The successful prediction of protein subcellular localization directly from protein primary sequence is useful to protein function prediction and drug discovery. In this paper, by using the concept of pseudo amino acid composition (PseAAC), the mycobacterial proteins are studied and predicted by support vector machine (SVM) and increment of diversity combined with modified Mahalanobis Discriminant (IDQD). The results of jackknife cross-validation for 450 nonredundant proteins show that the overall predicted successful rates of SVM and IDQD are 82.2% and 79.1%, respectively. Compared with other existing methods, SVM combined with PseAAC display higher accuracies.

Antigen epitopes provide valuable information useful for disease prevention, diagnosis, and treatment. Recently, more and more databases focusing on different types of epitopes have become available. Conformational epitopes are an important form of epitope formed by residues that are sequentially discontinuous but close together in three-dimensional space. These epitopes have implicit structural information, making them attractive for both theoretical and applied biomedical research. However, most existing databases focus on linear rather than conformational epitopes. We describe CED, a special database of well defined conformational epitopes. CED provides a collection of conformational epitopes and related information including the residue make up and location of the epitope, the immunological property of the epitope, the source antigen and corresponding antibody of the epitope. All entries in this database are manually curated from articles published in peer review journals. The database can be browsed or searched through a user-friendly web interface. Most epitopes in CED can also be viewed interactively in the context of their 3D structures. In addition, the entries are also hyperlinked to various databases such as Swiss-Prot, PDB, KEGG and PubMed, providing wide background information. A conformational epitope database called CED has been developed as an information resource for investigators involved in both theoretical and applied immunology research. It complements other existing specialised epitope databases. The database is freely available at

Specific noncovalent interactions that are indicative of attractive, directional intermolecular forces have always been of key interest to medicinal chemists in their search for the “glue” that holds drugs and their targets together. With the rapid increase in the number of solved biomolecular structures as well as the performance enhancement of computer hardware and software in recent years, it is now possible to give more comprehensive insight into the geometrical characteristics and energetic landscape of certain sophisticated noncovalent interactions present at the binding interface of protein receptors and small ligands based on accumulated knowledge gaining from the combination of two quite disparate but complementary approaches: crystallographic data analysis and quantum-mechanical ab initio calculation. In this perspective, we survey massive body of published works relating to structural characterization and theoretical investigation of three kinds of strong, specific, direct, enthalpy-driven intermolecular forces, including hydrogen bond, halogen bond and salt bridge, involved in the formation of protein-ligand complex architecture in order to characterize their biological functions in conferring affinity and specificity for ligand recognition by host protein. In particular, the biomedical implications of raised knowledge are discussed with respect to potential applications in rational drug design.

Background: The location of proteins in a cell can provide important clues to their functions in various biological processes. Thus, the application of machine learning method in the prediction of protein subcellular localization has become a hotspot in bioinformatics. As one of key organelles, the Golgi apparatus is in charge of protein storage, package, and distribution. Objective: The identification of protein location in Golgi apparatus will provide in-depth insights into their functions. Thus, the machine learning-based method of predicting protein location in Golgi apparatus has been extensively explored. The development of protein sub-Golgi apparatus localization prediction should be reviewed for providing a whole background for the fields. Method: The benchmark dataset, feature extraction, machine learning method and published results were summarized. Results: We briefly introduced the recent progresses in protein sub-Golgi apparatus localization prediction using machine learning methods and discussed their advantages and disadvantages. Conclusion: We pointed out the perspective of machine learning methods in protein sub-Golgi localization prediction.

Self-binding peptides (SBPs) represent a novel biomolecular phenomenon spanning between folding and binding, where a short peptide segment within a monomeric protein fulfills biological functions by dynamically binding to/unbinding from its target domain in the same monomer. Here, we were able to quantitatively reconstruct the complete structural dynamics picture of binding of free SBPs to their target domains for five representative SBP systems by carrying out the state-of-the-art molecular dynamics (MD) simulations. In the picture, a two-step binding mechanism for SBP–domain recognition and association was proposed, which includes a fast, nonspecific diffusive phase and a slow, specific organizational phase. The electrostatic interactions and desolvation effects play a predominant role in the first phase that leads to the formation of a metastable encounter complex, while conformational rearrangement is observed in the second phase to optimize the exquisite network of nonbonded chemical forces such as hydrogen bonds and salt bridges across the complex interface. From an energetic point of view, a funnel-shape enthalpy landscape steers these SBPs towards their native bound state and thus facilitates the binding process. However, the binding exhibits typical enthalpy–entropy compensation due to the high flexibility of peptides that results in a relatively low affinity for SBP–domain binding and forces the SBP systems to rapidly switch between the bound and unbound states. In addition, slight conformational changes in the target domain and/or in the polypeptide linker between the domain and peptide can significantly affect the energetic properties and dynamic behavior of the fine-tuned binding process of SBP–domain recognition.