Pairwise alignment incorporating dipeptide covariation

Motivation: Standard algorithms for pairwise protein sequence alignment make the simplifying assumption that amino acid substitutions at neighboring sites are uncorrelated. This assumption allows implementation of fast algorithms for pairwise sequence alignment, but it ignores information that could conceivably increase the power of remote homolog detection. We examine the validity of this assumption by constructing extended substitution matrixes that encapsulate the observed correlations between neighboring sites, by developing an efficient and rigorous algorithm for pairwise protein sequence alignment that incorporates these local substitution correlations, and by assessing the ability of this algorithm to detect remote homologies. Results: Our analysis indicates that local correlations between substitutions are not strong on the average. Furthermore, incorporating local substitution correlations into pairwise alignment did not lead to a statistically significant improvement in remote homology detection. Therefore, the standard assumption that individual residues within protein sequences evolve independently of neighboring positions appears to be an efficient and appropriate approximation

Of bits and bugs

Pur-α is a nucleic acid-binding protein involved in cell cycle control, transcription, and neuronal function. Initially no prediction of the three-dimensional structure of Pur-α was possible. However, recently we solved the X-ray structure of Pur-α from the fruitfly Drosophila melanogaster and showed that it contains a so-called PUR domain. Here we explain how we exploited bioinformatics tools in combination with X-ray structure determination of a bacterial homolog to obtain diffracting crystals and the high-resolution structure of Drosophila Pur-α. First, we used sensitive methods for remote-homology detection to find three repetitive regions in Pur-α. We realized that our lack of understanding how these repeats interact to form a globular domain was a major problem for crystallization and structure determination. With our information on the repeat motifs we then identified a distant bacterial homolog that contains only one repeat. We determined the bacterial crystal structure and found that two of the repeats interact to form a globular domain. Based on this bacterial structure, we calculated a computational model of the eukaryotic protein. The model allowed us to design a crystallizable fragment and to determine the structure of Drosophila Pur-α. Key for success was the fact that single repeats of the bacterial protein self-assembled into a globular domain, instructing us on the number and boundaries of repeats to be included for crystallization trials with the eukaryotic protein. This study demonstrates that the simpler structural domain arrangement of a distant prokaryotic protein can guide the design of eukaryotic crystallization constructs. Since many eukaryotic proteins contain multiple repeats or repeating domains, this approach might be instructive for structural studies of a range of proteins

MRFalign: Protein Homology Detection through Alignment of Markov Random Fields

Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/.Comment: Accepted by both RECOMB 2014 and PLOS Computational Biolog

Probabilistic protein homology modeling

Searching sequence databases and building 3D models for proteins are important tasks for biologists. When the structure of a query protein is given, its function can be inferred. However, experimental methods for structure prediction are both expensive and time consuming. Fully automatic homology modeling refers to building a 3D model for a query sequence from an alignment to related homologous proteins with known structure (templates) by a computer. Current prediction servers can provide accurate models within a few hours to days. Our group has developed HHpred, which is one of the top performing structure prediction servers in the field. In general, homology based structure modeling consists of four steps: (1) finding homologous templates in a database, (2) selecting and (3) aligning templates to the query, (4) building a 3D model based on the alignment. In part one of this thesis, we will present improvements of step (2) and (4). Specifically, homology modeling has been shown to work best when multiple templates are selected instead of only a single one. Yet, current servers are using rather ad-hoc approaches to combine information from multiple templates. We provide a rigorous statistical framework for multi-template homology modeling. Given an alignment, we employ Modeller to calculate the most probable structure for a query. The 3D model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions. We find that the query’s atomic distance restraints can be accurately described by two-component Gaussian mixtures. Moreover, we derive statistical weights to quantify the redundancy among related templates. This allows us to apply the standard rules of probability theory to combine restraints from several templates. Together with a heuristic template selection strategy, we have implemented this approach within HHpred and could significantly improve model quality. Furthermore, we took part in CASP, a community wide competition for structure prediction, where we were ranked first in template based modeling and, at the same time, were more than 450 times faster than all other top servers. Homology modeling heavily relies on detecting and correctly aligning templates to the query sequence (step (1) and (3) from above). But remote homologies are difficult to detect and hard to align on a pure sequence level. Hence, modern tools are based on profiles instead of sequences. A profile summarizes the evolutionary history of a given sequence and consists of position specific amino acid probabilities for each residue. In addition to the similarity score between profile columns, most methods use extra terms that compare 1D structural properties such as secondary structure or solvent accessibility. These can be predicted from local profile windows. In the second part of this thesis, we develop a new score that is independent of any predefined structural property. For this purpose, we learn a library of 32 profile patterns that are most conserved in alignments of remotely homologous, structurally aligned proteins. Each so called “context state” in the library consists of a 13-residue sequence profile. We integrate the new context score into our Hmm-Hmm alignment tool HHsearch and improve especially the sensitivity and precision of difficult pairwise alignments significantly. Taken together, we introduced probabilistic methods to improve all four main steps in homology based structure prediction

DeepSF: deep convolutional neural network for mapping protein sequences to folds

Motivation Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein se quence into one of 1195 known folds, which is useful for both fold recognition and the study of se quence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and map it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 77.0%. We compare our method with a top profile profile alignment method - HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 14.5%-29.1% higher than HHSearch on template-free modeling targets and 4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.Comment: 28 pages, 13 figure

Detecting Remote Sequence Homology in Disordered Proteins: Discovery of Conserved Motifs in the N-Termini of Mononegavirales phosphoproteins

Paramyxovirinae are a large group of viruses that includes measles virus and parainfluenza viruses. The viral Phosphoprotein (P) plays a central role in viral replication. It is composed of a highly variable, disordered N-terminus and a conserved C-terminus. A second viral protein alternatively expressed, the V protein, also contains the N-terminus of P, fused to a zinc finger. We suspected that, despite their high variability, the N-termini of P/V might all be homologous; however, using standard approaches, we could previously identify sequence conservation only in some Paramyxovirinae. We now compared the N-termini using sensitive sequence similarity search programs, able to detect residual similarities unnoticeable by conventional approaches. We discovered that all Paramyxovirinae share a short sequence motif in their first 40 amino acids, which we called soyuz1. Despite its short length (11–16aa), several arguments allow us to conclude that soyuz1 probably evolved by homologous descent, unlike linear motifs. Conservation across such evolutionary distances suggests that soyuz1 plays a crucial role and experimental data suggest that it binds the viral nucleoprotein to prevent its illegitimate self-assembly. In some Paramyxovirinae, the N-terminus of P/V contains a second motif, soyuz2, which might play a role in blocking interferon signaling. Finally, we discovered that the P of related Mononegavirales contain similarly overlooked motifs in their N-termini, and that their C-termini share a previously unnoticed structural similarity suggesting a common origin. Our results suggest several testable hypotheses regarding the replication of Mononegavirales and suggest that disordered regions with little overall sequence similarity, common in viral and eukaryotic proteins, might contain currently overlooked motifs (intermediate in length between linear motifs and disordered domains) that could be detected simply by comparing orthologous proteins

Exploring the function and evolution of proteins using domain families

Proteins are frequently composed of multiple domains which fold independently. These are often evolutionarily distinct units which can be adapted and reused in other proteins. The classification of protein domains into evolutionary families facilitates the study of their evolution and function. In this thesis such classifications are used firstly to examine methods for identifying evolutionary relationships (homology) between protein domains. Secondly a specific approach for predicting their function is developed. Lastly they are used in studying the evolution of protein complexes. Tools for identifying evolutionary relationships between proteins are central to computational biology. They aid in classifying families of proteins, giving clues about the function of proteins and the study of molecular evolution. The first chapter of this thesis concerns the effectiveness of cutting edge methods in identifying evolutionary relationships between protein domains. The identification of evolutionary relationships between proteins can give clues as to their function. The second chapter of this thesis concerns the development of a method to identify proteins involved in the same biological process. This method is based on the concept of domain fusion whereby pairs of proteins from one organism with a concerted function are sometimes found fused into single proteins in a different organism. Using protein domain classifications it is possible to identify these relationships. Most proteins do not act in isolation but carry out their function by binding to other proteins in complexes; little is understood about the evolution of such complexes. In the third chapter of this thesis the evolution of complexes is examined in two representative model organisms using protein domain families. In this work, protein domain superfamilies allow distantly related parts of complexes to be identified in order to determine how homologous units are reused

Protein Fold Recognition Using Neural Networks

To predict accurately the three-dimensional (3D) structures of proteins from their amino acid sequences alone remains a challenging problem. However, using protein fold recognition tools, it is often possible to achieve good models or at least to gain some more information, to aid scientists in their research. This thesis describes development of TUNE (Threading Using Neural Networks), a fold recognition program using artificial neural network (ANN) models. A new method to generate amino acid substitution matrices is described in chapter two. It uses an ANN to generalise amino acid substitutions observed in protein structure alignments. Matrices for alignment scoring from this approach were compared with classic alignment scoring schemes. From these neural network models, a series of encoding schemes were constructed. These schemes describe the amino acid types with a few numbers. They were generated to replace the orthogonal encoding scheme, so that smaller, faster and more accurate neural network models can be applied on bioinformatic problems. The TUNE model was introduced in chapter four to measure protein sequence-structure compatibility. Given the integrated residue structural environment descriptions, the model predicts probabilities of observing amino acid types in such environments. Using this model, a scoring function to measure the fitness of a residue in a protein structure model can be made for protein threading programs. The model in chapter two was extended by including the residue structural environment descriptions for predictions. A simple protein fold recognition program with a dynamic programming algorithm was developed using this model. The program was then tested in the fourth round of the Critical Assessment of protein Structure Prediction methods (CASP4) and produced reasonably good results