Chloroplast transit peptide prediction: a peek inside the black box

Previous work in predicting protein localization to the chloroplast organelle in plants led to the development of an artificial neural network-based approach capable of remarkable accuracy in its prediction (ChloroP). A common criticism against such neural network models is that it is difficult to interpret the criteria that are used in making predictions. We address this concern with several new prediction methods that base predictions explicitly on the abundance of different amino acid types in the N-terminal region of the protein. Our successful prediction accuracy suggests that ChloroP uses little positional information in its decision-making; an unexpected result given the elaborate ChloroP input scheme. By removing positional information, our simpler methods allow us to identify those amino acids that are useful for successful prediction. The identification of important sequence features, such as amino acid content, is advantageous if one of the goals of localization predictors is to gain an understanding of the biological process of chloroplast localization. Our most accurate predictor combines principal component analysis and logistic regression. Web-based prediction using this method is available online at http://apicoplast.cis.upenn.edu/pclr/.     

A Segmental Semi Markov Model for protein secondary structure prediction

Hidden Markov Models (HMMs) are practical tools which provide probabilistic base for protein secondary structure prediction. In these models, usually, only the information of the left hand side of an amino acid is considered. Accordingly, these models seem to be inefficient with respect to long range correlations. In this work we discuss a Segmental Semi Markov Model (SSMM) in which the information of both sides of amino acids are considered. It is assumed and seemed reasonable that the information on both sides of an amino acid can provide a suitable tool for measuring dependencies. We consider these dependencies by dividing them into shorter dependencies. Each of these dependency models can be applied for estimating the probability of segments in structural classes. Several conditional probabilities concerning dependency of an amino acid to the residues appeared on its both sides are considered. Based on these conditional probabilities a weighted model is obtained to calculate the probability of each segment in a structure. This results in 2.27% increase in prediction accuracy in comparison with the ordinary Segmental Semi Markov Models, SSMMs. We also compare the performance of our model with that of the Segmental Semi Markov Model introduced by Schmidler et al. [C.S. Schmidler, J.S. Liu, D.L. Brutlag, Bayesian segmentation of protein secondary structure, J. Comp. Biol. 7(1/2) (2000) 233-248]. The calculations show that the overall prediction accuracy of our model is higher than the SSMM introduced by Schmidler.     

Clustering of amino acids for protein secondary structure prediction

Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent amino acid coarse-graining schemes are designed for the models by means of proper mutual information. We compare models of different level of complexity, and establish a practical model with a high prediction accuracy.     

A "solvated rotamer" approach to modeling water-mediated hydrogen bonds at protein-protein interfaces

Water-mediated hydrogen bonds play critical roles at protein-protein and protein-nucleic acid interfaces, and the interactions formed by discrete water molecules cannot be captured using continuum solvent models. We describe a simple model for the energetics of water-mediated hydrogen bonds, and show that, together with knowledge of the positions of buried water molecules observed in X-ray crystal structures, the model improves the prediction of free-energy changes upon mutation at protein-protein interfaces, and the recovery of native amino acid sequences in protein interface design calculations. We then describe a "solvated rotamer" approach to efficiently predict the positions of water molecules, at protein-protein interfaces and in monomeric proteins, that is compatible with widely used rotamer-based side-chain packing and protein design algorithms. Finally, we examine the extent to which the predicted water molecules can be used to improve prediction of amino acid identities and protein-protein interface stability, and discuss avenues for overcoming current limitations of the approach.     

Stereochemical criteria for prediction of the effects of proline mutations on protein stability

When incorporated into a polypeptide chain, proline (Pro) differs from all other naturally occurring amino acid residues in two important respects. The phi dihedral angle of Pro is constrained to values close to -65 degrees and Pro lacks an amide hydrogen. Consequently, mutations which result in introduction of Pro can significantly affect protein stability. In the present work, we describe a procedure to accurately predict the effect of Pro introduction on protein thermodynamic stability. Seventy-seven of the 97 non-Pro amino acid residues in the model protein, CcdB, were individually mutated to Pro, and the in vivo activity of each mutant was characterized. A decision tree to classify the mutation as perturbing or nonperturbing was created by correlating stereochemical properties of mutants to activity data. The stereochemical properties including main chain dihedral angle phi and main chain amide H-bonds (hydrogen bonds) were determined from 3D models of the mutant proteins built using MODELLER. We assessed the performance of the decision tree on a large dataset of 163 single-site Pro mutations of T4 lysozyme, 74 nsSNPs, and 52 other Pro substitutions from the literature. The overall accuracy of this algorithm was found to be 81% in the case of CcdB, 77% in the case of lysozyme, 76% in the case of nsSNPs, and 71% in the case of other Pro substitution data. The accuracy of Pro scanning mutagenesis for secondary structure assignment was also assessed and found to be at best 69%. Our prediction procedure will be useful in annotating uncharacterized nsSNPs of disease-associated proteins and for protein engineering and design.     

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction

1 Introduction The prediction of protein structure and function from amino acid sequences is one of the most impor-tant problems in molecular biology. This problem is becoming more pressing as the number of known pro-tein sequences is explored as a result of genome and other sequencing projects, and the protein sequence- structure gap is widening rapidly[1]. Therefore, com-putational tools to predict protein structures are needed to narrow the widening gap. Although the prediction of three dim…     

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction

A novel method for predicting the secondary structures of proteins from amino acid sequence has been presented. The protein secondary structure seqlets that are analogous to the words in natural language have been extracted. These seqlets will capture the relationship between amino acid sequence and the secondary structures of proteins and further form the protein secondary structure dictionary. To be elaborate, the dictionary is organism-specific. Protein secondary structure prediction is formulated as an integrated word segmentation and part of speech tagging problem. The word-lattice is used to represent the results of the word segmentation and the maximum entropy model is used to calculate the probability of a seqlet tagged as a certain secondary structure type. The method is markovian in the seqlets, permitting efficient exact calculation of the posterior probability distribution over all possible word segmentations and their tags by viterbi algorithm. The optimal segmentations and their tags are computed as the results of protein secondary structure prediction. The method is applied to predict the secondary structures of proteins of four organisms respectively and compared with the PHD method. The results show that the performance of this method is higher than that of PHD by about 3.9% Q3 accuracy and 4.6% SOV accuracy. Combining with the local similarity protein sequences that are obtained by BLAST can give better prediction. The method is also tested on the 50 CASP5 target proteins with Q₃ accuracy 78.9% and SOV accuracy 77.1%. A web server for protein secondary structure prediction has been constructed which is available at http://www.insun.hit.edu.cn:81/demos/biology/index.html.     

Protein location prediction using atomic composition and global features of the amino acid sequence

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectively used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.     

Statistical model of amino acid code of protein secondary structure

In the previous paper (Shestopalov, 2003) we presented the amino acid code of protein secondary structure as a partial solution of the fundamental problem of the protein three-dimensional structure calculation from the amino acid sequence. Here a statistical model of the code is described. The model is based on the structural data from 2258 protein chains (417,112 amino acid residues used). 60 and 61% of the secondary structure, calculated using the model, coincide, respectively, with the observed secondary structure in the training subset and test subset (104 protein chains and 21,166 residues used). This is equal to the threshold value for all the secondary structure calculations, based on the models, where, similarly as here, only the nearest and middle-range interactions are considered. Therefore the constructed model can be applied for the protein structure prediction from the amino acid sequence, especially when additional information is used along with expert analysis, as in the most successful prediction methods. The model can be used for analysis of the secondary structure changes during protein folding by comparison of the calculated and observed secondary structures. The information about the conformationally invariant segments can serve for the simulation of the supersecondary structure formation. One can try to obtain and examine the protein subset, in which the calculated and observed secondary structures are very similar.     

Prediction of protein stability changes for single-site mutations using support vector machines

Accurate prediction of protein stability changes resulting from single amino acid mutations is important for understanding protein structures and designing new proteins. We use support vector machines to predict protein stability changes for single amino acid mutations leveraging both sequence and structural information. We evaluate our approach using cross-validation methods on a large dataset of single amino acid mutations. When only the sign of the stability changes is considered, the predictive method achieves 84% accuracy-a significant improvement over previously published results. Moreover, the experimental results show that the prediction accuracy obtained using sequence alone is close to the accuracy obtained using tertiary structure information. Because our method can accurately predict protein stability changes using primary sequence information only, it is applicable to many situations where the tertiary structure is unknown, overcoming a major limitation of previous methods which require tertiary information. The web server for predictions of protein stability changes upon mutations (MUpro), software, and datasets are available at http://www.igb.uci.edu/servers/servers.html.     

Efficient prediction of nucleic acid binding function from low-resolution protein structures

Structural genomics projects as well as ab initio protein structure prediction methods provide structures of proteins with no sequence or fold similarity to proteins with known functions. These are often low-resolution structures that may only include the positions of C alpha atoms. We present a fast and efficient method to predict DNA-binding proteins from just the amino acid sequences and low-resolution, C alpha-only protein models. The method uses the relative proportions of certain amino acids in the protein sequence, the asymmetry of the spatial distribution of certain other amino acids as well as the dipole moment of the molecule. These quantities are used in a linear formula, with coefficients derived from logistic regression performed on a training set, and DNA-binding is predicted based on whether the result is above a certain threshold. We show that the method is insensitive to errors in the atomic coordinates and provides correct predictions even on inaccurate protein models. We demonstrate that the method is capable of predicting proteins with novel binding site motifs and structures solved in an unbound state. The accuracy of our method is close to another, published method that uses all-atom structures, time-consuming calculations and information on conserved residues.     

氨基酸定点突变提高灵芝蛋白LZ-8热稳定性的研究

通过氨基酸定点突变技术提高灵芝免疫调节蛋白LZ-8的热稳定性。通过分子动力学模拟结合温度因子预测对LZ-8氨基酸突变位点进行理性设计,在毕赤酵母X33菌株内构建并表达LZ-8突变体蛋白,采用HeLa细胞生长抑制实验和差示量热扫描分析检测并比较了LZ-8突变前后生物学活性及热力学参数。结果显示,LZ-8 N-端α螺旋为理论预测的温度敏感区域,在该区域进行F8W和R9K氨基酸双位点突变,突变后的LZ-8热稳定性提高,相变温度Tm上升0.92℃,相转变焓值ΔH提高23.14 kJ/mol,但突变后LZ-8生物学活性基本不变,LZ-8和LZ-8突变体对HeLa细胞生长抑制的IC50值分别是2.238μg/mL和2.407μg/mL。通过理性设计氨基酸突变位点,获得了稳定性提高但生物学活性不变的灵芝免疫调节蛋白LZ-8的突变体。     

AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices

Identifying a protein's subcellular localization is an important step to understand its function. However, the involved experimental work is usually laborious, time consuming and costly. Computational prediction hence becomes valuable to reduce the inefficiency. Here we provide a method to predict protein subcellular localization by using amino acid composition and physicochemical properties. The method concatenates the information extracted from a protein's N-terminal, middle and full sequence. Each part is represented by amino acid composition, weighted amino acid composition, five-level grouping composition and five-level dipeptide composition. We divided our dataset into training and testing set. The training set is used to determine the best performing amino acid index by using five-fold cross validation, whereas the testing set acts as the independent dataset to evaluate the performance of our model. With the novel representation method, we achieve an accuracy of approximately 75% on independent dataset. We conclude that this new representation indeed performs well and is able to extract the protein sequence information. We have developed a web server for predicting protein subcellular localization. The web server is available at http://aaindexloc.bii.a-star.edu.sg .     

Hidden Markov model-derived structural alphabet for proteins: the learning of protein local shapes captures sequence specificity

Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence.     

Toward bacterial protein sub-cellular location prediction: single-class discrimminant models for all gram- and gram+ compartments

Based on Bayesian Networks, methods were created that address protein sequence-based bacterial subcellular location prediction. Distinct predictive algorithms for the eight bacterial subcellular locations were created. Several variant methods were explored. These variations included differences in the number of residues considered within the query sequence - which ranged from the N-terminal 10 residues to the whole sequence - and residue representation - which took the form of amino acid composition, percentage amino acid composition, or normalised amino acid composition. The accuracies of the best performing networks were then compared to PSORTB. All individual location methods outperform PSORTB except for the Gram+ cytoplasmic protein predictor, for which accuracies were essentially equal, and for outer membrane protein prediction, where PSORTB outperforms the binary predictor. The method described here is an important new approach to method development for subcellular location prediction. It is also a new, potentially valuable tool for candidate subunit vaccine selection.     

Neural network-based prediction of mutation-induced protein stability changes in Staphylococcal nuclease at 20 residue positions

Protein-based therapeutics are playing an increasingly important role in the treatment of diseases, including diabetes and cancer. The viability of these treatments, however, are highly dependent on the stability of the therapeutic, since stability affects both the shelf life of the therapeutic as well as its active life in the body. Stability engineering can, therefore, be used to increase the effectiveness of protein-based therapeutics. Computational methods of protein stability prediction have been under development for about a decade, but complex molecular interactions make stability prediction difficult and computationally intensive. A rapid computational method of protein stability prediction is developed using feed-forward neural networks and used to predict mutation-induced stability changes in Staphylococcal nuclease. The input to the neural network consisted of sequences of evolutionarily based amino acid similarity scores that were obtained through the comparison of the amino acids in a mutation containing sequence to their positional counterparts in the baseline wild-type amino acid sequence. A training set was created which consisted of similarity score sequences, for which the stabilities of the corresponding amino acid sequences were known, paired with the relative stabilities of the sequences to that of the baseline. Back-propagation of error was used to train the network to output accurate relative stability scores for the sequences in the training set. Neural network-based relative stability predictions for 55 sequences containing mutation combinations not found in the training set had an accuracy of 92.8%.     

Engineering a thermostable protein via optimization of charge-charge interactions on the protein surface

A simple theoretical model for increasing the protein stability by adequately redesigning the distribution of charged residues on the surface of the native protein was tested experimentally. Using the molecule of ubiquitin as a model system, we predicted possible amino acid substitutions on the surface of this protein which would lead to an increase in its stability. Experimental validation for this prediction was achieved by measuring the stabilities of single-site-substituted ubiquitin variants using urea-induced unfolding monitored by far-UV CD spectroscopy. We show that the generated variants of ubiquitin are indeed more stable than the wild-type protein, in qualitative agreement with the theoretical prediction. As a positive control, theoretical predictions for destabilizing amino acid substitutions on the surface of the ubiquitin molecule were considered as well. These predictions were also tested experimentally using correspondingly designed variants of ubiquitin. We found that these variants are less stable than the wild-type protein, again in agreement with the theoretical prediction. These observations provide guidelines for rational design of more stable proteins and suggest a possible mechanism of structural stability of proteins from thermophilic organisms.     

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein’s function in the cell. Understanding a protein’s secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.     

Modeling large regions in proteins: applications to loops, termini, and folding

Template-based methods for predicting protein structure provide models for a significant portion of the protein but often contain insertions or chain ends (InsEnds) of indeterminate conformation. The local structure prediction "problem" entails modeling the InsEnds onto the rest of the protein. A well-known limit involves predicting loops of ≤12 residues in crystal structures. However, InsEnds may contain as many as ~50 amino acids, and the template-based model of the protein itself may be imperfect. To address these challenges, we present a free modeling method for predicting the local structure of loops and large InsEnds in both crystal structures and template-based models. The approach uses single amino acid torsional angle "pivot" moves of the protein backbone with a C(β) level representation. Nevertheless, our accuracy for loops is comparable to existing methods. We also apply a more stringent test, the blind structure prediction and refinement categories of the CASP9 tournament, where we improve the quality of several homology based models by modeling InsEnds as long as 45 amino acids, sizes generally inaccessible to existing loop prediction methods. Our approach ranks as one of the best in the CASP9 refinement category that involves improving template-based models so that they can function as molecular replacement models to solve the phase problem for crystallographic structure determination.