Peisheng Cong

A novel structural position-specific scoring matrix for the prediction of protein secondary structures

MOTIVATION The precise prediction of protein secondary structure is of key importance for the prediction of 3D structure and biological function. Although the development of many excellent methods over the last few decades has allowed the achievement of prediction accuracies of up to 80%, progress seems to have reached a bottleneck, and further improvements in accuracy have proven difficult. RESULTS We propose for the first time a structural position-specific scoring matrix (SPSSM), and establish an unprecedented database of 9 million sequences and their SPSSMs. This database, when combined with a purpose-designed BLAST tool, provides a novel prediction tool: SPSSMPred. When the SPSSMPred was validated on a large dataset (10,814 entries), the Q3 accuracy of the protein secondary structure prediction was 93.4%. Our approach was tested on the two latest EVA sets; accuracies of 82.7 and 82.0% were achieved, far higher than can be achieved using other predictors. For further evaluation, we tested our approach on newly determined sequences (141 entries), and obtained an accuracy of 89.6%. For a set of low-homology proteins (40 entries), the SPSSMPred still achieved a Q3 value of 84.6%. AVAILABILITY The SPSSMPred server is available at http://cal.tongji.edu.cn/SPSSMPred/ CONTACT lith@tongji.edu.cn

Preliminary study on classification of rice and detection of paraffin in the adulterated samples by Raman spectroscopy combined with multivariate analysis.

Rice has played an important role in staple food supply of over approximately one-half of the world population. In this study, Raman spectroscopy and several multivariate data analysis methods were applied for discrimination of rice samples from different districts of China. A total of 42 samples were examined. It is shown that the representative Raman spectra in each group are different according to geographical origin after baseline correction to enhance spectral features. Moreover, adulteration of rice is a serious problem for consumers. In addition to the obvious effect on producer profits, adulteration can also cause severe health and safety problems. Paraffin was added to give the rice a desirable translucent appearance and increase its marketability. Detection of paraffin in the adulterated rice samples was preliminarily investigated as well. The results showed that Raman spectroscopy data with chemometric techniques can be applied to rapid detecting rice adulteration with paraffin.

DSP: a protein shape string and its profile prediction server.

Many studies have demonstrated that shape string is an extremely important structure representation, since it is more complete than the classical secondary structure. The shape string provides detailed information also in the regions denoted random coil. But few services are provided for systematic analysis of protein shape string. To fill this gap, we have developed an accurate shape string predictor based on two innovative technologies: a knowledge-driven sequence alignment and a sequence shape string profile method. The performance on blind test data demonstrates that the proposed method can be used for accurate prediction of protein shape string. The DSP server provides both predicted shape string and sequence shape string profile for each query sequence. Using this information, the users can compare protein structure or display protein evolution in shape string space. The DSP server is available at both http://cheminfo.tongji.edu.cn/dsp/ and its main mirror http://chemcenter.tongji.edu.cn/dsp/.

Combining nonlinear PLS with the numeric genetic algorithm for QSAR

Abstract In this paper, a new algorithm, the nonlinear PLS improved by the numeric genetic algorithm, called NPLSNGA, is applied to deal with nonlinear functions for inner relationship in QSAR. The NGA is used twice in NPLSNGA, once for nonlinear regression, and the other use is for nonlinear equations. Using the inner relationship of quadratic polynomial function, the fungicidal activity of a series of O -ethyl- N -isopropylphosphoro (thioureido) thioates was studied. The results are superior to the results of the reference. In QSAR of carboquinon derivatives and an anticarcinogenic drug for clinical media, the inner relation of sigmoid function was used. The results are equivalent to the results of ANN.

PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif

Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the highest accuracy (overall accuracy of 80.8%) of identification of plant protein SCLs as benchmarked by using a new test dataset compared other plant SCL prediction webservers. The ability of PlantLoc to predict multiple sites was also significantly higher than for any other webserver. The predicted substantiality motifs of queries also have great potential for analysis of relationships with protein functional regions. The PlantLoc server is available at http://cal.tongji.edu.cn/PlantLoc/.

Predicting gram-positive bacterial protein subcellular localization based on localization motifs.

The subcellular localization of proteins is closely related to their functions. In this work, we propose a novel approach based on localization motifs to improve the accuracy of predicting subcellular localization of Gram-positive bacterial proteins. Our approach performed well on a five-fold cross validation with an overall success rate of 89.5%. Besides, the overall success rate of an independent testing dataset was 97.7%. Moreover, our approach was tested using a new experimentally-determined set of Gram-positive bacteria proteins and achieved an overall success rate of 96.3%.

Accurate prediction of protein relative solvent accessibility using a balanced model

BackgroundProtein relative solvent accessibility provides insight into understanding protein structure and function. Prediction of protein relative solvent accessibility is often the first stage of predicting other protein properties. Recent predictors of relative solvent accessibility discriminate against exposed regions as compared with buried regions, resulting in higher prediction accuracy associated with buried regions relative to exposed regions.MethodsHere, we propose a more accurate and balanced predictor of protein relative solvent accessibility. First, we collected known proteins in three subsets according to sequence length and constructed a balanced dataset after reducing redundancy within each subset. Next, we measured the performance associated with different variables and variable combinations to determine the best variable combination. Finally, a predictor called BMRSA was constructed for modelling and prediction, which used the balanced set as the training set, the position- specific scoring matrix, predicted secondary structure, buried-exposed profile, and length of a query sequence as variables, and the conditional random field as the machine-learning method.ResultsBMRSA performance on test sets confirmed that our approach improved prediction accuracy relative to state-of-the-art approaches and was balanced in its comparison of buried and exposed regions. Our method is valuable when higher levels of accuracy in predicting exposed-residue states are required. The BMRSA is available at: http://cheminfo.tongji.edu.cn:8080/BMRSA/.

DomHR: Accurately Identifying Domain Boundaries in Proteins Using a Hinge Region Strategy

Motivation The precise prediction of protein domains, which are the structural, functional and evolutionary units of proteins, has been a research focus in recent years. Although many methods have been presented for predicting protein domains and boundaries, the accuracy of predictions could be improved. Results In this study we present a novel approach, DomHR, which is an accurate predictor of protein domain boundaries based on a creative hinge region strategy. A hinge region was defined as a segment of amino acids that covers part of a domain region and a boundary region. We developed a strategy to construct profiles of domain-hinge-boundary (DHB) features generated by sequence-domain/hinge/boundary alignment against a database of known domain structures. The DHB features had three elements: normalized domain, hinge, and boundary probabilities. The DHB features were used as input to identify domain boundaries in a sequence. DomHR used a nonredundant dataset as the training set, the DHB and predicted shape string as features, and a conditional random field as the classification algorithm. In predicted hinge regions, a residue was determined to be a domain or a boundary according to a decision threshold. After decision thresholds were optimized, DomHR was evaluated by cross-validation, large-scale prediction, independent test and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DomHR outperformed other well-established, publicly available domain boundary predictors for prediction accuracy. Availability The DomHR is available at http://cal.tongji.edu.cn/domain/.

Determination of the paracetamol degradation process with online UV spectroscopic and multivariate curve resolution-alternating least squares methods: comparative validation by HPLC

Paracetamol (N-acetyl-4-amino-phenol) is a popular antipyretic and analgesic medication which has few side effects and little toxicity when used in recommended dose. After ingestion of an overdose quantity of paracetamol, the accumulation of toxic metabolites may cause severe and even fatal hepatotoxicity and nephrotoxicity. In addition, p-aminophenol is the hydrolytic product of paracetamol and has high toxicity. It may be involved in the pharmaceutical preparation of paracetamol as a synthetic intermediate or a degradation product of paracetamol. Therefore, establishing an appropriate analytical method to research the stability of the medication is quite important and necessary. In this work, a kinetic alkaline degradation process of paracetamol was investigated by online two-way dimensional UV-Vis kinetic spectroscopy combined with the chemometric method Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). The extracted concentration profiles and pure spectra of the reacting species in the reaction were obtained. These profiles indicated that there are two intermediates in the process. The possible degradation pathway and reaction mechanism of the paracetamol was postulated based on the MCR-ALS results, and the rate constants of every reaction step were resolved through subsequent model-fitting. To validate these results, a comparative offline measurement method with high performance liquid chromatography was implemented experimentally. Moreover, an ab initio calculation was also performed to evaluate the estimated reaction mechanism theoretically in energy terms.

Predicting Turns in Proteins with a Unified Model

Motivation Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. Results In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i) using newly exploited features of structural evolution information (secondary structure and shape string of protein) based on structure homologies, (ii) considering all types of turns in a unified model, and (iii) practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries) by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.