CaMELS: In silico prediction of calmodulin binding proteins and their binding sites

Due to Ca²⁺‐dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet‐lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet‐lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large‐margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM‐binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome‐wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif‐based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub‐sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels .     

Predicting the helix‐helix interactions from correlated residue mutations

Helix‐helix interactions are crucial in the structure assembly, stability and function of helix‐rich proteins including many membrane proteins. In spite of remarkable progresses over the past decades, the accuracy of predicting protein structures from their amino acid sequences is still far from satisfaction. In this work, we focused on a simpler problem, the prediction of helix‐helix interactions, the results of which could facilitate practical protein structure prediction by constraining the sampling space. Specifically, we started from the noisy 2D residue contact maps derived from correlated residue mutations, and utilized ridge detection to identify the characteristic residue contact patterns for helix‐helix interactions. The ridge information as well as a few additional features were then fed into a machine learning model HHConPred to predict interactions between helix pairs. In an independent test, our method achieved an F‐measure of ～60% for predicting helix‐helix interactions. Moreover, although the model was trained mainly using soluble proteins, it could be extended to membrane proteins with at least comparable performance relatively to previous approaches that were generated purely using membrane proteins. All data and source codes are available at http://166.111.152.91/Downloads.html or https://github.com/dpxiong/HHConPred .     

Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information

Sequence-based approach for motif prediction is of great interest and remains a challenge. In this work, we develop a local combinational variable approach for sequence-based helix-turn-helix (HTH) motif prediction. First we choose a sequence data set for 88 proteins of 22 amino acids in length to launch an optimized traversal for extracting local combinational segments (LCS) from the data set. Then after LCS refinement, local combinational variables (LCV) are generated to construct prediction models for HTH motifs. Prediction ability of LCV sets at different thresholds is calculated to settle a moderate threshold. The large data set we used comprises 13 HTH families, with 17 455 sequences in total. Our approach predicts HTH motifs more precisely using only primary protein sequence information, with 93.29% accuracy, 93.93% sensitivity and 92.66% specificity. Prediction results of newly reported HTH-containing proteins compared with other prediction web service presents a good prediction model derived from the LCV approach. Comparisons with profile-HMM models from the Pfam protein families database show that the LCV approach maintains a good balance while dealing with HTH-containing proteins and non-HTH proteins at the same time. The LCV approach is to some extent a complementary to the profile-HMM models for its better identification of false-positive data. Furthermore, genome-wide predictions detect new HTH proteins in both Homo sapiens and Escherichia coli organisms, which enlarge applications of the LCV approach. Software for mining LCVs from sequence data set can be obtained from anonymous ftp site ftp://cheminfo.tongji.edu.cn/LCV/freely.     

基于添加模体信息和功率谱密度的组合向量预测27类蛋白质折叠子

以序列相似性低于40%的1895条蛋白质序列构建涵盖27个折叠类型的蛋白质折叠子数据库,从蛋白质序列出发,用模体频数值、低频功率谱密度值、氨基酸组分、预测的二级结构信息和自相关函数值构成组合向量表示蛋白质序列信息,采用支持向量机算法,基于整体分类策略,对27类蛋白质折叠子的折叠类型进行预测,独立检验的预测精度达到了66.67%。同时,以同样的特征参数和算法对27类折叠子的4个结构类型进行了预测,独立检验的预测精度达到了89.24%。将同样的方法用于前人使用过的27类折叠子数据库,得到了好于前人的预测结果。     

Analysis and prediction of helix shift errors in homology modeling

High sequence identity between two proteins (e.g. > 60%) is a strong evidence for high structural similarity. However, internal shifts in one of the two proteins can sometimes give rise to unexpectedly high structural differences. This, in turn, causes unreliable structure predictions when two such proteins are used in homology modeling. Here, we perform a computational analysis of helix shifts and we show that their occurrence can be predicted with statistical learning methods. Our results indicate that helix shifts increase the RMS error by factor 2.6 compared to those protein pairs without a helix shift. Although helix shifts are rare (1.6% of helices and a commensurately higher number of proteins are affected), they therefore pose a significant problem for reliable structure prediction systems. In this paper, we prototype a new approach for model quality assessment and demonstrate that it can successfully warn against helix shifts. A support vector machine trained on a wide range of sequence and structure properties predicts the occurrence of helix shifts with a sensitivity of 74.2% and a specificity of 83.6%. On an equalized test dataset, this corresponds to an accuracy of 78.9%. Projected to the full dataset, it translates to an accuracy of 83.4%. Our analysis shows that helix shift detection is a valuable building block for highly reliable structure prediction systems. Furthermore, the statistical learning based approach to helix shift detection that we employ here is orthogonal to well-established model quality assessment methods (which use geometric constraint checking or mean force potentials). Therefore, a further increase of prediction accuracy is expected from the combination of these methods.     

Predicting protein sumoylation sites from sequence features

Protein sumoylation is a post-translational modification that plays an important role in a wide range of cellular processes. Small ubiquitin-related modifier (SUMO) can be covalently and reversibly conjugated to the sumoylation sites of target proteins, many of which are implicated in various human genetic disorders. The accurate prediction of protein sumoylation sites may help biomedical researchers to design their experiments and understand the molecular mechanism of protein sumoylation. In this study, a new machine learning approach has been developed for predicting sumoylation sites from protein sequence information. Random forests (RFs) and support vector machines (SVMs) were trained with the data collected from the literature. Domain-specific knowledge in terms of relevant biological features was used for input vector encoding. It was shown that RF classifier performance was affected by the sequence context of sumoylation sites, and 20 residues with the core motif ΨKXE in the middle appeared to provide enough context information for sumoylation site prediction. The RF classifiers were also found to outperform SVM models for predicting protein sumoylation sites from sequence features. The results suggest that the machine learning approach gives rise to more accurate prediction of protein sumoylation sites than the other existing methods. The accurate classifiers have been used to develop a new web server, called seeSUMO (http://bioinfo.ggc.org/seesumo/), for sequence-based prediction of protein sumoylation sites.     

fRMSDPred: predicting local RMSD between structural fragments using sequence information

The effectiveness of comparative modeling approaches for protein structure prediction can be substantially improved by incorporating predicted structural information in the initial sequence-structure alignment. Motivated by the approaches used to align protein structures, this article focuses on developing machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments. We present algorithms to solve this fragment-level RMSD prediction problem using a supervised learning framework based on support vector regression and classification that incorporates protein profiles, predicted secondary structure, effective information encoding schemes, and novel second-order pairwise exponential kernel functions. Our comprehensive empirical study shows superior results compared with the profile-to-profile scoring schemes. We also show that for protein pairs with low sequence similarity (less than 12% sequence identity) these new local structural features alone or in conjunction with profile-based information lead to alignments that are considerably accurate than those obtained by schemes that use only profile and/or predicted secondary structure information.     

用支持向量机预测人类基因5’／3’选择性剪切位点

选择性剪切是调解基因表达的重要机制.识别选择性剪切位点是后基因组时代的一个重要工作.本文从最新的EBI人类基因选择性剪切数据库中,选取5＇/3＇选择性剪切位点作为正集,选取在剪切位点附近的假剪切位点作为负集,并把所有的选择性剪切位点和假剪切位点随机分成训练集和测试集.本文选用的预测选择性剪切位点的方法是基于位置权重矩阵和离散增量的支持向量机方法.此方法仅基于训练集,以不同位点的单碱基概率和序列片断的三联体频数作为信息参数,利用位置权重矩阵和离散增量算法结合支持向量机,得到了选择性供体位点和受体位点的分类器,并用此分类器对测试集中的选择性供体位点和受体位点进行预测.对独立测试集中的选择性供体位点和选择性受体位点的预测成功率分别为88.74%和90.86%,特异性分别为85.62%和81.19%.本文预测选择性剪切位点的方法成功率高于其它选择性剪切位点预测方法预测成功率,此预测方法进一步提高了对选择性剪切位点的理论预测能力.     

Prediction of pi-turns in proteins using PSI-BLAST profiles and secondary structure information

Due to the structural and functional importance of tight turns, some methods have been proposed to predict gamma-turns, beta-turns, and alpha-turns in proteins. In the past, studies of pi-turns were made, but not a single prediction approach has been developed so far. It will be useful to develop a method for identifying pi-turns in a protein sequence. In this paper, the support vector machine (SVM) method has been introduced to predict pi-turns from the amino acid sequence. The training and testing of this approach is performed with a newly collected data set of 640 non-homologous protein chains containing 1931 pi-turns. Different sequence encoding schemes have been explored in order to investigate their effects on the prediction performance. With multiple sequence alignment and predicted secondary structure, the final SVM model yields a Matthews correlation coefficient (MCC) of 0.556 by a 7-fold cross-validation. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/piturn/.     

PAIRpred: Partner‐specific prediction of interacting residues from sequence and structure

We present a novel partner‐specific protein–protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter‐protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding‐associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/ . Proteins 2014; 82:1142–1155. © 2013 Wiley Periodicals, Inc.     

Solution structure of the Mu end DNA-binding ibeta subdomain of phage Mu transposase: modular DNA recognition by two tethered domains.

The phage Mu transposase (MuA) binds to the ends of the Mu genome during the assembly of higher order nucleoprotein complexes. We investigate the structure and function of the MuA end-binding domain (Ibetagamma). The three-dimensional solution structure of the Ibeta subdomain (residues 77-174) has been determined using multidimensional NMR spectroscopy. It comprises five alpha-helices, including a helix-turn-helix (HTH) DNA-binding motif formed by helices 3 and 4, and can be subdivided into two interacting structural elements. The structure has an elongated disc-like appearance from which protrudes the recognition helix of the HTH motif. The topology of helices 2-4 is very similar to that of helices 1-3 of the previously determined solution structure of the MuA Igamma subdomain and to that of the homeodomain family of HTH DNA-binding proteins. We show that each of the two subdomains binds to one half of the 22 bp recognition sequence, Ibeta to the more conserved Mu end distal half (beta subsite) and Igamma to the Mu end proximal half (gamma subsite) of the consensus Mu end-binding site. The complete Ibetagamma domain binds the recognition sequence with a 100- to 1000-fold higher affinity than the two subdomains independently, indicating a cooperative effect. Our results show that the Mu end DNA-binding domain of MuA has a modular organization, with each module acting on a specific part of the 22 bp binding site. Based on the present binding data and the structures of the Ibeta and Igamma subdomains, a model for the interaction of the complete Ibetagamma domain with DNA is proposed.     

DNA recognition for virus assembly through multiple sequence-independent interactions with a helix-turn-helix motif

The helix-turn-helix (HTH) motif features frequently in protein DNA-binding assemblies. Viral pac site-targeting small terminase proteins possess an unusual architecture in which the HTH motifs are displayed in a ring, distinct from the classical HTH dimer. Here we investigate how such a circular array of HTH motifs enables specific recognition of the viral genome for initiation of DNA packaging during virus assembly. We found, by surface plasmon resonance and analytical ultracentrifugation, that individual HTH motifs of the Bacillus phage SF6 small terminase bind the packaging regions of SF6 and related SPP1 genome weakly, with little local sequence specificity. Nuclear magnetic resonance chemical shift perturbation studies with an arbitrary single-site substrate suggest that the HTH motif contacts DNA similarly to how certain HTH proteins contact DNA non-specifically. Our observations support a model where specificity is generated through conformational selection of an intrinsically bent DNA segment by a ring of HTHs which bind weakly but cooperatively. Such a system would enable viral gene regulation and control of the viral life cycle, with a minimal genome, conferring a major evolutionary advantage for SPP1-like viruses.     

Prediction of turn types in protein structure by machine-learning classifiers

We present machine learning approaches for turn prediction from the amino acid sequence. Different turn classes and types were considered based on a novel turn classification scheme. We trained an unsupervised (self-organizing map) and two kernel-based classifiers, namely the support vector machine and a probabilistic neural network. Turn versus non-turn classification was carried out for turn families containing intramolecular hydrogen bonds and three to six residues. Support vector machine classifiers yielded a Matthews correlation coefficient (mcc) of approximately 0.6 and a prediction accuracy of 80%. Probabilistic neural networks were developed for beta-turn type prediction. The method was able to distinguish between five types of beta-turns yielding mcc > 0.5 and at least 80% overall accuracy. We conclude that the proposed new turn classification is distinct and well-defined, and machine learning classifiers are suited for sequence-based turn prediction. Their potential for sequence-based prediction of turn structures is discussed.     

Detecting DNA-binding helix-turn-helix structural motifs using sequence and structure information

In this work, we analyse the potential for using structural knowledge to improve the detection of the DNA-binding helix–turn–helix (HTH) motif from sequence. Starting from a set of DNA-binding protein structures that include a functional HTH motif and have no apparent sequence similarity to each other, two different libraries of hidden Markov models (HMMs) were built. One library included sequence models of whole DNA-binding domains, which incorporate the HTH motif, the second library included shorter models of ‘partial’ domains, representing only the fraction of the domain that corresponds to the functionally relevant HTH motif itself. The libraries were scanned against a dataset of protein sequences, some containing the HTH motifs, others not. HMM predictions were compared with the results obtained from a previously published structure-based method and subsequently combined with it. The combined method proved more effective than either of the single-featured approaches, showing that information carried by motif sequences and motif structures are to some extent complementary and can successfully be used together for the detection of DNA-binding HTHs in proteins of unknown function.     

Statistical models for discerning protein structures containing the DNA-binding helix-turn-helix motif

A method for discerning protein structures containing the DNA-binding helix-turn-helix (HTH) motif has been developed. The method uses statistical models based on geometrical measurements of the motif. With a decision tree model, key structural features required for DNA binding were identified. These include a high average solvent-accessibility of residues within the recognition helix and a conserved hydrophobic interaction between the recognition helix and the second alpha helix preceding it. The Protein Data Bank was searched using a more accurate model of the motif created using the Adaboost algorithm to identify structures that have a high probability of containing the motif, including those that had not been reported previously.