Systematic evaluation of CS‐Rosetta for membrane protein structure prediction with sparse NOE restraints

We critically test and validate the CS‐Rosetta methodology for de novo structure prediction of ‐helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase‐1 (mPGES‐1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X‐ray structures are available, resolution‐adapted structural recombination (RASREC) CS‐Rosetta yields structures that are as close to the X‐ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES‐1 and Bacillus cereus TSPO, where only X‐ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS‐Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close‐to‐native structures were obtained with one randomly picked long‐range NOEs for every 14, 31, 38, and 8 residues for full‐length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES‐1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS‐Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812–826. © 2016 Wiley Periodicals, Inc.     

TOUCHSTONEX: protein structure prediction with sparse NMR data

TOUCHSTONEX, a new method for folding proteins that uses a small number of long-range contact restraints derived from NMR experimental NOE (nuclear Overhauser enhancement) data, is described. The method employs a new lattice-based, reduced model of proteins that explicitly represents C(alpha), C(beta), and the sidechain centers of mass. The force field consists of knowledge-based terms to produce protein-like behavior, including various short-range interactions, hydrogen bonding, and one-body, pairwise, and multibody long-range interactions. Contact restraints were incorporated into the force field as an NOE-specific pairwise potential. We evaluated the algorithm using a set of 125 proteins of various secondary structure types and lengths up to 174 residues. Using N/8 simulated, long-range sidechain contact restraints, where N is the number of residues, 108 proteins were folded to a C(alpha)-root-mean-square deviation (RMSD) from native below 6.5 A. The average RMSD of the lowest RMSD structures for all 125 proteins (folded and unfolded) was 4.4 A. The algorithm was also applied to limited experimental NOE data generated for three proteins. Using very few experimental sidechain contact restraints, and a small number of sidechain-main chain and main chain-main chain contact restraints, we folded all three proteins to low-to-medium resolution structures. The algorithm can be applied to the NMR structure determination process or other experimental methods that can provide tertiary restraint information, especially in the early stage of structure determination, when only limited data are available.     

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵N-¹H residual dipolar coupling data, typical of that obtained for ¹⁵N,¹³C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.     

Balancing exploration and exploitation in population‐based sampling improves fragment‐based de novo protein structure prediction

Conformational search space exploration remains a major bottleneck for protein structure prediction methods. Population‐based meta‐heuristics typically enable the possibility to control the search dynamics and to tune the balance between local energy minimization and search space exploration. EdaFold is a fragment‐based approach that can guide search by periodically updating the probability distribution over the fragment libraries used during model assembly. We implement the EdaFold algorithm as a Rosetta protocol and provide two different probability update policies: a cluster‐based variation (EdaRose_c) and an energy‐based one (EdaRose_en). We analyze the search dynamics of our new Rosetta protocols and show that EdaRose_c is able to provide predictions with lower C RMSD to the native structure than EdaRose_en and Rosetta AbInitio Relax protocol. Our software is freely available as a C++ patch for the Rosetta suite and can be downloaded from http://www.riken.jp/zhangiru/software/ . Our protocols can easily be extended in order to create alternative probability update policies and generate new search dynamics. Proteins 2017; 85:852–858. © 2016 Wiley Periodicals, Inc.     

Using scores derived from statistical coupling analysis to distinguish correct and incorrect folds in de-novo protein structure prediction

Distinguishing native from non-native folds remains a challenging problem for protein structure prediction. We describe a method, SCA-distance scoring, based on results from statistical coupling analysis which discriminates between native and non-native folds produced by a de novo protein structure prediction method for four out of five test proteins. The method is particularly good at discriminating non-native folds which are close in RMSD to the true fold but contain a change in an internal structural element. SCA-distance scoring is a useful addition to the tools available for distinguishing native from non-native folds in protein structure prediction.     

Finding the needle in the haystack: towards solving the protein-folding problem computationally

Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as “the protein folding problem,” has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.     

Use of structure comparison methods for the refinement of protein structure predictions. I. Identifying the structural family of a protein from low-resolution models.

Predicting the three-dimensional structure of proteins is still one of the most challenging problems in molecular biology. Despite its difficulty, several investigators have started to produce consistently low-resolution predictions for small proteins. However, in most of these cases, the prediction accuracy is still too low to make them useful. In the present article, we address the problem of obtaining better-quality predictions, starting from low-resolution models. To this end, we have devised a new procedure that uses these models, together with structure comparison methods, to identify the structural family of the target protein. This would allow, in a second step not described in the present work, to refine the predictions using conserved features of the identified family. In our approach, the structure database is investigated using predictions, at different accuracy levels, for a given protein. As query structures, we used both low-resolution versions of the native structures, as well as different sets of low accuracy predictions. In general, we found that for predictions with a resolution of > or =5-7 A, structure comparison methods were able to identify the fold of a protein in the top positions.     

Surber and kick sampling: a comparison for the assessment of macroinvertebrate community structure in streams of south-western Australia

Macroinvertebrate community structure was compared in benthic samples taken by Surber and kick methods from a lotic system in south-western Australia. Eleven sites were sampled concurrently in winter, spring and summer 1987.Surber samples contained fewer individuals and more taxa, particularly those with a low frequency of occurrence. This was attributed to the lower surface area, but greater intensity of Surber sampling. It is proposed that the Surber method is more suited to taking cryptic and closely adherent taxa in sites with a highly heterogeneous substratum.Percentage similarity between paired Surber and kick samples was determined by Sorensen's and Czekanowski's coefficients, with mean values of 66% and 60% for June, 61% and 49% for September and 66% and 49% for December respectively. Ordination demonstrated a division of upland from lowland sites on axis 1, with a separation of paired-samples on axis 2. This pattern held across qualitative and quantitative datasets, with and without a downweighting on rare taxa. At each level of classification fewer paired-samples separated in qualitative than quantitative datasets.Kick sampling provided a substantial saving in costs over Surber sampling, particularly when qualitative data were utilised, making the method suitable for routine, biological monitoring. However, the initial use of replicated Surber sampling, particularly in areas that have not been previously sampled is recommended for environmental impact studies to detect rare taxa, that may be endangered.     

Role of a two-residue spacer in an alpha,beta-Didehydrophenylalanine containing hexapeptide: crystal and solution structure of Boc-val-deltaPhe-Leu-Ala-deltaPhe-Ala-OMe.

The peptide Boc-Val1-deltaPhe2-Leu3-Ala4-deltaPhe5-Ala6-OMe has been examined for the structural consequence of placing a two-residue segment between the deltaPhe residues. The peptide is stabilized by four consecutive beta-turns. The overall conformation of the molecule is a right-handed 3(10)-helix, with average (phi, psi) values (-67.7 degrees, -22.7 degrees), unwound at the C-terminus. The 1H NMR results also suggest that the peptide maintains its 3(10)-helical structure in solution as observed in the crystal state. The crystal structure is stabilized through head-to-tail hydrogen bonds and a repertoire of aromatic interactions laterally directed between adjacent helices, which are antiparallel to each other. The aromatic ring of deltaPhe5 forms the hub of multicentred interactions, namely as a donor in aromatic C-H...pi and aromatic C-H...O=C interactions and as an acceptor in a CH3...pi interaction. The present structure uniquely illustrates the unusual capability of a deltaPhe ring to host such concerted interactions and suggests its exploitation in introducing long-range interactions in the folding of supersecondary structures.     

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1–2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug‐specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans‐membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α‐helical MPs as well as β‐barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge‐based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade. Proteins 2015; 83:1–24. © 2014 Wiley Periodicals, Inc.     

Genetic population structure in horse chestnut (Aesculus hippocastanum L.): effects of human‐mediated expansion across Europe

The geographical distribution of existing populations of horse chestnut (Aesculus hippocastanum L.) in Europe is determined by past demographic events during the Quaternary. In the present study we evaluate the imprints that northward expansions originated from common ancestry at southern Europe may have left on the present patterns of genetic variation for horse chestnut across the continent. Genetic diversity and levels of population structure in a European south–north gradient, ranging from the Balkans to the Scandinavian Peninsula, were determined with Amplified Fragment Length Polymorphism (AFLP) markers in 159 loci. A family of rarefaction techniques for the estimation of gene diversity was used to exclude potential confounding effects as a result of the unequal sample sizes. The results indicate that northern populations are not more genetically depleted than southern populations, thus suggesting that diversity for this species is not correlated with latitudinal distribution. Detailed hypotheses based on prediction models for different historical events associated with human‐mediated spread of cultivation are examined for a better understanding of the current genetic patterns of regional differentiation.