Protein Conformational Search Research Articles

Analysis of the Structural Mechanism of ATP Inhibition at the AAA1 Subunit of Cytoplasmic Dynein-1 Using a Chemical "Toolkit".

Dynein is a ~1.2 MDa cytoskeletal motor protein that carries organelles via retrograde transport in eukaryotic cells. The motor protein belongs to the ATPase family of proteins associated with diverse cellular activities and plays a critical role in transporting cargoes to the minus end of the microtubules. The motor domain of dynein possesses a hexameric head, where ATP hydrolysis occurs. The presented work analyzes the structure–activity relationship (SAR) of dynapyrazole A and B, as well as ciliobrevin A and D, in their various protonated states and their 46 analogues for their binding in the AAA1 subunit, the leading ATP hydrolytic site of the motor domain. This study exploits in silico methods to look at the analogues’ effects on the functionally essential subsites of the motor domain of dynein 1, since no similar experimental structural data are available. Ciliobrevin and its analogues bind to the ATP motifs of the AAA1, namely, the walker-A (W-A) or P-loop, the walker-B (W-B), and the sensor I and II. Ciliobrevin A shows a better binding affinity than its D analogue. Although the double bond in ciliobrevin A and D was expected to decrease the ligand potency, they show a better affinity to the AAA1 binding site than dynapyrazole A and B, lacking the bond. In addition, protonation of the nitrogen atom in ciliobrevin A and D, as well as dynapyrazole A and B, at the N9 site of ciliobrevin and the N7 of the latter increased their binding affinity. Exploring ciliobrevin A geometrical configuration suggests the E isomer has a superior binding profile over the Z due to binding at the critical ATP motifs. Utilizing the refined structure of the motor domain obtained through protein conformational search in this study exhibits that Arg1852 of the yeast cytoplasmic dynein could involve in the “glutamate switch” mechanism in cytoplasmic dynein 1 in lieu of the conserved Asn in AAA+ protein family.

Three-dimensional protein structure prediction based on memetic algorithms

Tertiary protein structure prediction is a challenging problem in Structural Bioinformatics and is classified according to the computational complexity theory as a NP-hard problem. In this paper, we proposed a first-principle method that makes use of a priori information about known protein structures to tackle the three-dimensional protein structure prediction problem. We do so by designing a multimodal memetic algorithm that uses an evolutionary approach with a ternary tree-structured population allied to a local search strategy. The method has been developed based on an incremental approach using the combination of promising evolutionary components to address the concerned multimodal problem. Three memetic algorithms focused on the problem are proposed. The first one modifies a basic version of a memetic algorithm by introducing modified global search operators. The second uses a different population structure for the memetic algorithm. And finally, the last algorithm consists of the integration of global operators and multimodal strategies to deal with the inherent multimodality of the protein structure prediction problem. The implementations take advantage of structural knowledge stored in the Protein Data Bank to guide the exploiting and restrict the protein conformational search space. Predicted three-dimensional protein structures were analyzed regarding root mean square deviation and the global distance total score test. Obtained results for the three versions outperformed the basic version of the memetic algorithm. The third algorithm overcomes the results of the previous two, demonstrating the importance of adapting the method to deal with the complexities of the problem. In addition, the achieved results are topologically compatible with the experimental correspondent, confirming the promising performance of our approach.

Detecting intermediate protein conformations using algebraic topology

BackgroundUnderstanding protein structure and dynamics is essential for understanding their function. This is a challenging task due to the high complexity of the conformational landscapes of proteins and their rugged energy levels. In particular, it is important to detect highly populated regions which could correspond to intermediate structures or local minima.ResultsWe present a hierarchical clustering and algebraic topology based method that detects regions of interest in protein conformational space. The method is based on several techniques. We use coarse grained protein conformational search, efficient robust dimensionality reduction and topological analysis via persistent homology as the main tools. We use two dimensionality reduction methods as well, robust Principal Component Analysis (PCA) and Isomap, to generate a reduced representation of the data while preserving most of the variance in the data.ConclusionsOur hierarchical clustering method was able to produce compact, well separated clusters for all the tested examples.

Soft Computing Methods for Disulfide Connectivity Prediction

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

Relaxation of backbone bond geometry improves protein energy landscape modeling

A key issue in macromolecular structure modeling is the granularity of the molecular representation. A fine-grained representation can approximate the actual structure more accurately, but may require many more degrees of freedom than a coarse-grained representation and hence make conformational search more challenging. We investigate this tradeoff between the accuracy and the size of protein conformational search space for two frequently used representations: one with fixed bond angles and lengths and one that has full flexibility. We performed large-scale explorations of the energy landscapes of 82 protein domains under each model, and find that the introduction of bond angle flexibility significantly increases the average energy gap between native and non-native structures. We also find that incorporating bonded geometry flexibility improves low resolution X-ray crystallographic refinement. These results suggest that backbone bond angle relaxation makes an important contribution to native structure energetics, that current energy functions are sufficiently accurate to capture the energetic gain associated with subtle deformations from chain ideality, and more speculatively, that backbone geometry distortions occur late in protein folding to optimize packing in the native state.

Exploring a New Role for Molecular Chaperones in Protein Folding

Using force spectroscopy, Mashaghi et al. have demonstrated that the general chaperone trigger factor (TF) promotes the correct folding of the maltose binding protein (MBP) by binding to partially folded states along the folding pathway. Protein folding is a remarkable biological process in which proteins are transformed from linear amino acid sequences into complex three-dimensional structures. Errors during protein folding can lead to neurodegenerative ailments such as Alzheimer's disease and Parkinson's disease. Although the precise in vivo mechanism of this important process remains poorly understood, a network of molecular chaperone proteins are thought to play a crucial role in guiding a protein towards its correct conformation. It has been previously reported that these chaperones assist protein folding by suppressing aggregation, as well as by rescuing misfolded states (Hartl et al., 2011). However, a recent study has shown that molecular chaperones directly influence a protein's conformational search to attain the native structure (Mashaghi et al., 2013). The primary technique used in this study is single- molecule force spectroscopy, in which small amounts of forces (in the piconewton range) are applied to unfold (and refold) single protein molecules, using an instrument called optical tweezers (Jagannathan and Marqusee, 2013). The magnitude of the applied force and the end-to-end extension of the protein molecule are monitored as a function of time. Rare intermediate states can be precisely identified because they have different end-to-end extensions than the native state or the completely unfolded state. Using a combination of force spectroscopy and biochemical essays, Mashaghi and co-workers explored the role of the general chaperone trigger factor (TF) in promoting the folding of maltose binding protein (MBP). In the absence of TF, MBP unfolds predominantly via a single intermediate state, although additional rare protein states are sampled transiently (for less than 1 s). After the addition of TF, the rare, transient states were observed much more frequently and were stable for longer times, suggesting that TF promotes and stabilizes the partially folded states. Furthermore, it was shown that TF does not bind to natively folded MBP, indicating that the molecular chaperone only binds partial folds along the folding pathway, until MBP attains its native state. Because TF association could be particularly beneficial for complex multi-domain proteins, the folding of a construct composed of four MBP repeats (MBP4) was studied. In the absence of TF, MBP4 folds to a compact, non-native state that does not unfold, suggesting the presence of tight misfolding interactions between the domains. The presence of TF significantly restored native folding of MBP4, and the tightly misfolded states were only rarely observed. The work described in this study is of importance to the protein folding field because it demonstrates that molecular chaperones can reshape the folding energy landscape by binding and protecting partially folded states. The stabilization of these states guides the folding pathway towards the correct native structure and away from misfolded conformations.

MOIRAE: A computational strategy to extract and represent structural information from experimental protein templates

The prediction and analysis of the three- dimensional (3D) structure of proteins is a key research problem in Structural Bioinformatics. The 1990's Genome Projects resulted in a large increase in the number of available protein sequences. However, the number of identified 3D protein structures have not followed the same growth trend. Currently, the number of available protein sequences greatly exceeds the number of known 3D structures. Many computational methodologies, systems and algorithms have been proposed to address the protein structure prediction problem. However, the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. The most significant progress in the last Critical Assessment of protein Structure Prediction was achieved by methods that use database information. Nevertheless, a major challenge remains in the development of better strategies for template identification and representation. This article describes a computational strategy to acquire and represent structural information of experimentally determined 3D protein structures. A clustering strategy was combined with artificial neural networks in order to extract structural information from experimental protein structure templates. In the proposed strategy, the main efforts focus on the acquisition of useful and accurate structural information from 3D protein templates stored in the Protein Data Bank (PDB). The proposed method was tested in twenty protein sequences whose sizes vary from 14 to 70 amino acid residues. Our results show that the proposed method is a good way to extract and represent valuable information obtained from the PDB and also significantly reduce the 3D protein conformational search space.

A GMDH polynomial neural network-based method to predict approximate three-dimensional structures of polypeptides

Tertiary Protein Structure Prediction is one of the most important problems in Structural Bioinformatics. Along the last 20years many algorithms have been proposed as to solve this problem. However, it still remains a challenging issue because of the complexity and of the dimensionality of the protein conformational search space. In this article a first principle method which uses database information for the prediction of the 3-D structure of polypeptides is presented. The technique is based on the Group Method of Data Handling (GMDH) algorithm, implemented by a software tool introduced on this work. GMDH Polynomial Neural Networks have been used with success in many fields such as data mining, knowledge discovery, pattern recognition and prediction. The proposed method was tested with seven protein sequences whose sizes vary from 14 to 54 amino acid residues. Results show that the predicted tertiary structures adopt a fold similar to the experimental structures. RMSD and secondary structure analysis reveal that the proposed method present accurate results in their predictions. The predicted structures can be used as input structures in refinement methods based on molecular mechanics (MM), e.g. molecular dynamics (MD) simulations. The search space is expected to be greatly reduced and the ab initio methods can demand a much reduced computational time to achieve a more accurate polypeptide structure.

Prediction of Protein Tertiary Structure using Genetic Algorithm

Proteins are essential for the biological processes in the human body. They can only perform their functions when they fold into their tertiary structure .Protein structure can be determined experimentally and computationally. Experimental methods are time consuming and high-priced and it is not always feasible to identify the protein structure experimentally. In order to predict the protein structure using computational methods, the problem is formulated as an optimization problem and the goal is to find the lowest free energy conformation. In this paper, Genetic Algorithm (GA) based optimization is used. This algorithm is adapted to search the protein conformational search space to find the lowest free energy conformation. Interestingly, the algorithm was able to find the lowest free energy conformation for a test protein (i.e. Met enkephalin) using ECEPP force fields.

IN SEARCH OF THE PROTEIN NATIVE STATE WITH A PROBABILISTIC SAMPLING APPROACH

The three-dimensional structure of a protein is a key determinant of its biological function. Given the cost and time required to acquire this structure through experimental means, computational models are necessary to complement wet-lab efforts. Many computational techniques exist for navigating the high-dimensional protein conformational search space, which is explored for low-energy conformations that comprise a protein's native states. This work proposes two strategies to enhance the sampling of conformations near the native state. An enhanced fragment library with greater structural diversity is used to expand the search space in the context of fragment-based assembly. To manage the increased complexity of the search space, only a representative subset of the sampled conformations is retained to further guide the search towards the native state. Our results make the case that these two strategies greatly enhance the sampling of the conformational space near the native state. A detailed comparative analysis shows that our approach performs as well as state-of-the-art ab initio structure prediction protocols.

Feature space resampling for protein conformational search

De novo protein structure prediction requires location of the lowest energy state of the polypeptide chain among a vast set of possible conformations. Powerful approaches include conformational space annealing, in which search progressively focuses on the most promising regions of conformational space, and genetic algorithms, in which features of the best conformations thus far identified are recombined. We describe a new approach that combines the strengths of these two approaches. Protein conformations are projected onto a discrete feature space which includes backbone torsion angles, secondary structure, and beta pairings. For each of these there is one "native" value: the one found in the native structure. We begin with a large number of conformations generated in independent Monte Carlo structure prediction trajectories from Rosetta. Native values for each feature are predicted from the frequencies of feature value occurrences and the energy distribution in conformations containing them. A second round of structure prediction trajectories are then guided by the predicted native feature distributions. We show that native features can be predicted at much higher than background rates, and that using the predicted feature distributions improves structure prediction in a benchmark of 28 proteins. The advantages of our approach are that features from many different input structures can be combined simultaneously without producing atomic clashes or otherwise physically inviable models, and that the features being recombined have a relatively high chance of being correct.

Enhanced bounding techniques to reduce the protein conformational search space

The complexity and enormous size of the conformational space that must be explored for the protein tertiary structure prediction problem has led to the development of a wide assortment of algorithmic approaches. In this study, we apply state-of-the-art tertiary structure prediction algorithms and instead focus on the development of bounding techniques to reduce the conformational search space. Dihedral angle bounds on the φ and ψ angles are established based on the predicted secondary structure and studies of the allowed regions of φ/ψ space. Distance bounds are developed based on predicted secondary structure information (including β-sheet topology predictions) to further reduce the search space. This bounding strategy is entirely independent of the degree of homology between the target protein and the database of proteins with experimentally-determined structures. The proposed approach is applied to the structure prediction of protein G as an illustrative example, yielding a significantly higher number of near-native protein tertiary structure predictions.

Exploring zipping and assembly as a protein folding principle

It has been proposed that proteins fold by a process called "Zipping and Assembly" (Z&A). Zipping refers to the growth of local substructures within the chain, and assembly refers to the coming together of already-formed pieces. Our interest here is in whether Z&A is a general method that can fold most of sequence space, to global minima, efficiently. Using the HP model, we can address this question by enumerating full conformation and sequence spaces. We find that Z&A reaches the global energy minimum native states, even though it searches only a very small fraction of conformational space, for most sequences in the full sequence space. We find that Z&A, a mechanism-based search, is more efficient in our tests than the replica exchange search method. Folding efficiency is increased for chains having: (a) small loop-closure steps, consistent with observations by Plaxco et al. 1998;277;985-994 that folding rates correlate with contact order, (b) neither too few nor too many nucleation sites per chain, and (c) assembly steps that do not occur too early in the folding process. We find that the efficiency increases with chain length, although our range of chain lengths is limited. We believe these insights may be useful for developing faster protein conformational search algorithms.