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11.
  • Illergård, Kristoffer, 1980- (author)
  • On the effects of structure and function on protein evolution
  • 2010
  • Doctoral thesis (other academic/artistic)abstract
    • Many proteins can be described as working machines that make sure that everything functions in the cell. Their specific molecular functions are largely dependent on their three-dimensional structures, which in turn are mainly predetermined by their linear sequences of amino acid residues. Therefore, there is a relation between the sequence, structure and function of a protein, in which knowledge about the structure is crucial for understanding the functions. The structure is generally difficult to determine experimentally, but should in principle be possible to predict from the sequence by computational methods. The instructions of how to build the linear proteins sequences are copied during cell division and are passed on to successive generations. Although the copying process is a very efficient and accurate system, it does not function correctly on every occasion. Sometimes errors, or mutations can result from the process. These mutations gradually accumulate over time, so that the sequences and thereby also the structures and functions of proteins evolve overtime. This thesis is based on four papers concerning the relationship between function, structure and sequence and how it changes during the evolution of proteins. Paper I shows that the structural change is linearly related to sequence change and that structures are 3 to 10 times more conserved than sequences. In Paper II and Paper III we investigated non-helical structures and polar residues, respectively, positioned in the nonpolar membrane core environment of α-helical membrane proteins. Both types were found to be evolutionary conserved and functionally important. Paper IV includes the development of a method to predict the residues in α-helical membrane proteins that after folding become exposed to the solvent environment.
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12.
  • Lamb, John, 1983- (author)
  • Transmembrane Proteins and Protein Structure Prediction : What we can learn from Computational Methods
  • 2021
  • Doctoral thesis (other academic/artistic)abstract
    • A protein’s 3D-structure is essential to understand how proteins function and interact and how biochemical processes proceed in organic life. Despite the advancement in experimental methods, it remains expensive and time-consuming to determine protein structure experimentally. There have been significant advances in machine learning and computational methods where, in many cases, models of protein structure can be determined to a high level of quality. Using computational methods helps predict protein 3D-structure and is often used complementary to experimental methods to give better insight and understanding of biological processes.This thesis presents studies focusing on the simplicity and transparency of the 3D-structure pipeline. This is done with a new interactive database with full access to the pipeline’s data and code together with tools to analyse and compare models and structures. I present a new module for the last step in this pipeline, the final folding of the protein chain, which both simplifies the current pipeline and uses new input data based on the current research. This module predicts better models than its predecessor and produces models more than a magnitude faster than the current state-of-the-art tools. This module also contains a novel way of both folding and docking dimers in one single step. There are many examples of how machine learning models contain biases that originate in biased training data, translating into models that do not generalise well. I present a study where experts collaborate to create a high-quality database of Intrinsically Disordered Proteins. Through manual annotation and quality protocols, high-quality training data has been produced that is well suited for machine learning tasks and protein disorder analysis. In this thesis, I also present computational methods pertaining to transmembrane proteins and how they can increase our insight into membrane protein structure. In one study, we use computational methods together with experimental methods to investigate how differently charged residue pairs that form salt bridges inside the membrane of membrane proteins changes the insertion potential. We show that amino acid pairs that form salt bridges in this setting contribute 0.5-0.7 kcal/mol to membrane insertion’s apparent free energy. This gives new insight and advances in how we calculate insertion and can lead to better membrane protein topology predictors. In the final study, we investigate the CPA/AT-transporter family of transmembrane proteins and create a new integrated topology annotation method and structural classification, resulting in new insight into how this family evolved through time. The entire pipeline is published as an interactive database with complete transparency for both the method and data used. The study shows how this family has evolved by duplicating internal regions and how this has caused a structural symmetry in the family. This thesis, therefore, contributes to a more accessible and more transparent path of using computational methods to give a more extensive insight into protein structure prediction and how these structures pertain to biochemical processes.
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13.
  • Larsson, Per, 1978- (author)
  • Prediction, modeling, and refinement of protein structure
  • 2010
  • Doctoral thesis (other academic/artistic)abstract
    • Accurate predictions of protein structure are important for understanding many processes in cells. The interactions that govern protein folding and structure are complex, and still far from completely understood. However, progress is being made in many areas. Here, efforts to improve the overall quality of protein structure models are described. From a pure evolutionary perspective, in which proteins are viewed in the light of gradually accumulated mutations on the sequence level, it is shown how information from multiple sources helps to create more accurate models. A very simple but surprisingly accurate method for assigning confidence measures for protein structures is also tested. In contrast to models based on evolution, physics based methods view protein structures as the result of physical interactions between atoms. Newly implemented methods are described that both increase the time-scales accessible for molecular dynamics simulations almost 10-fold, and that to some extent might be able to refine protein structures. Finally, I compare the efficiency and properties of different techniques for protein structure refinement.
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14.
  • Light, Sara, 1975- (author)
  • Investigations into the evolution of biological networks
  • 2006
  • Doctoral thesis (other academic/artistic)abstract
    • Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory.The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks.This thesis describes three investigations into the evolution of metabolic and protein-protein interaction networks. We present a comparative study of the importance of retrograde evolution, the scenario that pathways assemble backward compared to the direction of the pathway, and patchwork evolution, where enzymes evolve from a broad to narrow substrate specificity. Shifting focus toward network topology, a suggested mechanism for the evolution of biological networks, preferential attachment, is investigated in the context of metabolism. Early in the investigation of biological networks it seemed clear that the networks often display a particular, 'scale-free', topology. This topology is characterized by many nodes with few interaction partners and a few nodes (hubs) with a large number of interaction partners. While the second paper describes the evidence for preferential attachment in metabolic networks, the final paper describes the characteristics of the hubs in the physical interaction network of S. cerevisiae.
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15.
  • Michel, Mirco, 1986- (author)
  • From Sequence to Structure : Using predicted residue contacts to facilitate template-free protein structure prediction
  • 2017
  • Doctoral thesis (other academic/artistic)abstract
    • Despite the fundamental role of experimental protein structure determination, computational methods are of essential importance to bridge the ever growing gap between available protein sequence and structure data. Common structure prediction methods rely on experimental data, which is not available for about half of the known protein families.Recent advancements in amino acid contact prediction have revolutionized the field of protein structure prediction. Contacts can be used to guide template-free structure predictions that do not rely on experimentally solved structures of homologous proteins. Such methods are now able to produce accurate models for a wide range of protein families.We developed PconsC2, an approach that improved existing contact prediction methods by recognizing intra-molecular contact patterns and noise reduction. An inherent problem of contact prediction based on maximum entropy models is that large alignments with over 1000 effective sequences are needed to infer contacts accurately. These are however not available for more than 80% of all protein families that do not have a representative structure in PDB. With PconsC3, we could extend the applicability of contact prediction to families as small as 100 effective sequences by combining global inference methods with machine learning based on local pairwise measures.By introducing PconsFold, a pipeline for contact-based structure prediction, we could show that improvements in contact prediction accuracy translate to more accurate models. Finally, we applied a similar technique to Pfam, a comprehensive database of known protein families. In addition to using a faster folding protocol we employed model quality assessment methods, crucial for estimating the confidence in the accuracy of predicted models. We propose models tobe accurate for 558 families that do not have a representative known structure. Out of those, over 75% have not been reported before.
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16.
  • Moruz, Luminita, 1982- (author)
  • Chromatographic retention time prediction and its applications in mass spectrometry-based proteomics
  • 2013
  • Doctoral thesis (other academic/artistic)abstract
    • Mass spectrometry-based methods are among the most commonly used techniques to characterize proteins in biological samples. With rapid technological developments allowing increasing throughput, thousands of proteins can now be monitored in a matter of hours. However, these advances brought a whole new set of analytical challenges. At the moment, it is no longer possible to rely on human experts to process the data. Instead, accurate computational tools are required.In line with these observations, my research work has involved development of computational methods to facilitate the analysis of mass spectrometry-based experiments. In particular, the projects included in this thesis revolve around the chromatography step of such experiments, where peptides are separated according to their hydrophobicity.The first part of the thesis describes an algorithm to predict retention time from peptide sequences. The method provides more accurate predictions compared to previous approaches, while being easily transferable to other chromatography setups. In addition, it gives equally good predictions for peptides carrying arbitrary posttranslational modifications as for unmodified peptides.The second part of the thesis includes two applications of retention time predictions in the context of mass spectrometry-based proteomics experiments. First, we show how theoretical calculations of masses and retention times can be used to infer proteins in shotgun proteomics experiments. Secondly, we illustrate the use of retention time predictions to calculate optimized gradient functions for reversed-phase liquid chromatography.
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17.
  • Ohlson, Tomas, 1977- (author)
  • The use of evolutionary information in protein alignments and homology identification
  • 2006
  • Doctoral thesis (other academic/artistic)abstract
    • For the vast majority of proteins no experimental information about the three-dimensional structure is known, but only its sequence. Therefore, the easiest way to obtain some understanding of the structure and function of these proteins is by relating them to well studied proteins. This can be done by searching for homologous proteins. It is easy to identify a homologous sequence if the sequence identity is above 30%. However, if the sequence identity drops below 30% then more sophisticated methods have to be used. These methods often use evolutionary information about the sequences, which makes it possible to identify homologous sequences with a low sequence identity.In order to build a three--dimensional model from the sequence based on a protein structure the two sequences have to be aligned. Here the aligned residues serve as a first approximation of the structure.This thesis focuses on the development of fold recognition and alignment methods based on evolutionary information. The use of evolutionary information for both query and target proteins was shown to improve both recognition and alignments. In a benchmark of profile--profile methods it was shown that the probabilistic methods were best, although the difference between several of the methods was quite small once optimal gap-penalties were used. An artificial neural network based alignment method ProfNet was shown to be at least as good as the best profile--profile method, and by adding information from a self-organising map and predicted secondary structure we were able to further improve ProfNet.
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18.
  • Pozzati, Gabriele, 1989- (author)
  • Deep learning solutions to protein quaternary structure
  • 2023
  • Doctoral thesis (other academic/artistic)abstract
    • Interactions between proteins are directly involved in most biological processes and are essential for the correct functioning of every form of life. The nature of protein-protein interactions allows functional assemblies of hundreds of protein chains. Given the enormous complexity and the pivotal role of protein interactions in life’s mechanics, the necessity to obtain a complete comprehension of such mechanisms is just as big as the challenge to achieve such knowledge. In the last few decades, experimental procedures constantly improved, dramatically increasing the available structural data for protein interactions. Unfortunately, experimental methods require a lot of time and resources and cannot always be applied with the same degree of success. Several computational methods have been developed in parallel with experimental procedures to overcome such limitations. Therefore, this thesis focused on screening existing computational methods and adopting them to improve the overall accuracy in solving structures of protein-complexes. In the first paper, I propose a simple rigid-body docking framework to test several interface predictors and their ability to drive a protein-protein docking procedure. Next, in the second paper, I display a method to adapt the trRosetta deep neural network to predict inter-residues distances and dihedral angle constraints for full protein complexes. The same concept is then improved in the third paper with FoldDock, an adaptation of Alphafold2 to work on multiple protein sequences and produce the corresponding complex. Finally, in the fourth paper, the FoldDock pipeline is applied to a large dataset of protein pairwise interactions derived from the hu.MAP and HuRI datasets, resulting in the characterization of more than 3000 high-confidence structural models.
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19.
  • Sagit, Rauan, 1985- (author)
  • Variation in length of proteins by repeats and disorder regions
  • 2013
  • Doctoral thesis (other academic/artistic)abstract
    • Protein-coding genes evolve together with their genome and acquire changes, some of which affect the length of their protein products. This explains why equivalent proteins from different species can exhibit length differences. Variation in length of proteins during evolution arguably presents a large number of possibilities for improvement and innovation of protein structure and function. In order to contribute to an increased understanding of this process, we have studied variation caused by tandem domain duplications and insertions or deletions of intrinsically disordered residues.The study of two proteins, Nebulin and Filamin, together with a broader study of long repeat proteins (>10 domain repeats), began by confirming that tandem domains evolve by internal duplications. Next, we show that vertebrate Nebulins evolved by duplications of a seven-domain unit, yet the most recent duplications utilized different gene parts as duplication units. However, Filamin exhibits a checkered duplication pattern, indicating that duplications were followed by similarity erosions that were hindered at particular domains due to the presence of equivalent binding motifs. For long repeat proteins, we found that human segmental duplications are over-represented in long repeat genes. Additionally, domains that have formed long repeats achieved this primarily by duplications of two or more domains at a time.The study of homologous protein pairs from the well-characterized eukaryotes nematode, fruit fly and several fungi, demonstrated a link between variation in length and variation in the number of intrinsically disordered residues. Next, insertions and deletions (indels) estimated from HMM-HMM pairwise alignments showed that disordered residues are clearly more frequent among indel than non-indel residues. Additionally, a study of raw length differences showed that more than half of the variation in fungi proteins is composed of disordered residues. Finally, a model of indels and their immediate surroundings suggested that disordered indels occur in already disordered regions rather than in ordered regions.
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20.
  • Shenoy, Aditi, 1995- (author)
  • Unlocking protein sequences : Advances in protein structure and ligand-binding site prediction
  • 2024
  • Doctoral thesis (other academic/artistic)abstract
    • The protein sequence determines how it will fold into its unique three-dimensional structure. Once folded, proteins perform their functions by interacting with other proteins or molecules called ligands within the cell. Experimental determination of protein structure and function is tedious. Computational approaches aim to accurately predict the properties of proteins to complement experimental efforts of understanding biochemical mechanisms within the cell. This thesis introduces computational techniques that predict the structure of protein complexes and identify protein residues involved in interactions with common biomolecules, such as metal ions and nucleic acids, based on sequence information. AlphaFold, a method that predicted protein structure using sequence information with almost experimental accuracy, was a critical breakthrough that shaped the field of protein structure prediction. Subsequently, approaches such as FoldDock adapted the AlphaFold pipeline for dimer complexes. Paper I applies the FoldDock protocol to understand toxin-antitoxin systems. These protein complexes are highly evolutionary conserved, and high-confidence dimer predictions were generated. Paper II applies the FoldDock protocol to study protein-protein interactions in the human proteome. To verify the reliability of machine-learning-based computational methods, they must be tested on independent data different from the data used to train the method. Paper III involves generating and using a homology-reduced independent test set to benchmark the performance of protein complex structure predictors, including the recent AlphaFold release adapted for multi-chain proteins – AlphaFold-Multimer. A confidence score (pDockQ2) was proposed to estimate the quality of the interfaces within multimers. Paper I, Paper II and Paper III are associated with predicting and evaluating protein-protein interactions. Representation learning involves finding effective representations of input data to maximise available information, making it easier to understand and process them for downstream prediction tasks. A recent advance in protein representation learning is Protein Language models (pLMs), where large language models are trained on a massive corpus of protein sequences. Highly contextualised and informative vector representations contained in the last hidden layer of the model have been used to predict numerous properties, such as ligand binding sites, subcellular localisation, and post-translational modifications, among others. Paper IV uses residue-level embeddings to predict whether a protein binds to one or more of the ten most common ions. It also predicts residue-level binding probabilities for multiple ions simultaneously. Paper V expands this approach beyond metals. It explores the impact of structure-informed features alongside sequence embeddings to predict whether a residue binds to nucleic acids, small molecules or metals.  Paper IV and Paper V are associated with developing machine learning methods to predict and evaluate protein-ligand interactions. In summary, the research conducted within this thesis offers valuable insights into three crucial levers to systematically harness the potential of machine learning for protein bioinformatics. These are (1) construction of homology-reduced non-redundant datasets, (2) finding optimal protein representations, and (3) rigorous evaluation and inference. 
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  • Result 11-20 of 30
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