Splitting statistical potentials into meaningful scoring functions: testing the prediction of near-native structures from decoy conformations

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions

BACKGROUND

Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities.

RESULTS

Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models.

CONCLUSIONS

While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .

Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions

Protein-protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state-of-the-art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state-of-art methods.

The crystal structure of a cardiovirus RNA-dependent RNA polymerase reveals an unusual conformation of the polymerase active site

Encephalomyocarditis virus (EMCV) is a member of the Cardiovirus genus within the large Picornaviridae family, which includes a number of important human and animal pathogens. The RNA-dependent RNA polymerase (RdRp) 3Dpol is a key enzyme for viral genome replication. In this study, we report the X-ray structures of two different crystal forms of the EMCV RdRp determined at 2.8- and 2.15-Å resolution. The in vitro elongation and VPg uridylylation activities of the purified enzyme have also been demonstrated. Although the overall structure of EMCV 3Dpol is shown to be similar to that of the known RdRps of other members of the Picornaviridae family, structural comparisons show a large reorganization of the active-site cavity in one of the crystal forms. The rearrangement affects mainly motif A, where the conserved residue Asp240, involved in ribonucleoside triphosphate (rNTP) selection, and its neighbor residue, Phe239, move about 10 Å from their expected positions within the ribose binding pocket toward the entrance of the rNTP tunnel. This altered conformation of motif A is stabilized by a cation-π interaction established between the aromatic ring of Phe239 and the side chain of Lys56 within the finger domain. Other contacts, involving Phe239 and different residues of motif F, are also observed. The movement of motif A is connected with important conformational changes in the finger region flanked by residues 54 to 63, harboring Lys56, and in the polymerase N terminus. The structures determined in this work provide essential information for studies on the cardiovirus RNA replication process and may have important implications for the development of new antivirals targeting the altered conformation of motif A.

IMPORTANCE

The Picornaviridae family is one of the largest virus families known, including many important human and animal pathogens. The RNA-dependent RNA polymerase (RdRp) 3Dpol is a key enzyme for picornavirus genome replication and a validated target for the development of antiviral therapies. Solving the X-ray structure of the first cardiovirus RdRp, EMCV 3Dpol, we captured an altered conformation of a conserved motif in the polymerase active site (motif A) containing the aspartic acid residue involved in rNTP selection and binding. This altered conformation of motif A, which interferes with the correct positioning of the rNTP substrate in the active site, is stabilized by a number of residues strictly conserved among picornaviruses. The rearrangements observed suggest that this motif A segment is a dynamic element that can be modulated by external effectors, either activating or inhibiting enzyme activity, and this type of modulation appears to be general to all picornaviruses.

On the use of knowledge-based potentials for the evaluation of models of protein-protein, protein-DNA, and protein-RNA interactions

Proteins are the bricks and mortar of cells, playing structural and functional roles. In order to perform their function, they interact with each other as well as with other biomolecules such as DNA or RNA. Therefore, to fathom the function of a protein, we require knowing its partners and the atomic details of its interactions (i.e., the structure of the complex). However, the amount of protein interactions with an experimentally determined three-dimensional structure is scarce. Therefore, computational techniques such as homology modeling are foremost to fill this gap. Protein interactions can be modeled using as templates the interactions of homologous proteins, if the structure of the complex is known, or using docking methods. In both approaches, the estimation of the quality of models is essential. There are several ways to address this problem. In this review, we focus on the use of knowledge-based potentials for the analysis of protein interactions. We describe the procedure to derive statistical potentials and split them into different energetic terms that can be used for different purposes. We extensively discuss the fields where knowledge-based potentials have been successfully applied to (1) model protein-protein, protein-DNA, and protein-RNA interactions and (2) predict binding sites (in the protein and in the DNA). Moreover, we provide ready-to-use resources for docking and benchmarking protein interactions.

On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking

Development of effective methods to screen binary interactions obtained by rigid-body protein-protein docking is key for structure prediction of complexes and for elucidating physicochemical principles of protein-protein binding. We have derived empirical knowledge-based potential functions for selecting rigid-body docking poses. These potentials include the energetic component that provides the residues with a particular secondary structure and surface accessibility. These scoring functions have been tested on a state-of-art benchmark dataset and on a decoy dataset of permanent interactions. Our results were compared with a residue-pair potential scoring function (RPScore) and an atomic-detailed scoring function (Zrank). We have combined knowledge-based potentials to score protein-protein poses of decoys of complexes classified either as transient or as permanent protein-protein interactions. Being defined from residue-pair statistical potentials and not requiring of an atomic level description, our method surpassed Zrank for scoring rigid-docking decoys where the unbound partners of an interaction have to endure conformational changes upon binding. However, when only moderate conformational changes are required (in rigid docking) or when the right conformational changes are ensured (in flexible docking), Zrank is the most successful scoring function. Finally, our study suggests that the physicochemical properties necessary for the binding are allocated on the proteins previous to its binding and with independence of the partner. This information is encoded at the residue level and could be easily incorporated in the initial grid scoring for Fast Fourier Transform rigid-body docking methods.

Prediction of a new class of RNA recognition motif

The observation that activation domains (AD) of procarboxypeptidases are rather long compared to the pro-regions of other zymogens raises the possibility that they could play additional roles apart from precluding enzymatic activity within the proenzyme and helping in its folding process. In the present work, we compared the overall pro-domain tertiary structure with several proteins belonging to the same fold in the structural classification of proteins (SCOP) database by using structure and sequence comparisons. The best score obtained was between the activation domain of human procarboxypeptidase A4 (ADA4h) and the human U1A protein from the U1 snRNP. Structural alignment revealed the existence of RNP1- and RNP2-related sequences in ADA4h. After modeling ADA4h on U1A, the new structure was used to extract a new sequence pattern characteristic for important residues at key positions. The new sequence pattern allowed scanning protein sequences to predict the RNA-binding function for 32 sequences undetected by PFAM. Unspecific RNA electrophoretic mobility shift assays experimentally supported the prediction that ADA4h binds an RNA motif similar to the U1A binding-motif of stem-loop II of U1 small nuclear RNA. The experiments carried out with ADA4h in the present work suggest the sharing of a common ancestor with other RNA recognition motifs. However, the fact that key residues preventing activity within the proenzyme are also key residues for RNA binding might have induced the activation domains of procarboxypeptidases to evolve from the canonical RNP1 and RNP2 sequences.

A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction

BACKGROUND

An accurate potential function is essential to attack protein folding and structure prediction problems. The key to developing efficient knowledge-based potential functions is to design reference states that can appropriately counteract generic interactions. The reference states of many knowledge-based distance-dependent atomic potential functions were derived from non-interacting particles such as ideal gas, however, which ignored the inherent sequence connectivity and entropic elasticity of proteins.

METHODOLOGY

We developed a new pair-wise distance-dependent, atomic statistical potential function (RW), using an ideal random-walk chain as reference state, which was optimized on CASP models and then benchmarked on nine structural decoy sets. Second, we incorporated a new side-chain orientation-dependent energy term into RW (RWplus) and found that the side-chain packing orientation specificity can further improve the decoy recognition ability of the statistical potential.

SIGNIFICANCE

RW and RWplus demonstrate a significantly better ability than the best performing pair-wise distance-dependent atomic potential functions in both native and near-native model selections. It has higher energy-RMSD and energy-TM-score correlations compared with other potentials of the same type in real-life structure assembly decoys. When benchmarked with a comprehensive list of publicly available potentials, RW and RWplus shows comparable performance to the state-of-the-art scoring functions, including those combining terms from multiple resources. These data demonstrate the usefulness of random-walk chain as reference states which correctly account for sequence connectivity and entropic elasticity of proteins. It shows potential usefulness in structure recognition and protein folding simulations. The RW and RWplus potentials, as well as the newly generated I-TASSER decoys, are freely available in http://zhanglab.ccmb.med.umich.edu/RW.