore mutant p53's tumor suppressor function, leading to apoptosis in several types of cancer cells. For this reason, we believe that potential direct targets of PRIMA-1 identified in silico should be experimentally screened for their ability to inhibit cancer cell growth. The highest-ranked human protein of our PRIMA-1 docking results is oxidosqualene cyclase (OSC), which is part of the cholesterol synthetic pathway. The results of two followup experiments which treat OSC as a possible anti-cancer target are promising. We show that both PRIMA-1 and Ro 48-8071, a known potent OSC inhibitor, significantly reduce the viability of BT-474 and T47-D breast cancer cells relative to normal mammary cells. In addition, like PRIMA-1, we find that Ro 48-8071 results in increased binding of p53 to DNA in BT-474 cells (which express mutant p53). For the first time, Ro 48-8071 is shown as a potent agent in killing human breast cancer cells. The potential of OSC as a new target for developing anticancer therapies is worth further investigation.

TiO 2 nanotube arrays doped by Sn up to 12 at% have been prepared using template-based liquid phase deposition method. Their morphologies, structures and optical properties have been investigated by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, UV–vis absorption spectroscopy and photoluminescence spectroscopy. The photocatalytic properties of the samples were evaluated with the degradation of methylene blue under UV irradiation. The result shows that doping an appropriate amount of Sn can effectively improve the photocatalytic activity of TiO 2 nanotube arrays, and the optimum dopant amount is found to be 5.6 at% in our experiments.

An increasing attention has been dedicated to the characterization of complex networks within the protein world. Before now most investigations about protein structures were only considered where the interactive cutoff distance R c =5 or 7 A. It is noteworthy that the length of peptide bond is about 1.5 A, the length of hydrogen bond is about 3 A, the range of London-van der Waals force is about 5 A and the range of hydrophobic effect can reach to 12 A in protein molecule. Present work reports a study on the topological properties of the amino acid network constructed by different interactions above. The results indicate that the small-world property of amino acid network constructed by the peptide and hydrogen bond, London-van der Waals force and the hydrophobic effect is strong, very strong and relatively weak, respectively. Besides, there exists a precise exponential relation C ∝ k −0.5 at R c =12 A. It means that the amino acid network constructed by the hydrophobic effect tend to be hierarchical. Functional modules could be the cause for hierarchical modularity architecture in protein structures. This study on amino acid interactive network for different interactions facilitates the identification of binding sites which is strongly linked with protein function, and furthermore provides reasonable understanding of the underlying laws of evolution in genomics and proteomics.

The cobweb-like network was constructed, and its structure properties and synchronizability were studied by numerical simulation and probability theory. For cobweb-like networks, the behaviors of structure properties with the adding probability are similar to those for the small-world network. However, the properties of cobweb-like networks also distinctly depend on the spoke–ring ratio r . With increasing r , the average path length decreases, and the average clustering coefficient increases. Simultaneously, the connectivity distribution becomes heterogeneous. Moreover, in the case of bounded synchronized region, the synchronizability shows complicated behaviors with r , and is enhanced favorably at r = 2.28 with homogeneous load distribution. The reason is that homogeneous load distribution has an advantage over the communication between oscillators, which efficiently leads to global synchronization. This work could be useful for design and kinetic property research of cobweb-like networks.

The preparation of Zn deposits has been performed by thin-layer electrodeposition in acidic zinc sulphate solution with and without agar, respectively. The morphological and structural characteristics of the deposits have been investigated by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The study shows that the presence of agar has a significant influence on the morphology of the Zn deposits. Under the chosen conditions and without agar, the deposit shows a dendritic morphology, which is made of orderly arranged grains with a preferred orientation in [0001]. In the presence of agar, the deposit shows a dense branch morphology, which has a randomly oriented grain texture and diminished grain size in comparison with that of the dendritic morphology. This work contributes to understanding the influence of agar gel on the pattern formation in thin-layer electrodeposition.

Prediction of protein domain structural classes is an important topic in protein science. In this paper, we proposed a new conception: structural class tendency of polypeptides (SCTP), which is based on the fact that a given amino acid fragment tends to be presented in certain type of proteins. The SCTP is obtained from an available training data set PDB40-B. When using the SCTP to predict protein structural classes by Intimate Sorting predictive method, we got the predictive accuracy (jack knife test) with 93.7%, 96.5%, and 78.6% for the testing data set PDB40-j, Chou&Maggiora and CHOU. These results indicate that the SCTP approach is quite encouraging and promising. This new conception provides an effective tool to extract valuable information from protein sequences. (c) 2007 Elsevier B.V. All rights reserved.

Deposition, diffusion, and aggregation (DDA) on a two-dimensional small-world network have been investigated by computer simulations. This model is characterized by two parameters: the clustering exponent α and long-range connection rate ϕ . The results show that as α and ϕ vary there exists a continuous crossover in the fractal dimension D f from 1.65 to 2, which corresponds to the crossover from the DDA pattern to dense one. The change of the aggregation pattern results from the long-range connection in the network, which reduces the effect of screening during the aggregation. With primitive analysis, we obtain the expressions of the fractal dimension D f and the crossover point α c . These results may be useful to describe the vapor deposition, molecular-beam epitaxy, and similar experiments on defective substrates.

Using NVT molecular dynamics method, we investigated the critical scaling and universality in the short-time dynamics for the two-dimensional vapor–liquid fluid system by the evolution of the potential energy. Our results for the NVT ensemble confirmed that there exist universal scaling behaviors already in the short-time regime. The critical exponents θ , z and β/ν were determined from the short-time dynamic scaling relations. It showed that the static critical exponent β/ν obtained from the present short-time dynamics is consistent with that of the two-dimensional Ising model.  2002 Elsevier Science B.V. All rights reserved.

Particle–cluster aggregation on nonuniform substrates was investigated by Monte Carlo simulations. The nonuniform substrates are represented by percolations with occupied probability p. Elastic repulsive interactions between particles through the substrates have been involved. It was found, for uniform substrates (p∼1), the aggregates change from diffusion-limited aggregates to thick net-like branched structures with the increase of repulsive interaction. As p decreases, the effect of repulsive interaction on aggregates lessens gradually. When p is close to the critical value pc, the cluster changes very little for variant repulsive interactions.

A particle–cluster aggregation model is presented, in which particle motion is controlled by randomness and directive correlation. The directive correlation means that the walking direction of the particle at next step relates to the moving direction at previous step. The influence of competition between randomness and directive correlation of particle motion on aggregation is investigated by Monte Carlo simulations.

We investigate the influence of long-range interactions on the energy localization in a one-dimensional Morse chain with an impurity by molecular dynamics simulations. The energy distribution around the impurity is studied. It shows that in the high temperature limit there is no difference upon the potential energy localization whether the interaction includes a long-range one or not; but at low and intermediate temperatures, the difference is obvious. When the long-range interaction is taken into account, in the low temperature limit the potential energy localization is limited to the impurity and its next-nearest-neighbor particles, and the localization does not occur at its nearest-neighbor sites; in the intermediate temperature range, the potential energy localization takes place at the impurity and its nearest-neighbor sites, as well as the next-nearest-neighbor sites. The average potential energies 〈 u i 〉 and 〈 u 〉 of the impurity and host particles, respectively, are also investigated. The results show that corresponding to the soft (hard) impurity, at low temperatures, 〈 u i 〉 is lower (higher) than 〈 u 〉 and the softer (harder) impurity leads to lower (higher) 〈 u i 〉; however, at high temperatures, 〈 u i 〉 is higher (lower) than 〈 u 〉 and increases (decreases) with decreasing (increasing) stiffness of the impurity. The results are explained by the soliton fusion theory of Toda lattice.

By means of molecular dynamics simulations we have investigated local energy excitations of impurities with different types (stiffness, size or mass) solved in a one-dimensional lattice. The basic configuration studied is that an impurity particle is imbedded into a chain which consists of host particles interacting via a modified Morse potential. The results show that in thermal equilibrium the impurity may lead to local energy excitations at the impurity site. The stiffness and size impurities bring about a localization of the potential energy, while the mass impurity causes that of the kinetic energy. The variation of energies with the impurity parameters and temperature is obtained and explained by the soliton picture of a Toda lattice.

One of the challenges in protein structure prediction is the development of an accurate scoring function that yields a global minimum free energy for the native state. Although the knowledge-based scoring function has proven to be a successful scoring approach to protein structure prediction, there exists a hurdle in deriving knowledge-based potentials. Namely, the ideal reference state is inaccessible. In this work, we have developed a general physics-based iterative method to extract distance-dependent all-atom potentials for protein structure prediction. Our method circumvented the long-standing reference state problem. The derived scoring function was extensively evaluated with three diverse test sets, and showed significant improvement over other well-known scoring functions. The results suggest the efficacy of our scoring function for protein structure prediction.