Andrey Gorin

Solution structure of P22 transcriptional antitermination N peptide-boxB RNA complex.

We have determined the solution structure of a 15-mer boxB RNA hairpin complexed with a 20-mer basic peptide of the N protein involved in bacteriophage P22 transcriptional antitermination. Complex formation involves adaptive binding with the N peptide adopting a bent α-helical conformation that packs tightly through hydrophobic and electrostatic interactions against the major groove face of the boxB RNA hairpin, orienting the open opposite face for potential interactions with host factors and/or RNA polymerase. Four nucleotides in the boxB RNA hairpin pentaloop form a stable GNRA like tetraloop structural scaffold on complex formation, allowing the looped out fifth nucleotide to make extensive hydrophobic contacts with the bound peptide. The guanidinium group of a key arginine is hydrogen-bonded to the guanine in a loop-closing sheared G·A mismatch and to adjacent backbone phosphates. The identified intermolecular contacts account for the consequences of N peptide and boxB RNA mutations on bacteriophage transcriptional antitermination.

RNA architecture dictates the conformations of a bound peptide.

BACKGROUND The biological function of several viral and bacteriophage proteins, and their arginine-rich subdomains, involves RNA-mediated interactions. It has been shown recently that bound peptides adopt either beta-hairpin or alpha-helical conformations in viral and phage peptide-RNA complexes. We have compared the structures of the arginine-rich peptide domain of HIV-1 Rev bound to two RNA aptamers to determine whether RNA architecture can dictate the conformations of a bound peptide. RESULTS The core-binding segment of the HIV-1 Rev peptide class II RNA aptamer complex spans the two-base bulge and hairpin loop of the bound RNA and the carboxy-terminal segment of the bound peptide. The bound peptide is anchored in place by backbone and sidechain intermolecular hydrogen bonding and van der Waals stacking interactions. One of the bulge bases participates in U*(A*U) base triple formation, whereas the other is looped out and flaps over the bound peptide in the complex. The seven-residue hairpin loop is closed by a sheared G*A mismatch pair with several pyrimidines looped out of the hairpin fold. CONCLUSIONS Our structural studies establish that RNA architecture dictates whether the same HIV-1 Rev peptide folds into an extended or alpha-helical conformation on complex formation. Arginine-rich peptides can therefore adapt distinct secondary folds to complement the tertiary folds of their RNA targets. This contrasts with protein-RNA complexes in which elements of RNA secondary structure adapt to fit within the tertiary folds of their protein targets.

Towards Structural Genomics of RNA: Rapid NMR Resonance Assignment and Simultaneous RNA Tertiary Structure Determination Using Residual Dipolar Couplings

We report a new residual dipolar couplings (RDCs) based NMR procedure for rapidly determining RNA tertiary structure demonstrated on a uniformly (15)N/(13)C-labeled 27 nt variant of the trans-activation response element (TAR) RNA from HIV-I. In this procedure, the time-consuming nuclear Overhauser enhancement (NOE)-based sequential assignment step is replaced by a fully automated RDC-based assignment strategy. This approach involves examination of all allowed sequence-specific resonance assignment permutations for best-fit agreement between measured RDCs and coordinates for sub-structures in a target RNA. Using idealized A-form geometries to model Watson-Crick helices and coordinates from a previous X-ray structure to model a hairpin loop in TAR, the best-fit RDC assignment solutions are determined very rapidly (<five minutes of computational time) and are in complete agreement with corresponding NOE-based assignments. Orientational constraints derived from RDCs are used simultaneously to assemble sub-structures into an RNA tertiary conformation. Through enhanced speeds of application and reduced reliance on chemical shift dispersion, this RDC-based approach lays the foundation for rapidly determining RNA conformations in a structural genomics context, and may increase the size limit of RNAs that can be examined by NMR.

Anchoring an extended HTLV-1 Rex peptide within an RNA major groove containing junctional base triples

BACKGROUND The Rex protein of the human T cell leukemia virus type 1 (HTLV-1) belongs to a family of proteins that use arginine-rich motifs (ARMs) to recognize their RNA targets. Previously, an in vitro selected RNA aptamer sequence was identified that mediates mRNA transport in vivo when placed in the primary binding site on stem-loop IID of the Rex response element. We present the solution structure of the HTLV-1 arginine-rich Rex peptide bound to its RNA aptamer target determined by multidimensional heteronuclear NMR spectroscopy. RESULTS The Rex peptide in a predominantly extended conformation threads through a channel formed by the shallow and widened RNA major groove and a looped out guanine. The RNA aptamer contains three stems separated by a pair of two-base bulges, and adopts an unanticipated fold in which both junctional sites are anchored through base triple formation. Binding specificity is associated with intermolecular hydrogen bonding between guanidinium groups of three non-adjacent arginines and the guanine base edges of three adjacent G.C pairs. CONCLUSIONS The extended S-shaped conformation of the Rex peptide, together with previous demonstrations of a beta-hairpin conformation for the bovine immunodeficiency virus (BIV) Tat peptide and an alpha-helical conformation for the human immunodeficiency virus (HIV) Rev peptide in complex with their respective RNA targets, expands our understanding of the strategies employed by ARMs for adaptive recognition and highlights the importance of RNA tertiary structure in accommodating minimalist elements of protein secondary structure.

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces

BackgroundProtein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB).ResultsWe have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section).ConclusionOur method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.

Protein docking using surface matching and supervised machine learning.

Computational prediction of protein complex structures through docking offers a means to gain a mechanistic understanding of protein interactions that mediate biological processes. This is particularly important as the number of experimentally determined structures of isolated proteins exceeds the number of structures of complexes. A comprehensive docking procedure is described in which efficient sampling of conformations is achieved by matching surface normal vectors, fast filtering for shape complementarity, clustering by RMSD, and scoring the docked conformations using a supervised machine learning approach. Contacting residue pair frequencies, residue propensities, evolutionary conservation, and shape complementarity score for each docking conformation are used as input data to a Random Forest classifier. The performance of the Random Forest approach for selecting correctly docked conformations was assessed by cross‐validation using a nonredundant benchmark set of X‐ray structures for 93 heterodimer and 733 homodimer complexes. The single highest rank docking solution was the correct (near‐native) structure for slightly more than one third of the complexes. Furthermore, the fraction of highly ranked correct structures was significantly higher than the overall fraction of correct structures, for almost all complexes. A detailed analysis of the difficult to predict complexes revealed that the majority of the homodimer cases were explained by incorrect oligomeric state annotation. Evolutionary conservation and shape complementarity score as well as both underrepresented and overrepresented residue types and residue pairs were found to make the largest contributions to the overall prediction accuracy. Finally, the method was also applied to docking unbound subunit structures from a previously published benchmark set. Proteins 2007.

A decentralized parallel implementation for parallel tempering algorithm

Parallel tempering (PT), also known as replica exchange, is a powerful Markov Chain Monte Carlo sampling approach, which aims at reducing the relaxation time in simulations of physical systems. In this paper, we present a novel decentralized parallel implementation of PT using the message passing interface (MPI) and the scalable parallel random number generators (SPRNG) library. By taking advantage of the characteristics of pseudorandom number generators, this implementation eliminates global synchronization and reduces the overhead caused by interprocessor communication in replica exchange in PT. Moreover, our proposed non-blocking replica exchange reduces communication overhead in pair-wise process replica exchanges by allowing the process reaching the replica exchange point to leap-ahead while waiting for the other one to reach the common replica exchange point. Also, temperature exchange instead of conformation replica exchange is proposed to reduce communication and achieve load balancing in the participating processors in the PT computation. All these enable one to efficiently apply PT to large-scale massively parallel systems. The efficiency of this parallel PT implementation is demonstrated in the context of minimizing various benchmark functions with complicated landscapes as objective functions. Our computational results and analysis have shown that the decentralized PT is scalable, reproducible, load-balanced, and yields insignificant communication overhead.

A peptide-linkage deletion procedure for estimate of energetic contributions of individual peptide groups in a complex environment: Application to parallel β-Sheets

A peptide-linkage deletion procedure is introduced for extracting the quantum mechanical (QM) interaction energies of individual groups in a complex environment and applied for the determination of the energetic contributions of the individual hydrogen bond acceptors (C=O’s) and donors (N-H’s) in parallel β-sheets. For the β-sheets studied here, the results show that the contributions from the H-bond acceptors (C=O) can be significantly greater than the contributions from the donors (N-H). It is suggested that this imbalance may be induced, at least in part, by the inter-strand Cα-H⋯O=C interactions which may play an important role in stabilizing β-sheets. The results demonstrate the usefulness of the approach proposed in this paper to study interactions in complex protein environments.

PARALLEL TEMPERING IN ROSETTA PRACTICE

Parallel Tempering (PT) is an effective algorithm to overcome the slow convergence in low-temperature protein simulation by initiating multiple systems to run at multiple temperature levels and randomly switch with neighbor temperature levels. We implemented the PT scheme in the Rosetta to explore the rough energy landscape in protein folding and to improve the success rate of Rosetta in topologically complex structures. Compared to the original Simulated Annealing (SA) scheme in Rosetta, our preliminary computational results show that the PT scheme in Rosetta program exhibits a wider range sampling in the potential energy surface in protein folding.

PanFP: pangenome-based functional profiles for microbial communities.

BackgroundFor decades there has been increasing interest in understanding the relationships between microbial communities and ecosystem functions. Current DNA sequencing technologies allows for the exploration of microbial communities in two principle ways: targeted rRNA gene surveys and shotgun metagenomics. For large study designs, it is often still prohibitively expensive to sequence metagenomes at both the breadth and depth necessary to statistically capture the true functional diversity of a community. Although rRNA gene surveys provide no direct evidence of function, they do provide a reasonable estimation of microbial diversity, while being a very cost-effective way to screen samples of interest for later shotgun metagenomic analyses. However, there is a great deal of 16S rRNA gene survey data currently available from diverse environments, and thus a need for tools to infer functional composition of environmental samples based on 16S rRNA gene survey data.ResultsWe present a computational method called pangenome-based functional profiles (PanFP), which infers functional profiles of microbial communities from 16S rRNA gene survey data for Bacteria and Archaea. PanFP is based on pangenome reconstruction of a 16S rRNA gene operational taxonomic unit (OTU) from known genes and genomes pooled from the OTU’s taxonomic lineage. From this lineage, we derive an OTU functional profile by weighting a pangenome’s functional profile with the OTUs abundance observed in a given sample. We validated our method by comparing PanFP to the functional profiles obtained from the direct shotgun metagenomic measurement of 65 diverse communities via Spearman correlation coefficients. These correlations improved with increasing sequencing depth, within the range of 0.8–0.9 for the most deeply sequenced Human Microbiome Project mock community samples. PanFP is very similar in performance to another recently released tool, PICRUSt, for almost all of survey data analysed here. But, our method is unique in that any OTU building method can be used, as opposed to being limited to closed-reference OTU picking strategies against specific reference sequence databases.ConclusionsWe developed an automated computational method, which derives an inferred functional profile based on the 16S rRNA gene surveys of microbial communities. The inferred functional profile provides a cost effective way to study complex ecosystems through predicted comparative functional metagenomes and metadata analysis. All PanFP source code and additional documentation are freely available online at GitHub (https://github.com/srjun/PanFP).