An integrated approach to the analysis and modeling of protein sequences and structures. III. A comparative study of sequence conservation in protein structural families using multiple structural alignments

The information required to generate a protein structure is contained in its amino acid sequence, but how three-dimensional information is mapped onto a linear sequence is still incompletely understood. Multiple structure alignments of similar protein structures have been used to investigate conserved sequence features but contradictory results have been obtained, due, in large part, to the absence of subjective criteria to be used in the construction of sequence profiles and in the quantitative comparison of alignment results. Here, we report a new procedure for multiple structure alignment and use it to construct structure-based sequence profiles for similar proteins. The definition of "similar" is based on the structural alignment procedure and on the protein structural distance (PSD) described in paper I of this series, which offers an objective measure for protein structure relationships. Our approach is tested in two well-studied groups of proteins; serine proteases and Ig-like proteins. It is demonstrated that the quality of a sequence profile generated by a multiple structure alignment is quite sensitive to the PSD used as a threshold for the inclusion of proteins in the alignment. Specifically, if the proteins included in the aligned set are too distant in structure from one another, there will be a dilution of information and patterns that are relevant to a subset of the proteins are likely to be lost.In order to understand better how the same three-dimensional information can be encoded in seemingly unrelated sequences, structure-based sequence profiles are constructed for subsets of proteins belonging to nine superfolds. We identify patterns of relatively conserved residues in each subset of proteins. It is demonstrated that the most conserved residues are generally located in the regions where tertiary interactions occur and that are relatively conserved in structure. Nevertheless, the conservation patterns are relatively weak in all cases studied, indicating that structure-determining factors that do not require a particular sequential arrangement of amino acids, such as secondary structure propensities and hydrophobic interactions, are important in encoding protein fold information. In general, we find that similar structures can fold without having a set of highly conserved residue clusters or a well-conserved sequence profile; indeed, in some cases there is no apparent conservation pattern common to structures with the same fold. Thus, when a group of proteins exhibits a common and well-defined sequence pattern, it is more likely that these sequences have a close evolutionary relationship rather than the similarities having arisen from the structural requirements of a given fold.     

An integrated approach to the analysis and modeling of protein sequences and structures. II. On the relationship between sequence and structural similarity for proteins that are not obviously related in sequence

Here, we discuss the relationship between protein sequence and protein structural similarity. It is established that a protein structural distance (PSD) of 2.0 is a threshold above which two proteins are unlikely to have a detectable pairwise sequence relationship. A precise correlation is established between the level of sequence similarity, defined by a normalized Smith-Waterman score, and the probability that two proteins will have a similar structure (defined by pairwise PSD<2). This correlation can be used in evaluating the likelihood for success in a comparative modeling procedure. We establish the existence of a correlation between sequence and structural similarity for pairs of proteins that are related in structure but whose sequence relationship is not detectable using standard pairwise sequence alignments. Although it is well known that there is a close relationship between sequence and structural similarity for pairwise sequence identities greater than about 30 %, there has been little discussion as to the possible existence of such a relationship for pairs of proteins in or below the twilight zone of sequence similarity (<25 % pairwise sequence identity). Possible implications of our results for the evolution of protein structure are discussed.     

From analysis of protein structural alignments toward a novel approach to align protein sequences

Alignment of protein sequences is a key step in most computational methods for prediction of protein function and homology-based modeling of three-dimensional (3D)-structure. We investigated correspondence between "gold standard" alignments of 3D protein structures and the sequence alignments produced by the Smith-Waterman algorithm, currently the most sensitive method for pair-wise alignment of sequences. The results of this analysis enabled development of a novel method to align a pair of protein sequences. The comparison of the Smith-Waterman and structure alignments focused on their inner structure and especially on the continuous ungapped alignment segments, "islands" between gaps. Approximately one third of the islands in the gold standard alignments have negative or low positive score, and their recognition is below the sensitivity limit of the Smith-Waterman algorithm. From the alignment accuracy perspective, the time spent by the algorithm while working in these unalignable regions is unnecessary. We considered features of the standard similarity scoring function responsible for this phenomenon and suggested an alternative hierarchical algorithm, which explicitly addresses high scoring regions. This algorithm is considerably faster than the Smith-Waterman algorithm, whereas resulting alignments are in average of the same quality with respect to the gold standard. This finding shows that the decrease of alignment accuracy is not necessarily a price for the computational efficiency.     

An Eulerian path approach to global multiple alignment for DNA sequences.

With the rapid increase in the dataset of genome sequences, the multiple sequence alignment problem is increasingly important and frequently involves the alignment of a large number of sequences. Many heuristic algorithms have been proposed to improve the speed of computation and the quality of alignment. We introduce a novel approach that is fundamentally different from all currently available methods. Our motivation comes from the Eulerian method for fragment assembly in DNA sequencing that transforms all DNA fragments into a de Bruijn graph and then reduces sequence assembly to a Eulerian path problem. The paper focuses on global multiple alignment of DNA sequences, where entire sequences are aligned into one configuration. Our main result is an algorithm with almost linear computational speed with respect to the total size (number of letters) of sequences to be aligned. Five hundred simulated sequences (averaging 500 bases per sequence and as low as 70% pairwise identity) have been aligned within three minutes on a personal computer, and the quality of alignment is satisfactory. As a result, accurate and simultaneous alignment of thousands of long sequences within a reasonable amount of time becomes possible. Data from an Arabidopsis sequencing project is used to demonstrate the performance.     

SOMAP: a novel interactive approach to multiple protein sequences alignment

A novel interactive method for generating multiple protein sequencealignments is described. The program has no internal limit tothe number or length of sequences it can handle and is designedfor use with DEC VAX processors running the VMS operating system.The approach used is essentially one of manual sequence manipulation,aided by built-in symbolic displays of identities and similarities,and strict and ‘fuzzy’ (ambiguous) pattern-matchingfacilities. Additional flexibility is provided by means of aninterface to a publicly available automatic alignment systemand to a comprehensive sequence analysis package. Received on August 28, 1990; accepted on November 20, 1990     

Prediction of protein structural class using a complexity-based distance measure

Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach achieves a high prediction accuracy over some classical methods.     

Classification of protein sequences by homology modeling and quantitative analysis of electrostatic similarity

Protein electrostatics plays a key role in ligand binding and protein-protein interactions. Therefore, similarities or dissimilarities in electrostatic potentials can be used as indicators of similarities or dissimilarities in protein function. We here describe a method to compare the electrostatic properties within protein families objectively and quantitatively. Three-dimensional structures are built from database sequences by comparative modeling. Molecular potentials are then computed for these with a continuum solvation model by finite difference solution of the Poisson-Boltzmann equation or analytically as a multipole expansion that permits rapid comparison of very large datasets. This approach is applied to 104 members of the Pleckstrin homology (PH) domain family. The deviation of the potentials of the homology models from those of the corresponding experimental structures is comparable to the variation of the potential in an ensemble of structures from nuclear magnetic resonance data or between snapshots from a molecular dynamics simulation. For this dataset, the results for analysis of the full electrostatic potential and the analysis using only monopole and dipole terms are very similar. The electrostatic properties of the PH domains are generally conserved despite the extreme sequence divergence in this family. Notable exceptions from this conservation are seen for PH domains linked to a Db1 homology (DH) domain and in proteins with internal PH domain repeats.     

Database of homology-derived protein structures and the structural meaning of sequence alignment

The database of known protein three-dimensional structures can be significantly increased by the use of sequence homology, based on the following observations. (1) The database of known sequences, currently at more than 12,000 proteins, is two orders of magnitude larger than the database of known structures. (2) The currently most powerful method of predicting protein structures is model building by homology. (3) Structural homology can be inferred from the level of sequence similarity. (4) The threshold of sequence similarity sufficient for structural homology depends strongly on the length of the alignment. Here, we first quantify the relation between sequence similarity, structure similarity, and alignment length by an exhaustive survey of alignments between proteins of known structure and report a homology threshold curve as a function of alignment length. We then produce a database of homology-derived secondary structure of proteins (HSSP) by aligning to each protein of known structure all sequences deemed homologous on the basis of the threshold curve. For each known protein structure, the derived database contains the aligned sequences, secondary structure, sequence variability, and sequence profile. Tertiary structures of the aligned sequences are implied, but not modeled explicitly. The database effectively increases the number of known protein structures by a factor of five to more than 1800. The results may be useful in assessing the structural significance of matches in sequence database searches, in deriving preferences and patterns for structure prediction, in elucidating the structural role of conserved residues, and in modeling three-dimensional detail by homology.     

Protein structural analysis as a tool for protein design

The analysis of known protein structures is a very valuable and indispensable tool for deciphering the complex rules relating sequence to structure in proteins. On the other hand, the design of novel proteins is certainly the most severe test of our understanding of such rules. In this report we describe our own attempt to develop appropriate tools for the investigation of known protein structure properties and their applications to the design of a novel, all β protein. The success of the design project is a demonstration of the usefulness of careful analysis of the data base of known protein structures. © 1994 John Wiley & Sons, Inc.     

Exploring an alignment free approach for protein classification and structural class prediction

Alignment free methods based on Chaos Game Representation (CGR), also known as sequence signature approaches, have proven of great interest for DNA sequence analysis. Indeed, they have been successfully applied for sequence comparison, phylogeny, detection of horizontal transfers or extraction of representative motifs in regulation sequences. Transposing such methods to proteins poses several fundamental questions related to representation space dimensionality. Several studies have tackled these points, but none has, so far, brought the application of CGRs to proteins to their fully expected potential. Yet, several studies have shown that techniques based on n-peptide frequencies can be relevant for proteins. Here, we investigate the effectiveness of a strategy based on the CGR approach using a fixed reverse encoding of amino acids into nucleic sequences. We first explore its relevance to protein classification into functional families. We then attempt to apply it to the prediction of protein structural classes. Our results suggest that the reverse encoding approach could be relevant in both cases. We show that it is able to classify functional families of proteins by extracting signatures close to the ProSite patterns. Applied to structural classification, the approach reaches scores of correct classification close to 84%, i.e. close to the scores of related methods in the field. Various optimizations of the approach are still possible, which open the door for future applications.     

An integrated computational workflow for efficient and quantitative modeling of renin inhibitors

A new integrated computational workflow that couples the strength of the molecular overlay methods to achieve rapid and automated alignments along with 3D-QSAR techniques like CoMFA and CoMSIA for quantitative binding affinity prediction is presented. The results obtained from such techniques are compared with rule-based Topomer CoMFA method, where possible. The developed 3D-QSAR models were prospectively used to predict the affinities of new compounds designed through R-group deconvolution starting from the core chemical scaffold and subsequent virtual combinatorial library enumeration. The general applicability of the seamless in silico modeling workflow is demonstrated using several datasets reported for small molecule inhibitors of renin.     

An integrated approach to patient-specific predictive modeling for single ventricle heart palliation

In patients with congenital heart disease and a single ventricle (SV), ventricular support of the circulation is inadequate, and staged palliative surgery (usually 3 stages) is needed for treatment. In the various palliative surgical stages individual differences in the circulation are important and patient-specific surgical planning is ideal. In this study, an integrated approach between clinicians and engineers has been developed, based on patient-specific multi-scale models, and is here applied to predict stage 2 surgical outcomes. This approach involves four distinct steps: (1) collection of pre-operative clinical data from a patient presenting for SV palliation, (2) construction of the pre-operative model, (3) creation of feasible virtual surgical options which couple a three-dimensional model of the surgical anatomy with a lumped parameter model (LPM) of the remainder of the circulation and (4) performance of post-operative simulations to aid clinical decision making. The pre-operative model is described, agreeing well with clinical flow tracings and mean pressures. Two surgical options (bi-directional Glenn and hemi-Fontan operations) are virtually performed and coupled to the pre-operative LPM, with the hemodynamics of both options reported. Results are validated against postoperative clinical data. Ultimately, this work represents the first patient-specific predictive modeling of stage 2 palliation using virtual surgery and closed-loop multi-scale modeling.     

Super pairwise alignment (SPA): an efficient approach to global alignment for homologous sequences.

Sequence analysis is the basis of bioinformatics, while sequence alignment is a fundamental task for sequence analysis. The widely used alignment algorithm, Dynamic Programming, though generating optimal alignment, takes too much time due to its high computation complexity O(N(2)). In order to reduce computation complexity without sacrificing too much accuracy, we have developed a new approach to align two homologous sequences. The new approach presented here, adopting our novel algorithm which combines the methods of probabilistic and combinatorial analysis, reduces the computation complexity to as low as O(N). The computation speed by our program is at least 15 times faster than traditional pairwise alignment algorithms without a loss of much accuracy. We hence named the algorithm Super Pairwise Alignment (SPA). The pairwise alignment execution program based on SPA and the detailed results of the aligned sequences discussed in this article are available upon request.     

Multiple mapping method: a novel approach to the sequence-to-structure alignment problem in comparative protein structure modeling

A major bottleneck in comparative protein structure modeling is the quality of input alignment between the target sequence and the template structure. A number of alignment methods are available, but none of these techniques produce consistently good solutions for all cases. Alignments produced by alternative methods may be superior in certain segments but inferior in others when compared to each other; therefore, an accurate solution often requires an optimal combination of them. To address this problem, we have developed a new approach, Multiple Mapping Method (MMM). The algorithm first identifies the alternatively aligned regions from a set of input alignments. These alternatively aligned segments are scored using a composite scoring function, which determines their fitness within the structural environment of the template. The best scoring regions from a set of alternative segments are combined with the core part of the alignments to produce the final MMM alignment. The algorithm was tested on a dataset of 1400 protein pairs using 11 combinations of two to four alignment methods. In all cases MMM showed statistically significant improvement by reducing alignment errors in the range of 3 to 17%. MMM also compared favorably over two alignment meta-servers. The algorithm is computationally efficient; therefore, it is a suitable tool for genome scale modeling studies.     

An integrated sustainable development approach to modeling the eco-environmental effects from urbanization

Urbanization induces detrimental effects on the eco-environment that are beginning to be extensively described. However, the adoption of suitable indicators and reliable methods still requires attention to provide a basis for a more accurate understanding of eco-environmental effects from urbanization. The aggregated index system representing the coupling relationships between urbanization and eco-environment was introduced and an integrated sustainable development approach (ISD) to simulate and evaluate the integrative effects was developed in this paper. The ISD consists of modules of the coupling relationship assessments, systematic analysis of potential eco-environmental changes, and sustainability evaluation. An interpretative structural modeling (ISM) and grey relative technique (GRT) are used to analyze the systematical structure and assess the coupling relationships. A system dynamics model (SD) and artificial neural network (ANN) are used to simulate the potential environmental changes and compare the sustainability, respectively. An application study of Jiangsu province in China was performed, where the general eco-environmental integrity is under significant urbanization pressure. The potential eco-environmental scenarios from 2010 to 2015 under natural, populated, economic, spatial and social urbanization patterns were simulated and analyzed. The result reveals that the urbanization sustainability may be met on condition that either populated urbanization or social urbanization pattern is adopted in Jiangsu province, China. The aggregated indexes and ISD may help local authorities better understand and address the complex coupling relationship, and develop improved regional environmental management strategies that better balance urbanization and eco-environmental conservation.     

Evolution of protein sequences and structures.

The relationship between sequence similarity and structural similarity has been examined in 36 protein families with five or more diverse members whose structures are known. The structural similarity within a family (as determined with the DALI structure comparison program) is linearly related to sequence similarity (as determined by a Smith-Waterman search of the protein sequences in the structure database). The correlation between structural similarity and sequence similarity is very high; 18 of the 36 families had linear correlation coefficients r>/=0.878, and only nine had correlation coefficients r相似文献   

An integrated approach for inference and mechanistic modeling for advancing drug development

An important challenge facing researchers in drug development is how to translate multi-omic measurements into biological insights that will help advance drugs through the clinic. Computational biology strategies are a promising approach for systematically capturing the effect of a given drug on complex molecular networks and on human physiology. This article discusses a two-pronged strategy for inferring biological interactions from large-scale multi-omic measurements and accounting for known biology via mechanistic dynamical simulations of pathways, cells, and organ- and tissue level models. These approaches are already playing a role in driving drug development by providing a rational and systematic computational framework.     

Domain assignment for protein structures using a consensus approach: characterization and analysis.

A consensus approach for the assignment of structural domains in proteins is presented. The approach combines a number of previously published algorithms, and takes advantage of the elevated accuracy obtained when assignments from the individual algorithms are in agreement. The consensus approach is tested on a data set of 55 protein chains, for which domain assignments from four automated methods were known, and for which crystallographers assignments had been reported in the literature. Accuracy was found to increase in this test from 72% using individual algorithms to 100% when all four methods were in agreement. However a consensus prediction using all four methods was only possible for 52% of the dataset. The consensus approach [using three publicly available domain assignment algorithms (PUU, DETECTIVE, DOMAK)] was then used to make domain assignments for a data set of 787 protein chains from the Protein Data Bank. Analysis of the assignments showed 55.7% of assignments could be made automatically, and of these, 13.5% were multi-domain proteins. Of the remaining 44.3% that could not be assigned by the consensus procedure 90.4% had their domain boundaries assigned correctly by at least one of the algorithms. Once identified, these domains were analyzed for trends in their size and secondary structure class. In addition, the discontinuity of each domain along the protein chain was considered.