Sub-AQUA: real-value quality assessment of protein structure models

Using steered molecular dynamic tension for assessing quality of computational protein structure models

The native structures of proteins, except for notable exceptions of intrinsically disordered proteins, in general take their most stable conformation in the physiological condition to maintain their structural framework so that their biological function can be properly carried out. Experimentally, the stability of a protein can be measured by several means, among which the pulling experiment using the atomic force microscope (AFM) stands as a unique method. AFM directly measures the resistance from unfolding, which can be quantified from the observed force-extension profile. It has been shown that key features observed in an AFM pulling experiment can be well reproduced by computational molecular dynamics simulations. Here, we applied computational pulling for estimating the accuracy of computational protein structure models under the hypothesis that the structural stability would positively correlated with the accuracy, i.e. the closeness to the native, of a model. We used in total 4929 structure models for 24 target proteins from the Critical Assessment of Techniques of Structure Prediction (CASP) and investigated if the magnitude of the break force, that is, the force required to rearrange the model's structure, from the force profile was sufficient information for selecting near-native models. We found that near-native models can be successfully selected by examining their break forces suggesting that high break force indeed indicates high stability of models. On the other hand, there were also near-native models that had relatively low peak forces. The mechanisms of the stability exhibited by the break forces were explored and discussed.

How 5 f Electron Polarisability Drives Covalency and Selectivity in Actinide N-Donor Complexes

We report a series of isostructural tetravalent actinide (Th, U−Pu) complexes with the N‐donor ligand N,N’‐ethylene‐bis((pyrrole‐2‐yl)methanimine) (H₂L, H₂pyren). Structural data from SC‐XRD analysis reveal [An(pyren)₂] complexes with different An−N_imine versus An−N_pyrrolide bond lengths. Quantum chemical calculations elucidated the bonding situation, including differences in the covalent character of the coordinative bonds. A comparison to the intensely studied analogous N,N′‐ethylene‐bis(salicylideneimine) (H₂salen)‐based complexes [An(salen)₂] displays, on average, almost equal electron sharing of pyren or salen with the An^IV, pointing to a potential ligand‐cage‐driven complex stabilisation. This is shown in the fixed ligand arrangement of pyren and salen in the respective An^IV complexes. The overall bond strength of the pure N‐donor ligand pyren to An^IV (An=Th, U, Np, Pu) is slightly weaker than to salen, with the exception of the Pa^IV complex, which exhibits extraordinarily high electron sharing of pyren with Pa^IV. Such an altered ligand preference within the early An^IV series points to a specificity of the 5f¹ configuration, which can be explained by polarisation effects of the 5 f electrons, allowing the strongest f electron backbonding from Pa^IV (5f¹) to the N donors of pyren.

Characterization of Inducible HSP70 Genes in an Antarctic Yeast, Glaciozyma antarctica PI12, in Response to Thermal Stress

The induction of highly conserved heat shock protein 70 (HSP70) is often related to a cellular response due to harmful stress or adverse life conditions. In this study, we determined the expression of Hsp70 genes in the Antarctic yeast, Glaciozyma antarctica, under different several thermal treatments for several exposure periods. The main aims of the present study were (1) to determine if stress-induced Hsp70 could be used to monitor the exposure of the yeast species G. antarctica to various types of thermal stress; (2) to analyze the structures of the G. antarctica HSP70 proteins using comparative modeling; and (3) to evaluate the relationship between the function and structure of HSP70 in G. antarctica. In this study, we managed to amplify and clone 2 Hsp70 genes from G. antarctica named GaHsp70-1 and GaHsp70-2. The cells of G. antarctica expressed significantly inducible Hsp70 genes after the heat and cold shock treatments. Interestingly, GaHsp70-1 showed 2–6-fold higher expression than GaHsp70-2 after the heat and cold exposure. ATP hydrolysis analysis on both G. antarctica HSP70s proved that these psychrophilic chaperones can perform activities in a wide range of temperatures, such as at 37, 25, 15, and 4 °C. The 3D structures of both HSP70s revealed several interesting findings, such as the substitution of a β-sheet to loop in the N-terminal ATPase binding domain and some modest residue substitutions, which gave the proteins the flexibility to function at low temperatures and retain their functional activity at ambient temperatures. In conclusion, both analyzed HSP70s played important roles in the physiological adaptation of G. antarctica.

Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models

Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method (GMQ), explicitly considers the predicted quality of spatially neighboring residues using a graph representation of a query protein structure model. GMQ uses conditional random field as its core of the algorithm, and performs a binary prediction of the quality of each residue in a model, indicating if a residue position is likely to be within an error cutoff or not. The accuracy of GMQ was improved by considering larger graphs to include quality information of more surrounding residues. Moreover, we found that using different edge weights in graphs reflecting different secondary structures further improves the accuracy. GMQ showed competitive performance on a benchmark for quality assessment of structure models from the Critical Assessment of Techniques for Protein Structure Prediction (CASP).

Computational methods for constructing protein structure models from 3D electron microscopy maps

Protein structure determination by cryo-electron microscopy (EM) has made significant progress in the past decades. Resolutions of EM maps have been improving as evidenced by recently reported structures that are solved at high resolutions close to 3Å. Computational methods play a key role in interpreting EM data. Among many computational procedures applied to an EM map to obtain protein structure information, in this article we focus on reviewing computational methods that model protein three-dimensional (3D) structures from a 3D EM density map that is constructed from two-dimensional (2D) maps. The computational methods we discuss range from de novo methods, which identify structural elements in an EM map, to structure fitting methods, where known high resolution structures are fit into a low-resolution EM map. A list of available computational tools is also provided.

Three-Dimensional Molecular Modeling of a Diverse Range of SC Clan Serine Proteases

Serine proteases are involved in a variety of biological processes and are classified into clans sharing structural homology. Although various three-dimensional structures of SC clan proteases have been experimentally determined, they are mostly bacterial and animal proteases, with some from archaea, plants, and fungi, and as yet no structures have been determined for protozoa. To bridge this gap, we have used molecular modeling techniques to investigate the structural properties of different SC clan serine proteases from a diverse range of taxa. Either SWISS-MODEL was used for homology-based structure prediction or the LOOPP server was used for threading-based structure prediction. The predicted models were refined using Insight II and SCRWL and validated against experimental structures. Investigation of secondary structures and electrostatic surface potential was performed using MOLMOL. The structural geometry of the catalytic core shows clear deviations between taxa, but the relative positions of the catalytic triad residues were conserved. Evolutionary divergence was also exhibited by large variation in secondary structure features outside the core, differences in overall amino acid distribution, and unique surface electrostatic potential patterns between species. Encompassing a wide range of taxa, our structural analysis provides an evolutionary perspective on SC clan serine proteases.

Modeling and structural analysis of PA clan serine proteases

BACKGROUND

Serine proteases account for over a third of all known proteolytic enzymes; they are involved in a variety of physiological processes and are classified into clans sharing structural homology. The PA clan of endopeptidases is the most abundant and over two thirds of this clan is comprised of the S1 family of serine proteases, which bear the archetypal trypsin fold and have a catalytic triad in the order Histidine, Aspartate, Serine. These proteases have been studied in depth and many three dimensional structures have been experimentally determined. However, these structures mostly consist of bacterial and animal proteases, with a small number of plant and fungal proteases and as yet no structures have been determined for protozoa or archaea. The core structure and active site geometry of these proteases is of interest for many applications. This study investigated the structural properties of different S1 family serine proteases from a diverse range of taxa using molecular modeling techniques.

RESULTS

Our predicted models from protozoa, archaea, fungi and plants were combined with the experimentally determined structures of 16 S1 family members and used for analysis of the catalytic core. Amino acid sequences were submitted to SWISS-MODEL for homology-based structure prediction or the LOOPP server for threading-based structure prediction. Predicted models were refined using INSIGHT II and SCRWL and validated against experimental structures. Investigation of secondary structures and electrostatic surface potential was performed using MOLMOL. The structural geometry of the catalytic core shows clear deviations between taxa, but the relative positions of the catalytic triad residues were conserved. Some highly conserved residues potentially contributing to the stability of the structural core were identified. Evolutionary divergence was also exhibited by large variation in secondary structure features outside the core, differences in overall amino acid distribution, and unique surface electrostatic potential patterns between species.

CONCLUSIONS

Encompassing a wide range of taxa, our structural analysis provides an evolutionary perspective on S1 family serine proteases. Focusing on the common core containing the catalytic site of the enzyme, this analysis is beneficial for future molecular modeling strategies and structural analysis of serine protease models.

Protein structure validation by generalized linear model root-mean-square deviation prediction

Large-scale initiatives for obtaining spatial protein structures by experimental or computational means have accentuated the need for the critical assessment of protein structure determination and prediction methods. These include blind test projects such as the critical assessment of protein structure prediction (CASP) and the critical assessment of protein structure determination by nuclear magnetic resonance (CASD-NMR). An important aim is to establish structure validation criteria that can reliably assess the accuracy of a new protein structure. Various quality measures derived from the coordinates have been proposed. A universal structural quality assessment method should combine multiple individual scores in a meaningful way, which is challenging because of their different measurement units. Here, we present a method based on a generalized linear model (GLM) that combines diverse protein structure quality scores into a single quantity with intuitive meaning, namely the predicted coordinate root-mean-square deviation (RMSD) value between the present structure and the (unavailable) "true" structure (GLM-RMSD). For two sets of structural models from the CASD-NMR and CASP projects, this GLM-RMSD value was compared with the actual accuracy given by the RMSD value to the corresponding, experimentally determined reference structure from the Protein Data Bank (PDB). The correlation coefficients between actual (model vs. reference from PDB) and predicted (model vs. "true") heavy-atom RMSDs were 0.69 and 0.76, for the two datasets from CASD-NMR and CASP, respectively, which is considerably higher than those for the individual scores (-0.24 to 0.68). The GLM-RMSD can thus predict the accuracy of protein structures more reliably than individual coordinate-based quality scores.

Evaluation of model quality predictions in CASP9

CASP has been assessing the state of the art in the a priori estimation of accuracy of protein structure prediction since 2006. The inclusion of model quality assessment category in CASP contributed to a rapid development of methods in this area. In the last experiment, 46 quality assessment groups tested their approaches to estimate the accuracy of protein models as a whole and/or on a per-residue basis. We assessed the performance of these methods predominantly on the basis of the correlation between the predicted and observed quality of the models on both global and local scales. The ability of the methods to identify the models closest to the best one, to differentiate between good and bad models, and to identify well modeled regions was also analyzed. Our evaluations demonstrate that even though global quality assessment methods seem to approach perfection point (weighted average per-target Pearson's correlation coefficients are as high as 0.97 for the best groups), there is still room for improvement. First, all top-performing methods use consensus approaches to generate quality estimates, and this strategy has its own limitations. Second, the methods that are based on the analysis of individual models lag far behind clustering techniques and need a boost in performance. The methods for estimating per-residue accuracy of models are less accurate than global quality assessment methods, with an average weighted per-model correlation coefficient in the range of 0.63-0.72 for the best 10 groups.

Structural Bioinformatics Approach of Cyclin-Dependent Kinases 1 and 3 Complexed with Inhibitors

The cyclin-dependent kinases or CDKs participate in the regulation of both the cell progression cycle and the RNA polymerase-II transcription cycle. In several human tumours deregulation of CDK-related mechanisms have been detected, e.g., overexpression of cyclins or deletion of genes encoding for CKIs. Regarding these observations, CDKs came up to be interesting targets for elaboration of novel antitumour drugs. Based on the importance of the CDKs, this research aimed to describe, to characterize and to compare the molecular models of CDK1 and CDK3. Since the structures of human CDK1 and CDK3 are unavailable in the Protein Data Bank -PDB, homology models were created based on the CDK2 as the template, once they share a substantial identity. The structural studies of the CDK1 and CDK3 biding sites were conducted by molecular docking with 15 different CDK inhibitors previously identified to CDK2. This study allowed the understanding of the structure of the complexes between CDK1/ CDK3 with inhibitors. The knowledge of their structural features mainly the biding sites might be useful to discovery and rationalization of drug design process.

Energetics-based discovery of protein-ligand interactions on a proteomic scale

Biochemical functions of proteins in cells frequently involve interactions with various ligands. Proteomic methods for the identification of proteins that interact with specific ligands such as metabolites, signaling molecules, and drugs are valuable in investigating the regulatory mechanisms of cellular metabolism, annotating proteins with unknown functions, and elucidating pharmacological mechanisms. Here we report an energetics-based target identification method in which target proteins in a cell lysate are identified by exploiting the effect of ligand binding on their stabilities. Urea-induced unfolding of proteins in cell lysates is probed by a short pulse of proteolysis, and the effect of a ligand on the amount of folded protein remaining is monitored on a proteomic scale. As proof of principle, we identified proteins that interact with ATP in the Escherichia coli proteome. Literature and database mining confirmed that a majority of the identified proteins are indeed ATP-binding proteins. Four identified proteins that were previously not known to interact with ATP were cloned and expressed to validate the result. Except for one protein, the effects of ATP on urea-induced unfolding were confirmed. Analyses of the protein sequences and structure models were also employed to predict potential ATP binding sites in the identified proteins. Our results demonstrate that this energetics-based target identification approach is a facile method to identify proteins that interact with specific ligands on a proteomic scale.

Effect of using suboptimal alignments in template-based protein structure prediction

Computational protein structure prediction remains a challenging task in protein bioinformatics. In the recent years, the importance of template-based structure prediction is increasing because of the growing number of protein structures solved by the structural genomics projects. To capitalize the significant efforts and investments paid on the structural genomics projects, it is urgent to establish effective ways to use the solved structures as templates by developing methods for exploiting remotely related proteins that cannot be simply identified by homology. In this work, we examine the effect of using suboptimal alignments in template-based protein structure prediction. We showed that suboptimal alignments are often more accurate than the optimal one, and such accurate suboptimal alignments can occur even at a very low rank of the alignment score. Suboptimal alignments contain a significant number of correct amino acid residue contacts. Moreover, suboptimal alignments can improve template-based models when used as input to Modeller. Finally, we use suboptimal alignments for handling a contact potential in a probabilistic way in a threading program, SUPRB. The probabilistic contacts strategy outperforms the partly thawed approach, which only uses the optimal alignment in defining residue contacts, and also the re-ranking strategy, which uses the contact potential in re-ranking alignments. The comparison with existing methods in the template-recognition test shows that SUPRB is very competitive and outperforms existing methods.