Prediction of functionally important residues in globular proteins from unusual central distances of amino acids

Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation

Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.

CSmetaPred: a consensus method for prediction of catalytic residues

Background

Knowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc.

Results

Both meta-predictors are evaluated on two comprehensive benchmark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves. The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform their constituent methods and CSmetaPred_poc is the best of evaluated methods. For instance, on CSAMAC dataset CSmetaPred_poc (CSmetaPred) achieves highest Mean Average Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96). Importantly, median predicted rank of catalytic residues is the lowest (best) for CSmetaPred_poc. Considering residues ranked ≤20 classified as true positive in binary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset. Moreover, on the same dataset CSmetaPred_poc predicts all catalytic residues within top 20 ranks for ~73% of enzymes. Furthermore, benchmarking of prediction on comparative modelled structures showed that models result in better prediction than only sequence based predictions. These analyses suggest that CSmetaPred_poc is able to rank putative catalytic residues at lower (better) ranked positions, which can facilitate and expedite their experimental characterization.

Conclusions

The benchmarking studies showed that employing meta-approach in combining residue-level scores derived from well-known catalytic residue predictors can improve prediction accuracy as well as provide improved ranked positions of known catalytic residues. Hence, such predictions can assist experimentalist to prioritize residues for mutational studies in their efforts to characterize catalytic residues. Both meta-predictors are available as webserver at: http://14.139.227.206/csmetapred/.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1987-z) contains supplementary material, which is available to authorized users.

Role of Ordered Proteins in the Folding-Upon-Binding of Intrinsically Disordered Proteins

In this work, we quantitatively investigate the thermodynamic analogy between the folding of monomeric proteins and the interactions of intrinsically disordered proteins (IDPs). Motivated by the hypothesis that similar hydrophobic forces guide both globular protein folding and also IDP interactions, we present a unified experimental and computational investigation of the coupling between the folding and binding of the intrinsically disordered tail of FCP1 when interacting with the cooperatively folding winged-helix domain of Rap74. Our calorimetric measurements quantitatively demonstrate the significance of hydrophobic interactions for this binding event. Our computational studies indicate that IDPs relieve frustration at the surface of ordered proteins to generate a minimally frustrated complex that is strikingly similar to a globular monomeric protein. In summary, these results not only quantify the thermodynamic forces driving disordered protein interactions but also highlight the role of ordered proteins for IDP function.

Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data

Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires ‘ground-truth’ functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the ‘responsible’ isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the ‘responsible’ isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions.

In mammalian genomes, a single gene can be alternatively spliced into multiple isoforms which greatly increase the functional diversity of the genome. In the human, more than 95% of multi-exon genes undergo alternative splicing. It is hard to computationally differentiate the functions for the splice isoforms of the same gene, because they are almost always annotated with the same functions and share similar sequences. In this paper, we developed a generic framework to identify the ‘responsible’ isoform(s) for each function that the gene carries out, and therefore predict functional assignment on the isoform level instead of on the gene level. Within this generic framework, we implemented and evaluated several related algorithms for isoform function prediction. We tested these algorithms through both computational evaluation and experimental validation of the predicted ‘responsible’ isoform(s) and the predicted disparate functions of the isoforms of Cdkn2a and of Anxa6. Our algorithm represents the first effort to predict and differentiate isoforms through large-scale genomic data integration.

Recharging oxidative protein repair: catalysis by methionine sulfoxide reductases towards their amino acid, protein, and model substrates

The sulfur-containing amino acid methionine (Met) in its free and amino acid residue forms can be readily oxidized to the R and S diastereomers of methionine sulfoxide (MetO). Methionine sulfoxide reductases A (MSRA) and B (MSRB) reduce MetO back to Met in a stereospecific manner, acting on the S and R forms, respectively. A third MSR type, fRMSR, reduces the R form of free MetO. MSRA and MSRB are spread across the three domains of life, whereas fRMSR is restricted to bacteria and unicellular eukaryotes. These enzymes protect against abiotic and biotic stresses and regulate lifespan. MSRs are thiol oxidoreductases containing catalytic redox-active cysteine or selenocysteine residues, which become oxidized by the substrate, requiring regeneration for the next catalytic cycle. These enzymes can be classified according to the number of redox-active cysteines (selenocysteines) and the strategies to regenerate their active forms by thioredoxin and glutaredoxin systems. For each MSR type, we review catalytic parameters for the reduction of free MetO, low molecular weight MetO-containing compounds, and oxidized proteins. Analysis of these data reinforces the concept that MSRAs reduce various types of MetO-containing substrates with similar efficiency, whereas MSRBs are specialized for the reduction of MetO in proteins.

Methionine sulfoxide reductases preferentially reduce unfolded oxidized proteins and protect cells from oxidative protein unfolding

Reduction of methionine sulfoxide (MetO) residues in proteins is catalyzed by methionine sulfoxide reductases A (MSRA) and B (MSRB), which act in a stereospecific manner. Catalytic properties of these enzymes were previously established mostly using low molecular weight MetO-containing compounds, whereas little is known about the catalysis of MetO reduction in proteins, the physiological substrates of MSRA and MSRB. In this work we exploited an NADPH-dependent thioredoxin system and determined the kinetic parameters of yeast MSRA and MSRB using three different MetO-containing proteins. Both enzymes showed Michaelis-Menten kinetics with the K(m) lower for protein than for small MetO-containing substrates. MSRA reduced both oxidized proteins and low molecular weight MetO-containing compounds with similar catalytic efficiencies, whereas MSRB was specialized for the reduction of MetO in proteins. Using oxidized glutathione S-transferase as a model substrate, we showed that both MSR types were more efficient in reducing MetO in unfolded than in folded proteins and that their activities increased with the unfolding state. Biochemical quantification and identification of MetO reduced in the substrates by mass spectrometry revealed that the increased activity was due to better access to oxidized MetO in unfolded proteins; it also showed that MSRA was intrinsically more active with unfolded proteins regardless of MetO availability. Moreover, MSRs most efficiently protected cells from oxidative stress that was accompanied by protein unfolding. Overall, this study indicates that MSRs serve a critical function in the folding process by repairing oxidatively damaged nascent polypeptides and unfolded proteins.