An account of in silico identification tools of secreted effector proteins in bacteria and future challenges.

Bacterial pathogens secrete numerous effector proteins via six secretion systems, type I to type VI secretion systems, to adapt to new environments or to promote virulence by bacterium-host interactions. Many computational approaches have been used in the identification of effector proteins before the subsequent experimental verification because they tolerate laborious biological procedures and are genome scale, automated and highly efficient. Prevalent examples include machine learning methods and statistical techniques. In this article, we summarize the computational progress toward predicting secreted effector proteins in bacteria, with an opening of an introduction of features that are used to discriminate effectors from non-effectors. The mechanism, contribution and deficiency of previous developed detection tools are presented, which are further benchmarked based on a curated testing data set. According to the results of benchmarking, potential improvements of the prediction performance are discussed, which include (1) more informative features for discriminating the effectors from non-effectors; (2) the construction of comprehensive training data set of the machine learning algorithms; (3) the advancement of reliable prediction methods and (4) a better interpretation of the mechanisms behind the molecular processes. The future of in silico identification of bacterial secreted effectors includes both opportunities and challenges.

Modulation, bioinformatic screening, and assessment of small molecular peptides targeting the vascular endothelial growth factor receptor.

Vascular endothelial growth factor (VEGF) and VEGF receptor (VEGFR) are important factors in tumor growth and metastasis. Molecular probes or drugs designed to target VEGF/VEGFR interactions are crucial in tumor molecular imaging and targeted therapy. Bioinformatic methods enable molecular design based on the structure of bio-macromolecules and their interactions. This study was aimed to identify tumor-targeting small-molecule peptides with high affinity for VEGFR using bioinformatics screening. The VEGFR extracellular immunoglobulin-like modules Ig1-Ig3 were used as the target to systematically alter the primary peptide sequence of VEGF125-136. Molecular docking and surface functional group interaction methods were combined in an in silico screen for polypeptides, which in theory, would have higher affinities for VEGFR. In vitro receptor competition binding assays were used to assess the affinity of the putative VEGFR-binding polypeptides. Rhodamine-conjugated peptides were used to label and visualize peptide-binding sites on A549 cells. Using bioinformatic screening, we identified 20 polypeptides with potentially higher affinity for VEGFR. The polypeptides were capable of inhibiting the binding of (125)I-VEGF to VEGFR in a dose-dependent manner. The IC50 values of QKRKRKKSRKKH and RKRKRKKSRYIVLS (80 and 185 nmol/L, respectively) were significantly lower than that of VEGF125-136 (464 nmol/L); thus, the affinity of these peptides for VEGFR was 6- and 2.5-fold higher, respectively, than that of VEGF125-136. Rhodamine labeling of A549 cells revealed peptide binding mainly on the plasma membrane and in the cytoplasm. Bioinformatic approaches hold promise for the development of molecular imaging probes. Using this approach, we designed two peptides that showed higher affinity toward VEGFR. These polypeptides may be used as molecular probes or drugs targeting VEGFR, which can be utilized in molecular imaging and targeted therapy of certain tumors.

Combined prediction of transmembrane topology and signal peptide of beta-barrel proteins: using a hidden Markov model and genetic algorithms.

BACKGROUND: Hidden Markov models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. The design of the model architecture and the algorithms for parameter estimation and decoding are extremely important for improve the performance of HMM. In topology prediction of transmembrane beta-barrels proteins (TMBs), the Baum-Welch algorithm is widely adapted for HMM training but usually leads to a sub-optimal model in practice. In addition, all the existing HMM-based predictors are only designed to model the transmembrane segment without a submodel to model the signal peptide (SP) for full-length sequences. It is not convenient for users to investigate the structures of full-length TMB sequences. RESULTS: We present here, an HMM that combine a transmembrane barrel submodel and an SP submodel for both topology and SP predictions. A new genetic algorithm (GA) is presented here to training the model, at the same time the Posterior-Viterbi algorithm is adopted for decoding. A dataset including 33 TMBs that is the most so far in literature are collected for model training and testing. Results of self-consistency and jackknife tests shows the GA has better global performance than the Baum-Welch algorithm. Results of jackknife tests show that this method performs better than all well known existing methods for topology predictions. Furthermore, it provides a function to predict SP in full-length TMBs sequences with fairish accuracy. CONCLUSION: We show that our combined HMM-based method is a better choice for TMB topology prediction, which implements topology predictions with higher accuracy and additional SP predictions for full-length TMB sequences.

Prediction of subcellular localization of eukaryotic proteins using position-specific profiles and neural network with weighted inputs.

Subcellular location is one of the key biological characteristics of proteins. Position-specific profiles (PSP) have been introduced as important characteristics of proteins in this article. In this study, to obtain position-specific profiles, the Position Specific Iterative-Basic Local Alignment Search Tool (PSI-BLAST) has been used to search for protein sequences in a database. Position-specific scoring matrices are extracted from the profiles as one class of characteristics. Four-part amino acid compositions and 1st-7th order dipeptide compositions have also been calculated as the other two classes of characteristics. Therefore, twelve characteristic vectors are extracted from each of the protein sequences. Next, the characteristic vectors are weighed by a simple weighing function and inputted into a BP neural network predictor named PSP-Weighted Neural Network (PSP-WNN). The Levenberg-Marquardt algorithm is employed to adjust the weight matrices and thresholds during the network training instead of the error back propagation algorithm. With a jackknife test on the RH2427 dataset, PSP-WNN has achieved a higher overall prediction accuracy of 88.4% rather than the prediction results by the general BP neural network, Markov model, and fuzzy k-nearest neighbors algorithm on this dataset. In addition, the prediction performance of PSP-WNN has been evaluated with a five-fold cross validation test on the PK7579 dataset and the prediction results have been consistently better than those of the previous method on the basis of several support vector machines, using compositions of both amino acids and amino acid pairs. These results indicate that PSP-WNN is a powerful tool for subcellular localization prediction. At the end of the article, influences on prediction accuracy using different weighting proportions among three characteristic vector categories have been discussed. An appropriate proportion is considered by increasing the prediction accuracy.