A technique for visualizing dihedral signal of large protein sequences

This paper presents a clustering and visualization technique for analyzing dihedral angles of large protein sequences. The clustering is used for discovering and grouping those similar dihedrals while the visualization can display and navigate sequences of dihedral angles of several proteins as well as their clustered property. In order to visualize a very large sequence of hundred thousands of dihedral signals, we plot them on a spiral coordinate system. This spiral visualization ensures the linear distribution without distortion or interruption of a very long sequence of points. A clustering algorithm is also provided to group those dihedral signals into different clusters so that it can enhance the analysis process. Our system can also zoom to display a number of selected proteins interactively. © 2006 IEEE

Biological Systems Workbook: Data modelling and simulations at molecular level

Nowadays, there are huge quantities of data surrounding the different fields of biology derived from experiments and theoretical simulations, where results are often stored in biological databases that are growing at a vertiginous rate every year. Therefore, there is an increasing research interest in the application of mathematical and physical models able to produce reliable predictions and explanations to understand and rationalize that information. All these investigations are helping to overcome biological questions pushing forward in the solution of problems faced by our society. In this Biological Systems Workbook, we aim to introduce the basic pieces allowing life to take place, from the 3D structural point of view. We will start learning how to look at the 3D structure of molecules from studying small organic molecules used as drugs. Meanwhile, we will learn some methods that help us to generate models of these structures. Then we will move to more complex natural organic molecules as lipid or carbohydrates, learning how to estimate and reproduce their dynamics. Later, we will revise the structure of more complex macromolecules as proteins or DNA. Along this process, we will refer to different computational tools and databases that will help us to search, analyze and model the different molecular systems studied in this course

Protein structure and dynamics

Proteins are essential components of biological processes, this explains why understanding their structure, function and dynamics is so important. In the following, we give an overview on various methods for the determination of three-dimensional structure and dynamics of proteins. We discuss the most important experimental methods, X-ray diffraction and NMR spectroscopy, as well as computer modelling techniques and their application to the construction of graphics models, which can be inspected visually. We also treat prediction as well as molecular graphics representation of protein structures. We devote special attention to dynamics, where time scales of protein movement, structures and interactions are discussed. We wish to demonstrate that protein structure determination and computer representation is now at a very high degree of sophistication and reliability

Machine learning in the analysis of biomolecular simulations

Machine learning has rapidly become a key method for the analysis and organization of large-scale data in all scientific disciplines. In life sciences, the use of machine learning techniques is a particularly appealing idea since the enormous capacity of computational infrastructures generates terabytes of data through millisecond simulations of atomistic and molecular-scale biomolecular systems. Due to this explosion of data, the automation, reproducibility, and objectivity provided by machine learning methods are highly desirable features in the analysis of complex systems. In this review, we focus on the use of machine learning in biomolecular simulations. We discuss the main categories of machine learning tasks, such as dimensionality reduction, clustering, regression, and classification used in the analysis of simulation data. We then introduce the most popular classes of techniques involved in these tasks for the purpose of enhanced sampling, coordinate discovery, and structure prediction. Whenever possible, we explain the scope and limitations of machine learning approaches, and we discuss examples of applications of these techniques.Peer reviewe

The Integration of Oxidative Surface Mapping and Molecular Dynamics Simulation Techniques as a Strategy for Studying Protein Conformational Change

The range and number of new and unknown proteins is increasing at a staggering rate due to the recent genome sequencing projects. The next step in understanding how biological systems, even including the human body, work is by understanding the function of all the various proteins. Solving the structure of a protein is an important first step in elucidating its function; however, the study of its dynamic movements can specifically implicate regions involved in its function and even demonstrate the mechanism by which function is performed. Molecular dynamics simulations are a powerful computational approach for visualizing the dynamic movement of proteins. Computational tools are predominantly theory based predictions. Therefore, they require validation by experimental results. Oxidative surface mapping is an experimental labeling method which can be used to identify “buried” vs. “solvent-accessible” regions in a folded protein. Movement in specific regions of a protein can be mapped and monitored using this method. β-lactoglobulin is a well studied protein that undergoes a pH induced conformational change. It was chosen as the target protein for this study because it has been the focus of numerous studies in the past and much information is known about it. Even so, many aspects of this protein’s structure still remain a mystery. This thesis work is an attempt to integrate computational and experimental techniques as a strategy for studying the protein conformational change of a well studied protein system. The degree of overlap displayed by the integration of these two techniques is limited, however it provides a foundation from which improvements can be implemented for future attempts of studying protein systems using this approach

Integrative determination of the atomic structure of mutant huntingtin exon 1 fibrils from Huntington's disease

Neurodegeneration in Huntington's disease (HD) is accompanied by the aggregation of fragments of the mutant huntingtin protein, a biomarker of disease progression. A particular pathogenic role has been attributed to the aggregation-prone huntingtin exon 1 (HttEx1) fragment, whose polyglutamine (polyQ) segment is expanded. Unlike amyloid fibrils from Parkinson's and Alzheimer's diseases, the atomic-level structure of HttEx1 fibrils has remained unknown, limiting diagnostic and treatment efforts. We present and analyze the structure of fibrils formed by polyQ peptides and polyQ-expanded HttEx1. Atomic-resolution perspectives are enabled by an integrative analysis and unrestrained all-atom molecular dynamics (MD) simulations incorporating experimental data from electron microscopy (EM), solid-state NMR, and other techniques. Visualizing the HttEx1 subdomains in atomic detail helps explaining the biological properties of these protein aggregates, as well as paves the way for targeting them for detection and degradation.</p

Membrane interactions with membrane type 1 matrix metalloproteinase

Membrane type 1 matrix metalloproteinase (MT1-MMP) is essential to a myriad of extracellular activities including tumor cell migration and angiogenesis. At the cell surface, MT1-MMP is a major factor in the proteolysis of receptors, growth factors, and collagen. MT1-MMP extracellular domains bind the cell surface which can be influential in bringing these complexes together. This study uses new techniques to uncover the interactions between MT1-MMP and the cell surface. Described here is the development of techniques in protein and lipid preparations, NMR data acquisition, and structure determination by molecular dynamics simulations. Through these methods, the HPX domain was shown to bind nanodiscs by opposing tips of blade II and blade IV. The protruding part of these tips contain an EPGYPK sequence that are seen dipping into the membrane surface making contact with the lipid head groups. Blade IV membrane binding allows collagen to bind unhindered. Both blade II and blade IV membrane binding structures are shown to be favorable for homodimerization without disruption of the collagen binding site. The catalytic domain is shown to at least transiently bind membranes. This study then hypothesizes and discusses how these interactions impact both future peripheral protein membrane interaction studies and uncover similarities between the MMP family.Includes bibliographical reference

Relaxation Dispersion NMR Spectroscopy

Relaxation dispersion nuclear magnetic resonance (NMR) spectroscopy has been developed since the 1950s and has now evolved into a very sensitive and versatile tool to study chemical and conformational exchange processes on the micro- to milliseconds (µs–ms) time scale. While relaxation dispersion NMR was originally designed with small molecules in mind, it has become a very attractive tool to also study the dynamics of biological macromolecules, after major advances had been made in hardware, experimental design and isotope labelling

The investigation of type-specific features of the copper coordinating AA9 proteins and their effect on the interaction with crystalline cellulose using molecular dynamics studies

AA9 proteins are metallo-enzymes which are crucial for the early stages of cellulose degradation. AA9 proteins have been suggested to cleave glycosidic bonds linking cellulose through the use of their Cu2+ coordinating active site. AA9 proteins possess different regioselectivities depending on the resulting cleavage they form and as result, are grouped accordingly. Type 1 AA9 proteins cleave the C1 carbon of cellulose while Type 2 AA9 proteins cleave the C4 carbon and Type 3 AA9 proteins cleave either C1 or C4 carbons. The steric congestion of the AA9 active site has been proposed to be a contributor to the observed regioselectivity. As such, a bioinformatics characterisation of type-specific sequence and structural features was performed. Initially AA9 protein sequences were obtained from the Pfam database and multiple sequence alignment was performed. The sequences were phylogenetically characterised and sequences were grouped into their respective types and sub-groups were identified. A selection analysis was performed on AA9 LPMO types to determine the selective pressure acting on AA9 protein residues. Motif discovery was then performed to identify conserved sequence motifs in AA9 proteins. Once type-specific sequence features were identified structural mapping was performed to assess possible effects on substrate interaction. Physicochemical property analysis was also performed to assess biochemical differences between AA9 LPMO types. Molecular dynamics (MD) simulations were then employed to dynamically assess the consequences of the discovered type-specific features on AA9-cellulose interaction. Due to the absence of AA9 specific force field parameters MD simulations were not readily applicable. As a result, Potential Energy Surface (PES) scans were performed to evaluate the force field parameters for the AA9 active site using the PM6 semi empirical approach and least squares fitting. A Type 1 AA9 active site was constructed from the crystal structure 4B5Q, encompassing only the Cu2+ coordinating residues, the Cu2+ ion and two water residues. Due to the similarity in AA9 active sites, the Type force field parameters were validated on all three AA9 LPMO types. Two MD simulations for each AA9 LPMO types were conducted using two separate Lennard-Jones parameter sets. Once completed, the MD trajectories were analysed for various features including the RMSD, RMSF, radius of gyration, coordination during simulation, hydrogen bonding, secondary structure conservation and overall protein movement. Force field parameters were successfully evaluated and validated for AA9 proteins. MD simulations of AA9 proteins were able to reveal the presence of unique type-specific binding modes of AA9 active sites to cellulose. These binding modes were characterised by the presence of unique type-specific loops which were present in Type 2 and 3 AA9 proteins but not in Type 1 AA9 proteins. The loops were found to result in steric congestion that affects how the Cu2+ ion interacts with cellulose. As a result, Cu2+ binding to cellulose was observed for Type 1 and not Type 2 and 3 AA9 proteins. In this study force field parameters have been evaluated for the Type 1 active site of AA9 proteins and this parameters were evaluated on all three types and binding. Future work will focus on identifying the nature of the reactive oxygen species and performing QM/MM calculations to elucidate the reactive mechanism of all three AA9 LPMO types