A spatio-temporal mining approach towards summarizing and analyzing protein folding trajectories

Understanding the protein folding mechanism remains a grand challenge in structural biology. In the past several years, computational theories in molecular dynamics have been employed to shed light on the folding process. Coupled with high computing power and large scale storage, researchers now can computationally simulate the protein folding process in atomistic details at femtosecond temporal resolution. Such simulation often produces a large number of folding trajectories, each consisting of a series of 3D conformations of the protein under study. As a result, effectively managing and analyzing such trajectories is becoming increasingly important.     

A data mining approach for analyzing density maps representing macromolecular structures

Results of electron microscopy-based three-dimensional reconstructions of macromolecules or their complexes are usually stored as density maps. Each point ("voxel") in the map represents a density value and one approach for studying details of the map is to display an isosurface enclosing areas of interest. We have taken a data mining approach not only focusing on the areas of immediate interest but determining all possible separate entities ("blobs") from a density map. After the entire density map is analyzed with our mining program BLOBBER, properties of all detected blobs can be browsed and sets of blobs can be visualized using our VIZBLOB program. Since BLOBBER analyzes density maps using only density information and relates it to spatial relationships, BLOBBER can be used to analyze symmetrical or asymmetrical density maps from any source. To test our program we have analyzed published bacteriophage PRD1 reconstructions. We identified various structural details ranging from individual proteins to major complexes such as the whole capsid shell and more elaborate details of possible connections between membrane interfaces. This approach can also be a useful preprocessing tool for visualizing reconstructions.     

A new approach to predicting protein folding types

A new method is proposed for predicting the folding type of a protein according to its amino acid composition based on the following physical picture: (1) a protein is characterized as a vector of 20-dimensional space, in which its 20 components are defined by the compositions of its 20 amino acids; and (2) the similarity of two proteins is proportional to the mutual projection of their characterized vectors, and hence inversely proportional to the size of their correlation angle. Thus, the prediction is performed by calculating the correlation angles of the vector for the predicted protein with a set of standard vectors representing the norms of four protein folding types (i.e., alla, all ,a+, anda/). In comparison with the existing methods, the new method has the merits of yielding a higher rate of correct prediction, displaying a more intuitive physical picture, and being convenient in application. For instance, in predicting the 64 proteins in the development set based on which the standard vectors are derived, the average accuracy rate is 83.6%, which is higher than that obtained for the same set of proteins by any of the existing methods. The average accuracy predicted for an independent set of 35 proteins of known X-ray structure is 91.4%, which is significantly higher than any of the reported accuracies so far, implying that the new method is of great value in practical application. All of these have demonstrated that the new method as proposed in this paper is characterized by an improved feature in both self-consistency and extrapolating-effectiveness.On sabbatical leave from Department of Physics, Tianjin University, Tianjin, China.     

A new computational approach for real protein folding prediction

An effective and fast minimization approach is proposed for the prediction of protein folding, in which the 'relative entropy' is used as a minimization function and the off-lattice model is used. In this approach, we only use the information of distances between the consecutive Calpha atoms along the peptide chain and a generalized form of the contact potential for 20 types of amino acids. Tests of the algorithm are performed on the real proteins. The root mean square deviations of the structures of eight folded target proteins versus the native structures are in a reasonable range. In principle, this method is an improvement on the energy minimization approach.     

A computational approach to simplifying the protein folding alphabet.

What is the minimal number of residue types required to form a structured protein? This question is important for understanding protein modeling and design. Recently, an experimental finding by Baker and coworkers suggested a five-residue solution to this problem. We were motivated by their results and by the arguments of Wolynes to study reductions of protein representation based on the concept of mismatch between a reduced interaction matrix and the Miyazawa and Jernigan (MJ) matrix. We find several possible simplified schemes from the relationship of minimized mismatch versus the number of residue types (N = approximately 2-20). As a specific case, an optimal reduction with five types of residues has the same form as the simplified palette of Baker and coworkers. Statistical and kinetic features of a number of sequences are tested. Comparison of results from sequences with 20 residue types and their reduced representations indicates that the reduction by mismatch minimization is successful. For example, sequences with five types of residues have good folding ability and kinetic accessibility in model studies.     

A model-based approach for mining membrane protein crystallization trials

MOTIVATION: Membrane proteins are known to play crucial roles in various cellular functions. Information about their function can be derived from their structure, but knowledge of these proteins is limited, as their structures are difficult to obtain. Crystallization has proved to be an essential step in the determination of macromolecular structure. Unfortunately, the bottleneck is that the crystallization process is quite complex and extremely sensitive to experimental conditions, the selection of which is largely a matter of trial and error. Even under the best conditions, it can take a large amount of time, from weeks to years, to obtain diffraction-quality crystals. Other issues include the time and cost involved in taking multiple trials and the presence of very few positive samples in a wide and largely undetermined parameter space. Therefore, any help in directing scientists' attention to the hot spots in the conceptual crystallization space would lead to increased efficiency in crystallization trials. RESULTS: This work is an application case study on mining membrane protein crystallization trials to predict novel conditions that have a high likelihood of leading to crystallization. We use suitable supervised learning algorithms to model the data-space and predict a novel set of crystallization conditions. Our preliminary wet laboratory results are very encouraging and we believe this work shows great promise. We conclude with a view of the crystallization space that is based on our results, which should prove useful for future studies in this area.     

A one-pot analysis approach to simplify measurements of protein stability and folding kinetics

To achieve a good understanding of the characteristics of a protein, it is important to study its stability and folding kinetics. Investigations of protein stability have been recently applied to drug-target identification, drug screening, and proteomic studies. The efficiency of the experiments performed to study protein stability and folding kinetics is now a crucial factor that needs to be optimized for these potential applications. However, the standard procedures used to carry out these experiments are usually complicated and time consuming. Large number of measurements is the bottleneck that limits the application of protein folding to large-scale experiments. To overcome this limitation, we developed a method denoted as “one-pot analysis” which is based on taking a single measurement from a mixture of samples rather than from every sample. We combined one-pot analysis with pulse proteolysis to determine the effects of the binding of maltose to maltose-binding protein on the protein folding properties. After carrying out a simple optimization, we demonstrated that protein stability or unfolding kinetics could be measured accurately with just one detection measurement. We then further applied the optimized conditions to cellular thermal shift assay (CETSA). Combining one-pot analysis with CETSA led to a successful determination of the effects of the binding of methotrexate to dihydrofolate reductase in HCT116 cancer cells. Our results demonstrated the applicability of one-pot analysis to energetics-based methods for studying protein folding. We expect the combination of one-pot analysis and energetics-based methods to significantly benefit studies such as drug-target identification, proteomic investigations, and drug screening.     

An eigenvalue-eigenvector approach to predicting protein folding types

The accuracy of predicting protein folding types can be significantly enhanced by a recently developed algorithm in which the coupling effect among different amino acid components is taken into account [Chou and Zhang (1994)J. Biol. Chem. 269, 22014-22020]. However, in practical calculations using this powerful algorithm, one may sometimes face illconditioned matrices. To overcome such a difficulty, an effective eigenvalue-eigenvector approach is proposed. Furthermore, the new approach has been used to predict a recently constructed set of 76 proteins not included in the training set, and the accuracy of prediction is also much higher than those of other methods.     

A coarse-grained approach to protein design: learning from design to understand folding

Computational studies have given a great contribution in building our current understanding of the complex behavior of protein molecules; nevertheless, a complete characterization of their free energy landscape still represents a major challenge. Here, we introduce a new coarse-grained approach that allows for an extensive sampling of the conformational space of a large number of sequences. We explicitly discuss its application in protein design, and by studying four representative proteins, we show that the method generates sequences with a relatively smooth free energy surface directed towards the target structures.     

Pathway diversity and concertedness in protein folding: an ab-initio approach

Making use of an ab-initio folding simulator, we generate in vitro pathways leading to the native fold in moderate size single- domain proteins. The assessment of pathway diversity is not biased by any a priori information on the native fold. We focus on two study cases, hyperthermophile variant of protein G domain (1gb4) and ubiquitin (1ubi), with the same topology but different context dependence in their native folds. We demonstrate that a quenching of structural fluctuations is achieved once the proteins find a stationary plateau maximizing the number of highly protected hydrogen bonds. This enables us to identify the folding nucleus and show that folding does not become expeditious until a concerted event takes place generating a topology able to prevent water attack on a maximal number of hydrogen bonds. This result is consistent with the standard nucleation mechanism postulated for two-state folders. Pathway diversity is correlated with the extent of conflict between local structural propensity and large-scale context, rather than with contact order: In highly context-dependent proteins, the success of folding cannot rely on a single fortuitous event in which local propensity is overruled by large-scale effects. We predict mutational Pi values on individual pathways, compute ensemble averages and predict extent of surface burial and percentage of hydrogen bonding on each component of the transition state ensemble, thus deconvoluting individual folding-route contributions to the averaged two-state kinetic picture. Our predicted kinetic isotopic effects find experimental support and lead to further probes. Finally, the molecular redesign potentiality of the method, aimed at increasing folding expediency, is explored.     

Xylanase homology modeling using the inverse protein folding approach.

Xylanase has been used in wood pulp bleaching in an effort to reduce chlorine release into the environment and pollution associated with paper production. The three-dimensional structure of xylanase is important to enable better understanding of the enzyme mechanism and to help design a more thermostable xylanase mutant. At the time this work was begun, there was no sequence homologous protein available for traditional sequence-based homology modeling. In order to circumvent this problem, the inverse protein folding approach was undertaken to find a suitable template structure. Model structures of Bacillus circulans xylanase were built based on the data-base search results of related proteins. The model structures were refined and compared to the recently solved xylanase X-ray crystal structure. The overall structural similarity between the theoretical model and experimental structure demonstrate the usefulness of this approach. Disagreement in folding topology, however, warrants further research into the inverse protein folding approach.     

A statistical-reaction-diffusion approach for analyzing expansion processes

In this article, we propose a method for analyzing the spatial variations in the range expansion of the pine processionary moth (PPM), an invasive species in France. Based on binary measurements - the presence or absence of PPM nests - the proposed method allows us to infer the local effect of the environment on PPM population expansion. This effect is estimated at each position x using a parameter F(x) that corresponds to the local PPM fitness. The data type and the two stage PPM life cycle make estimating this parameter difficult. To overcome these difficulties we adopt a mechanistic-statistical approach that combines a statistical model for the observation process with a hierarchical,reaction-diffusion based mechanistic model for the expansion process. Bayesian inference of the parameter F(x) reveals that PPM fitness is spatially heterogeneous and highlights the existence of large regions associated with lower fitness. The factors underlying this lower fitness are yet to be determined.     

Topology fingerprint approach to the inverse protein folding problem.

We describe the most general solution to date of the problem of matching globular protein sequences to the appropriate three-dimensional structures. The screening template, against which sequences are tested, is provided by a protein "structural fingerprint" library based on the contact map and the buried/exposed pattern of residues. Then, a lattice Monte Carlo algorithm validates or dismisses the stability of the proposed fold. Examples of known structural similarities between proteins having weakly or unrelated sequences such as the globins and phycocyanins, the eight-member alpha/beta fold of triose phosphate isomerase and even a close structural equivalence between azurin and immunoglobulins are found.     

Chaperones and protein folding

Chaperones are centrally involved in the control of protein structure, function, localization and transport. A flurry of scientific activity continues to examine the molecular nature of chaperone-substrate recognition and the role of auxiliary chaperones (cohort proteins) and small molecules that expedite these processes. Chaperones have been implicated in processes as diverse as protein secretion, nuclear transport, thermotolerance, the steroid receptor signal transduction pathway, T-cell receptor and major histocompatibility complex class I and II multimeric assembly and bacterial virulence.     

Coding nucleic acids are chaperons for protein folding: A novel theory of protein folding

The arguments for nucleic acid chaperons are reviewed and three new lines of evidence are added. (1) It was found that amino acids encoded by codons in short nucleic acid loops frequently form turns and helices in the corresponding protein structures. (2) The amino acids encoded by partially complementary (1st and 3rd nucleotides) codons are more frequently co-located in the encoded proteins than expected by chance. (3) There are significant correlations between thermodynamic changes (ddG) caused by codon mutations in nucleic acids and the thermodynamic changes caused by the corresponding amino acid mutations in the encoded proteins. We conclude that the concept of the Proteomic Code and nucleic acid chaperons seems correct from the bioinformatics point of view, and we expect to see direct biochemical experiments and evidence in the near future.     

A kinetic approach to determining the conformational stability of a protein that dimerizes after folding

A strongly stabilized form of the β1 domain of the streptococcal protein G, termed Gβ1-M2, was previously obtained by an in vitro selection method for stabilized protein variants. It contains four substitutions, but how they contribute to the Gibbs free energy of denaturation (ΔG(D)) could not be determined, because, unlike the wild-type protein, Gβ1-M2 dimerizes in a spectroscopically silent reaction. Here we determined the ΔG(D) of the folded Gβ1-M2 monomer by using a kinetic approach that uncouples the folding of the monomer from dimerization. The conformational equilibration of the monomer is faster than dimer formation, and therefore, its stability constant could be determined from the ratio of the rate constants for monomer unfolding and refolding. In this approach, double-mixing experiments were essential for uncovering the unfolding kinetics of the transient Gβ1-M2 monomer and the association of the monomers after their folding. The analysis revealed that the selected substitutions stabilize the Gβ1-M2 monomer by 15 kJ mol(-1) in an additive fashion. The combination of single- and double-mixing kinetic experiments thus allowed us to determine the thermodynamic stability of a transient species that is inaccessible in equilibrium experiments. It can be applied for proteins in which monomer folding and oligomerization are kinetically uncoupled.     

A text mining approach to detect mentions of protein glycosylation in biomedical text

Protein Glycosylation is an important post translational event that plays a pivotal role in protein folding and protein is trafficking. We describe a dictionary based and a rule based approach to mine ‘mentions‘ of protein glycosylation in text. The dictionary based approach relies on a set of manually curated dictionaries specially constructed to address this task. Abstracts are then screened for the ‘mentions‘ of words from these dictionaries which are further scored followed by classification on the basis of a threshold. The rule based approaches also relies on the words in the dictionary to arrive at the features which are used for classification. The performance of the system using both the approaches has been evaluated using a manually curated corpus of 3133 abstracts. The evaluation suggests that the performance of the Rule based approach supersedes that of the Dictionary based approach.