Disorder transitions and conformational diversity cooperatively modulate biological function in proteins

Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered‐disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure‐function relationships related to order‐disorder transitions.     

Identification of the critical structural determinants of the EF-hand domain arrangements in calcium binding proteins

EF-hand calcium binding proteins (CaBPs) share strong sequence homology, but exhibit great diversity in structure and function. Thus although calmodulin (CaM) and calcineurin B (CNB) both consist of four EF hands, their domain arrangements are quite distinct. CaM and the CaM-like proteins are characterized by an extended architecture, whereas CNB and the CNB-like proteins have a more compact form. In this study, we performed structural alignments and molecular dynamics (MD) simulations on 3 CaM-like proteins and 6 CNB-like proteins, and quantified their distinct structural and dynamical features in an effort to establish how their sequences specify their structures and dynamics. Alignments of the EF2-EF3 region of these proteins revealed that several residues (not restricted to the linker between the EF2 and EF3 motifs) differed between the two groups of proteins. A customized inverse folding approach followed by structural assessments and MD simulations established the critical role of these residues in determining the structure of the proteins. Identification of the critical determinants of the two different EF-hand domain arrangements and the distinct dynamical features relevant to their respective functions provides insight into the relationships between sequence, structure, dynamics and function among these EF-hand CaBPs.     

Exploring the factors determining the dynamics of different protein folds

Normal mode analyses of homologous proteins at the family and superfamily level show that slow dynamics are similar and are preserved through evolution. This study investigates how the slow dynamics of proteins is affected by variation in the protein architecture and fold. For this purpose, we have used computer-generated protein models based on idealized protein structures with varying folds. These are shown to be protein-like in their behavior, and they are used to investigate the influence of architecture and fold on the slow dynamics. We compared the dynamics of models having different folds but similar architecture and found the architecture to be the dominant factor for the slow dynamics.     

Dynamical structure of αB-crystallin

The human small heat-shock protein αB-crystallin is an extremely difficult molecule to study, with its inherent structural dynamics posing unique challenges to all biophysical and structural biology techniques. Here we highlight how the polydispersity and quaternary dynamics of αB-crystallin are intrinsically inter-twined, and how this can impact on measurements of the oligomeric distribution. We show that, in spite of these difficulties, considerable understanding of the varied fluctuations αB-crystallin undergoes at equilibrium has emerged in the last few years. By reporting on data obtained from a variety of biophysical techniques, we demonstrate how the αB-crystallin solution ensemble is governed by molecular motions of varying amplitude and time-scales spanning several orders of magnitude. We describe how these diverse measurements are being used to construct an integrated view of the dynamical structure of αB-crystallin, and highlight areas that require further interrogation. With its study motivating the refinement of experimental techniques, and the development of new approaches to combine the hybrid datasets, we conclude that αB-crystallin continues to represent a paradigm for dynamical biology.     

Exploring the effects of sparse restraints on protein structure prediction

One of the main barriers to accurate computational protein structure prediction is searching the vast space of protein conformations. Distance restraints or inter‐residue contacts have been used to reduce this search space, easing the discovery of the correct folded state. It has been suggested that about 1 contact for every 12 residues may be sufficient to predict structure at fold level accuracy. Here, we use coarse‐grained structure‐based models in conjunction with molecular dynamics simulations to examine this empirical prediction. We generate sparse contact maps for 15 proteins of varying sequence lengths and topologies and find that given perfect secondary‐structural information, a small fraction of the native contact map (5%‐10%) suffices to fold proteins to their correct native states. We also find that different sparse maps are not equivalent and we make several observations about the type of maps that are successful at such structure prediction. Long range contacts are found to encode more information than shorter range ones, especially for α and αβ‐proteins. However, this distinction reduces for β‐proteins. Choosing contacts that are a consensus from successful maps gives predictive sparse maps as does choosing contacts that are well spread out over the protein structure. Additionally, the folding of proteins can also be used to choose predictive sparse maps. Overall, we conclude that structure‐based models can be used to understand the efficacy of structure‐prediction restraints and could, in future, be tuned to include specific force‐field interactions, secondary structure errors and noise in the sparse maps.     

Dynameomics: large-scale assessment of native protein flexibility

Structure is only the first step in understanding the interactions and functions of proteins. In this paper, we explore the flexibility of proteins across a broad database of over 250 solvated protein molecular dynamics simulations in water for an aggregate simulation time of approximately 6 micros. These simulations are from our Dynameomics project, and these proteins represent approximately 75% of all known protein structures. We employ principal component analysis of the atomic coordinates over time to determine the primary axis and magnitude of the flexibility of each atom in a simulation. This technique gives us both a database of flexibility for many protein fold families and a compact visual representation of a particular protein's native-state conformational space, neither of which are available using experimental methods alone. These tools allow us to better understand the nature of protein motion and to describe its relationship to other structural and dynamical characteristics. In addition to reporting general properties of protein flexibility and detailing many dynamic motifs, we characterize the relationship between protein native-state flexibility and early events in thermal unfolding and show that flexibility predicts how a protein will begin to unfold. We provide evidence that fold families have conserved flexibility patterns, and family members who deviate from the conserved patterns have very low sequence identity. Finally, we examine novel aspects of highly inflexible loops that are as important to structural integrity as conventional secondary structure. These loops, which are difficult if not impossible to locate without dynamic data, may constitute new structural motifs.     

Homology modeling in a dynamical world

A key concept in template‐based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well‐established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure‐function relationship. We show that protein families with low conformational diversity show a well‐correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.     

Can morphing methods predict intermediate structures?

Movement is crucial to the biological function of many proteins, yet crystallographic structures of proteins can give us only a static snapshot. The protein dynamics that are important to biological function often happen on a timescale that is unattainable through detailed simulation methods such as molecular dynamics as they often involve crossing high-energy barriers. To address this coarse-grained motion, several methods have been implemented as web servers in which a set of coordinates is usually linearly interpolated from an initial crystallographic structure to a final crystallographic structure. We present a new morphing method that does not extrapolate linearly and can therefore go around high-energy barriers and which can produce different trajectories between the same two starting points. In this work, we evaluate our method and other established coarse-grained methods according to an objective measure: how close a coarse-grained dynamics method comes to a crystallographically determined intermediate structure when calculating a trajectory between the initial and final crystal protein structure. We test this with a set of five proteins with at least three crystallographically determined on-pathway high-resolution intermediate structures from the Protein Data Bank. For simple hinging motions involving a small conformational change, segmentation of the protein into two rigid sections outperforms other more computationally involved methods. However, large-scale conformational change is best addressed using a nonlinear approach and we suggest that there is merit in further developing such methods.     

Identification of the critical structural determinants of the EF-hand domain arrangements in calcium binding proteins

EF-hand calcium binding proteins (CaBPs) share strong sequence homology, but exhibit great diversity in structure and function. Thus although calmodulin (CaM) and calcineurin B (CNB) both consist of four EF hands, their domain arrangements are quite distinct. CaM and the CaM-like proteins are characterized by an extended architecture, whereas CNB and the CNB-like proteins have a more compact form. In this study, we performed structural alignments and molecular dynamics (MD) simulations on 3 CaM-like proteins and 6 CNB-like proteins, and quantified their distinct structural and dynamical features in an effort to establish how their sequences specify their structures and dynamics. Alignments of the EF2-EF3 region of these proteins revealed that several residues (not restricted to the linker between the EF2 and EF3 motifs) differed between the two groups of proteins. A customized inverse folding approach followed by structural assessments and MD simulations established the critical role of these residues in determining the structure of the proteins. Identification of the critical determinants of the two different EF-hand domain arrangements and the distinct dynamical features relevant to their respective functions provides insight into the relationships between sequence, structure, dynamics and function among these EF-hand CaBPs.     

Structural compliance: A new metric for protein flexibility

Proteins are the active players in performing essential molecular activities throughout biology, and their dynamics has been broadly demonstrated to relate to their mechanisms. The intrinsic fluctuations have often been used to represent their dynamics and then compared to the experimental B-factors. However, proteins do not move in a vacuum and their motions are modulated by solvent that can impose forces on the structure. In this paper, we introduce a new structural concept, which has been called the structural compliance, for the evaluation of the global and local deformability of the protein structure in response to intramolecular and solvent forces. Based on the application of pairwise pulling forces to a protein elastic network, this structural quantity has been computed and sometimes is even found to yield an improved correlation with the experimental B-factors, meaning that it may serve as a better metric for protein flexibility. The inverse of structural compliance, namely the structural stiffness, has also been defined, which shows a clear anticorrelation with the experimental data. Although the present applications are made to proteins, this approach can also be applied to other biomolecular structures such as RNA. This present study considers only elastic network models, but the approach could be applied further to conventional atomic molecular dynamics. Compliance is found to have a slightly better agreement with the experimental B-factors, perhaps reflecting its bias toward the effects of local perturbations, in contrast to mean square fluctuations. The code for calculating protein compliance and stiffness is freely accessible at https://jerniganlab.github.io/Software/PACKMAN/Tutorials/compliance .     

Manual classification strategies in the ECOD database

ECOD (Evolutionary Classification Of protein Domains) is a comprehensive and up‐to‐date protein structure classification database. The majority of new structures released from the PDB (Protein Data Bank) each week already have close homologs in the ECOD hierarchy and thus can be reliably partitioned into domains and classified by software without manual intervention. However, those proteins that lack confidently detectable homologs require careful analysis by experts. Although many bioinformatics resources rely on expert curation to some degree, specific examples of how this curation occurs and in what cases it is necessary are not always described. Here, we illustrate the manual classification strategy in ECOD by example, focusing on two major issues in protein classification: domain partitioning and the relationship between homology and similarity scores. Most examples show recently released and manually classified PDB structures. We discuss multi‐domain proteins, discordance between sequence and structural similarities, difficulties with assessing homology with scores, and integral membrane proteins homologous to soluble proteins. By timely assimilation of newly available structures into its hierarchy, ECOD strives to provide a most accurate and updated view of the protein structure world as a result of combined computational and expert‐driven analysis. Proteins 2015; 83:1238–1251. © 2015 Wiley Periodicals, Inc.     

Inactivation and reactivation of ribonuclease A studied by computer simulation

The year 2011 marked the half-centenary of the publication of what came to be known as the Anfinsen postulate, that the tertiary structure of a folded protein is prescribed fully by the sequence of its constituent amino acid residues. This postulate has become established as a credo, and, indeed, no contradictions seem to have been found to date. However, the experiments that led to this postulate were conducted on only a single protein, bovine ribonuclease A (RNAse). We conduct molecular dynamics (MD) simulations on this protein with the aim of mimicking this experiment as well as making the methodology available for use with basically any protein. There have been many attempts to model denaturation and refolding processes of globular proteins in silico using MD, but only a few examples where disulphide-bond containing proteins were studied. We took the view that if the reductive deactivation and oxidative reactivation processes of RNAse could be modelled in silico, this would provide valuable insights into the workings of the classical Anfinsen experiment.     

Integrative Structural Biology in the Era of Accurate Structure Prediction

Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy—applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure–function relationships.     

On the Potential of Machine Learning to Examine the Relationship Between Sequence,Structure, Dynamics and Function of Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs—and intrinsically disordered regions (IDRs) interspersed between folded domains—are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.     

The first report of polymorphisms and genetic characteristics of the prion protein gene (PRNP) in horses

ABSTRACT

Prion diseases have a wide host range, but prion-infected cases have never been reported in horses. Genetic polymorphisms that can directly impact the structural stability of horse prion protein have not been investigated thus far. In addition, we noticed that previous studies focusing on horse-specific amino acids and secondary structure predictions of prion protein were performed for limited parts of the protein. In this study, we found genetic polymorphisms in the horse prion protein gene (PRNP) in 201 Thoroughbred horses. The identified polymorphism was assessed to determine whether this polymorphism impedes stability of protein using PolyPhen-2, PROVEAN and PANTHER. In addition, we evaluated horse-specific amino acids in horse and mouse prion proteins using same methods. We found only one single nucleotide polymorphism (SNP) in the horse prion protein, and three annotation tools predicted that the SNP is benign. In addition, horse-specific amino acids showed different effects on horse and mouse prion proteins, respectively.

Abbreviations: PRNP: prion protein gene; SNP: single nucleotide polymorphism; CJD: Creutzfeldt-Jakob disease; CWD: chronic wasting disease; TME: transmissible mink encephalopathy; FSE: feline spongiform encephalopathy; MD: molecular dynamics; ER: endoplasmic reticulum; GPI: glycosylphosphatidylinositol; NMR: nuclear magnetic resonance; ORF: open reading frame; GWAS: genome-wide association study; NAPA: non-adaptive prion amplification; HMM: hidden Markov model; NCBI: National Center for Biotechnology Information     

Molecular Dynamics Study of Naturally Existing Cavity Couplings in Proteins

Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results.     

Next Generation Protein Structure Predictions and Genetic Variant Interpretation

The need to make sense of the thousands of genetic variants uncovered every day in terms of pathology or biological mechanism is acute. Many insights into how genetic changes impact protein function can be gleaned if three-dimensional structures of the associated proteins are available. The availability of a highly accurate method of predicting structures from amino acid sequences (e.g. Alphafold2) is thus potentially a great boost to those wanting to understand genetic changes. In this paper we discuss the current state of protein structures known for the human and other proteomes and how Alphafold2 might impact on variant interpretation efforts. For the human proteome in particular, the state of the available structural data suggests that the impact on variant interpretation might be less than anticipated. We also discuss additional efforts in structure prediction that could further aid the understanding of genetic variants.