Relationship between energy distribution and fold stability: Insights from molecular dynamics simulations of native and mutant proteins

Conformational Dynamics, Energetics, and the Divergent Evolution of Allosteric Regulation: The Case of the Yeast MAPK Family

Allosteric mechanisms provide finely-tuned control over signalling proteins. Proteins of the same family may share high sequence identity and structural similarity but show distinct traits of allosteric control and evolutionary divergent regulation. Revealing the determinants of such properties may be important to understand the molecular bases of different regulatory pathways. Herein, we investigate whether and how evolutionarily-divergent traits of allosteric regulation in homologous proteins can be decoded in terms of internal dynamics and interaction networks that support functionally oriented conformations. In this framework, we start from the comparative analysis of the dynamics and energetics of the yeast MAP Kinases (MAPKs) Fus3 and Kss1 in their native basins. Importantly, distinctive dynamic and energetic stabilization features emerge, which can be related to the two proteins' differential ability to be phosphorylated and engage with the allosteric activator Ste5. We then expanded our study to other evolutionarily-related MAPKs. We show that the dynamical and energetical traits defining the distinct regulatory profiles of Fus3 and Kss1 can be traced along their evolutionary tree. Overall, our approach is able to reconnect (latent) allostery with the principal elements of protein structural stabilization and dynamics, showing how allosteric regulation was encrypted in MAPKs structure well before Ste5 appearance.

Molecular mechanisms of chaperone-directed protein folding: Insights from atomistic simulations

Molecular chaperones, a family of proteins of which Hsp90 and Hsp70 are integral members, form an essential machinery to maintain healthy proteomes by controlling the folding and activation of a plethora of substrate client proteins. This is achieved through cycles in which Hsp90 and Hsp70, regulated by task-specific co-chaperones, process ATP and become part of a complex network that undergoes extensive compositional and conformational variations. Despite impressive advances in structural knowledge, the mechanisms that regulate the dynamics of functional assemblies, their response to nucleotides, and their relevance for client remodeling are still elusive. Here, we focus on the glucocorticoid receptor (GR):Hsp90:Hsp70:co-chaperone Hop client-loading and the GR:Hsp90:co-chaperone p23 client-maturation complexes, key assemblies in the folding cycle of glucocorticoid receptor (GR), a client strictly dependent upon Hsp90/Hsp70 for activity. Using a combination of molecular dynamics simulation approaches, we unveil with unprecedented detail the mechanisms that underpin function in these chaperone machineries. Specifically, we dissect the processes by which the nucleotide-encoded message is relayed to the client and how the distinct partners of the assemblies cooperate to (pre)organize partially folded GR during Loading and Maturation. We show how different ligand states determine distinct dynamic profiles for the functional interfaces defining the interactions in the complexes and modulate their overall flexibility to facilitate progress along the chaperone cycle. Finally, we also show that the GR regions engaged by the chaperone machinery display peculiar energetic signatures in the folded state, which enhance the probability of partial unfolding fluctuations. From these results, we propose a model where a dynamic cross-talk emerges between the chaperone dynamics states and remodeling of client-interacting regions. This factor, coupled to the highly dynamic nature of the assemblies and the conformational heterogeneity of their interactions, provides the basis for regulating the functions of distinct assemblies during the chaperoning cycle.

Computing allostery: from the understanding of biomolecular regulation and the discovery of cryptic sites to molecular design

The concept of allostery has become a central tenet in the study of biological systems. In parallel, the discovery of allosteric drugs is generating new opportunities to selectively modulate difficult targets involved in pathologic mechanisms. Molecular simulations can provide atomistically detailed insight into the processes involved in allosteric regulation and signaling, and at the same time, they have the potential to unveil regulatory hotspots or cryptic sites that are not immediately evident from the analysis of static structures. In this context, computational approaches should be able to connect the study of allosteric regulation at different scales to the possibility of leveraging this knowledge to expand the chemical space of new, active drugs. Here, we will discuss recent advances in the study of allosteric regulation via computational methods and connect the mechanistic knowledge generated to the possibility of designing new small molecules that can tweak the functions of their receptors.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

NLRP3 monomer functional dynamics: From the effects of allosteric binding to implications for drug design

The protein NLRP3 and its complexes are associated with an array of inflammatory pathologies, among which neurodegenerative, autoimmune, and metabolic diseases. Targeting the NLRP3 inflammasome represents a promising strategy for easing the symptoms of pathologic neuroinflammation. When the inflammasome is activated, NLRP3 undergoes a conformational change triggering the production of pro-inflammatory cytokines IL-1β and IL-18, as well as cell death by pyroptosis. NLRP3 nucleotide-binding and oligomerization (NACHT) domain plays a crucial role in this function by binding and hydrolysing ATP and is primarily responsible, together with conformational transitions involving the PYD domain, for the complex-assembly process. Allosteric ligands proved able to induce NLRP3 inhibition. Herein, we examine the origins of allosteric inhibition of NLRP3. Through the use of molecular dynamics (MD) simulations and advanced analysis methods, we provide molecular-level insights into how allosteric binding affects protein structure and dynamics, remodelling of the conformational ensembles populated by the protein, with key reverberations on how NLRP3 is preorganized for assembly and ultimately function. The data are used to develop a Machine Learning model to define the protein as Active or Inactive, only based on the analysis of its internal dynamics. We propose this model as a novel tool to select allosteric ligands.

Conformational Behavior of SARS-Cov-2 Spike Protein Variants: Evolutionary Jumps in Sequence Reverberate in Structural Dynamic Differences

SARS-CoV-2 has evolved rapidly in the first 3 years of pandemic diffusion. The initial evolution of the virus appeared to proceed through big jumps in sequence changes rather than through the stepwise accumulation of point mutations on already established variants. Here, we examine whether this nonlinear mutational process reverberates in variations of the conformational dynamics of the SARS-CoV-2 Spike protein (S-protein), the first point of contact between the virus and the human host. We run extensive microsecond-scale molecular dynamics simulations of seven distinct variants of the protein in their fully glycosylated state and set out to elucidate possible links between the mutational spectrum of the S-protein and the structural dynamics of the respective variant, at global and local levels. The results reveal that mutation-dependent structural and dynamic modulations mostly consist of increased coordinated motions in variants that acquire stability and in an increased internal flexibility in variants that are less stable. Importantly, a limited number of functionally important substructures (the receptor binding domain, in particular) share the same time of movements in all variants, indicating efficient preorganization for functional regions dedicated to host interactions. Our results support a model in which the internal dynamics of the S-proteins from different strains varies in a way that reflects the observed random and non-stepwise jumps in sequence evolution, while conserving the functionally oriented traits of conformational dynamics necessary to support productive interactions with host receptors.

Integrating Protein Interaction Surface Prediction with a Fragment-Based Drug Design: Automatic Design of New Leads with Fragments on Energy Surfaces

Protein-protein interactions (PPIs) have emerged in the past years as significant pharmacological targets in the development of new therapeutics due to their key roles in determining pathological pathways. Herein, we present fragments on energy surfaces, a simple and general design strategy that integrates the analysis of the dynamic and energetic signatures of proteins to unveil the substructures involved in PPIs, with docking, selection, and combination of drug-like fragments to generate new PPI inhibitor candidates. Specifically, structural representatives of the target protein are used as inputs for the blind physics-based prediction of potential protein interaction surfaces using the matrix of low coupling energy decomposition method. The predicted interaction surfaces are subdivided into overlapping windows that are used as templates to direct the docking and combination of fragments representative of moieties typically found in active drugs. This protocol is then applied and validated using structurally diverse, important PPI targets as test systems. We demonstrate that our approach facilitates the exploration of the molecular diversity space of potential ligands, with no requirement of prior information on the location and properties of interaction surfaces or on the structures of potential lead compounds. Importantly, the hit molecules that emerge from our ab initio design share high chemical similarity with experimentally tested active PPI inhibitors. We propose that the protocol we describe here represents a valuable means of generating initial leads against difficult targets for further development and refinement.

Computational Epitope Prediction and Design for Antibody Development and Detection

The design of optimized protein antigens is a fundamental step in the development of new vaccine candidates and in the detection of therapeutic antibodies. A fundamental prerequisite is the identification of antigenic regions that are most prone to interact with antibodies, namely, B-cell epitopes. Here, we describe an efficient structure-based computational method for epitope prediction, called MLCE. In this approach, all that is required is the 3D structure of the antigen of interest. MLCE can be applied to glycosylated proteins, facilitating the identification of immunoreactive versus immune-shielding carbohydrates.

Human prion protein: exploring the thermodynamic stability and structural dynamics of its pathogenic mutants

Human familial prion diseases are known to be associated with different single-point mutants of the gene coding for prion protein with a primary focus at several locations of the globular domain. We have identified 12 different single-point pathogenic mutants of human prion protein (HuPrP) with the help of extensive perturbations/mutation technique at multiple locations of HuPrP sequence related to potentiality towards conformational disorders. Among these, some of the mutants include pathogenic variants that corroborate well with the literature reported proteins while majority include some unique single-point mutants that are either not explicitly studied early or studied for variants with different residues at the specific position. Primarily, our study sheds light on the unfolding mechanism of the above mentioned mutants in depth. Besides, we could identify some mutants under investigation that demonstrates not only unfolding of the helical structures but also extension and generation of the β-sheet structures and or simultaneously have highly exposed hydrophobic surface which is assumed to be linked with the production of aggregate/fibril structures of the prion protein. Among the identified mutants, Q212E needs special attention due to its maximum exposure of hydrophobic core towards solvent and E200Q is found to be important due to its maximum extent of β-content. We are also able to identify different respective structural conformations of the proteins according to their degree of structural unfolding and those conformations can be extracted and further studied in detail. Communicated by Ramaswamy H. Sarma.

Evaluation of docking procedures reliability in affitins-partners interactions

Affitins constitute a class of small proteins belonging to Sul7d family, which, in microorganisms such as Sulfolobus acidocaldarius, bind DNA preventing its denaturation. Thanks to their stability and small size (60–66 residues in length) they have been considered as ideal candidates for engineering and have been used for more than 10 years now, for different applications. The individuation of a mutant able to recognize a specific target does not imply the knowledge of the binding geometry between the two proteins. However, its identification is of undoubted importance but not always experimentally accessible. For this reason, computational approaches such as protein-protein docking can be helpful for an initial structural characterization of the complex. This method, which produces tens of putative binding geometries ordered according to a binding score, needs to be followed by a further reranking procedure for finding the most plausible one. In the present paper, we use the server ClusPro for generating docking models of affitins with different protein partners whose experimental structures are available in the Protein Data Bank. Then, we apply two protocols for reranking the docking models. The first one investigates their stability by means of Molecular Dynamics simulations; the second one, instead, compares the docking models with the interacting residues predicted by the Matrix of Local Coupling Energies method. Results show that the more efficient way to deal with the reranking problem is to consider the information given by the two protocols together, i.e. employing a consensus approach.

Cancer-related Mutations with Local or Long-range Effects on an Allosteric Loop of p53

The tumor protein 53 (p53) is involved in transcription-dependent and independent processes. Several p53 variants related to cancer have been found to impact protein stability. Other variants, on the contrary, might have little impact on structural stability and have local or long-range effects on the p53 interactome. Our group previously identified a loop in the DNA binding domain (DBD) of p53 (residues 207-213) which can recruit different interactors. Experimental structures of p53 in complex with other proteins strengthen the importance of this interface for protein-protein interactions. We here characterized with structure-based approaches somatic and germline variants of p53 which could have a marginal effect in terms of stability and act locally or allosterically on the region 207-213 with consequences on the cytosolic functions of this protein. To this goal, we studied 1132 variants in the p53 DBD with structure-based approaches, accounting also for protein dynamics. We focused on variants predicted with marginal effects on structural stability. We then investigated each of these variants for their impact on DNA binding, dimerization of the p53 DBD, and intramolecular contacts with the 207-213 region. Furthermore, we identified variants that could modulate long-range the conformation of the region 207-213 using a coarse-grain model for allostery and all-atom molecular dynamics simulations. Our predictions have been further validated using enhanced sampling methods for 15 variants. The methodologies used in this study could be more broadly applied to other p53 variants or cases where conformational changes of loop regions are essential in the function of disease-related proteins.

Structure-function relationships of the disease-linked A218T oxytocin receptor variant

Various single nucleotide polymorphisms (SNPs) in the oxytocin receptor (OXTR) gene have been associated with behavioral traits, autism spectrum disorder (ASD) and other diseases. The non-synonymous SNP rs4686302 results in the OXTR variant A218T and has been linked to core characteristics of ASD, trait empathy and preterm birth. However, the molecular and intracellular mechanisms underlying those associations are still elusive. Here, we uncovered the molecular and intracellular consequences of this mutation that may affect the psychological or behavioral outcome of oxytocin (OXT)-treatment regimens in clinical studies, and provide a mechanistic explanation for an altered receptor function. We created two monoclonal HEK293 cell lines, stably expressing either the wild-type or A218T OXTR. We detected an increased OXTR protein stability, accompanied by a shift in Ca²⁺ dynamics and reduced MAPK pathway activation in the A218T cells. Combined whole-genome and RNA sequencing analyses in OXT-treated cells revealed 7823 differentially regulated genes in A218T compared to wild-type cells, including 429 genes being associated with ASD. Furthermore, computational modeling provided a molecular basis for the observed change in OXTR stability suggesting that the OXTR mutation affects downstream events by altering receptor activation and signaling, in agreement with our in vitro results. In summary, our study provides the cellular mechanism that links the OXTR rs4686302 SNP with genetic dysregulations associated with aspects of ASD.

Markov state models and NMR uncover an overlooked allosteric loop in p53

The tumor suppressor p53 is the most frequently mutated gene in human cancer, and thus reactivation of mutated p53 is a promising avenue for cancer therapy. Analysis of wildtype p53 and the Y220C cancer mutant long-timescale molecular dynamics simulations with Markov state models and validation by NMR relaxation studies has uncovered the involvement of loop L6 in the slowest motions of the protein. Due to its distant location from the DNA-binding surface, the conformational dynamics of this loop has so far remained largely unexplored. We observe mutation-induced stabilization of alternate L6 conformations, distinct from all experimentally-determined structures, in which the loop is both extended and located further away from the DNA-interacting surface. Additionally, the effect of the L6-adjacent Y220C mutation on the conformational landscape of the functionally-important loop L1 suggests an allosteric role to this dynamic loop and the inactivation mechanism of the mutation. Finally, the simulations reveal a novel Y220C cryptic pocket that can be targeted for p53 rescue efforts. Our approach exemplifies the power of the MSM methodology for uncovering intrinsic dynamic and kinetic differences among distinct protein ensembles, such as for the investigation of mutation effects on protein function.

Wildtype and Y220C L1 and L6 loops conformational landscape, with MSM-identified L6 states highlighted on the right.

The Answer Lies in the Energy: How Simple Atomistic Molecular Dynamics Simulations May Hold the Key to Epitope Prediction on the Fully Glycosylated SARS-CoV-2 Spike Protein

SARS-CoV-2 is a health threat with dire socioeconomical consequences. As the crucial mediator of infection, the viral glycosylated spike protein (S) has attracted the most attention and is at the center of efforts to develop therapeutics and diagnostics. Herein, we use an original decomposition approach to identify energetically uncoupled substructures as antibody binding sites on the fully glycosylated S. Crucially, all that is required are unbiased MD simulations; no prior knowledge of binding properties or ad hoc parameter combinations is needed. Our results are validated by experimentally confirmed structures of S in complex with anti- or nanobodies. We identify poorly coupled subdomains that are poised to host (several) epitopes and potentially involved in large functional conformational transitions. Moreover, we detect two distinct behaviors for glycans: those with stronger energetic coupling are structurally relevant and protect underlying peptidic epitopes, and those with weaker coupling could themselves be prone to antibody recognition.

Simple Model of Protein Energetics To Identify Ab Initio Folding Transitions from All-Atom MD Simulations of Proteins

A fundamental requirement to predict the native conformation, address questions of sequence design and optimization, and gain insights into the folding mechanisms of proteins lies in the definition of an unbiased reaction coordinate that reports on the folding state without the need to compare it to reference values, which might be unavailable for new (designed) sequences. Here, we introduce such a reaction coordinate, which does not depend on previous structural knowledge of the native state but relies solely on the energy partition within the protein: the spectral gap of the pair nonbonded energy matrix (ENergy Gap, ENG). This quantity can be simply calculated along unbiased MD trajectories. We show that upon folding the gap increases significantly, while its fluctuations are reduced to a minimum. This is consistently observed for a diverse set of systems and trajectories. Our approach allows one to promptly identify residues that belong to the folding core as well as residues involved in non-native contacts that need to be disrupted to guide polypeptides to the folded state. The energy gap and fluctuations criteria are then used to develop an automatic detection system which allows us to extract and analyze folding transitions from a generic MD trajectory. We speculate that our method can be used to detect conformational ensembles in dynamic and intrinsically disordered proteins, revealing potential preorganization for binding.

Chemical Perturbation of Oncogenic Protein Folding: from the Prediction of Locally Unstable Structures to the Design of Disruptors of Hsp90-Client Interactions

Protein folding quality control in cells requires the activity of a class of proteins known as molecular chaperones. Heat shock protein-90 (Hsp90), a multidomain ATP driven molecular machine, is a prime representative of this family of proteins. Interactions between Hsp90, its co-chaperones, and client proteins have been shown to be important in facilitating the correct folding and activation of clients. Hsp90 levels and functions are elevated in tumor cells. Here, we computationally predict the regions on the native structures of clients c-Abl, c-Src, Cdk4, B-Raf and Glucocorticoid Receptor, that have the highest probability of undergoing local unfolding, despite being ordered in their native structures. Such regions represent potential ideal interaction points with the Hsp90-system. We synthesize mimics spanning these regions and confirm their interaction with partners of the Hsp90 complex (Hsp90, Cdc37 and Aha1) by Nuclear Magnetic Resonance (NMR). Designed mimics selectively disrupt the association of their respective clients with the Hsp90 machinery, leaving unrelated clients unperturbed and causing apoptosis in cancer cells. Overall, selective targeting of Hsp90 protein-protein interactions is achieved without causing indiscriminate degradation of all clients, setting the stage for the development of therapeutics based on specific chaperone:client perturbation.

Impact of Mutations on NPAC Structural Dynamics: Mechanistic Insights from MD Simulations

NPAC is a cytokine-like nuclear factor involved in chromatin modification and regulation of gene expression. In humans, the C-terminal domain of NPAC has the conserved structure of the β-hydroxyacid dehydrogenases (β-HAD) protein superfamily, which forms a stable tetrameric core scaffold for demethylase enzymes and organizes multiple sites for chromatin interactions. In spite of the close structural resemblance to other β-HAD family members, the human NPAC dehydrogenase domain lacks a highly conserved catalytic lysine, substituted by a methionine. The reintroduction of the catalytic lysine by M437 K mutation results in a significant decrease of stability of the tetramer. Here, we have computationally investigated the molecular determinants of the functional differences between methionine and lysine-containing NPAC proteins. We find that the single mutation can determine strong consequences in terms of dynamics, stability, and ultimately ability to assemble in supramolecular complexes: the higher stability and lower flexibility of the methionine variant structurally preorganizes the monomer for tetramerization, whereas lysine increases flexibility and favors conformations that, while catalytically active, are not optimal for tetrameric assembly. We combine structure-dynamics analysis to an evolutionary study of NPAC sequences, showing that the methionine mutation occurs in a specifically flexible region of the lysine-containing protein, flanked by two domains that concentrate most of the stabilizing interactions. In our model, such separation of stability nuclei and flexible regions appears to favor the functional innovability of the protein.

The Subtle Trade-Off between Evolutionary and Energetic Constraints in Protein-Protein Interactions

Life machinery, although overwhelmingly complex, is rooted on a rather limited number of molecular processes. One of the most important is protein-protein interaction. Metabolic regulation, protein folding control, and cellular motility are examples of processes based on the fine-tuned interaction of several protein partners. The region on the protein surface devoted to the recognition of a specific partner is essential for the function of the protein and is, therefore, likely to be conserved during evolution. On the other hand, the physical chemistry of amino acids underlies the mechanism of interactions. Both evolutionary and energetic constraints can then be used to build scoring functions capable of recognizing interaction sites. Our working hypothesis is that residues within the interaction interface tend at the same time to be evolutionarily conserved (to preserve their function) and to provide little contribution to the internal stabilization of the structure of their cognate protein, to facilitate conformational adaptation to the partner. Here, we show that for some classes of protein partners (for example, those involved in signal transduction and in enzymes) evolutionary constraints play the key role in defining the interaction surface. In contrast, energetic constraints emerge as more important in protein partners involved in immune response, in inhibitor proteins, and in structural proteins. Our results indicate that a general-purpose scoring function for protein-protein interaction should not be agnostic of the biological function of the partners.

Peptides for Infectious Diseases: From Probe Design to Diagnostic Microarrays

Peptides and peptidomimetics have attracted revived interest regarding their applications in chemical biology over the last few years. Their chemical versatility, synthetic accessibility and the ease of storage and management compared to full proteins have made peptides particularly interesting in diagnostic applications, where they proved to efficiently recapitulate the molecular recognition properties of larger protein antigens, and were proven to be able to capture antibodies circulating in the plasma and serum of patients previously exposed to bacterial or viral infections. Here, we describe the development, integration and application of strategies for computational prediction and design, advanced chemical synthesis, and diagnostic deployment in multiplexed assays of peptide-based materials which are able to bind antibodies of diagnostic as well as therapeutic interest. By presenting successful applications of such an integrated strategy, we argue that they will have an ever-increasing role in both basic and clinical realms of research, where important advances can be expected in the next few years.

Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins

With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.

How the Ligand-Induced Reorganization of Protein Internal Energies Is Coupled to Conformational Events

Here, we introduce a novel computational method to identify the protein substructures most likely to support the functionally oriented structural deformations that occur upon ligand-binding. To this aim, we study the modulation of protein energetics along the trajectory of a molecular dynamics simulation of different proteins in the presence and in the absence of their respective ligands, namely, human FGF, human second PDZ from human PTP1E/PTPL1, and the N terminal domain of human Hsp90. The method is based on the idea that a subset of protein residues (hotspots) may initiate the global response via the disassembly and reassembly of interactions, which is reflected in the modulation of the overall protein energetics. To identify structural hotspots and dynamic states linked to the onset of functionally relevant conformational transitions, we define an energy profile to monitor the protein energetics, based on a previously introduced approach that highlights the essential nonbonded couplings among all residues. The energy profiles are calculated along the trajectory to yield a time-dependent evolution, and their relative population in the presence and absence of the ligand is evaluated by means of a clustering procedure. It is found that interconversion between clusters, as well as their population and the density of specific energy profiles in the vicinity of structural transitions, provides specific information on the impact of the ligand in driving the protein conformational response. This analysis also highlights the hotspot residues that are most responsive to the presence of the ligand. Importantly, identified hotspots are in agreement with experimental evidence in the three considered systems. We propose that this approach can be generally used in the prediction of "allosteric hotspots" and ligand-induced conformational responses, as well as to select conformations more likely to support functional transitions (e.g., in the framework of adaptive sampling approaches).

Targeting Difficult Protein-Protein Interactions with Plain and General Computational Approaches

Investigating protein-protein interactions (PPIs) holds great potential for therapeutic applications, since they mediate intricate cell signaling networks in physiological and disease states. However, their complex and multifaceted nature poses a major challenge for biochemistry and medicinal chemistry, thereby limiting the druggability of biological partners participating in PPIs. Molecular Dynamics (MD) provides a solid framework to study the reciprocal shaping of proteins’ interacting surfaces. Here, we review successful applications of MD-based methods developed in our group to predict interfacial areas involved in PPIs of pharmaceutical interest. We report two interesting examples of how structural, dynamic and energetic information can be combined into efficient strategies which, complemented by experiments, can lead to the design of new small molecules with promising activities against cancer and infections. Our advances in targeting key PPIs in angiogenic pathways and antigen-antibody recognition events will be discussed for their role in drug discovery and chemical biology.

DNA Polymerase Conformational Dynamics and the Role of Fidelity-Conferring Residues: Insights from Computational Simulations

Herein we investigate the molecular bases of DNA polymerase I conformational dynamics that underlie the replication fidelity of the enzyme. Such fidelity is determined by conformational changes that promote the rejection of incorrect nucleotides before the chemical ligation step. We report a comprehensive atomic resolution study of wild type and mutant enzymes in different bound states and starting from different crystal structures, using extensive molecular dynamics (MD) simulations that cover a total timespan of ~5 ms. The resulting trajectories are examined via a combination of novel methods of internal dynamics and energetics analysis, aimed to reveal the principal molecular determinants for the (de)stabilization of a certain conformational state. Our results show that the presence of fidelity-decreasing mutations or the binding of incorrect nucleotides in ternary complexes tend to favor transitions from closed toward open structures, passing through an ensemble of semi-closed intermediates. The latter ensemble includes the experimentally observed ajar conformation which, consistent with previous experimental observations, emerges as a molecular checkpoint for the selection of the correct nucleotide to incorporate. We discuss the implications of our results for the understanding of the relationships between the structure, dynamics, and function of DNA polymerase I at the atomistic level.

Surface energetics and protein-protein interactions: analysis and mechanistic implications

Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions.

Mechanisms of Differential Allosteric Modulation in Homologous Proteins: Insights from the Analysis of Internal Dynamics and Energetics of PDZ Domains

Allostery is a general phenomenon in proteins whereby a perturbation at one site reverberates into a functional change at another one, through modulation of its conformational dynamics. Herein, we address the problem of how the molecular signal encoded by a ligand is differentially transmitted through the structures of two homologous PDZ proteins: PDZ2, which responds to binding with structural and dynamical changes in regions distal from the ligand site, and PDZ3, which is characterized by less-intense dynamical variations. We use novel methods of analysis of MD simulations in the unbound and bound states to investigate the determinants of the differential allosteric behavior of the two proteins. The analysis of the correlations between the redistribution of stabilization energy and local fluctuation patterns highlights the nucleus of residues responsible for the stabilization of the 3D fold, the stability core, as the substructure that defines the difference in the allosteric response: in PDZ2, it undergoes a consistent dynamic and energetic reorganization, whereas in PDZ3, it remains largely unperturbed. Specifically, we observe for PDZ2 a significant anticorrelation between the motions of distal loops and residues of the stability core and differences in the correlation patterns between the bound and unbound states. Such variation is not observed in PDZ3, indicating that its energetics and internal dynamics are less affected by the presence/absence of the ligand. Finally, we propose a model with a direct link between the modulation of the structural, energetic and dynamic properties of a protein, and its allosteric response to a perturbation.

Sequence- and Structure-Based Immunoreactive Epitope Discovery for Burkholderia pseudomallei Flagellin

Burkholderia pseudomallei is a Gram-negative bacterium responsible for melioidosis, a serious and often fatal infectious disease that is poorly controlled by existing treatments. Due to its inherent resistance to the major antibiotic classes and its facultative intracellular pathogenicity, an effective vaccine would be extremely desirable, along with appropriate prevention and therapeutic management. One of the main subunit vaccine candidates is flagellin of Burkholderia pseudomallei (FliC_Bp). Here, we present the high resolution crystal structure of FliC_Bp and report the synthesis and characterization of three peptides predicted to be both B and T cell FliC_Bp epitopes, by both structure-based in silico methods, and sequence-based epitope prediction tools. All three epitopes were shown to be immunoreactive against human IgG antibodies and to elicit cytokine production from human peripheral blood mononuclear cells. Furthermore, two of the peptides (F51-69 and F270-288) were found to be dominant immunoreactive epitopes, and their antibodies enhanced the bactericidal activities of purified human neutrophils. The epitopes derived from this study may represent potential melioidosis vaccine components.

Melioidosis is an infectious disease caused by Burkolderia pseudomallei that poses a major public health problem in Southeast Asia and northern Australia. This bacterium is difficult to treat due to its intrinsic resistance to antibiotics, poor diagnosis, and the lack of a licensed vaccine. Vaccine safety is a prime concern, therefore recombinant protein subunit and/or peptide vaccine components, may represent safer alternatives. In this context, we targeted one of the main subunit vaccine candidates tested to date, flagellin from B. pseudomallei (FliC_Bp) that comprises the flagellar filament that mediates bacterial motility. Based on the knowledge that activation of both cell-mediated and antibody-mediated responses must be addressed in a melioidosis vaccine, we identified B and T cell immunoreactive peptides from FliC_Bp, using both sequence-based and structure-based computational prediction programs, for further in vitro immunological testing. Our data confirm the accuracy of sequence-based epitope prediction tools, and two structure-based methods applied to the FliC_Bp crystal structure (here-described), in predicting both T- and B-cell epitopes. Moreover, we identified two epitope peptides with significant joint T-cell and B-cell activities for further development as melioidosis vaccine components.

Structural Stability and Flexibility Direct the Selection of Activating Mutations in Epidermal Growth Factor Receptor Kinase

Herein we investigate the potential of novel methods of molecular dynamics analysis to provide information on the key factors that underlie the preferential localization and the effects of mutations modulating protein activities. Epidermal growth factor receptor (EGFR) kinases are selected as a test case. The combined analysis of protein energetics and internal dynamics indicates a clear polarization in the native protein, whereby a highly stable and ordered scaffold in one domain, namely the C-lobe, is combined to a flexible and loosely stabilized domain, the N-lobe. The subdivision in two portions with different properties directs the presence of point mutations mainly to the N-lobe. This allows modulating protein flexibility so that the protein can more efficiently sample the conformations necessary for substrate recognition, while leaving the stability of the protein unperturbed. In this context, comparative simulations of EGFR in the wild type sequence and in the presence of the activating oncogenic mutation G719S reveal flexibility changes in several key regions, involving in particular the part of the kinase devoted to the regulation of substrate recognition (regulatory core) and an increase in the number of stabilizing interactions in the N-lobe for the activated mutant. Our approaches represent a promising and simple strategy toward rationalizing the effects of mutations in modulating enzymatic activities.

Molecular dynamics, monte carlo simulations, and langevin dynamics: a computational review

Macromolecular structures, such as neuraminidases, hemagglutinins, and monoclonal antibodies, are not rigid entities. Rather, they are characterised by their flexibility, which is the result of the interaction and collective motion of their constituent atoms. This conformational diversity has a significant impact on their physicochemical and biological properties. Among these are their structural stability, the transport of ions through the M2 channel, drug resistance, macromolecular docking, binding energy, and rational epitope design. To assess these properties and to calculate the associated thermodynamical observables, the conformational space must be efficiently sampled and the dynamic of the constituent atoms must be simulated. This paper presents algorithms and techniques that address the abovementioned issues. To this end, a computational review of molecular dynamics, Monte Carlo simulations, Langevin dynamics, and free energy calculation is presented. The exposition is made from first principles to promote a better understanding of the potentialities, limitations, applications, and interrelations of these computational methods.

Energetic and dynamic aspects of the affinity maturation process: characterizing improved variants from the bevacizumab antibody with molecular simulations

Antibody affinity maturation is one of the fundamental processes of immune defense against invading pathogens. From the biological point of view, the clonal selection hypothesis represents the most accepted mechanism to explain how mutations increasing the affinity for target antigens are introduced and selected in antibody molecules. However, understanding at the molecular level how protein modifications, such as point mutation, can modify and modulate the affinity of an antibody for its antigen is still a major open issue in molecular biology. In this paper, we address various aspects of this problem by analyzing and comparing atomistic simulations of 17 variants of the bevacizumab antibody, all directed against the common target protein VEGF-A. In particular, we examine MD-based descriptors of the internal energetics and dynamics of mutated antibodies and their possible correlations with experimentally determined affinities for the antigens. Our results show that affinity improvement is correlated with a variation of the internal stabilization energy of the antibody molecule when bound to the antigen, compensated by the variation in the interaction energy between the antigen and the antibody, paralleled by an overall modulation of internal coordination within the antibody molecular structure. A possible model of the mechanism of rigidification and of the main residues involved is proposed. Overall, our results can help in understanding the molecular determinants of antigen recognition and have implications in the rational design of new antibodies with optimized affinities.

A Hamiltonian replica exchange molecular dynamics (MD) method for the study of folding, based on the analysis of the stabilization determinants of proteins

Herein, we present a novel Hamiltonian replica exchange protocol for classical molecular dynamics simulations of protein folding/unfolding. The scheme starts from the analysis of the energy-networks responsible for the stabilization of the folded conformation, by means of the energy-decomposition approach. In this framework, the compact energetic map of the native state is generated by a preliminary short molecular dynamics (MD) simulation of the protein in explicit solvent. This map is simplified by means of an eigenvalue decomposition. The highest components of the eigenvector associated with the lowest eigenvalue indicate which sites, named "hot spots", are likely to be responsible for the stability and correct folding of the protein. In the Hamiltonian replica exchange protocol, we use modified force-field parameters to treat the interparticle non-bonded potentials of the hot spots within the protein and between protein and solvent atoms, leaving unperturbed those relative to all other residues, as well as solvent-solvent interactions. We show that it is possible to reversibly simulate the folding/unfolding behavior of two test proteins, namely Villin HeadPiece HP35 (35 residues) and Protein A (62 residues), using a limited number of replicas. We next discuss possible implications for the study of folding mechanisms via all atom simulations.

Rational epitope design for protein targeting

We present a new multidisciplinary strategy integrating computational biology with high-throughput microarray analysis aimed to translate molecular understanding of protein-antibody recognition into the design of efficient and selective protein-based analytical and diagnostic tools. The structures of two proteins with different folds and secondary structure contents, namely, the beta-barrel FABP and the α-helical S100B, were used as the basis for the prediction and design of potential antibody-binding epitopes using the recently developed MLCE computational method. Starting from the idea that the structure, dynamics, and stability of a protein-antigen play a key role in the interaction with antibodies, MLCE integrates the analysis of the dynamical and energetic properties of proteins to identify nonoptimized, low-intensity energetic interaction-networks on the surface of the isolated antigens, which correspond to substructures that can aptly be recognized by a binding partner. The identified epitopes were next synthesized as free peptides and used to elicit specific antibodies in rabbits. Importantly, the resulting antibodies were proven to specifically and selectively recognize the original, full-length proteins in microarray-based tests. Competition experiments further demonstrated the specificity of the molecular recognition between the target immobilized proteins and the generated antibodies. Our integrated computational and microarray-based results demonstrate the possibility to rationally discover and design synthetic epitopes able to elicit antibodies specific for full-length proteins starting only from three-dimensional structural information on the target. We discuss implications for diagnosis and vaccine development purposes.

Investigating allostery in molecular recognition: insights from a computational study of multiple antibody-antigen complexes

Antibody-antigen recognition plays a key role in the immune response against pathogens. Here, we have investigated various aspects of this problem by analyzing a large and diverse set of antibodies and their respective complexes with protein antigens through atomistic simulations. Common features of antibody response to the presence of antigens are elucidated by the analysis of the proteins' internal dynamics and coordination in different ligand states, combined with the analysis of the interaction networks implicated in the stabilization of functional structures. The use of a common structural reference reveals preferential changes in the dynamic coordination and intramolecular interaction networks induced by antigen binding and shared by all antibodies. Such changes propagate from the binding region through the whole immunoglobulin domains. Overall, complexed antibodies show more diffuse networks of nonbonded interactions and a general higher internal dynamic coordination, which preferentially involve the immunoglobulin (Ig) domains of the heavy chain. The combined results provide atomistic insights into the correlations between the modulation of conformational dynamics, structural stability, and allosteric signal transduction. In particular, the results suggest that specific networks of residues, shared among all the analyzed proteins, define the molecular pathways by which antibody structures respond to antigen binding. Our studies may have implications in practical use, such as the rational design of antibodies with specifically modulated antigen-binding affinities.

A structure-based strategy for epitope discovery in Burkholderia pseudomallei OppA antigen

We present an approach integrating structural and computational biology with immunological tests to identify epitopes in the OppA antigen from the Gram-negative pathogen Burkholderia pseudomallei, the etiological agent of melioidosis. The crystal structure of OppA(Bp), reported here at 2.1 Å resolution, was the basis for a computational analysis that identified three potential epitopes. In parallel, antigen proteolysis and immunocapturing allowed us to identify three additional peptides. All six potential epitopes were synthesized as free peptides and tested for their immunoreactivity against sera from healthy seronegative, healthy seropositive, and recovered melioidosis patients. Three synthetic peptides allowed the different patient groups to be distinguished, underlining the potential of this approach. Extension of the computational analysis, including energy-based decomposition methods, allowed rationalizing results of the predictive analyses and the immunocapture epitope mapping. Our results illustrate a structure-based epitope discovery process, whose application may expand our perspectives in the diagnostic and vaccine design fields.

Computational tools for rational protein engineering of aldolases

In this mini-review we describe the different strategies for rational protein engineering and summarize the computational tools available. Computational tools can either be used to design focused libraries, to predict sequence-function relationships or for structure-based molecular modelling. This also includes de novo design of enzymes. Examples for protein engineering of aldolases and transaldolases are given in the second part of the mini-review.

Molecular dynamics simulations of hsp90 with an eye to inhibitor design

Proteins carry out their functions through interactions with different partners. Dynamic conformational switching among different structural sub-states favors the adaptation to the shapes of the different partners. Such conformational changes can be determined by diverse biochemical factors, such as ligand-binding. Atomic level investigations of the mechanisms that underlie functional dynamics may provide new opportunities for the discovery of leads that target disease-related proteins. In this review, we report our views and approaches on the development of novel and accurate physical-chemistry-based models for the characterization of the salient aspects of the ligand-regulated dynamics of Hsp90, and on the exploitation of such new knowledge for the rational discovery of inhibitors of the chaperone.

Identification of domains in protein structures from the analysis of intramolecular interactions

The subdivision of protein structures into smaller and independent structural domains has a fundamental importance in understanding protein evolution and function and in the development of protein classification methods as well as in the interpretation of experimental data. Due to the rapid growth in the number of solved protein structures, the need for devising new accurate algorithmic methods has become more and more urgent. In this paper, we propose a new computational approach that is based on the concept of domain as a compact and independent folding unit and on the analysis of the residue-residue energy interactions obtainable through classical all-atom force field calculations. In particular, starting from the analysis of the nonbonded interaction energy matrix associated with a protein, our method filters out and selects only those specific subsets of interactions that define possible independent folding nuclei within a complex protein structure. This allows grouping different protein fragments into energy clusters that are found to correspond to structural domains. The strategy has been tested using proper benchmark data sets, and the results have shown that the new approach is fast and reliable in determining the number of domains in a totally ab initio manner and without making use of any training set or knowledge of the systems in exam. Moreover, our method, identifying the most relevant residues for the stabilization of each domain, may complement the results given by other classification techniques and may provide useful information to design and guide new experiments.

Protein design in metabolic engineering and synthetic biology

Starting from experimental data on sequence, structure or biochemical properties of enzymes, protein design seeks to construct enzymes with desired activity, stability, specificity and selectivity. Two strategies are widely used to investigate sequence-structure-function relationships: statistical methods to analyse protein families or mutant libraries, and molecular modelling methods to study proteins and their interaction with ligands or substrates. On the basis of these methods, protein design has been successfully applied to fine-tune bottleneck enzymes in metabolic engineering and to design enzymes with new substrate spectra and new functions. However, constructing efficient metabolic pathways by integrating individual enzymes into a complex system is challenging. The field of synthetic biology is still in its infancy, but promising results have demonstrated the feasibility and usefulness of the concept.

Investigating dynamic and energetic determinants of protein nucleic acid recognition: analysis of the zinc finger zif268-DNA complexes

BACKGROUND

Protein-DNA recognition underlies fundamental biological processes ranging from transcription to replication and modification. Herein, we present a computational study of the sequence modulation of internal dynamic properties and of intraprotein networks of aminoacid interactions that determine the stability and specificity of protein-DNA complexes.

RESULTS

To this aim, we apply novel theoretical approaches to analyze the dynamics and energetics of biological systems starting from MD trajectories. As model system, we chose different sequences of Zinc Fingers (ZF) of the Zif268 family bound with different sequences of DNA. The complexes differ for their experimental stability properties, but share the same overall 3 D structure and do not undergo structural modifications during the simulations. The results of our analysis suggest that the energy landscape for DNA binding may be populated by dynamically different states, even in the absence of major conformational changes. Energetic couplings between residues change in response to protein and/or DNA sequence variations thus modulating the selectivity of recognition and the relative importance of different regions for binding.

CONCLUSIONS

The results show differences in the organization of the intra-protein energy-networks responsible for the stabilization of the protein conformations recognizing and binding DNA. These, in turn, are reflected into different modulation of the ZF's internal dynamics. The results also show a correlation between energetic and dynamic properties of the different proteins and their specificity/selectivity for DNA sequences. Finally, a dynamic and energetic model for the recognition of DNA by Zinc Fingers is proposed.

Exploiting antigenic diversity for vaccine design: the chlamydia ArtJ paradigm

We present an interdisciplinary approach that, by incorporating a range of experimental and computational techniques, allows the identification and characterization of functional/immunogenic domains. This approach has been applied to ArtJ, an arginine-binding protein whose orthologs in Chlamydiae trachomatis (CT ArtJ) and pneumoniae (CPn ArtJ) are shown to have different immunogenic properties despite a high sequence similarity (60% identity). We have solved the crystallographic structures of CT ArtJ and CPn ArtJ, which are found to display a type II transporter fold organized in two α-β domains with the arginine-binding region at their interface. Although ArtJ is considered to belong to the periplasm, we found that both domains contain regions exposed on the bacterial surface. Moreover, we show that recombinant ArtJ binds to epithelial cells in vitro, suggesting a role for ArtJ in host-cell adhesion during Chlamydia infection. Experimental epitope mapping and computational analysis of physicochemical determinants of antibody recognition revealed that immunogenic epitopes reside mainly in the terminal (D1) domain of both CPn and CT ArtJ, whereas the surface properties of the respective binding-prone regions appear sufficiently different to assume divergent immunogenic behavior. Neutralization assays revealed that sera raised against CPn ArtJ D1 partially reduce both CPn and CT infectivity in vitro, suggesting that functional antibodies directed against this domain may potentially impair chlamydial infectivity. These findings suggest that the approach presented here, combining functional and structure-based analyses of evolutionary-related antigens can be a valuable tool for the identification of cross-species immunogenic epitopes for vaccine development.

Predicting interaction sites from the energetics of isolated proteins: a new approach to epitope mapping

An increasing number of functional studies of proteins have shown that sequence and structural similarities alone may not be sufficient for reliable prediction of their interaction properties. This is particularly true for proteins recognizing specific antibodies, where the prediction of antibody-binding sites, called epitopes, has proven challenging. The antibody-binding properties of an antigen depend on its structure and related dynamics. Aiming to predict the antibody-binding regions of a protein, we investigate a new approach based on the integrated analysis of the dynamical and energetic properties of antigens, to identify nonoptimized, low-intensity energetic interaction networks in the protein structure isolated in solution. The method is based on the idea that recognition sites may correspond to localized regions with low-intensity energetic couplings with the rest of the protein, which allows them to undergo conformational changes, to be recognized by a binding partner, and to tolerate mutations with minimal energetic expense. Upon analyzing the results on isolated proteins and benchmarking against antibody complexes, it is found that the method successfully identifies binding sites located on the protein surface that are accessible to putative binding partners. The combination of dynamics and energetics can thus discriminate between epitopes and other substructures based only on physical properties. We discuss implications for vaccine design.

Computational study of the resistance shown by the subtype B/HIV-1 protease to currently known inhibitors

Human immunodeficiency virus type 1 protease (HIV-1 PR) is an essential enzyme in the HIV-1 life cycle. As such, this protein represents a major drug target in AIDS therapy, but emerging resistance to antiretroviral inhibitor cocktails, caused by high viral mutation rates, represents a significant challenge in AIDS treatment. Many mutations are not located within the active site or binding pocket, nor they do significantly modify the three-dimensional structural organization of the enzyme; hence, the mechanism(s) by which they alter inhibitor affinity for the protease remains uncertain. In this article, we present an all-atom computational analysis of the dynamic residue-residue coordination between the active site residues and the rest of the protein and of the energetic properties of different HIV-1 PR complexes. We analyze both the wild-type form and mutated forms that induce drug resistance. In particular, the results show differences between the wild type and the mutants in their mechanism of dynamic coordination, in the signal propagation between the active site residues and the rest of the protein, and in the energy networks responsible for the stabilization of the bound inhibitor conformation. Finally, we propose a dynamic and energetic explanation for HIV-1 protease drug resistance, and, through this model, we identify a possible new site that could be helpful in the design of a new family of HIV-1 PR allosteric inhibitors.

Human allelic variation: perspective from protein function, structure, and evolution

It is widely anticipated that the coming year will be marked by the complete characterization of DNA sequence of protein-coding regions of thousands of human individuals. A number of existing computational methods use comparative protein sequence analysis and analysis of protein structure to predict the functional effect of coding human alleles. Functional and structural analysis of coding allelic variants can inform various aspects of research on human genetic variation. In population and evolutionary genetics it helps estimate the strength of purifying selection against deleterious missense mutations and study the imprint of demographic history on deleterious genetic variation. In medical genetics it may assist in the interpretation of uncharacterized mutations in genes involved in monogenic and oligogenic diseases. It has a potential to facilitate medical sequencing studies searching for genes underlying Mendelian diseases or harboring rare alleles involved in complex traits.

Selecting sequences that fold into a defined 3D structure: A new approach for protein design based on molecular dynamics and energetics

The problem of finding amino acid sequences able to fold into a defined three-dimensional (3D) structure is at the basis of successful protein design efforts. Herein, we present the results of the application of a novel, all-atom molecular dynamics based, energy decomposition approach to the selection of sequences able to fold into a given 3D conformation. First, the energy decomposition approach is applied to natural sequences associated to a well-defined structure to identify the principal energetic coupling interactions necessary to stabilize it, defining the specific energetic signature for the fold. Then, several different sequences are threaded on the defined 3D structure and only those sequences whose energetic signature (pattern) is close to that of the natural sequence, according to a similarity criterion, are selected as able to populate the specific fold. Furthermore, it is possible to evaluate the fitness of a certain sequence for a fold by combining the information provided by the energetic signature to that contained in the contact map, which recapitulates the fold topology. The results show that the better fit between the energetic properties of a sequence and the topology corresponds to a better stabilization of the protein fold by that sequence. We applied this approach to a library of natural and artificial WW domain sequences, previously developed by the Ranganathan group, containing sequences that are experimentally known to be able and unable to fold into native structures. The results show that our approach can correctly identify 70% of the sequences known to populate the typical WW domain fold.

Computational protein design as a tool for fold recognition

Computationally designed protein sequences have been proposed as a basis to perform fold recognition and homology searching. To investigate this possibility, an automated procedure is used to completely redesign 24 SH3 proteins and 22 SH2 proteins. We use the experimental backbone coordinates as fixed templates in the folded state and a molecular mechanics model to compute the pairwise interaction energies between all sidechain types and conformations. Energy calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is then used to scan the sequence and conformational space for optimal solutions. We produced 200,000-450,000 sequences for each backbone template. The designed sequences ressemble moderately-distant, natural homologues of the initial templates, according to their identity scores and their similarity with respect to the Pfam sets of SH2 and SH3 domains. Standard homology detection tools document their native-like character: the Conserved Domain Database recognizes 61% (52%) of our low-energy sequences as SH3 (SH2) domains; the SUPERFAMILY, Hidden-Markov Model library recognizes 81% (84%). Conversely, position specific scoring matrices (PSSMs) derived from our designed sequences can be used to detect natural homologues in sequence databases. Within SwissProt, a set of natural SH3 PSSMs detects 772 SH3 domains, for example; our designed PSSMs detect 67% of these, plus one additional sequence and two false positives. If six amino acids involved in substrate binding (a selective pressure not accounted for in our design) are reset to their experimental types, then 77% of the experimental SH3 domains are detected. Results for the SH2 domains are similar. Several directions to improve the method further are discussed.

Methionine sulfoxides on prion protein Helix-3 switch on the alpha-fold destabilization required for conversion

Background

The conversion of the cellular prion protein (PrP^C) into the infectious form (PrP^Sc) is the key event in prion induced neurodegenerations. This process is believed to involve a multi-step conformational transition from an α-helical (PrP^C) form to a β-sheet-rich (PrP^Sc) state. In addition to the conformational difference, PrP^Sc exhibits as covalent signature the sulfoxidation of M213. To investigate whether such modification may play a role in the misfolding process we have studied the impact of methionine oxidation on the dynamics and energetics of the HuPrP(125–229) α-fold.

Methodology/Principal Findings

Using molecular dynamics simulation, essential dynamics, correlated motions and signal propagation analysis, we have found that substitution of the sulfur atom of M213 by a sulfoxide group impacts on the stability of the native state increasing the flexibility of regions preceding the site of the modification and perturbing the network of stabilizing interactions. Together, these changes favor the population of alternative states which maybe essential in the productive pathway of the pathogenic conversion. These changes are also observed when the sulfoxidation is placed at M206 and at both, M206 and M213.

Conclusions/Significance

Our results suggest that the sulfoxidation of Helix-3 methionines might be the switch for triggering the initial α-fold destabilization required for the productive pathogenic conversion.