Harpreet Kaur

Prediction of beta-turns in proteins from multiple alignment using neural network.

A neural network‐based method has been developed for the prediction of β‐turns in proteins by using multiple sequence alignment. Two feed‐forward back‐propagation networks with a single hidden layer are used where the first‐sequence structure network is trained with the multiple sequence alignment in the form of PSI‐BLAST–generated position‐specific scoring matrices. The initial predictions from the first network and PSIPRED‐predicted secondary structure are used as input to the second structure‐structure network to refine the predictions obtained from the first net. A significant improvement in prediction accuracy has been achieved by using evolutionary information contained in the multiple sequence alignment. The final network yields an overall prediction accuracy of 75.5% when tested by sevenfold cross‐validation on a set of 426 nonhomologous protein chains. The corresponding Qpred, Qobs, and Matthews correlation coefficient values are 49.8%, 72.3%, and 0.43, respectively, and are the best among all the previously published β‐turn prediction methods. The Web server BetaTPred2 (http://www.imtech.res.in/raghava/betatpred2/) has been developed based on this approach.

PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides.

Among secondary structure elements, beta-turns are ubiquitous and major feature of bioactive peptides. We analyzed 77 biologically active peptides with length varying from 9 to 20 residues. Out of 77 peptides, 58 peptides were found to contain at least one beta-turn. Further, at the residue level, 34.9% of total peptide residues were found to be in beta-turns, higher than the number of helical (32.3%) and beta-sheet residues (6.9%). So, we utilized the predicted beta-turns information to develop an improved method for predicting the three-dimensional (3D) structure of small peptides. In principle, we built four different structural models for each peptide. The first model I was built by assigning all the peptide residues an extended conformation (phi = Psi = 180 degrees ). Second model II was built using the information of regular secondary structures (helices, beta-strands and coil) predicted from PSIPRED. In third model III, secondary structure information including beta-turn types predicted from BetaTurns method was used. The fourth model IV had main-chain phi, Psi angles of model III and side chain angles assigned using standard Dunbrack backbone dependent rotamer library. These models were further refined using AMBER package and the resultant C(alpha) rmsd values were calculated. It was found that adding the beta-turns to the regular secondary structures greatly reduces the rmsd values both before and after the energy minimization. Hence, the results indicate that regular and irregular secondary structures, particularly beta-turns information can provide valuable and vital information in the tertiary structure prediction of small bioactive peptides. Based on the above study, a web server PEPstr (http://www.imtech.res.in/raghava/pepstr/) was developed for predicting the tertiary structure of small bioactive peptides.

Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods

This article describes a method developed for predicting transmembrane β‐barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed‐forward neural network with a standard back‐propagation training algorithm. The accuracy of the ANN‐based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI‐BLAST. We have also developed an SVM‐based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN‐ and SVM‐based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using “leave one out cross‐validation” (LOOCV). Based on this study, we have developed a Web server, TBBPred, for predicting transmembrane β‐barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred). Proteins 2004.

A neural network method for prediction of β-turn types in proteins using evolutionary information

MOTIVATIONnThe prediction of beta-turns is an important element of protein secondary structure prediction. Recently, a highly accurate neural network based method Betatpred2 has been developed for predicting beta-turns in proteins using position-specific scoring matrices (PSSM) generated by PSI-BLAST and secondary structure information predicted by PSIPRED. However, the major limitation of Betatpred2 is that it predicts only beta-turn and non-beta-turn residues and does not provide any information of different beta-turn types. Thus, there is a need to predict beta-turn types using an approach based on multiple sequence alignment, which will be useful in overall tertiary structure prediction.nnnRESULTSnIn the present work, a method has been developed for the prediction of beta-turn types I, II, IV and VIII. For each turn type, two consecutive feed-forward back-propagation networks with a single hidden layer have been used where the first sequence-to-structure network has been trained on single sequences as well as on PSI-BLAST PSSM. The output from the first network along with PSIPRED predicted secondary structure has been used as input for the second-level structure-to-structure network. The networks have been trained and tested on a non-homologous dataset of 426 proteins chains by 7-fold cross-validation. It has been observed that the prediction performance for each turn type is improved significantly by using multiple sequence alignment. The performance has been further improved by using a second level structure-to-structure network and PSIPRED predicted secondary structure information. It has been observed that Type I and II beta-turns have better prediction performance than Type IV and VIII beta-turns. The final network yields an overall accuracy of 74.5, 93.5, 67.9 and 96.5% with MCC values of 0.29, 0.29, 0.23 and 0.02 for Type I, II, IV and VIII beta-turns, respectively, and is better than random prediction.nnnAVAILABILITYnA web server for prediction of beta-turn types I, II, IV and VIII based on above approach is available at http://www.imtech.res.in/raghava/betaturns/ and http://bioinformatics.uams.edu/mirror/betaturns/ (mirror site).

Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure.

The present study is an attempt to develop a neural network‐based method for predicting the real value of solvent accessibility from the sequence using evolutionary information in the form of multiple sequence alignment. In this method, two feed‐forward networks with a single hidden layer have been trained with standard back‐propagation as a learning algorithm. The Pearsons correlation coefficient increases from 0.53 to 0.63, and mean absolute error decreases from 18.2 to 16% when multiple‐sequence alignment obtained from PSI‐BLAST is used as input instead of a single sequence. The performance of the method further improves from a correlation coefficient of 0.63 to 0.67 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields a mean absolute error value of 15.2% between the experimental and predicted values, when tested on two different nonhomologous and nonredundant datasets of varying sizes. The method consists of two steps: (1) in the first step, a sequence‐to‐structure network is trained with the multiple alignment profiles in the form of PSI‐BLAST‐generated position‐specific scoring matrices, and (2) in the second step, the output obtained from the first network and PSIPRED‐predicted secondary structure information is used as an input to the second structure‐to‐structure network. Based on the present study, a server SARpred (http://www.imtech.res.in/raghava/sarpred/) has been developed that predicts the real value of solvent accessibility of residues for a given protein sequence. We have also evaluated the performance of SARpred on 47 proteins used in CASP6 and achieved a correlation coefficient of 0.68 and a MAE of 15.9% between predicted and observed values. Proteins 2005.

A neural-network based method for prediction of γ-turns in proteins from multiple sequence alignment

In the present study, an attempt has been made to develop a method for predicting γ‐turns in proteins. First, we have implemented the commonly used statistical and machine‐learning techniques in the field of protein structure prediction, for the prediction of γ‐turns. All the methods have been trained and tested on a set of 320 nonhomologous protein chains by a fivefold cross‐validation technique. It has been observed that the performance of all methods is very poor, having a Matthews Correlation Coefficient (MCC) ≤ 0.06. Second, predicted secondary structure obtained from PSIPRED is used in γ‐turn prediction. It has been found that machine‐learning methods outperform statistical methods and achieve an MCC of 0.11 when secondary structure information is used. The performance of γ‐turn prediction is further improved when multiple sequence alignment is used as the input instead of a single sequence. Based on this study, we have developed a method, GammaPred, for γ‐turn prediction (MCC = 0.17). The GammaPred is a neural‐network‐based method, which predicts γ‐turns in two steps. In the first step, a sequence‐to‐structure network is used to predict the γ‐turns from multiple alignment of protein sequence. In the second step, it uses a structure‐to‐structure network in which input consists of predicted γ‐turns obtained from the first step and predicted secondary structure obtained from PSIPRED. (A Web server based on GammaPred is available at http://www.imtech.res.in/raghava/gammapred/.)

Association studies of catechol-O-methyltransferase (COMT) gene with schizophrenia and response to antipsychotic treatment

AIMnWe investigated the catechol-O-methyltrasferase (COMT) gene, which is a strong functional and positional candidate gene for schizophrenia and therapeutic response to antipsychotic medication.nnnMATERIALS & METHODSnSingle-locus as well as detailed haplotype-based association analysis of the COMT gene with schizophrenia and antipsychotic treatment response was carried out using seven COMT polymorphisms in 398 schizophrenia patients and 241 healthy individuals from a homogeneous south Indian population. Further responsiveness to risperidone treatment was assessed in 117 schizophrenia patients using Clinical Global Impressions (CGI). A total of 69 patients with a CGI score of 2 or less met the criteria of good responders and 48 were patients who continued to have a score of 3 and above and were classified as poor responders to risperidone treatment.nnnRESULTSnThe association of SNP rs4680 with schizophrenia did not remain significant after adjusting for multiple testing. Haplotype analysis showed highly significant association of seven COMT marker haplotypes with schizophrenia (CLUMP T4 p-value = 0.0001). Our results also demonstrated initial significant allelic associations of two SNPs with drug response (rs4633: chi(2) = 4.36, p-value = 0.036, OR: 1.80, 95% CI: 1.03-3.15; and rs4680: chi(2) = 4.02, p-value = 0.044, OR: 1.76, 95% CI: 1.01-3.06) before multiple correction. We employed two-marker sliding window analysis for haplotype association and observed a significant association of markers located between intron 1 and intron 2 (rs737865, rs6269: CLUMP T4 p-value = 0.021); and in exon 4 (rs4818, rs4680: CLUMP T4 p-value = 0.028) with drug response.nnnCONCLUSIONnThe present study thus indicates that the interacting effects within the COMT gene polymorphisms may influence the disease status and response to risperidone in schizophrenia patients. However, the study needs to be replicated in a larger sample set for confirmation, followed by functional studies.

Locating probable genes using Fourier transform approach.

FTG is a web server for analyzing nucleotide sequences to predict the genes using Fourier transform techniques. This server implements the existing Fourier transform algorithms for gene prediction and allows the rapid visualization of analysis by output in GIF format.

An evaluation of β-turn prediction methods

MOTIVATION: beta-turn is an important element of protein structure. In the past three decades, numerous beta-turn prediction methods have been developed based on various strategies. For a detailed discussion about the importance of beta-turns and a systematic introduction of the existing prediction algorithms for beta-turns and their types, please see a recent review (Chou, Analytical Biochemistry, 286, 1-16, 2000). However at present, it is still difficult to say which method is better than the other. This is because of the fact that these methods were developed on different sets of data. Thus, it is important to evaluate the performance of beta-turn prediction methods. RESULTS: We have evaluated the performance of six methods of beta-turn prediction. All the methods have been tested on a set of 426 non-homologous protein chains. It has been observed that the performance of the neural network based method, BTPRED, is significantly better than the statistical methods. One of the reasons for its better performance is that it utilizes the predicted secondary structure information. We have also trained, tested and evaluated the performance of all methods except BTPRED and GORBTURN, on new data set using a 7-fold cross-validation technique. There is a significant improvement in performance of all the methods when secondary structure information is incorporated. Moreover, after incorporating secondary structure information, the Sequence Coupled Model has yielded better results in predicting beta-turns as compared with other methods. In this study, both threshold dependent and independent (ROC) measures have been used for evaluation.

Prediction of α‐turns in proteins using PSI‐BLAST profiles and secondary structure information

In this paper a systematic attempt has been made to develop a better method for predicting α‐turns in proteins. Most of the commonly used approaches in the field of protein structure prediction have been tried in this study, which includes statistical approach “Sequence Coupled Model” and machine learning approaches; i) artificial neural network (ANN); ii) Weka (Waikato Environment for Knowledge Analysis) Classifiers and iii) Parallel Exemplar Based Learning (PEBLS). We have also used multiple sequence alignment obtained from PSIBLAST and secondary structure information predicted by PSIPRED. The training and testing of all methods has been performed on a data set of 193 non‐homologous protein X‐ray structures using five‐fold cross‐validation. It has been observed that ANN with multiple sequence alignment and predicted secondary structure information outperforms other methods. Based on our observations we have developed an ANN‐based method for predicting α‐turns in proteins. The main components of the method are two feed‐forward back‐propagation networks with a single hidden layer. The first sequence‐structure network is trained with the multiple sequence alignment in the form of PSI‐BLAST‐generated position specific scoring matrices. The initial predictions obtained from the first network and PSIPRED predicted secondary structure are used as input to the second structure–structure network to refine the predictions obtained from the first net. The final network yields an overall prediction accuracy of 78.0% and MCC of 0.16. A web server AlphaPred (http://www.imtech.res.in/raghava/alphapred/) has been developed based on this approach. Proteins 2004.