Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce dif-fraction quality crystals for X-ray crystallogra-phy based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of in- silico methods for sequence-based prediction/ assessment of the protein crystallization pro-pensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discov-ered to correlate with the ability to form crystals. We also contrast and empirically compare mo- dern sequence-based predictors of crystalliza-tion propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and compli-mentary predictions. Although their average ac- curacy is similar at around 70%, we show that application of a simple majority-vote based en-semble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystalli-zation propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.

Sequence based prediction of relative solvent accessibility using two-stage support vector regression with confidence values

Predicted relative solvent accessibility (RSA) provides useful information for prediction of binding sites and reconstruction of the 3D-structure based on a protein sequence. Recent years observed development of several RSA prediction methods including those that generate real values and those that predict discrete states (buried vs. exposed). We propose a novel method for real value prediction that aims at minimizing the prediction error when compared with six existing methods. The proposed method is based on a two-stage Support Vector Regression (SVR) predictor. The improved prediction quality is a result of the developed composite sequence representation, which includes a custom-selected subset of features from the PSI-BLAST profile, secondary structure predicted with PSI-PRED, and binary code that indicates position of a given residue with respect to sequence termini. Cross validation tests on a benchmark dataset show that our method achieves 14.3 mean absolute error and 0.68 correlation. We also propose a confidence value that is associated with each predicted RSA values. The confidence is computed based on the difference in predictions from the two-stage SVR and a second two-stage Linear Regression (LR) predictor. The confidence values can be used to indicate the quality of the output RSA predictions.

NetEPD: A Network-Based Essential Protein Discovery Platform

Proteins drive virtually all cellular-level processes.The proteins that are critical to cell proliferation and survival are defined as essential.These essential proteins are implicated in key metabolic and regulatory networks,and are important in the context of rational drug design efforts.The computational identification of the essential proteins benefits from the proliferation of publicly available protein interaction datasets.Scientists have developed several algorithms that use these interaction datasets to predict essential proteins.However,a comprehensive web platform that facilitates the analysis and prediction of essential proteins is missing.In this study,we design,implement,and release Net EPD:a network-based essential protein discovery platform.This resource integrates data on Protein–Protein Interaction(PPI)networks,gene expression,subcellular localization,and a native set of essential proteins.It also computes a variety of node centrality measures,evaluates the predictions of essential proteins,and visualizes PPI networks.This comprehensive platform functions by implementing four activities,which include the collection of datasets,computation of centrality measures,evaluation,and visualization.The results produced by Net EPD are visualized on its website,and sent to a user-provided email,and they are available to download in a parsable format.This platform is freely available at http://bioinformatics.csu.edu.cn/netepd.