Spectral Sequence Motif Discovery

Sequence discovery tools play a central role in several fields of computational biology. In the framework of Transcription Factor binding studies, motif finding algorithms of increasingly high performance are required to process the big datasets produced by new high-throughput sequencing technologies. Most existing algorithms are computationally demanding and often cannot support the large size of new experimental data. We present a new motif discovery algorithm that is built on a recent machine learning technique, referred to as Method of Moments. Based on spectral decompositions, this method is robust under model misspecification and is not prone to locally optimal solutions. We obtain an algorithm that is extremely fast and designed for the analysis of big sequencing data. In a few minutes, we can process datasets of hundreds of thousand sequences and extract motif profiles that match those computed by various state-of-the-art algorithms.Comment: 20 pages, 3 figures, 1 tabl

MODER2: First-order Markov Modeling and Discovery of Monomeric and Dimeric Binding Motifs

Motivation: Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results: We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average.Peer reviewe

Study of protein-DNA interaction using new generation data

Ph.DDOCTOR OF PHILOSOPH

A Generalized Biophysical Model of Transcription Factor Binding Specificity and Its Application on High-Throughput SELEX Data

The interaction between transcription factors (TFs) and DNA plays an important role in gene expression regulation. In the past, experiments on protein–DNA interactions could only identify a handful of sequences that a TF binds with high affinities. In recent years, several high-throughput experimental techniques, such as high-throughput SELEX (HT-SELEX), protein-binding microarrays (PBMs) and ChIP-seq, have been developed to estimate the relative binding affinities of large numbers of DNA sequences both in vitro and in vivo. The large volume of data generated by these techniques proved to be a challenge and prompted the development of novel motif discovery algorithms. These algorithms are based on a range of TF binding models, including the widely used probabilistic model that represents binding motifs as position frequency matrices (PFMs). However, the probabilistic model has limitations and the PFMs extracted from some of the high-throughput experiments are known to be suboptimal. In this dissertation, we attempt to address these important questions and develop a generalized biophysical model and an expectation maximization (EM) algorithm for estimating position weight matrices (PWMs) and other parameters using HT-SELEX data. First, we discuss the inherent limitations of the popular probabilistic model and compare it with a biophysical model that assumes the nucleotides in a binding site contribute independently to its binding energy instead of binding probability. We use simulations to demonstrate that the biophysical model almost always provides better fits to the data and conclude that it should take the place of the probabilistic model in charactering TF binding specificity. Then we describe a generalized biophysical model, which removes the assumption of known binding locations and is particularly suitable for modeling protein–DNA interactions in HT-SELEX experiments, and BEESEM, an EM algorithm capable of estimating the binding model and binding locations simultaneously. BEESEM can also calculate the confidence intervals of the estimated parameters in the binding model, a rare but useful feature among motif discovery algorithms. By comparing BEESEM with 5 other algorithms on HT-SELEX, PBM and ChIP-seq data, we demonstrate that BEESEM provides significantly better fits to in vitro data and is similar to the other methods (with one exception) on in vivo data under the criterion of the area under the receiver operating characteristic curve (AUROC). We also discuss the limitations of the AUROC criterion, which is purely rank-based and thus misses quantitative binding information. Finally, we investigate whether adding DNA shape features can significantly improve the accuracy of binding models. We evaluate the ability of the gradient boosting classifiers generated by DNAshapedTFBS, an algorithm that takes account of DNA shape features, to differentiate ChIP-seq peaks from random background sequences, and compare them with various matrix-based binding models. The results indicate that, compared with optimized PWMs, adding DNA shape features does not produce significantly better binding models and may increase the risk of overfitting on training datasets

Protein-DNA Recognition Models for the Homeodomain and C2H2 Zinc Finger Transcription Factor Families

Transcription factors: TFs) play a central role in the gene regulatory network of each cell. They can stimulate or inhibit transcription of their target genes by binding to short, degenerate DNA sequence motifs. The goal of this research is to build improved models of TF binding site recognition. This can facilitate the determination of regulatory networks and also allow for the prediction of binding site motifs based only on the TF protein sequence. Recent technological advances have rapidly expanded the amount of quantitative TF binding data available. PBMs: Protein Binding Microarrays) have recently been implemented in a format that allows all 10mers to be assayed in parallel. There is now PBM data available for hundreds of transcription factors. Another fairly recent technique for determining the binding preference of a TF is an in vivo bacterial one-hybrid assay: B1H). In this approach a TF is expressed in E. coli where it can be used to select strong binding sites from a library of randomized sites located upstream of a weak promoter, driving expression of a selectable gene. When coupled with high throughput sequencing and a newly developed analysis method, quantitative binding data can be obtained. In the last few years, the binding specificities of hundreds of TFs have been determined using B1H. The two largest eukaryotic transcription factor families are the zf-C2H2 and homeodomain TF families. Newly available PBM and B1H specificity models were used to develop recognition models for these two families, with the goal of being able to predict the binding specific of a TF from its protein sequence. We developed a feature selection method based on adjusted mutual information that automatically recovers nearly all of the known key residues for the homeodomain and zf-C2H2 families. Using those features we find that, for both families, random forest: RF) and support vector machine: SVM) based recognition models outperform the nearest neighbor method, which has previously been considered the best method

CSI-Tree: a regression tree approach for modeling binding properties of DNA-binding molecules based on cognate site identification (CSI) data

The identification and characterization of binding sites of DNA-binding molecules, including transcription factors (TFs), is a critical problem at the interface of chemistry, biology and molecular medicine. The Cognate Site Identification (CSI) array is a high-throughput microarray platform for measuring comprehensive recognition profiles of DNA-binding molecules. This technique produces datasets that are useful not only for identifying binding sites of previously uncharacterized TFs but also for elucidating dependencies, both local and nonlocal, between the nucleotides at different positions of the recognition sites. We have developed a regression tree technique, CSI-Tree, for exploring the spectrum of binding sites of DNA-binding molecules. Our approach constructs regression trees utilizing the CSI data of unaligned sequences. The resulting model partitions the binding spectrum into homogeneous regions of position specific nucleotide effects. Each homogeneous partition is then summarized by a position weight matrix (PWM). Hence, the final outcome is a binding intensity rank-ordered collection of PWMs each of which spans a different region in the binding spectrum. Nodes of the regression tree depict the critical position/nucleotide combinations. We analyze the CSI data of the eukaryotic TF Nkx-2.5 and two engineered small molecule DNA ligands and obtain unique insights into their binding properties. The CSI tree for Nkx-2.5 reveals an interaction between two positions of the binding profile and elucidates how different nucleotide combinations at these two positions lead to different binding affinities. The CSI trees for the engineered DNA ligands exhibit a common preference for the dinucleotide AA in the first two positions, which is consistent with preference for a narrow and relatively flat minor groove. We carry out a reanalysis of these data with a mixture of PWMs approach. This approach is an advancement over the simple PWM model and accommodates position dependencies based on only sequence data. Our analysis indicates that the dependencies revealed by the CSI-Tree are challenging to discover without the actual binding intensities. Moreover, such a mixture model is highly sensitive to the number and length of the sequences analyzed. In contrast, CSI-Tree provides interpretable and concise summaries of the complete recognition profiles of DNA-binding molecules by utilizing binding affinities

DEEP LEARNING METHODS FOR PREDICTION OF AND ESCAPE FROM PROTEIN RECOGNITION

Protein interactions drive diverse processes essential to living organisms, and thus numerous biomedical applications center on understanding, predicting, and designing how proteins recognize their partners. While unfortunately the number of interactions of interest still vastly exceeds the capabilities of experimental determination methods, computational methods promise to fill the gap. My thesis pursues the development and application of computational methods for several protein interaction prediction and design tasks. First, to improve protein-glycan interaction specificity prediction, I developed GlyBERT, which learns biologically relevant glycan representations encapsulating the components most important for glycan recognition within their structures. GlyBERT encodes glycans with a branched biochemical language and employs an attention-based deep language model to embed the correlation between local and global structural contexts. This approach enables the development of predictive models from limited data, supporting applications such as lectin binding prediction. Second, to improve protein-protein interaction prediction, I developed a unified geometric deep neural network, ‘PInet’ (Protein Interface Network), which leverages the best properties of both data- and physics-driven methods, learning and utilizing models capturing both geometrical and physicochemical molecular surface complementarity. In addition to obtaining state-of-the-art performance in predicting protein-protein interactions, PInet can serve as the backbone for other protein-protein interaction modeling tasks such as binding affinity prediction. Finally, I turned from ii prediction to design, addressing two important tasks in the context of antibodyantigen recognition. The first problem is to redesign a given antigen to evade antibody recognition, e.g., to help biotherapeutics avoid pre-existing immunity or to focus vaccine responses on key portions of an antigen. The second problem is to design a panel of variants of a given antigen to use as “bait” in experimental identification of antibodies that recognize different parts of the antigen, e.g., to support classification of immune responses or to help select among different antibody candidates. I developed a geometry-based algorithm to generate variants to address these design problems, seeking to maximize utility subject to experimental constraints. During the design process, the algorithm accounts for and balances the effects of candidate mutations on antibody recognition and on antigen stability. In retrospective case studies, the algorithm demonstrated promising precision, recall, and robustness of finding good designs. This work represents the first algorithm to systematically design antigen variants for characterization and evasion of polyclonal antibody responses