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Three-dimensional structure of myoglobin.The low-resolution structure of myoglobin that was published by John Kendrew and colleagues in 1958 (Ref. 1). This figure in the Nature paper was reconstructed by the author using the original figures in the archives of the Medical Research Council Laboratory of Molecular Biology, Cambridge, UK. Polypeptide chains are in white and the grey disc represents the haem group. The three spheres show positions at which heavy atoms were attached to the molecule (black, Hg of p-chloro-mercuri-benzene-sulphonate; dark grey, Hg of mercury diammine; light grey, Au of auri-chloride). The marks on the scale are 1 Å apart.
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Every breakthrough that opens new vistas also removes the ground from under the feet of other scientists. The scientific joy of those who have seen the new light is accompanied by the dismay of those whose way of life has been changed for ever. The publication of the first structures of proteins at atomic resolution 50 years ago astounded and inspi...
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... right to left: the structure of the native state (Ns) was solved by nuclear magnetic resonance (NMr) and X-ray crystallography; the transition state (Ts) by Φ analysis (colour-coded from red, mean- ing unstructured, to blue, meaning highly native-like); the folding intermediate (I) was generated as a stable entity using protein engineering and its structure was solved by NMr; the structure of the denatured state (U), under conditions that favour folding, was simulated using molecular dynamics; and the entire unfolding pathway was simulated by molecular dynamics. Nearly 50 years of techno- logical advances were needed to proceed from the structural resolution of Figure 1 to the dynamics and structures in Figure 3. H1, H2 and H3 represent helices 1, 2 and 3, respectively. ...
Citations
... This allows for full customizability of the extracted region without having to edit PDB files. Currently, three methods are implemented: (1) No cutting. The full enzyme object is used in the network. ...
Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we present TopEC, a 3D graph neural network based on a localized 3D descriptor to learn chemical reactions of enzymes from enzyme structures and predict Enzyme Commission (EC) classes. Using message-passing frameworks, we include distance and angle information to significantly improve the predictive performance for EC classification (F-score: 0.72) compared to regular 2D graph neural networks. We trained networks without fold bias that can classify enzyme structures for a vast functional space (>800 ECs). Our model is robust to uncertainties in binding site locations and similar functions in distinct binding sites. We observe that TopEC networks learn from an interplay between biochemical features and local shape-dependent features. TopEC is available as a repository on GitHub: https://github.com/IBG4-CBCLab/TopEC and https://doi.org/10.25838/d5p-66.
... A large proportion of proteins are intrinsically disordered, "After half a century of structural studies on beautifully folded globular proteins, it is perhaps a shock to discover that up to some 40% of the proteins in the human proteome are estimated to be intrinsically disordered and become fully or partly structured on binding to binding partners in the cell.", [29]. A recent literature analysis showed that there are approximately 1150 non-redundant proteins in the list of validated intrinsically disordered proteins, [30]. ...
A single molecule theory for protein dynamics has been developed since 2012. It consists of the concepts of conformational Gibbs free energy function (CGF) and single molecule thermodynamic hypothesis (STH) that claims that all stable conformations are (local or global) minimizers of CGF. These are enough to give a unified explanations and mechanisms to many aspects of protein dynamics such as protein folding; allostery; denaturation; and intrin-sically disordered proteins. Formulas of CGF in water environment had been derived via quantum statistics. Applications of them to soluble proteins are: docking Gibbs free energy difference formula and a practical way to search better docking site; single molecule binding affinity; predicting and explaining why structures of a monomeric globular protein looks like a globule and is tightly packed with a hydrophobic core; a representation of the hydro-phobic effect; and a wholistic view to structures of water soluble proteins.
... As such, understanding their structure and function has been a major focus in molecular biology. X-ray crystallography and NMR spectroscopy have successfully been used for this purpose, albeit with limitations, especially when dealing with large and complex biological macromolecules [6][7][8][9][10][11][12]. More recently, cryogenic electron microscopy (Cryo-EM) has emerged as a revolutionary method, allowing the determination of near-atomic and even atomic resolution structures of isolated macromolecules [13][14][15][16][17] (see Figure 1). ...
Fluorescence microscopy has witnessed many clever innovations in the last two decades, leading to new methods such as structured illumination and super-resolution microscopies. The attainable resolution in biological samples is, however, ultimately limited by residual motion within the sample or in the microscope setup. Thus, such experiments are typically performed on chemically fixed samples. Cryogenic light micros-copy (Cryo-LM) has been investigated as an alternative, drawing on various preservation techniques developed for cryogenic electron microscopy (Cryo-EM). Moreover, this approach offers a powerful platform for correlative microscopy. Another key advantage of Cryo-LM is the strong reduction in photobleaching at low temperatures, facilitating the collection of orders of magnitude more photons from a single fluorophore. This results in much higher localization precision, leading to Angstrom resolution. In this review, we discuss the general development and progress of Cryo-LM with an emphasis on its application in harnessing structural information on proteins and protein complexes.
... According to the sequence-structure-function paradigm raised by Anfinsen, 1 the function of a protein is governed by the unique three-dimensional structure adopted by the protein on the basis of its amino-acid sequence. 1,2 However, in order to understand how proteins work, it is also necessary to shed light on their behavior from a dynamic point of view. [3][4][5][6] Proteins at physiological temperatures exhibit a wide range of intrinsic motions, 7 which span over different spatial and temporal scales and are governed by the (highly multidimensional) energy landscape. ...
The application of terahertz radiation has been shown to affect both protein structure and cellular function. As the key to such structural changes lies in the dynamic response of a protein, we focus on the susceptibility of the protein’s internal dynamics to mechanical stress induced by acoustic pressure waves. We use the open-boundary molecular dynamics method, which allows us to simulate the protein exposed to acoustic waves. By analyzing the dynamic fluctuations of the protein subunits, we demonstrate that the protein is highly susceptible to acoustic waves with frequencies corresponding to those of the internal protein vibrations. This is confirmed by changes in the compactness of the protein structure. As the amplitude of the pressure wave increases, even larger deviations from average positions and larger changes in protein compactness are observed. Furthermore, performing the mode-projection analysis, we show that the breathing-like character of collective modes is enhanced at frequencies corresponding to those used to excite the protein.
... The longer they are, the more likely they will knot. For a long time, however, many have considered proteins to be knot-free as reptation seems incompatible with most commonly described protein folding mechanisms that favor rapid collapse of polypeptide chains to form ordered structures without entanglements [2]. While the first topologically knotted protein structure was reported in 1977 [3] (Figure 1), knotted protein structures were considered anomalies and even artifacts [4]. ...
Topologically knotted proteins have entangled structural elements within their native structures that cannot be disentangled simply by pulling from the N-and C-termini. Systematic surveys have identified different types of knotted protein structures, constituting as much as 1% of the total entries within the Protein Data Bank. Many knotted proteins rely on their knotted structural elements to carry out evolutionarily conserved biological functions. Being knotted may also provide mechanical stability to withstand unfolding-coupled proteolysis. Reconfiguring a knotted protein topology by circular permutation or cyclization provides insights into the importance of being knotted in the context of folding and functions. With the explosion of predicted protein structures by artificial intelligence , we are now entering a new era of exploring the entangled protein universe.
... Some of the fundamental questions about protein folding arose during the 1950s such as how mRNA codons dictate the amino acid sequence (9). The information about the native three-dimensional structure of the proteins is present in the amino acid sequence but how biologically proteins fold so fast (10). Christian Anfinsen was one of the pioneer scientists in the biochemistry field who attempted in the year 1959 to merge the recently developing area of protein chemistry with classical genetics in his manuscript entitled "The molecular basis of evolution. ...
Proteins are unique macromolecules made up of a long chain of amino acids and are classified based on their function, structure, shape, chemical composition and solubility in different solvents. A wide variety of proteins are prone to misfold and create intracellular or extracellular aggregates that cause severe cellular malfunction. The importance of the protein folding problem was recognized and put forward 50 years back by distinguished scientists. Understanding the dynamics of protein folding is crucial and this can help us predict the ultimate configuration of functional protein. Many of the life-threatening diseases are caused by the misfolding of proteins. The reason for the misfolding can be point mutations since the three-dimensional structure of proteins depends on the primary sequence of its amino acid. Despite fifty years of research, we still need to fill the knowledge gap and accelerate our understanding, particularly in computational biology for the accurate prediction of protein structure. Homology modeling is utilized to predict protein structure in absence of experimental structure. Artificial intelligence, machine learning, and deep learning are being used extensively by researchers to computationally estimate a protein's structure based only on its amino acid sequence. AlphaFold which is in a second iteration tool has changed the perception about protein folding by solving the unsolved structures.
... Research conducted to expand this benchmark through the identification and analysis of other such structures is discussed in Chapter 3. A complete understanding of the function of a protein is often impossible without knowledge of its three dimensional structure. Pioneering work in the 1950s produced the first experimentally resolved protein structures (Fersht, 2008). Over the subsequent decades, tremendous progress has been made in the determination of protein structures through innovations in x-ray crystallography, nuclear magnetic resonance (NMR) and, more recently, cryo-electron microscopy (cryo-EM) (Hameduh et al., 2020). ...
The human adaptive immune system has evolved to provide a sophisticated response to a vast body of pathogenic microbes and toxic substances. The primary mediators of this response are T and B lymphocytes. Antigenic peptides presented at the surface of infected cells by major histocompatibility complex (MHC) molecules are recognised by T cell receptors (TCRs) with exceptional specificity. This specificity arises from the enormous diversity in TCR sequence and structure generated through an imprecise process of somatic gene recombination that takes place during T cell development. Quantification of the TCR repertoire through the analysis of data produced by high-throughput RNA sequencing allows for a characterisation of the immune response to disease over time and between patients, and the development of methods for diagnosis and therapeutic design. The latest version of the software package Decombinator extracts and quantifies the TCR repertoire with improved accuracy and compatibility with complementary experimental protocols and external computational tools. The software has been extended for analysis of fragmented short-read data from single cells, comparing favourably with two alternative tools. The development of cell-based therapeutics and vaccines is incomplete without an understanding of molecular level interactions. The breadth of TCR diversity and cross-reactivity presents a barrier for comprehensive structural resolution of the repertoire by traditional means. Computational modelling of TCR structures and TCR-pMHC complexes provides an efficient alternative. Four generalpurpose protein-protein docking platforms were compared in their ability to accurately model TCR-pMHC complexes. Each platform was evaluated against an expanded benchmark of docking test cases and in the context of varying additional information about the binding interface. Continual innovation in structural modelling techniques sets the stage for novel automated tools for TCR design. A prototype platform has been developed, integrating structural modelling and an optimisation routine, to engineer desirable features into TCR and TCR-pMHC complex models.
... A full understanding of their spatial arrangements and associated heterogeneous configurations is crucial for elucidating their molecular mechanisms, helps guide the engineering of new proteins, and is a great asset for drug discovery (Renaud et al., 2018). Indeed, since the pioneering work of Perutz on protein crystals (Fersht, 2008), a variety of techniques such as X-ray crystallography (Shi, 2014) and nuclear magnetic resonance (NMR) spectroscopy (Kanelis et al., 2001) have been explored for gaining insight into protein structure and function. Advances in sample preparation, detector technology, and image processing based on single-particle analysis have also ushered in atomic resolution in cryogenic electron microscopy (cryoEM) studies of protein structure (Nakane et al., 2020;Kühlbrandt, 2014). ...
Cryogenic optical localization in three dimensions (COLD) was recently shown to resolve up to four binding sites on a single protein. However, because COLD relies on intensity fluctuations that result from the blinking behavior of fluorophores, it is limited to cases where individual emitters show different brightness. This significantly lowers the measurement yield. To extend the number of resolved sites as well as the measurement yield, we employ partial labeling and combine it with polarization encoding in order to identify single fluorophores during their stochastic blinking. We then use a particle classification scheme to identify and resolve heterogenous subsets and combine them to reconstruct the three-dimensional arrangement of large molecular complexes. We showcase this method (polarCOLD) by resolving the trimer arrangement of proliferating cell nuclear antigen (PCNA) and six different sites of the hexamer protein Caseinolytic Peptidase B (ClpB) of Thermus thermophilus in its quaternary structure, both with Angstrom resolution. The combination of polarCOLD and single-particle cryogenic electron microscopy (cryoEM) promises to provide crucial insight into intrinsic heterogeneities of biomolecular structures. Furthermore, our approach is fully compatible with fluorescent protein labeling and can, thus, be used in a wide range of studies in cell and membrane biology.
... Since the first protein structure of myoglobin was determined, there has been a struggle to interpret protein structures in terms of their functions (Fersht, 2008;Kendrew et al., 1958), even though there has long been a widespread consensus that dynamics is key to such an understanding. But a simple interpretation of dynamics from structure has not been available, and protein researchers have been saddled with interpreting the complexities and randomness manifested in atomic molecular dynamics simulations. ...
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A new dynamic community identifier (DCI) is presented that relies upon protein residue dynamic cross-correlations generated by Gaussian elastic network models to identify those residue clusters exhibiting motions within a protein. A number of examples of communities are shown for diverse proteins, including GPCRs. It is a tool that can immediately simplify and clarify the most essential functional moving parts of any given protein. Proteins usually can be subdivided into groups of residues that move as communities. These are usually densely packed local sub-structures, but in some cases can be physically distant residues identified to be within the same community. The set of these communities for each protein are the moving parts. The ways in which these are organized overall can aid in understanding many aspects of functional dynamics and allostery. DCI enables a more direct understanding of functions including enzyme activity, action across membranes, and changes in the community structure from mutations or ligand binding. The DCI server is freely available on a web site (https://dci.bb.iastate.edu/).
Supplementary information:
Supplementary data are available at Bioinformatics online.
... In parallel with the efforts to establish the nature of the genetic code and the decoding process, it was determined that the amino acid sequence of a protein contains the protein folding code, i.e., information necessary to specify its unique three-dimensional structure [45]. While the exact nature of the protein folding code was (and in part remains) unknown, it was further concluded that the native folded state of a protein is not achieved by a random search among all possible conformations en route to the native structure (which would require enormous amount of time to accomplish), but likely is achieved through a set of limited and well-defined pathways exploring just a set of available conformational spaces (see reviews [46][47][48][49][50][51]). ...
... Seminal experiments performed by Christian Anfinsen and his colleagues (in the 1950-60s) on the reversible denaturation of ribonuclease [45] and many similar subsequent experiments by other researchers, which used a multitude of different other proteins indicated that amino acid sequence of a protein contains all the information necessary to specify its unique three dimensional structure [46][47][48][49][50][51]. Many theoretical and computational studies, including recent breakthrough research showing that it is possible to accurately predict protein structures from the amino acid sequence [62,63] further supported this key postulate of the "protein folding problem". ...
... The concept of the folding funnel suggesting that there could be distinct multiple pathways, which guide protein folding to a native conformation with the lowest free energy minimum, has been developed to explain how proteins fold (see reviews [46][47][48][49][50][51]). This concept was further amended by the new model for protein folding, which described the folding process through formation of the separately cooperative folding units (foldons) (see reviews [65][66][67]). ...
The genetic code sets the correspondence between the sequence of a given nucleotide triplet in an mRNA molecule, called a codon, and the amino acid that is added to the growing polypeptide chain during protein synthesis. With four bases (A, G, U, and C), there are 64 possible triplet codons: 61 sense codons (encoding amino acids) and 3 nonsense codons (so-called, stop codons that define termination of translation). In most organisms, there are 20 common/standard amino acids used in protein synthesis; thus, the genetic code is redundant with most amino acids (with the exception of Met and Trp) are being encoded by more than one (synonymous) codon. Synonymous codons were initially presumed to have entirely equivalent functions, however, the finding that synonymous codons are not present at equal frequencies in mRNA suggested that the specific codon choice might have functional implications beyond coding for amino acid. Observation of nonequivalent use of codons in mRNAs implied a possibility of the existence of auxiliary information in the genetic code. Indeed, it has been found that genetic code contains several layers of such additional information and that synonymous codons are strategically placed within mRNAs to ensure a particular translation kinetics facilitating and fine-tuning co-translational protein folding in the cell via step-wise/sequential structuring of distinct regions of the polypeptide chain emerging from the ribosome at different points in time. This review summarizes key findings in the field that have identified the role of synonymous codons and their usage in protein folding in the cell.
