Protein Loop Structure Research Articles

Accurate Structure Prediction for Protein Loops Based on Molecular Dynamics Simulations with RSFF2C.

Protein loops, connecting the α-helices and β-strands, are involved in many important biological processes. However, due to their conformational flexibility, it is still challenging to accurately determine three-dimensional (3D) structures of long loops experimentally and computationally. Herein, we present a systematic study of the protein loop structure prediction via a total of ∼850 μs molecular dynamics (MD) simulations. For a set of 15 long (10-16 residues) and solvent-exposed loops, we first evaluated the performance of four state-of-the-art loop modeling algorithms, DaReUS-Loop, Sphinx, Rosetta-NGK, and MODELLER, on each loop, and none of them could accurately predict the structures for most loops. Then, temperature replica exchange molecular dynamics (REMD) simulations were conducted with three recent force fields, RSFF2C with TIP3P water model, CHARMM36m with CHARMM-modified TIP3P, and AMBER ff19SB with OPC. We found that our recently developed residue-specific force field RSFF2C performed the best and successfully predicted 12 out of 15 loops with a root-mean-square deviation (RMSD) < 1.5 Å. As an alternative with lower computational cost, normal MD simulations at high temperatures (380, 500, and 620 K) were investigated. Temperature-dependent performance was observed for each force field, and, for RSFF2C+TIP3P, we found that three independent 100-ns MD simulations at 500 K gave comparable results with REMD simulations. These results suggest that MD simulations, especially with enhanced sampling techniques such as replica exchange, with the RSFF2C force field could be useful for accurate loop structure prediction.

Increasing the accuracy of protein loop structure prediction with evolutionary constraints.

Accurate prediction of loop structures remains challenging. This is especially true for long loops where the large conformational space and limited coverage of experimentally determined structures often leads to low accuracy. Co-evolutionary contact predictors, which provide information about the proximity of pairs of residues, have been used to improve whole-protein models generated through de novo techniques. Here we investigate whether these evolutionary constraints can enhance the prediction of long loop structures. As a first stage, we assess the accuracy of predicted contacts that involve loop regions. We find that these are less accurate than contacts in general. We also observe that some incorrectly predicted contacts can be identified as they are never satisfied in any of our generated loop conformations. We examined two different strategies for incorporating contacts, and on a test set of long loops (10 residues or more), both approaches improve the accuracy of prediction. For a set of 135 loops, contacts were predicted and hence our methods were applicable in 97 cases. Both strategies result in an increase in the proportion of near-native decoys in the ensemble, leading to more accurate predictions and in some cases improving the root-mean-square deviation of the final model by more than 3Å. Supplementary data are available at Bioinformatics online.

Protein Loop Structure Prediction Using Conformational Space Annealing.

We have developed a protein loop structure prediction method by combining a new energy function, which we call EPLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance-dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of EPLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, EPLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, EPLM outperformed EDFIRE for both decoy sets. This new approach equipped with EPLM and CSA can serve as the state-of-the-art de novo loop modeling method.

Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

An improved algorithm for prediction of protein loop structure based on position specificity of amino acids

Loop region is necessary structural element of protein molecule, and plays significant roles in protein functioning, e.g., in signaling, ligand recognition. Unlike the well-defined secondary structures (i.e., helix, sheet), however, loop regions vary in structure and some of them are even not able to be measured by ordinary experimental methods. For these reasons, computer-aided prediction of loop structure became a hotspot in bioinformatics and biophysics. Sorts of algorithms have been developed for this purpose. So far, however, the prediction of long loop is still a challenge. Among all the common algorithms, LEAP algorithm achieves the highest precision on long loop prediction. Our investigation on a test data set with LEAP algorithm reveals that the ultimate loop structure predicted by LEAP is almost entirely determined by the initial sampling of the conformation of the loop backbone. If all the backbone conformations in the initial sampling are quite distant from the real (native) conformation, the ultimately predicted structure is also distant from the native conformation, and the prediction accuracy cannot be improved obviously only by increasing the computation time. In the original LEAP, the initial sampling is based on the rough distribution of the backbone torsion angle (Ramachandran plot, R-plot) which doesn't consider the sequence information of the loop region. Many conformations which are far from the native conformation are most likely generated in the sampling. So there raises the open question, is it possible to enhance the initial sampling to be more targeted to the native conformation? In this paper, we suggest an approach to introduce the position-specific amino-acid sequence information into the initial sampling of the backbone conformation, which may generate more targeted initial decoys. An algorithm of protein secondary structure prediction, SPINE X, is used to generate rough but reasonable estimates of torsion angles of each amino acid of the loop backbone in sequence-dependent way. We then combine these values with the original R-plot to reconstruct a new R-plot for each amino acid in the loop, and the initial sampling is performed according to the new R-plot. We applied this new algorithm to a test set of loops (generated from single-chain proteins in CASP 10), and found the medians/means of RMSDs can reduce about 0.12 /0.13 , 0.25 /0.27 , 0.47 /0.27 for loop sets of length 10, 11, 12, respectively. Comparing to the original LEAP algorithm, the probability of making more accurate predictions is almost doubled when using the refined algorithm. The logic of our approach is not limited to LEAP, and can be extended to other algorithms which are also significantly dependent on initial sampling.

Modeling Protein Loop Structure by Cyclic Coordinate Descent-based Approach.

In both predicting protein loop structure and flexible areas in a docking interface, modeling local loop conformation plays a vital role. We address the challenge with a topological manipulation method based on the cyclic coordinate descent (CCD) algorithm. The protein loop conformation sampler (LMbCCD2) presented here allows us to move some key points of the loop toward some specific positions. We test the method's performance on sets of loops, and the results show that LMbCCD2 accurately controls the loop's topology. At last we use LMbCCD2 on flexible protein-peptide docking, and it shows that LMbCCD2 can improve the accuracy of flexible peptide folding and the full docking result.

Clustering and percolation in protein loop structures.

BackgroundHigh precision protein loop modelling remains a challenge, both in template based and template independent approaches to protein structure prediction.MethodWe introduce the concepts of protein loop clustering and percolation, to develop a quantitative approach to systematically classify the modular building blocks of loops in crystallographic folded proteins. These fragments are all different parameterisations of a unique kink solution to a generalised discrete nonlinear Schrödinger (DNLS) equation. Accordingly, the fragments are also local energy minima of the ensuing energy function.ResultsWe show how the loop fragments cover practically all ultrahigh resolution crystallographic protein structures in Protein Data Bank (PDB), with a 0.2 Ångström root-mean-square (RMS) precision. We find that no more than 12 different loop fragments are needed, to describe around 38 % of ultrahigh resolution loops in PDB. But there is also a large number of loop fragments that are either unique, or very rare, and examples of unique fragments are found even in the structure of a myoglobin.ConclusionsProtein loops are built in a modular fashion. The loops are composed of fragments that can be modelled by the kink of the DNLS equation. The majority of loop fragments are also common, which are shared by many proteins. These common fragments are probably important for supporting the overall protein conformation. But there are also several fragments that are either unique to a given protein, or very rare. Such fragments are probably related to the function of the protein. Furthermore, we have found that the amino acid sequence does not determine the structure in a unique fashion. There are many examples of loop fragments with an identical amino acid sequence, but with a very different structure.Electronic supplementary materialThe online version of this article (doi:10.1186/s12900-015-0049-x) contains supplementary material, which is available to authorized users.

Fast protein loop sampling and structure prediction using distance-guided sequential chain-growth Monte Carlo method.

Loops in proteins are flexible regions connecting regular secondary structures. They are often involved in protein functions through interacting with other molecules. The irregularity and flexibility of loops make their structures difficult to determine experimentally and challenging to model computationally. Conformation sampling and energy evaluation are the two key components in loop modeling. We have developed a new method for loop conformation sampling and prediction based on a chain growth sequential Monte Carlo sampling strategy, called Distance-guided Sequential chain-Growth Monte Carlo (DiSGro). With an energy function designed specifically for loops, our method can efficiently generate high quality loop conformations with low energy that are enriched with near-native loop structures. The average minimum global backbone RMSD for 1,000 conformations of 12-residue loops is Å, with a lowest energy RMSD of Å, and an average ensemble RMSD of Å. A novel geometric criterion is applied to speed up calculations. The computational cost of generating 1,000 conformations for each of the x loops in a benchmark dataset is only about cpu minutes for 12-residue loops, compared to ca cpu minutes using the FALCm method. Test results on benchmark datasets show that DiSGro performs comparably or better than previous successful methods, while requiring far less computing time. DiSGro is especially effective in modeling longer loops (– residues).

How long is a piece of loop?

Loops are irregular structures which connect two secondary structure elements in proteins. They often play important roles in function, including enzyme reactions and ligand binding. Despite their importance, their structure remains difficult to predict. Most protein loop structure prediction methods sample local loop segments and score them. In particular protein loop classifications and database search methods depend heavily on local properties of loops. Here we examine the distance between a loop’s end points (span). We find that the distribution of loop span appears to be independent of the number of residues in the loop, in other words the separation between the anchors of a loop does not increase with an increase in the number of loop residues. Loop span is also unaffected by the secondary structures at the end points, unless the two anchors are part of an anti-parallel beta sheet. As loop span appears to be independent of global properties of the protein we suggest that its distribution can be described by a random fluctuation model based on the Maxwell–Boltzmann distribution. It is believed that the primary difficulty in protein loop structure prediction comes from the number of residues in the loop. Following the idea that loop span is an independent local property, we investigate its effect on protein loop structure prediction and show how normalised span (loop stretch) is related to the structural complexity of loops. Highly contracted loops are more difficult to predict than stretched loops.

Sampling multiple scoring functions can improve protein loop structure prediction accuracy.

Accurately predicting loop structures is important for understanding functions of many proteins. In order to obtain loop models with high accuracy, efficiently sampling the loop conformation space to discover reasonable structures is a critical step. In loop conformation sampling, coarse-grain energy (scoring) functions coupling with reduced protein representations are often used to reduce the number of degrees of freedom as well as sampling computational time. However, due to implicitly considering many factors by reduced representations, the coarse-grain scoring functions may have potential insensitivity and inaccuracy, which can mislead the sampling process and consequently ignore important loop conformations. In this paper, we present a new computational sampling approach to obtain reasonable loop backbone models, so-called the Pareto optimal sampling (POS) method. The rationale of the POS method is to sample the function space of multiple, carefully selected scoring functions to discover an ensemble of diversified structures yielding Pareto optimality to all sampled conformations. The POS method can efficiently tolerate insensitivity and inaccuracy in individual scoring functions and thereby lead to significant accuracy improvement in loop structure prediction. We apply the POS method to a set of 4-12-residue loop targets using a function space composed of backbone-only Rosetta and distance-scale finite ideal-gas reference (DFIRE) and a triplet backbone dihedral potential developed in our lab. Our computational results show that in 501 out of 502 targets, the model sets generated by POS contain structure models are within subangstrom resolution. Moreover, the top-ranked models have a root mean square deviation (rmsd) less than 1 A in 96.8, 84.1, and 72.2% of the short (4-6 residues), medium (7-9 residues), and long (10-12 residues) targets, respectively, when the all-atom models are generated by local optimization from the backbone models and are ranked by our recently developed Pareto optimal consensus (POC) method. Similar sampling effectiveness can also be found in a set of 13-residue loop targets.

Exact distribution of a pattern in a set of random sequences generated by a Markov source: applications to biological data.

BackgroundIn bioinformatics it is common to search for a pattern of interest in a potentially large set of rather short sequences (upstream gene regions, proteins, exons, etc.). Although many methodological approaches allow practitioners to compute the distribution of a pattern count in a random sequence generated by a Markov source, no specific developments have taken into account the counting of occurrences in a set of independent sequences. We aim to address this problem by deriving efficient approaches and algorithms to perform these computations both for low and high complexity patterns in the framework of homogeneous or heterogeneous Markov models.ResultsThe latest advances in the field allowed us to use a technique of optimal Markov chain embedding based on deterministic finite automata to introduce three innovative algorithms. Algorithm 1 is the only one able to deal with heterogeneous models. It also permits to avoid any product of convolution of the pattern distribution in individual sequences. When working with homogeneous models, Algorithm 2 yields a dramatic reduction in the complexity by taking advantage of previous computations to obtain moment generating functions efficiently. In the particular case of low or moderate complexity patterns, Algorithm 3 exploits power computation and binary decomposition to further reduce the time complexity to a logarithmic scale. All these algorithms and their relative interest in comparison with existing ones were then tested and discussed on a toy-example and three biological data sets: structural patterns in protein loop structures, PROSITE signatures in a bacterial proteome, and transcription factors in upstream gene regions. On these data sets, we also compared our exact approaches to the tempting approximation that consists in concatenating the sequences in the data set into a single sequence.ConclusionsOur algorithms prove to be effective and able to handle real data sets with multiple sequences, as well as biological patterns of interest, even when the latter display a high complexity (PROSITE signatures for example). In addition, these exact algorithms allow us to avoid the edge effect observed under the single sequence approximation, which leads to erroneous results, especially when the marginal distribution of the model displays a slow convergence toward the stationary distribution. We end up with a discussion on our method and on its potential improvements.

Improving predicted protein loop structure ranking using a Pareto-optimality consensus method

BackgroundAccurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction.ResultsWe have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of ~20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD < 0.5A from the native) as top-ranked, and selecting at least one near-native model in the top-5-ranked models, respectively. Similar effectiveness of the POC method is also found in the decoy sets from membrane protein loops. Furthermore, the POC method outperforms the other popularly-used consensus strategies in model ranking, such as rank-by-number, rank-by-rank, rank-by-vote, and regression-based methods.ConclusionsBy integrating multiple knowledge- and physics-based scoring functions based on Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

Prediction of Protein Loop Structures Using a Local Move Monte Carlo Approach and a Grid-Based Force Field

We have developed an improved local move Monte Carlo (LMMC) loop sampling approach for loop predictions. The method generates loop conformations based on simple moves of the torsion angles of side chains and local moves of backbone of loops. To reduce the computational costs for energy evaluations, we developed a grid-based force field to represent the protein environment and solvation effect. Simulated annealing has been used to enhance the efficiency of the LMMC loop sampling and identify low-energy loop conformations. The prediction quality is evaluated on a set of protein loops with known crystal structure that has been previously used by others to test different loop prediction methods. The results show that this approach can reproduce the experimental results with

Prediction of protein loop structures using a local move Monte Carlo approach and a grid-based force field.

We have developed an improved local move Monte Carlo (LMMC) loop sampling approach for loop predictions. The method generates loop conformations based on simple moves of the torsion angles of side chains and local moves of backbone of loops. To reduce the computational costs for energy evaluations, we developed a grid-based force field to represent the protein environment and solvation effect. Simulated annealing has been used to enhance the efficiency of the LMMC loop sampling and identify low-energy loop conformations. The prediction quality is evaluated on a set of protein loops with known crystal structure that has been previously used by others to test different loop prediction methods. The results show that this approach can reproduce the experimental results with the root mean square deviation within 1.8 A for all the test cases. The LMMC loop prediction approach developed here could be useful for improvement in the quality the loop regions in homology models, flexible protein-ligand and protein-protein docking studies.

Protein loop structure prediction with flexible stem geometries

The structure prediction of loops with flexible stem residues is addressed in this article. While the secondary structure of the stem residues is assumed to be known, the geometry of the protein into which the loop must fit is considered to be unknown in our methodology. As a consequence, the compatibility of the loop with the remainder of the protein is not used as a criterion to reject loop decoys. The loop structure prediction with flexible stems is more difficult than fitting loops into a known protein structure in that a larger conformational space has to be covered. The main focus of the study is to assess the precision of loop structure prediction if no information on the protein geometry is available. The proposed approach is based on (1) dihedral angle sampling, (2) structure optimization by energy minimization with a physically based energy function, (3) clustering, and (4) a comparison of strategies for the selection of loops identified in (3). Steps (1) and (2) have similarities to previous approaches to loop structure prediction with fixed stems. Step (3) is based on a new iterative approach to clustering that is tailored for the loop structure prediction problem with flexible stems. In this new approach, clustering is not only used to identify conformers that are likely to be close to the native structure, but clustering is also employed to identify far-from-native decoys. By discarding these decoys iteratively, the overall quality of the ensemble and the loop structure prediction is improved. Step (4) provides a comparative study of criteria for loop selection based on energy, colony energy, cluster density, and a hybrid criterion introduced here. The proposed method is tested on a large set of 3215 loops from proteins in the Pdb-Select25 set and to 179 loops from proteins from the Casp6 experiment.

Crystal packing effects on protein loops

The effects of crystal packing on protein loop structures are examined by (1) a comparison of loops in proteins that have been crystallized in alternate packing arrangements, and (2) theoretical prediction of loops both with and without the inclusion of the crystal environment. Results show that in a minority of cases, loop geometries are dependent on crystal packing effects. Explicit representation of the crystal environment in a loop prediction algorithm can be used to model these effects and to reconstruct the structures, and relative energies, of a loop in alternative packing environments. By comparing prediction results with and without the inclusion of the crystal environment, the loop prediction algorithm can further be used to identify cases in which a crystal structure does not represent the most stable state of a loop in solution. We anticipate that this capability has implications for structural biology.

Generation of accurate protein loop conformations through low-barrier molecular dynamics.

Prediction and refinement of protein loop structures are important and challenging tasks for which no general solution has been found. In addition to the accuracy of scoring functions, the main problems reside in (1) insufficient statistical sampling and (2) crossing energy barriers that impede conformational rearrangements of the loop. We approach these two issues by using "low-barrier molecular dynamics," a combination of energy smoothing techniques. To address statistical sampling, locally enhanced sampling (LES) is used to produce multiple copies of the loop, thus improving statistics and reducing energy barriers. We introduce a novel extension of LES that can improve local sampling even further through hierarchical subdivision of copies. Even though LES reduces energy barriers, it cannot provide for crossing infinite barriers, which can be problematic when substantial rearrangement of residues is necessary. To permit this kind of loop residue repacking, a "soft-core" potential energy function is introduced, so that atomic overlaps are temporarily allowed. We tested this new combined methodology to a loop in anti-influenza antibody Fab 17/9 (7 residues long) and to another loop in the antiprogesterone antibody DB3 (8 residues). In both cases, starting from random conformations, we were able to locate correct loop structures (including sidechain orientations) with heavy-atom root-mean-square deviation (fit to the nonloop region) of approximately 1.1 A in Fab 17/9 and approximately 1.8 A in DB3. We show that the combination of LES and soft-core potential substantially improves sampling compared to regular molecular dynamics. Moreover, the sampling improvement obtained with this combined approach is significantly better than that provided by either of the two methods alone.

The Proofreading Pathway of Bacteriophage T4 DNA Polymerase

The base analog, 2-aminopurine (2AP), was used as a fluorescent reporter of the biochemical steps in the proofreading pathway catalyzed by bacteriophage T4 DNA polymerase. "Mutator" DNA polymerases that are defective in different steps in the exonucleolytic proofreading pathway were studied so that transient changes in fluorescence intensity could be equated with specific reaction steps. The G255S- and D131N-DNA polymerases can hydrolyze DNA, the final step in the proofreading pathway, but the mutator phenotype indicates a defect in one or more steps that prepare the primer-terminus for the cleavage reaction. The hydrolysis-defective D112A/E114A-DNA polymerase was also examined. Fluorescent enzyme-DNA complexes were preformed in the absence of Mg2+, and then rapid mixing, stopped-flow techniques were used to determine the fate of the fluorescent complexes upon the addition of Mg2+. Comparisons of fluorescence intensity changes between the wild type and mutant DNA polymerases were used to model the exonucleolytic proofreading pathway. These studies are consistent with a proofreading pathway in which the protein loop structure that contains residue Gly255 functions in strand separation and transfer of the primer strand from the polymerase active center to form a preexonuclease complex. Residue Asp131 acts at a later step in formation of the preexonuclease complex.