Ab initio protein folding

Ab initio protein folding methods have been developing rapidly over the past few years and, at the last Critical assessment of methods of protein structure prediction (CASP) meeting, it was shown that important progress has been made in generating structure from sequence. Both methods based on statistical potentials and methods using physics-based potentials have shown improvements. Most current methods use statistics-based potentials and the development of these is ongoing. Additionally, the inclusion of multiple sequence data in the algorithms in order to aid in finding the native structure is a common theme. The use of physics-based potentials is less developed, which means that less progress has been made in understanding why a sequence forms a structure.     

Specific interactions for ab initio folding of protein terminal regions with secondary structures

Proteins fold into unique three-dimensional structures by specific, orientation-dependent interactions between amino acid residues. Here, we extract orientation-dependent interactions from protein structures by treating each polar atom as a dipole with a direction. The resulting statistical energy function successfully refolds 13 out of 16 fully unfolded secondary-structure terminal regions of 10-23 amino acid residues in 15 small proteins. Dissecting the orientation-dependent energy function reveals that the orientation preference between hydrogen-bonded atoms is not enough to account for the structural specificity of proteins. The result has significant implications on the theoretical and experimental searches for specific interactions involved in protein folding and molecular recognition between proteins and other biologically active molecules.     

Accuracy of side-chain prediction upon near-native protein backbones generated by ab initio folding methods

The ab initio folding problem can be divided into two sequential tasks of approximately equal computational complexity: the generation of native-like backbone folds and the positioning of side chains upon these backbones. The prediction of side-chain conformation in this context is challenging, because at best only the near-native global fold of the protein is known. To test the effect of displacements in the protein backbones on side-chain prediction for folds generated ab initio, sets of near-native backbones (≤ 4 Å Cα RMS error) for four small proteins were generated by two methods. The steric environment surrounding each residue was probed by placing the side chains in the native conformation on each of these decoys, followed by torsion-space optimization to remove steric clashes on a rigid backbone. We observe that on average 40% of the χ1 angles were displaced by 40° or more, effectively setting the limits in accuracy for side-chain modeling under these conditions. Three different algorithms were subsequently used for prediction of side-chain conformation. The average prediction accuracy for the three methods was remarkably similar: 49% to 51% of the χ1 angles were predicted correctly overall (33% to 36% of the χ1+2 angles). Interestingly, when the inter-side-chain interactions were disregarded, the mean accuracy increased. A consensus approach is described, in which side-chain conformations are defined based on the most frequently predicted χ angles for a given method upon each set of near-native backbones. We find that consensus modeling, which de facto includes backbone flexibility, improves side-chain prediction: χ1 accuracy improved to 51–54% (36–42% of χ1+2). Implications of a consensus method for ab initio protein structure prediction are discussed. Proteins 33:204–217, 1998. © 1998 Wiley-Liss, Inc.     

CONFOLD: Residue‐residue contact‐guided ab initio protein folding

Predicted protein residue–residue contacts can be used to build three‐dimensional models and consequently to predict protein folds from scratch. A considerable amount of effort is currently being spent to improve contact prediction accuracy, whereas few methods are available to construct protein tertiary structures from predicted contacts. Here, we present an ab initio protein folding method to build three‐dimensional models using predicted contacts and secondary structures. Our method first translates contacts and secondary structures into distance, dihedral angle, and hydrogen bond restraints according to a set of new conversion rules, and then provides these restraints as input for a distance geometry algorithm to build tertiary structure models. The initially reconstructed models are used to regenerate a set of physically realistic contact restraints and detect secondary structure patterns, which are then used to reconstruct final structural models. This unique two‐stage modeling approach of integrating contacts and secondary structures improves the quality and accuracy of structural models and in particular generates better β‐sheets than other algorithms. We validate our method on two standard benchmark datasets using true contacts and secondary structures. Our method improves TM‐score of reconstructed protein models by 45% and 42% over the existing method on the two datasets, respectively. On the dataset for benchmarking reconstructions methods with predicted contacts and secondary structures, the average TM‐score of best models reconstructed by our method is 0.59, 5.5% higher than the existing method. The CONFOLD web server is available at http://protein.rnet.missouri.edu/confold/ . Proteins 2015; 83:1436–1449. © 2015 Wiley Periodicals, Inc.     

All-atom ab initio folding of a diverse set of proteins

Natural proteins fold to a unique, thermodynamically dominant state. Modeling of the folding process and prediction of the native fold of proteins are two major unsolved problems in biophysics. Here, we show successful all-atom ab initio folding of a representative diverse set of proteins by using a minimalist transferable-energy model that consists of two-body atom-atom interactions, hydrogen bonding, and a local sequence-energy term that models sequence-specific chain stiffness. Starting from a random coil, the native-like structure was observed during replica exchange Monte Carlo (REMC) simulation for most proteins regardless of their structural classes; the lowest energy structure was close to native-in the range of 2-6 A root-mean-square deviation (rmsd). Our results demonstrate that the successful folding of a protein chain to its native state is governed by only a few crucial energetic terms.     

Identification and ab initio simulations of early folding units in proteins

The location of protein subunits that form early during folding, constituted of consecutive secondary structure elements with some intrinsic stability and favorable tertiary interactions, is predicted using a combination of threading algorithms and local structure prediction methods. Two folding units are selected among the candidates identified in a database of known protein structures: the fragment 15-55 of 434 cro, an all-alpha protein, and the fragment 1-35 of ubiquitin, an alpha/beta protein. These units are further analyzed by means of Monte Carlo simulated annealing using several database-derived potentials describing different types of interactions. Our results suggest that the local interactions along the chain dominate in the first folding steps of both fragments, and that the formation of some of the secondary structures necessarily occurs before structure compaction. These findings led us to define a prediction protocol, which is efficient to improve the accuracy of the predicted structures. It involves a first simulation with a local interaction potential only, whose final conformation is used as a starting structure of a second simulation that uses a combination of local interaction and distance potentials. The root mean square deviations between the coordinates of predicted and native structures are as low as 2-4 A in most trials. The possibility of extending this protocol to the prediction of full proteins is discussed. Proteins 2001;42:164-176.     

Lattices for ab initio protein structure prediction

In the study of the protein folding problem with ab initio methods, the protein backbone can be built on some periodic lattices. Any vertex of these lattices can be occupied by a "ball," which can represent the mass center of an amino acid in a simplified coarse-grained model of the protein. The backbone, at a coarse-grained level, can be constituted of a No Reverse Self Avoiding Walk, which cannot intersect itself and cannot go back on itself. There is still much debate between those who use lattices to simplify the study of the protein folding problem and those preferring to work by using an off-lattice approach. Lattices can help to identify the protein tertiary structure in a computational less-expensive way, than off-lattice approaches that have to consider a potentially infinite number of possible structures. However, the use of a lattice, constituted of insufficiently accurate direction vectors, constrains the predictive ability of the model. The aim of this study is to perform a systematic screening of 7 known classic and 11 newly proposed lattices in terms of predictive power. The crystal structures of 42 different proteins (14 mainly alpha helical, 14 mainly beta sheet and 14 mixed structure proteins) were compared to the most accurate simulated models for each lattice. This strategy defines a scale of fitness for all the analyzed lattices and demonstrates that an increase in the coordination number and in the degrees of freedom is necessary but not sufficient to reach the best result. Instead, the introduction of a good set of direction vectors, as developed and tested in this study, strongly increases the lattice performance.     

Contact order and ab initio protein structure prediction

Although much of the motivation for experimental studies of protein folding is to obtain insights for improving protein structure prediction, there has been relatively little connection between experimental protein folding studies and computational structural prediction work in recent years. In the present study, we show that the relationship between protein folding rates and the contact order (CO) of the native structure has implications for ab initio protein structure prediction. Rosetta ab initio folding simulations produce a dearth of high CO structures and an excess of low CO structures, as expected if the computer simulations mimic to some extent the actual folding process. Consistent with this, the majority of failures in ab initio prediction in the CASP4 (critical assessment of structure prediction) experiment involved high CO structures likely to fold much more slowly than the lower CO structures for which reasonable predictions were made. This bias against high CO structures can be partially alleviated by performing large numbers of additional simulations, selecting out the higher CO structures, and eliminating the very low CO structures; this leads to a modest improvement in prediction quality. More significant improvements in predictions for proteins with complex topologies may be possible following significant increases in high-performance computing power, which will be required for thoroughly sampling high CO conformations (high CO proteins can take six orders of magnitude longer to fold than low CO proteins). Importantly for such a strategy, simulations performed for high CO structures converge much less strongly than those for low CO structures, and hence, lack of simulation convergence can indicate the need for improved sampling of high CO conformations. The parallels between Rosetta simulations and folding in vivo may extend to misfolding: The very low CO structures that accumulate in Rosetta simulations consist primarily of local up-down beta-sheets that may resemble precursors to amyloid formation.     

蛋白质结构从头预测方法研究进展

蛋白质结构从头预测是不依赖模板仅从氨基酸序列信息得到天然结构。它的关键是正确定义能量函数、精确选用计算机搜索算法来寻找能量最低值。基于此,本文系统介绍了能量函数和构象搜索策略,并列举了几种比较成功的从头预测方法,通过比较得出结论:基于统计学知识的能量函数是近年来从头预测发展的主要方向,现有从头预测的构象搜索都用到Monte Carlo法。这表明随着蛋白质结构预测研究的深入,能量函数的构建、构象搜索方法的选择、大分子蛋白质结构的从头预测等关键性问题都取得了突破性进展。     

Prospects for ab initio protein structural genomics

We present the results of a large-scale testing of the ROSETTA method for ab initio protein structure prediction. Models were generated for two independently generated lists of small proteins (up to 150 amino acid residues), and the results were evaluated using traditional rmsd based measures and a novel measure based on the structure-based comparison of the models to the structures in the PDB using DALI. For 111 of 136 all alpha and alpha/beta proteins 50 to 150 residues in length, the method produced at least one model within 7 A rmsd of the native structure in 1000 attempts. For 60 of these proteins, the closest structure match in the PDB to at least one of the ten most frequently generated conformations was found to be structurally related (four standard deviations above background) to the native protein. These results suggest that ab initio structure prediction approaches may soon be useful for generating low resolution models and identifying distantly related proteins with similar structures and perhaps functions for these classes of proteins on the genome scale.     

Direct correlation between proteins' folding rates and their amino acid compositions: an ab initio folding rate prediction

Discovering the mechanism of protein folding, in molecular biology, is a great challenge. A key step to this end is to find factors that correlate with protein folding rates. Over the past few years, many empirical parameters, such as contact order, long-range order, total contact distance, secondary structure contents, have been developed to reflect the correlation between folding rates and protein tertiary or secondary structures. However, the correlation between proteins' folding rates and their amino acid compositions has not been explored. In the present work, we examined systematically the correlation between proteins' folding rates and their amino acid compositions for two-state and multistate folders and found that different amino acids contributed differently to the folding progress. The relation between the amino acids' molecular weight and degeneracy and the folding rates was examined, and the role of hydrophobicity in the protein folding process was also inspected. As a consequence, a new indicator called composition index was derived, which takes no structure factors into account and is merely determined by the amino acid composition of a protein. Such an indicator is found to be highly correlated with the protein's folding rate (r > 0.7). From the results of this work, three points of concluding remarks are evident. (1) Two-state folders and multistate folders have different rate-determining amino acids. (2) The main determining information of a protein's folding rate is largely reflected in its amino acid composition. (3) Composition index may be the best predictor for an ab initio protein folding rate prediction directly from protein sequence from the standpoint of practical application.     

Functional inferences from blind ab initio protein structure predictions

Ab initio protein structure prediction methods have improved dramatically in the past several years. Because these methods require only the sequence of the protein of interest, they are potentially applicable to the open reading frames in the many organisms whose sequences have been and will be determined. Ab initio methods cannot currently produce models of high enough resolution for use in rational drug design, but there is an exciting potential for using the methods for functional annotation of protein sequences on a genomic scale. Here we illustrate how functional insights can be obtained from low-resolution predicted structures using examples from blind ab initio structure predictions from the third and fourth critical assessment of structure prediction (CASP3, CASP4) experiments.     

Toward optimal fragment generations for ab initio protein structure assembly

Fragment assembly using structural motifs excised from other solved proteins has shown to be an efficient method for ab initio protein‐structure prediction. However, how to construct accurate fragments, how to derive optimal restraints from fragments, and what the best fragment length is are the basic issues yet to be systematically examined. In this work, we developed a gapless‐threading method to generate position‐specific structure fragments. Distance profiles and torsion angle pairs are then derived from the fragments by statistical consistency analysis, which achieved comparable accuracy with the machine‐learning‐based methods although the fragments were taken from unrelated proteins. When measured by both accuracies of the derived distance profiles and torsion angle pairs, we come to a consistent conclusion that the optimal fragment length for structural assembly is around 10, and at least 100 fragments at each location are needed to achieve optimal structure assembly. The distant profiles and torsion angle pairs as derived by the fragments have been successfully used in QUARK for ab initio protein structure assembly and are provided by the QUARK online server at http://zhanglab.ccmb. med.umich.edu/QUARK/ . Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Osmolyte-induced protein folding free energy changes

Upon addition of protecting osmolyte to an aqueous solution of an intrinsically unstructured protein, spectral observables are often seen to change in a sigmoid fashion as a function of increasing osmolyte concentration. Commonly, such data are analyzed using the linear extrapolation model (LEM), a method that defines a scale from 0%-100% folded species at each osmolyte concentration by means of extending pre- and post-folding baselines into the transition region. Defining the 0%-100% folding scale correctly for each osmolyte is an important part of the analysis, leading to evaluation of the fraction of folded protein existing in the absence of osmolytes. In this study, we used reduced and carboxyamidated RNase T1 (RCAM-T1) as an intrinsically unstructured protein, and determined the thermodynamic stability of RCAM-T1 induced by naturally occurring osmolytes. Because the folded fraction of the protein population determined by experiments of thermal and urea-induced denaturation is nonzero in the absence of osmolytes at 15 degrees C, the commonly used LEM can lead to false values of DeltaG[stackD-->N0] for protein folding due to the arbitrary assumption that the protein is 100% unfolded in the presence of buffer alone. To correct this problem, titration of the protein solution with urea and extrapolating back to zero urea concentration gives the spectral value for 100% denatured protein. With fluorescence as the observable we redefine F/F0 to F/F0extrap = 1.0 and require that the denatured-state baseline have this value as its intercept. By so doing, the 0%-100% scale-corrected DeltaG[D-->N0] values of RCAM-T1 folding in the presence of various osmolytes are then found to be identical, with small error, demonstrating that DeltaG[D-->N0] is independent of the osmolytes used. Such a finding is an important step in validating this quantity derived from the LEM as having the properties expected of an authentic thermodynamic parameter. The rank order of osmolyte efficacies in stabilizing RCAM-T1 is sarcosine > sucrose > sorbitol > proline > betaine > glycerol.     

PreDisorder: ab initio sequence-based prediction of protein disordered regions

Background  

Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation.