Ab initio protein structure prediction

Steady progress has been made in the field of ab initio protein folding. A variety of methods now allow the prediction of low-resolution structures of small proteins or protein fragments up to approximately 100 amino acid residues in length. Such low-resolution structures may be sufficient for the functional annotation of protein sequences on a genome-wide scale. Although no consistently reliable algorithm is currently available, the essential challenges to developing a general theory or approach to protein structure prediction are better understood. The energy landscapes resulting from the structure prediction algorithms are only partially funneled to the native state of the protein. This review focuses on two areas of recent advances in ab initio structure prediction-improvements in the energy functions and strategies to search the caldera region of the energy landscapes.     

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.     

Prediction of structures of multidomain proteins from structures of the individual domains

We describe the development of a method for assembling structures of multidomain proteins from structures of isolated domains. The method consists of an initial low-resolution search in which the conformational space of the domain linker is explored using the Rosetta de novo structure prediction method, followed by a high-resolution search in which all atoms are treated explicitly and backbone and side chain degrees of freedom are simultaneously optimized. The method recapitulates, often with very high accuracy, the structures of existing multidomain proteins.     

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.     

植物类金属硫蛋白半胱氨酸富含区结构的建模

详细了解蛋白质的三级结构信息有助于理解其生物学功能.随着植物基因组研究的进展,已发现了50多个植物类金属硫蛋白(Metallothionein-Like, MT-L)基因.但至今只有少数几个MT-L蛋白得到了纯化,而其结构尚无报道,因此有必要建立分析这类蛋白结构特征的方法.本研究根据已知的哺乳动物MT的结构数据,分析得出了CXC、CXXC模式和金属-硫络合簇结构原子间的距离限制条件,并用距离几何算法计算得出预测蛋白可能的构象;然后通过统计分析筛选出目标函数值显著较小、构象能低的结构作为这些蛋白半胱氨酸富含区的预测结构,由此建成了适合于植物类金属硫蛋白半胱氨酸富含区的结构预测方法.从应用该方法正确地预测出了已知结构的蓝蟹MT的结构来看,该方法是可行的.并用该方法预测了油菜MT-L蛋白的半胱氨酸富含区的结构.     

植物类金属硫蛋白半胱氨酸富含区结构的建模

详细了解蛋白质的三级结构信息有助于理解其生物学功能。随着植物基因组研究的进展 ,已发现了 50多个植物类金属硫蛋白 (Metallothionein_Like ,MT_L)基因。但至今只有少数几个MT_L蛋白得到了纯化 ,而其结构尚无报道 ,因此有必要建立分析这类蛋白结构特征的方法。本研究根据已知的哺乳动物MT的结构数据 ,分析得出了CXC、CXXC模式和金属 硫络合簇结构原子间的距离限制条件 ,并用距离几何算法计算得出预测蛋白可能的构象 ;然后通过统计分析筛选出目标函数值显著较小、构象能低的结构作为这些蛋白半胱氨酸富含区的预测结构 ,由此建成了适合于植物类金属硫蛋白半胱氨酸富含区的结构预测方法。从应用该方法正确地预测出了已知结构的蓝蟹MT的结构来看 ,该方法是可行的。并用该方法预测了油菜MT_L蛋白的半胱氨酸富含区的结构。     

Protein structure modelling and evaluation based on a 4-distance description of side-chain interactions

Background  

Accurate evaluation and modelling of residue-residue interactions within and between proteins is a key aspect of computational structure prediction including homology modelling, protein-protein docking, refinement of low-resolution structures, and computational protein design.     

Ordered conformational change in the protein backbone: prediction of conformationally variable positions from sequence and low-resolution structural data

Ordered conformational changes are an important structural property of proteins and are involved in a variety of fundamental biological activities. Large-scale analyses of the implications of such changes for protein function and dysfunction require efficient methods for automated recognition of conformationally variable residue positions. The goal of this work was to study sequence and low-resolution structural properties of residue positions that change backbone conformation upon changes in protein environment and the utility of these properties for automated recognition of such conformationally variable positions. This study was performed using a large nonredundant set of experimentally characterized proteins that undergo ordered conformational transitions obtained from the Database of Macromolecular Movements. The results of this study show that ordered changes in backbone conformation are not limited to solvent accessible loop regions. A considerable fraction of conformationally variable positions is observed in helices and strands, and in buried positions. Conformationally variable positions are less conserved in evolution. Local patterns of (a) sequence neighbors, (b) evolutionary conservation, and (c) solvent accessibility can be used to predict conformationally variable positions with balanced sensitivity and specificity, albeit with large variance at the level of individual proteins. However, including a pattern of secondary structure into the prediction scheme results in a highly unbalanced performance when all conformationally variable positions located in regular secondary structure are misclassified. Application of the present methodology to the prion protein (PrP) shows that conformationally variable positions predicted in its ordered C-terminal domain are located within segments presumed to be involved in refolding of PrP.     

Folding elastic transmembrane helices to fit in a low-resolution image by electron microscopy

Structure prediction of membrane proteins could be constrained and thereby improved by introducing data of the observed molecular shape. We studied a coarse-grained molecular model that relied on residue-based dummy atoms to fold the transmembrane helices of a protein in the observed molecular shape. Based on the inter-residue potential, the α-helices were folded to contact each other in a simulated annealing protocol to search optimized conformation. Fitting the model into a three-dimensional volume was tested for proteins with known structures and resulted in a fairly reasonable arrangement of helices. In addition, the constraint to the packing transmembrane helix with the two-dimensional region was tested and found to work as a very similar folding guide. The obtained models nicely represented α-helices with the desired slight bend. Our structure prediction method for membrane proteins well demonstrated reasonable folding results using a low-resolution structural constraint introduced from recent cell-surface imaging techniques.     

A model for the three-dimensional structure of human plasma vitronectin from small-angle scattering measurements

Small-angle X-ray scattering (SAXS) measurements were used to characterize vitronectin, a circulatory protein found in human plasma that functions in regulating cell adhesion and migration, as well as proteolytic cascades that affect blood coagulation, fibrinolysis, and pericellular proteolysis. SAXS measurements were taken over a 3-fold range of protein concentrations, yielding data that characterize a monodisperse system of particles with an average radius of gyration of 30.3 +/- 0.6 A and a maximum linear dimension of 110 A. Shape restoration was applied to the data to produce two models of the solution structure of the ligand-free protein. A low-resolution model of the protein was generated that indicates the protein to be roughly peanut-shaped. A better understanding of the domain structure of vitronectin resulted from low-resolution models developed from available high-resolution structures of the domains. These domains include the N-terminal domain that was determined experimentally by NMR [Mayasundari, A., Whittemore, N. A., Serpersu, E. H., and Peterson, C. B. (2004) J. Biol. Chem. 279, 29359-29366] and the docked structure of the central and C-terminal domains that were determined by computational threading [Xu, D., Baburaj, K., Peterson, C. B., and Xu, Y. (2001) Proteins: Struct., Funct., Genet. 44, 312-320]. This model provides an indication of the disposition of the central domain and C-terminal heparin-binding domains of vitronectin with respect to the N-terminal somatomedin B (SMB) domain. This model constructed from the available domain structures, which agrees with the low-resolution model produced from the SAXS data, shows the SMB domain well separated from the central and heparin-binding domains by a disordered linker (residues 54-130). Also, binding sites within the SMB domain are predicted to be well exposed to the surrounding solvent for ease of access to its various ligands.     

An automatic search for similar spatial arrangements of alpha-helices and beta-strands in globular proteins

A fast search algorithm to reveal similar polypeptide backbone structural motifs in proteins is proposed. It is based on the vector representation of a polypeptide chain fold in which the elements of regular secondary structures are approximated by linear segments (Abagyan and Maiorov, J. Biomol. Struct. Dyn. 5, 1267-1279 (1988)). The algorithm permits insertions and deletions in the polypeptide chain fragments to be compared. The fast search algorithm implemented in FASEAR program is used for collecting beta alpha beta supersecondary structure units in a number of alpha/beta proteins of Brookhaven Data Bank. Variation of geometrical parameters specifying backbone chain fold is estimated. It appears that the conformation of the majority of the fragments, although almost all of them are right-handed, is quite different from that of standard beta alpha beta units. Apart from searching for specific type of secondary structure motif, the algorithm allows automatically to identify new recurrent folding patterns in proteins. It may be of particular interest for the development of tertiary template approach for prediction of protein three-dimensional structure as well for constructing artificial polypeptides with goal-oriented conformation.     

Structural insights into the beta-xylosidase from Trichoderma reesei obtained by synchrotron small-angle X-ray scattering and circular dichroism spectroscopy

The enzyme beta-xylosidase from Trichoderma reesei, a member of glycosil hydrolase family 3 (GH3), is a glycoside hydrolase which acts at the glycosidic linkages of 1,4-beta-xylooligosaccharides and that also exhibits alpha-l-arabinofuranosidase activity on 4-nitrophenyl alpha-l-arabinofuranoside. In this work, we show that the enzyme forms monomers in solution and derive the low-resolution molecular envelope of the beta-xylosidase from small-angle X-ray scattering (SAXS) data using the ab initio simulated annealing algorithm. The radius of gyration and the maximum dimension of the beta-xylosidase are 30.3 +/- 0.2 and 90 +/- 5 A, respectively. In contrast to the fold of the only two structurally characterized members of GH3, the barley beta-d-glucan exohydrolase and beta-hexosaminidase from Vibrio cholerae, which have respectively two or one distinct domains, the shape of the beta-xylosidase indicates the presence of three distinct structural modules. Domain recognition algorithms were used to show that the C-terminal part of the amino acid sequence of the protein forms the third domain. Circular dichroism spectroscopy and secondary structure prediction programs demonstrate that this additional domain adopts a predominantly beta conformation.     

OPUS-Dom: Applying the Folding-Based Method VECFOLD to Determine Protein Domain Boundaries

In this article, we present a de novo method for predicting protein domain boundaries, called OPUS-Dom. The core of the method is a novel coarse-grained folding method, VECFOLD, which constructs low-resolution structural models from a target sequence by folding a chain of vectors representing the predicted secondary-structure elements. OPUS-Dom generates a large ensemble of folded structure decoys by VECFOLD and labels the domain boundaries of each decoy by a domain parsing algorithm. Consensus domain boundaries are then derived from the statistical distribution of the putative boundaries and three empirical sequence-based domain profiles. OPUS-Dom generally outperformed several state-of-the-art domain prediction algorithms over various benchmark protein sets. Even though each VECFOLD-generated structure contains large errors, collectively these structures provide a more robust delineation of domain boundaries. The success of OPUS-Dom suggests that the arrangement of protein domains is more a consequence of limited coordination patterns per domain arising from tertiary packing of secondary-structure segments, rather than sequence-specific constraints.     

The Genetic Algorithm and the Conformational Search of Polypeptides and Proteins

Abstract

The genetic algorithm is a technique of function optimization derived from the principles of evolutionary theory. We have adapted it to perform conformational search on polypeptides and proteins. The algorithm was first tested on several small polypeptides and the 46 amino acid protein crambin under the AMBER potential energy function. The probable global minimum conformations of the polypeptides were located 90% of the time and a non-native conformation of crambin was located that was 150kcal/mol lower in potential energy than the minimized crystal structure conformation. Next, we used a knowledge-based potential function to predict the structures of melittin, pancreatic polypeptide, and crambin. A 2.31 Å ΔRMS conformation of melittin and a 5.33 Å ΔRMS conformation of pancreatic polypeptide were located by genetic algorithm-based conformational search under the knowledge-based potential function. Although the ΔRMS of pancreatic polypeptide was somewhat high, most of the secondary structure was correct. The secondary structure of crambin was predicted correctly, but the potential failed to promote packing interactions. Finally, we tested the packing aspects of our potential function by attempting to predict the tertiary structure of cytochrome b ₅₆₂ given correct secondary structure as a constraint. The final predicted conformation of cytochrome b ₅₆₂ was an almost completely extended continuous helix which indicated that the knowledge-based potential was useless for tertiary structure prediction. This work serves as a warning against testing potential functions designed for tertiary structure prediction on small proteins.     

Relationship between protein structure and geometrical constraints.

We evaluate to what extent the structure of proteins can be deduced from incomplete knowledge of disulfide bridges, surface assignments, secondary structure assignments, and additional distance constraints. A cost function taking such constraints into account was used to obtain protein structures using a simple minimization algorithm. For small proteins, the approximate structure could be obtained using one additional distance constraint for each amino acid in the protein. We also studied the effect of using predicted secondary structure and surface assignments. The constraints used in this approach typically may be obtained from low-resolution experimental data. When using a cost function based on distances, half of the resulting structures will be mirrored, because the resulting structure and its mirror image will have the same cost. The secondary structure assignments were therefore divided into chirality constraints and distance constraints. Here we report that the problem of mirrored structures, in some cases, can be solved by using a chirality term in the cost function.     

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.     

Core deformations in protein families: a physical perspective

An analysis is presented on how structural cores change shape within protein families, and whether or not there is a relationship between these structural changes and the vibrational modes that proteins experiment due to topological constraints. A set of 13 representative and well-populated protein families are studied. The evolutionary directions of deformation are obtained by applying a new multiple structural alignment technique to superimpose the structures and extract a conserved core, together with Principal Components Analysis (PCA) to extract the main deformation modes. A low-resolution Normal Mode Analysis (NMA) technique is used in parallel to study the properties of the mechanical core plasticity of the same proteins. We find that the evolutionary deformations span a low dimensional space. A statistically significant correspondence exists between these principal deformations and the vibrational modes accessible to a particular topology. We conclude that, to a significant extent, the structures of evolving proteins seem to respond to sequence changes by collective deformations along combinations of low-frequency modes. The findings have implications in structure prediction by homology modeling.     

An Automatic Search for Similar Spatial Arrangements of α-Helices and β-Strands in Globular Proteins

Abstract

A fast search algorithm to reveal similar polypeptide backbone structural motifs in proteins is proposed. It is based on the vector representation of a polypeptide chain fold in which the elements of regular secondary structures are approximated by linear segments (Abagyan and Maiorov, J. Biomol. Struct. Dyn. 5, 1267–1279 (1988)). The algorithm permits insertions and deletions in the polypeptide chain fragments to be compared. The fast search algorithm implemented in FASEAR program is used for collecting βαβ supersecondary structure units in a number of α/β proteins of Brookhaven Data Bank. Variation of geometrical parameters specifying backbone chain fold is estimated. It appears that the conformation of the majority of the fragments, although almost all of them are right-handed, is quite different from that of standard βαβ units. Apart from searching for specific type of secondary structure motif, the algorithm allows automatically to identify new recurrent folding patterns in proteins. It may be of particular interest for the development of tertiary template approach for prediction of protein three-dimensional structure as well for constructing artificial polypeptides with goal-oriented conformation.     

MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison

Advances in structural genomics and protein structure prediction require the design of automatic, fast, objective, and well benchmarked methods capable of comparing and assessing the similarity of low-resolution three-dimensional structures, via experimental or theoretical approaches. Here, a new method for sequence-independent structural alignment is presented that allows comparison of an experimental protein structure with an arbitrary low-resolution protein tertiary model. The heuristic algorithm is given and then used to show that it can describe random structural alignments of proteins with different folds with good accuracy by an extreme value distribution. From this observation, a structural similarity score between two proteins or two different conformations of the same protein is derived from the likelihood of obtaining a given structural alignment by chance. The performance of the derived score is then compared with well established, consensus manual-based scores and data sets. We found that the new approach correlates better than other tools with the gold standard provided by a human evaluator. Timings indicate that the algorithm is fast enough for routine use with large databases of protein models. Overall, our results indicate that the new program (MAMMOTH) will be a good tool for protein structure comparisons in structural genomics applications. MAMMOTH is available from our web site at http://physbio.mssm.edu/~ortizg/.