Theory of protein folding: the energy landscape perspective

Propagating Error through Traveling-Wave Ion Mobility Calibration

Native mass spectrometry (MS) is used to elucidate the stoichiometry of protein complexes and quantify binding interactions by maintaining native-like, noncovalent interactions in the gas phase. However, ionization forces proteins into specific conformations, losing the solution-phase dynamics associated with solvated protein structures. Comparison of gas-phase structures to those in solution, or to other gas-phase ion populations, has many biological implications. For one, analyzing the variety of conformations that are maintained in the gas-phase can provide insight into a protein's solution-phase energy landscape. The gas-phase conformations of proteins and complexes can be investigated using ion mobility (IM) spectrometry. Specifically, drift tube (DT)-IM utilizes uniform electric fields to propel a population of gas-phase ions through a region containing a neutral gas. By measuring the mobility (K) of gas-phase ions, users are able to calculate an average momentum transfer cross section (^DTCCS), which provides structural information on the ion. Conversely, in traveling-wave ion mobility spectrometry (TWIMS), ^TWCCS values cannot be derived directly from an ion's mobility but must be determined following calibration. Though the required calibration adds uncertainty, it is common to report only an average and standard deviation of the calculated ^TWCCS, accounting for uncertainty associated with replicate measurements, which is a fraction of the overall uncertainty. Herein, we calibrate a TWIMS instrument and derive ^TWCCS_N2 and ^TWCCS_N2→He values for four proteins: cytochrome c, ubiquitin, apo-myoglobin, and holo-myoglobin. We show that compared to reporting only the standard deviation of ^TWCCS, propagating error through the calibration results in a significant increase in the number of calculated ^TWCCS values that agree within experimental error with literature values (^DTCCS). Incorporating this additional uncertainty provides a more thorough assessment of a protein ion's gas-phase conformations, enabling the structures sampled by native IM-MS to be compared against other reported structures, both experimental and computational.

Binding of SARS-COV-2 (COVID-19) and SARS-COV to human ACE2: Identifying binding sites and consequences on ACE2 stiffness

The SARS-CoV-2 coronavirus (COVID-19) that is causing the massive global pandemic exhibits similar human cell invasion mechanism as the coronavirus SARS-CoV, which had significantly lower fatalities. The cell membrane protein Angiotensin-converting enzyme 2 (ACE2) is the initiation point for both the coronavirus infections in humans. Here, we model the molecular interactions and mechanical properties of ACE2 with both SARS-CoV and COVID-19 spike protein receptor-binding domains (RBD). We report that the COVID-19 spike RBD interacts with ACE2 more strongly and at only two protein residues, as compared to multi-residue interaction of the SARS-CoV. Although both coronaviruses stiffen the ACE2, the impact of COVID-19 is six times larger, which points towards differences in the severity of the reported respiratory distress. The recognition of specific residues of ACE2 attachments to coronaviruses is important as the residues suggest potential sites of intervention to inhibit attachment and subsequent entry of the COVID-19 into human host cells

Small Neutral Crowding Solute Effects on Protein Folding Thermodynamic Stability and Kinetics

Molecular crowding is a ubiquitous phenomenon in biological systems, with significant consequences on protein folding and stability. Small compounds, such as the osmolyte trimethylamine N-oxide (TMAO), can also present similar effects. To analyze the effects arising from these solute-like molecules, we performed a series of crowded coarse-grained folding simulations. Two well-known proteins were chosen, CI2 and SH3, modeled by the alpha-carbon-structure-based model. In the simulations, the crowding molecules were represented by low-sized neutral atom beads in different concentrations. The results show that a low level of the volume fraction occupied by neutral agents can change protein stability and folding kinetics for the two systems. However, the kinetics were shown to be unaffected in their respective folding temperatures. The faster kinetics correlates with changes in the folding route for systems at the same temperature, where non-native contacts were shown to be relevant. The transition states of the two systems with and without crowders are similar. In the case of SH3, there are differences in the structuring of two strands, which may be associated with the increase in its folding rate, in addition to the destabilization of the denatured ensemble. The present study also detected a crossover in the thermodynamic stability behavior, previously observed experimentally and theoretically. As the temperature increases, crowders change from destabilizing to stabilizing agents.

Where Biology and Traditional Polymers Meet: The Potential of Associating Sequence-Defined Polymers for Materials Science

Polymers with precisely defined monomeric sequences present an exquisite tool for controlling material properties by harnessing both the robustness of synthetic polymers and the ability to tailor the inter- and intramolecular interactions so crucial to many biological materials. While polymer scientists traditionally synthesized and studied the physics of long molecules best described by their statistical nature, many biological polymers derive their highly tailored functions from precisely controlled sequences. Therefore, significant effort has been applied toward developing new methods of synthesizing, characterizing, and understanding the physics of non-natural sequence-defined polymers. This perspective considers the synergistic advantages that can be achieved via tailoring both precise sequence control and attributes of traditional polymers in a single system. Here, we focus on the potential of sequence-defined polymers in highly associating systems, with a focus on the unique properties, such as enhanced proton conductivity, that can be attained by incorporating sequence. In particular, we examine these materials as key model systems for studying previously unresolvable questions in polymer physics including the role of chain shape near interfaces and how to tailor compatibilization between dissimilar polymer blocks. Finally, we discuss the critical challenges-in particular, truly scalable synthetic approaches, characterization and modeling tools, and robust control and understanding of assembly pathways-that must be overcome for sequence-defined polymers to attain their potential and achieve ubiquity.

Controlling solvent quality by time: Self-avoiding sprints in nonequilibrium polymerization

A fundamental paradigm in polymer physics is that macromolecular conformations in equilibrium can be described by universal scaling laws, being key for structure, dynamics, and function of soft (biological) matter and in the materials sciences. Here we reveal that during diffusion-influenced, nonequilibrium chain-growth polymerization, scaling laws change qualitatively, in particular, the growing polymers exhibit a surprising self-avoiding walk behavior in poor and θ solvents. Our analysis, based on monomer-resolved, off-lattice reaction-diffusion computer simulations, demonstrates that this phenomenon is a result of (i) nonequilibrium monomer density depletion correlations around the active polymerization site, leading to a locally directed and self-avoiding growth, in conjunction with (ii) chain (Rouse) relaxation times larger than the competing polymerization reaction time. These intrinsic nonequilibrium mechanisms are facilitated by fast and persistent reaction-driven diffusion ("sprints") of the active site, with analogies to pseudochemotactic active Brownian particles. Our findings have implications for time-controlled structure formation in polymer processing, as in, e.g., reactive self-assembly, photocrosslinking, and three-dimensional printing.

Protein dynamics implications of the low- and high-temperature denaturation of myoglobin

We reinvestigate a simple model used in the literature concerning the thermodynamic analysis of protein cold denaturation. We derive an exact thermodynamic expression for cold denaturation and give a better approximation than exists in the literature for predicting cold denaturation temperatures in the two-state model. We discuss the "dark-side" implications of this work for previous temperature-dependent protein dynamics experiments and discuss microfluidic experimental technologies, which could explore the thermal stability range of proteins below the bulk freezing point of water.

Identification of an optimal foldability criterion to design misfolding resistant protein

Proteins achieve their functional, active, and operative three dimensional native structures by overcoming the possibility of being trapped in non-native energy minima present in the energy landscape. The enormous and intricate interactions that play an important role in protein folding also determine the stability of the proteins. The large number of stabilizing/destabilizing interactions makes proteins to be only marginally stable as compared to the other competing structures. Therefore, there are some possibilities that they become trapped in the non-native conformations and thus get misfolded. These misfolded proteins lead to several debilitating diseases. This work performs a comparative study of some existing foldability criteria in the computational design of misfold resistant protein sequences based on self-consistent mean field theory. The foldability criteria selected for this study are E_f, Δ, and Φ that are commonly used in protein design procedures to determine the most efficient foldability criterion for the design of misfolding resistant proteins. The results suggest that the foldability criterion Δ is significantly better in designing a funnel energy landscape stabilizing the target state. The results also suggest that inclusion of negative design features is important for designing misfolding resistant proteins, but more information about the non-native conformations in terms of Φ leads to worse results compared to even simple positive design. The sequences designed using Δ show better resistance to misfolding in the Monte Carlo simulations performed in the study.

Structural Lessons From the Mutant Proinsulin Syndrome

Insight into folding mechanisms of proinsulin has been provided by analysis of dominant diabetes-associated mutations in the human insulin gene (INS). Such mutations cause pancreatic β-cell dysfunction due to toxic misfolding of a mutant proinsulin and impairment in trans of wild-type insulin secretion. Anticipated by the "Akita" mouse (a classical model of monogenic diabetes mellitus; DM), this syndrome illustrates the paradigm endoreticulum (ER) stress leading to intracellular proteotoxicity. Diverse clinical mutations directly or indirectly perturb native disulfide pairing leading to protein misfolding and aberrant aggregation. Although most introduce or remove a cysteine (Cys; leading in either case to an unpaired thiol group), non-Cys-related mutations identify key determinants of folding efficiency. Studies of such mutations suggest that the hormone's evolution has been constrained not only by structure-function relationships, but also by the susceptibility of its single-chain precursor to impaired foldability. An intriguing hypothesis posits that INS overexpression in response to peripheral insulin resistance likewise leads to chronic ER stress and β-cell dysfunction in the natural history of non-syndromic Type 2 DM. Cryptic contributions of conserved residues to folding efficiency, as uncovered by rare genetic variants, define molecular links between biophysical principles and the emerging paradigm of Darwinian medicine: Biosynthesis of proinsulin at the edge of non-foldability provides a key determinant of "diabesity" as a pandemic disease of civilization.

Enumeration and comprehensive in-silico modeling of three-helix bundle structures composed of typical αα-hairpins

BACKGROUND

The design of protein structures from scratch requires special attention to the combination of the types and lengths of the secondary structures and the loops required to build highly designable backbone structure models. However, it is difficult to predict the combinations that result in globular and protein-like conformations without simulations. In this study, we used single-chain three-helix bundles as simple models of protein tertiary structures and sought to thoroughly investigate the conditions required to construct them, starting from the identification of the typical αα-hairpin motifs.

RESULTS

First, by statistical analysis of naturally occurring protein structures, we identified three αα-hairpins motifs that were specifically related to the left- and right-handedness of helix-helix packing. Second, specifying these αα-hairpins motifs as junctions, we performed sequence-independent backbone-building simulations to comparatively build single-chain three-helix bundle structures and identified the promising combinations of the length of the α-helix and αα-hairpins types that results in tight packing between the first and third α-helices. Third, using those single-chain three-helix bundle backbone structures as template structures, we designed amino acid sequences that were predicted to fold into the target topologies, which supports that the compact single-chain three-helix bundles structures that we sampled show sufficient quality to allow amino-acid sequence design.

CONCLUSION

The enumeration of the dominant subsets of possible backbone structures for small single-chain three-helical bundle topologies revealed that the compact foldable structures are discontinuously and sparsely distributed in the conformational space. Additionally, although the designs have not been experimentally validated in the present research, the comprehensive set of computational structural models generated also offers protein designers the opportunity to skip building similar structures by themselves and enables them to quickly focus on building specialized designs using the prebuilt structure models. The backbone and best design models in this study are publicly accessible from the following URL: https://doi.org/10.5281/zenodo.4321632 .

Interpreting a black box predictor to gain insights into early folding mechanisms

Protein folding and function are closely connected, but the exact mechanisms by which proteins fold remain elusive. Early folding residues (EFRs) are amino acids within a particular protein that induce the very first stages of the folding process. High-resolution EFR data are only available for few proteins, which has previously enabled the training of a protein sequence-based machine learning 'black box' predictor (EFoldMine). Such a black box approach does not allow a direct extraction of the 'early folding rules' embedded in the protein sequence, whilst such interpretation is essential to improve our understanding of how the folding process works. We here apply and investigate a novel 'grey box' approach to the prediction of EFRs from protein sequence to gain mechanistic residue-level insights into the sequence determinants of EFRs in proteins. We interpret the rule set for three datasets, a default set comprised of natural proteins, a scrambled set comprised of the scrambled default set sequences, and a set of de novo designed proteins. Finally, we relate these data to the secondary structure adopted in the folded protein and provide all information online via http://xefoldmine.bio2byte.be/, as a resource to help understand and steer early protein folding.

Dissecting Multiple Pathways in the Relaxation Dynamics of Helix <==> Coil Transitions with Optimum Dimensionality Reduction

Fast kinetic experiments with dramatically improved time resolution have contributed significantly to understanding the fundamental processes in protein folding pathways involving the formation of a-helices and b-hairpin, contact formation, and overall collapse of the peptide chain. Interpretation of experimental results through application of a simple statistical mechanical model was key to this understanding. Atomistic description of all events observed in the experimental findings was challenging. Recent advancements in theory, more sophisticated algorithms, and a true long-term trajectory made way for an atomically detailed description of kinetics, examining folding pathways, validating experimental results, and reporting new findings for a wide range of molecular processes in biophysical chemistry. This review describes how optimum dimensionality reduction theory can construct a simplified coarse-grained model with low dimensionality involving a kinetic matrix that captures novel insights into folding pathways. A set of metastable states derived from molecular dynamics analysis generate an optimally reduced dimensionality rate matrix following transition pathway analysis. Analysis of the actual long-term simulation trajectory extracts a relaxation time directly comparable to the experimental results and confirms the validity of the combined approach. The application of the theory is discussed and illustrated using several examples of helix <==> coil transition pathways. This paper focuses primarily on a combined approach of time-resolved experiments and long-term molecular dynamics simulation from our ongoing work.

First-passage times in protein folding: exploring the native-like states vs. overcoming the free energy barrier

Using a model β-hairpin protein as a representative example of simple two-state folders whose kinetics are uncomplicated by the presence of on- and off-pathway intermediates, it is studied how the search for the protein's native state among native-like states affects the folding kinetics. It is revealed that the first-passage time (FPT) distributions are essentially single-exponential not only for the times to overcome the free energy barrier between the unfolded and native-like states but also for the times to find the native state among the native-like ones. The FPT distributions of this type are observed through all studied two-state-like regimes of protein folding, varying from a regime close to two-state folding to a regime close to downhill folding. If the protein explores native-like states for a time much longer than the time to overcome the free energy barrier, which is characteristic of high temperatures, the resulting FPT distribution to reach the native state remains close to exponential but the mean FPT (MFPT) is determined not by the height of the free energy barrier but by the time to explore native-like states. In particular, the mean time to overcome the free energy barrier is in reasonable agreement with the Kramers rate formula and generally far shorter than the overall MFPT to reach the native state. The observed increase of the overall MFPT, as a result of longer exploration of native-like states, may lead to an overestimate of the height of the free energy barrier between the unfolded and folded states when it is calculated from the overall MFPT.

Gaussian accelerated molecular dynamics (GaMD): principles and applications

Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., "Gaussian approximation"). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica exchange GaMD (rex-GaMD) and replica exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new "selective GaMD" algorithms including the ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein-protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design.

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike's full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

The local topological free energy of proteins

Protein folding, the process by which proteins attain a 3-dimensional conformation necessary for their function, remains an important unsolved problem in biology. A major gap in our understanding is how local properties of proteins relate to their global properties. In this manuscript, we use the Writhe and Torsion to introduce a new local topological/geometrical free energy that can be associated to 4 consecutive amino acids along the protein backbone. By analyzing a culled protein dataset from the PDB, our results show that high local topological free energy conformations are independent of sequence and may be involved in the rate limiting step in protein folding. By analyzing a set of 2-state single domain proteins, we find that the total local topological free energy of these proteins correlates with the experimentally observed folding rates reported in Plaxco et al. (2000).

Understanding the Folding Mediated Assembly of the Bacteriophage MS2 Coat Protein Dimers

The capsids of RNA viruses such as MS2 are great models for studying protein self-assembly because they are made almost entirely of multiple copies of a single coat protein (CP). Although CP is the minimal repeating unit of the capsid, previous studies have shown that CP exists as a homodimer (CP2) even in an acid-disassembled system, indicating that CP2 is an obligate dimer. Here, we investigate the molecular basis of this obligate dimerization using coarse-grained structure-based models and molecular dynamics simulations. We find that, unlike monomeric proteins of similar size, CP populates a single partially folded ensemble whose "foldedness" is sensitive to denaturing conditions. In contrast, CP2 folds similarly to single-domain proteins populating only the folded and the unfolded ensembles, separated by a prominent folding free energy barrier. Several intramonomer contacts form early, but the CP2 folding barrier is crossed only when the intermonomer contacts are made. A dissection of the structure of CP2 through mutant folding simulations shows that the folding barrier arises both from the topology of CP and the interface contacts of CP2. Together, our results show that CP2 is an obligate dimer because of kinetic stability, that is, dimerization induces a folding barrier and that makes it difficult for proteins in the dimer minimum to partially unfold and access the monomeric state without completely unfolding. We discuss the advantages of this obligate dimerization in the context of dimer design and virus stability.

Nanopore Technology for the Application of Protein Detection

Protein is an important component of all the cells and tissues of the human body and is the material basis of life. Its content, sequence, and spatial structure have a great impact on proteomics and human biology. It can reflect the important information of normal or pathophysiological processes and promote the development of new diagnoses and treatment methods. However, the current techniques of proteomics for protein analysis are limited by chemical modifications, large sample sizes, or cumbersome operations. Solving this problem requires overcoming huge challenges. Nanopore single molecule detection technology overcomes this shortcoming. As a new sensing technology, it has the advantages of no labeling, high sensitivity, fast detection speed, real-time monitoring, and simple operation. It is widely used in gene sequencing, detection of peptides and proteins, markers and microorganisms, and other biomolecules and metal ions. Therefore, based on the advantages of novel nanopore single-molecule detection technology, its application to protein sequence detection and structure recognition has also been proposed and developed. In this paper, the application of nanopore single-molecule detection technology in protein detection in recent years is reviewed, and its development prospect is investigated.

SlyD Accelerates trans-to-cis Prolyl Isomerization in a Mechanosignaling Protein under Load

Prolyl isomerization is recognized as one of the key regulatory mechanisms, which plays a crucial role in cell signaling, ion channel gating, phage virus infection, and molecular timing. This isomerization is usually slow but often accelerated by an enzyme, called peptidyl-prolyl isomerase (PPIase). In the current project, we investigate using single-molecule force spectroscopy (SMFS) the impact of a bacterial PPIase, SlyD, on the cis-trans isomerization of the proline 2225 (P2225) in an isolated 20th domain of a cytoskeletal mechanosensing protein filamin-A (FlnA20). To explore the FlnA20-PPIase interaction, we have used multiple SMFS modes, like constant velocity, constant distance, and jumping trap experiments. In our previous study, we reported the unique nature of the P2225, which is conserved in all naturally occurring filamins and can slowly (minutes) interconvert between cis-trans isomers, in absence of any PPIase. Our current results show a staggering 25-fold acceleration of the trans-to-cis isomerization rate in the presence of saturating SlyD concentration (7.25 μM) compared to the unenzymatic condition. A SlyD concentration-dependent depletion of the trans isomeric lifetime was also observed. Additionally, we observed that SlyD stabilizes the cis-isomer in the native state of FlnA20 by ∼2 k_BT. This is the first single-molecule observation of the cis-trans isomerization catalysis by a PPIase in a mechanosensing protein.

Recognition of N6-Methyladenosine by the YTHDC1 YTH Domain Studied by Molecular Dynamics and NMR Spectroscopy: The Role of Hydration

The YTH domain of YTHDC1 belongs to a class of protein "readers", recognizing the N6-methyladenosine (m⁶A) chemical modification in mRNA. Static ensemble-averaged structures revealed details of N6-methyl recognition via a conserved aromatic cage. Here, we performed molecular dynamics (MD) simulations along with nuclear magnetic resonance (NMR) and isothermal titration calorimetry (ITC) to examine how dynamics and solvent interactions contribute to the m⁶A recognition and negative selectivity toward an unmethylated substrate. The structured water molecules surrounding the bound RNA and the methylated substrate's ability to exclude bulk water molecules contribute to the YTH domain's preference for m⁶A. Intrusions of bulk water deep into the binding pocket disrupt binding of unmethylated adenosine. The YTHDC1's preference for the 5'-Gm⁶A-3' motif is partially facilitated by a network of water-mediated interactions between the 2-amino group of the guanosine and residues in the m⁶A binding pocket. The 5'-Im⁶A-3' (where I is inosine) motif can be recognized too, but disruption of the water network lowers affinity. The D479A mutant also disrupts the water network and destabilizes m⁶A binding. Our interdisciplinary study of the YTHDC1 protein-RNA complex reveals an unusual physical mechanism by which solvent interactions contribute toward m⁶A recognition.

Proteome plasticity in response to persistent environmental change

Temperature is a variable component of the environment, and all organisms must deal with or adapt to temperature change. Acute temperature change activates cellular stress responses, resulting in refolding or removal of damaged proteins. However, how organisms adapt to long-term temperature change remains largely unexplored. Here we report that budding yeast responds to long-term high temperature challenge by switching from chaperone induction to reduction of temperature-sensitive proteins and re-localizing a portion of its proteome. Surprisingly, we also find that many proteins adopt an alternative conformation. Using Fet3p as an example, we find that the temperature-dependent conformational difference is accompanied by distinct thermostability, subcellular localization, and, importantly, cellular functions. We postulate that, in addition to the known mechanisms of adaptation, conformational plasticity allows some polypeptides to acquire new biophysical properties and functions when environmental change endures.

Multiscale modeling of genome organization with maximum entropy optimization

Three-dimensional (3D) organization of the human genome plays an essential role in all DNA-templated processes, including gene transcription, gene regulation, and DNA replication. Computational modeling can be an effective way of building high-resolution genome structures and improving our understanding of these molecular processes. However, it faces significant challenges as the human genome consists of over 6 × 109 base pairs, a system size that exceeds the capacity of traditional modeling approaches. In this perspective, we review the progress that has been made in modeling the human genome. Coarse-grained models parameterized to reproduce experimental data via the maximum entropy optimization algorithm serve as effective means to study genome organization at various length scales. They have provided insight into the principles of whole-genome organization and enabled de novo predictions of chromosome structures from epigenetic modifications. Applications of these models at a near-atomistic resolution further revealed physicochemical interactions that drive the phase separation of disordered proteins and dictate chromatin stability in situ. We conclude with an outlook on the opportunities and challenges in studying chromosome dynamics.

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances

Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

Structural Analysis of a Genetically Encoded FRET Biosensor by SAXS and MD Simulations

Inspired by the modular architecture of natural signaling proteins, ligand binding proteins are equipped with two fluorescent proteins (FPs) in order to obtain Förster resonance energy transfer (FRET)-based biosensors. Here, we investigated a glucose sensor where the donor and acceptor FPs were attached to a glucose binding protein using a variety of different linker sequences. For three resulting sensor constructs the corresponding glucose induced conformational changes were measured by small angle X-ray scattering (SAXS) and compared to recently published single molecule FRET results (Höfig et al., ACS Sensors, 2018). For one construct which exhibits a high change in energy transfer and a large change of the radius of gyration upon ligand binding, we performed coarse-grained molecular dynamics simulations for the ligand-free and the ligand-bound state. Our analysis indicates that a carefully designed attachment of the donor FP is crucial for the proper transfer of the glucose induced conformational change of the glucose binding protein into a well pronounced FRET signal change as measured in this sensor construct. Since the other FP (acceptor) does not experience such a glucose induced alteration, it becomes apparent that only one of the FPs needs to have a well-adjusted attachment to the glucose binding protein.

Side-Chain Polarity Modulates the Intrinsic Conformational Landscape of Model Dipeptides

The intrinsic conformational preferences of small peptides may provide additional insight into the thermodynamics and kinetics of protein folding. In this study, we explore the underlying energy landscapes of two model peptides, namely, Ac-Ala-NH₂ and Ac-Ser-NH₂, using geometry-optimization-based tools developed within the context of energy landscape theory. We analyze not only how side-chain polarity influences the structural preferences of the dipeptides, but also other emergent properties of the landscape, including heat capacity profiles, and kinetics of conformational rearrangements. The contrasting topographies of the free energy landscape agree with recent results from Fourier transform microwave spectroscopy experiments, where Ac-Ala-NH₂ was found to exist as a mixture of two conformers, while Ac-Ser-NH₂ remained structurally locked, despite exhibiting an apparently rich conformational landscape.

Liquid-Liquid Phase Separation of Short Histidine- and Tyrosine-Rich Peptides: Sequence Specificity and Molecular Topology

The increasing realization of the prevalence of liquid-liquid phase separation (LLPS) across multiple length scales of biological constructs, from intracellular membraneless organelles to extracellular load-bearing tissues, has raised intriguing questions about intermolecular interactions regulating LLPS at the atomic level. Squid-beak derived histidine (His)- and tyrosine (Tyr)-rich peptides (HBpeps) have recently emerged as suitable short model peptides to precisely assess the roles of peptide motifs and single residues on the phase behavior and material properties of microdroplets obtained by LLPS. In this study, by systematically introducing single mutations in an HBpep, we have identified specific sticker residues that attract peptide chains together. We find that His and Tyr residues located near the sequence termini drive phase separation, forming interaction nodes that stabilize microdroplets. Combining quantum chemistry simulations with NMR studies, we predict atomic-level bond geometries and uncover inter-residue supramolecular interactions governing LLPS. These results are subsequently used to propose possible topological arrangements of the peptide chains, which upon expansion can help explain the three-dimensional network of microdroplets. The stability of the proposed topologies carried out through all-atom molecular dynamics simulations predicts chain topologies that are more likely to stabilize the microdroplets. Overall, this study provides useful guidelines for the de novo design of peptide coacervates with tunable phase behavior and material properties. In addition, the analysis of nanoscale topologies may pave the way to understand how client molecules can be trapped within microdroplets, with direct implications for the encapsulation and controlled release of therapeutics for drug delivery applications.

Behavior of Proteins under Pressure from Experimental Pressure-Dependent Structures

Structure-based models are coarse-grained representations of the interactions responsible for the protein folding process. In their simplest form, they use only the native contact map of a given protein to predict the main features of its folding process by computer simulation. Given their limitations, these models are frequently complemented with sequence-dependent contributions or additional information. Specifically, to analyze the effect of pressure on the folding/unfolding transition, special forms of these interaction potentials are employed, which may a priori determine the outcome of the simulations. In this work, we have tried to keep the original simplicity of structure-based models. Therefore, we have used folded structures that have been experimentally determined at different pressures to define native contact maps and thus interactions dependent on pressure. Despite the apparently tiny structural differences induced by pressure, our simulation results provide different thermodynamic and kinetic behaviors, which roughly correspond to experimental observations (when there is a possible comparison) of two proteins used as benchmarks, hen egg-white lysozyme and dihydrofolate reductase. Therefore, this work shows the feasibility of using experimental native structures at different pressures to analyze the global effects of this physical property on the protein folding process.

On the Role of Normal Aging Processes in the Onset and Pathogenesis of Diseases Associated with the Abnormal Accumulation of Protein Aggregates

Aging is a prime systemic cause of various age-related diseases, in particular, proteinopathies. In fact, most diseases associated with protein misfolding are sporadic, and their incidence increases with aging. This review examines the process of protein aggregate formation, the toxicity of such aggregates, the organization of cellular systems involved in proteostasis, and the impact of protein aggregates on important cellular processes leading to proteinopathies. We also analyze how manifestations of aging (mitochondrial dysfunction, dysfunction of signaling systems, changes in the genome and epigenome) facilitate pathogenesis of various proteinopathies either directly, by increasing the propensity of key proteins for aggregation, or indirectly, through dysregulation of stress responses. Such analysis might help in outlining approaches for treating proteinopathies and extending healthy longevity.

Coarse-Grained Modeling of Multiple Pathways in Conformational Transitions of Multi-Domain Proteins

Large-scale conformational transitions in multi-domain proteins are often essential for their functions. To investigate the transitions, it is necessary to explore multiple potential pathways, which involve different intermediate structures. Here, we present a multi-basin (MB) coarse-grained (CG) structure-based Go̅ model for describing transitions in proteins with more than two moving domains. This model is an extension of our dual-basin Go̅ model in which system-dependent parameters are determined systematically using the multistate Bennett acceptance ratio method. In the MB Go̅ model for multi-domain proteins, we assume that intermediate structures may have partial inter-domain native contacts. This approach allows us to search multiple transition pathways that involve distinct intermediate structures using the CG molecular dynamics (MD) simulations. We apply this scheme to an enzyme, adenylate kinase (AdK), which has three major domains and can move along two different pathways. Using the optimized mixing parameters for each pathway, AdK shows frequent transitions between the Open, Closed, and the intermediate basins and samples a wide variety of conformations within each basin. The explored multiple transition pathways could be compared with experimental data and examined in more detail by atomistic MD simulations.

Structure and dynamics of responsive colloids with dynamical polydispersity

Dynamical polydispersity in single-particle properties, for example a fluctuating particle size, shape, charge density, etc, is intrinsic to responsive colloids (RCs), such as biomacromolecules or microgels, but is typically not resolved in coarse-grained mesoscale simulations. Here, we present Brownian dynamics simulations of suspensions of RCs modeling soft hydrogel colloids, for which the size of the individual particles is an explicitly resolved (Gaussian) degree of freedom and dynamically responds to the local interacting environment. We calculate the liquid structure, emergent size distributions, long-time diffusion, and property (size) relaxation kinetics for a wide range of densities and intrinsic property relaxation times in the canonical ensemble. Comparison to interesting reference cases, such as conventional polydisperse suspensions with a frozen parent distribution, or conventional monodisperse systems interacting with an effective pair potential for one fixed size, shows a significant spread in the structure and dynamics. The differences, most apparent in the high density regimes, are due to many-body correlations and the dynamical coupling between property and translation in RC systems, not explicitly accounted for in the conventional treatments. In particular, the translational diffusion in the RC systems is surprisingly close to the free (single RC) diffusion, mainly due to a cancellation of crowding and size compression effects. We show that an effective monodisperse pair potential can be constructed that describes the many-body correlations reasonably well by convoluting the RC pair potential with the density-dependent emergent size distributions and using a mean effective diffusion constant.

Enhanced sampling method with coarse graining of conformational space

The sampling of conformations in the molecular simulations for systems with complicated free energy landscapes is always difficult. Here, we report a method for enhanced sampling based on the coarse-graining of conformational space. In this method, the locally converged region of the conformational space is coarse-grained with its population characterized by the related average residence time and visiting number, and at the same time, the direct simulations inside it are eliminated. The detailed balance is satisfied by updating the visiting number and generating outgoing trajectories of this region. This kind of coarse-graining operation can be further carried out by merging all the neighboring regions which are already converged together. The global equilibrium is achieved when the local equilibrated regions cover all the interested areas of the landscape. We tested the method by applying it to two model potentials and one protein system with multiple-basin energy landscapes. The sampling efficiency is found to be enhanced by more than three orders of magnitude compared to conventional molecular simulations, and are comparable with other widely used enhanced sampling methods. In addition, the kinetic information can also be well captured. All these results demonstrate that our method can help to solve the sampling problems efficiently and precisely without applying high temperatures or biasing potentials.

The Zero-Order Loop in Apoazurin Modulates Folding Mechanism In Silico

Pseudomonas aeruginosa apoazurin (apo, without the copper cofactor) has a single disulfide bond between residues 3 and 26 and unfolds in a two-state reaction in vitro. The disulfide bond covalently connects the N-termini of β-strands 1 and 3; thereby, it creates a zero-order loop or a "cinch" that restricts conformational space. Covalent loops and threaded topologies are emerging as important structural elements in folded proteins and may be important for function. In order to understand the role of a zero-order loop in the folding process of a protein, here we used coarse-grained molecular dynamics (CGMD) simulations in silico to compare two variants of apoazurin: one named "loop" which contained the disulfide, and another named "open" in which the disulfide bond between residues 3 and 26 was removed. CGMD simulations were performed to probe the stability and unfolding pathway of the two apoazurin variants at different urea concentrations and temperatures. Our results show that the covalent loop plays a prominent role in the unfolding mechanism of apoazurin; its removal alters both the folding-transition state and the unfolded-state ensemble of conformations. We propose that modulation of azurin's folding landscape by the disulfide bridge may be related to both copper capturing and redox sensing.

Diabetes mellitus due to toxic misfolding of proinsulin variants

BACKGROUND

Dominant mutations in the human insulin gene (INS) lead to pancreatic β-cell dysfunction and diabetes mellitus (DM) due to toxic misfolding of a mutant proinsulin. Analogous to a classical mouse model of monogenic DM ("Akita"), this syndrome highlights the susceptibility of β-cells to endoreticulum (ER) stress due to protein misfolding and aberrant aggregation.

SCOPE OF REVIEW

Diverse clinical mutations directly or indirectly perturb native disulfide pairing. Whereas most introduce or remove a cysteine (Cys; leading in either case to an unpaired thiol group), non-Cys-related mutations identify key determinants of folding efficiency. Studies of such mutations suggest that the hormone's evolution has been constrained not only by structure-function relationships but also by the susceptibility of its single-chain precursor to impaired foldability. An intriguing hypothesis posits that INS overexpression in response to peripheral insulin resistance likewise leads to chronic ER stress and β-cell dysfunction in the natural history of nonsyndromic Type 2 DM.

MAJOR CONCLUSIONS

Cryptic contributions of conserved residues to folding efficiency, as uncovered by rare genetic variants, define molecular links between biophysical principles and the emerging paradigm of Darwinian medicine: Biosynthesis of proinsulin at the edge of nonfoldability provides a key determinant of "diabesity" as a pandemic disease of civilization.

Energy landscape remodeling mechanism of Hsp70-chaperone-accelerated protein folding

Hsp70 chaperone is one of the key protein machines responsible for the quality control of protein production in cells. Facilitating in vivo protein folding by counteracting misfolding and aggregation is the essence of its biological function. Although the allosteric cycle during its functional actions has been well characterized both experimentally and computationally, the mechanism by which Hsp70 assists protein folding is still not fully understood. In this work, we studied the Hsp70-mediated folding of model proteins with rugged energy landscape by using molecular simulations. Different from the canonical scenario of Hsp70 functioning, which assumes that folding of substrate proteins occurs spontaneously after releasing from chaperones, our results showed that the substrate protein remains in contacts with the chaperone during its folding process. The direct chaperone-substrate interactions in the open conformation of Hsp70 tend to shield the substrate sites prone to form non-native contacts, which therefore avoids the frustrated folding pathway, leading to a higher folding rate and less probability of misfolding. Our results suggest that in addition to the unfoldase and holdase functions widely addressed in previous studies, Hsp70 can facilitate the folding of its substrate proteins by remodeling the folding energy landscape and directing the folding processes, demonstrating the foldase scenario. These findings add new, to our knowledge, insights into the general molecular mechanisms of chaperone-mediated protein folding.

Molecular heterogeneity in aqueous cosolvent systems

Aqueous cosolvent systems (ACoSs) are mixtures of small polar molecules such as amides, alcohols, dimethyl sulfoxide, or ions in water. These liquids have been the focus of fundamental studies due to their complex intermolecular interactions as well as their broad applications in chemistry, medicine, and materials science. ACoSs are fully miscible at the macroscopic level but exhibit nanometer-scale spatial heterogeneity. ACoSs have recently received renewed attention within the chemical physics community as model systems to explore the relationship between intermolecular interactions and microscopic liquid-liquid phase separation. In this perspective, we provide an overview of ACoS spatial segregation, dynamic heterogeneity, and multiscale relaxation dynamics. We describe emerging approaches to characterize liquid microstructure, H-bond networks, and dynamics using modern experimental tools combined with molecular dynamics simulations and network-based analysis techniques.

Fuzziness and Frustration in the Energy Landscape of Protein Folding, Function, and Assembly

Are all protein interactions fully optimized? Do suboptimal interactions compromise specificity? What is the functional impact of frustration? Why does evolution not optimize some contacts? Proteins and their complexes are best described as ensembles of states populating an energy landscape. These ensembles vary in breadth from narrow ensembles clustered around a single average X-ray structure to broader ensembles encompassing a few different functional "taxonomic" states on to near continua of rapidly interconverting conformations, which are called "fuzzy" or even "intrinsically disordered". Here we aim to provide a comprehensive framework for confronting the structural and dynamical continuum of protein assemblies by combining the concepts of energetic frustration and interaction fuzziness. The diversity of the protein structural ensemble arises from the frustrated conflicts between the interactions that create the energy landscape. When frustration is minimal after folding, it results in a narrow ensemble, but residual frustrated interactions result in fuzzy ensembles, and this fuzziness allows a versatile repertoire of biological interactions. Here we discuss how fuzziness and frustration play off each other as proteins fold and assemble, viewing their significance from energetic, functional, and evolutionary perspectives.We demonstrate, in particular, that the common physical origin of both concepts is related to the ruggedness of the energy landscapes, intramolecular in the case of frustration and intermolecular in the case of fuzziness. Within this framework, we show that alternative sets of suboptimal contacts may encode specificity without achieving a single structural optimum. Thus, we demonstrate that structured complexes may not be optimized, and energetic frustration is realized via different sets of contacts leading to multiplicity of specific complexes. Furthermore, we propose that these suboptimal, frustrated, or fuzzy interactions are under evolutionary selection and expand the biological repertoire by providing a multiplicity of biological activities. In accord, we show that non-native interactions in folding or interaction landscapes can cooperate to generate diverse functional states, which are essential to facilitate adaptation to different cellular conditions. Thus, we propose that not fully optimized structures may actually be beneficial for biological activities of proteins via an alternative set of suboptimal interactions. The importance of such variability has not been recognized across different areas of biology.This account provides a modern view on folding, function, and assembly across the protein universe. The physical framework presented here is applicable to the structure and dynamics continuum of proteins and opens up new perspectives for drug design involving not fully structured, highly dynamic protein assemblies.

Photoresponsive Prion-Mimic Foldamer to Induce Controlled Protein Aggregation

Proteins reconfigure their 3D-structure, and consequently their function, under the control of specific molecular interactions that sense, process and transmit information from the surrounding environment. When this fundamental process is hampered, many pathologies occur as in the case of protein misfolding diseases. In this work, we follow the early steps of α-synuclein (aS) aggregation, a process associated with Parkinson's disease etiopathogenesis, that is promptly promoted by a light-mediated binding between the protein and a photoactive foldamer. The latter can switch between two conformations, one of which generates supramolecular fibrillar seeds that act as molecular templates able to induce a fast β-sheet transition for aS monomers that successively undergo fibrillar polymerization. The proposed method represents a powerful tool to study protein aggregation relevant to misfolding diseases in a controlled and inducible system.

Insights into colloidal nanoparticle-protein corona interactions for nanomedicine applications

Colloidal nanoparticles (NPs) have attracted significant attention due to their unique physicochemical properties suitable for diagnosing and treating different human diseases. Nevertheless, the successful implementation of NPs in medicine demands a proper understanding of their interactions with the different proteins found in biological fluids. Once introduced into the body, NPs are covered by a protein corona (PC) that determines the biological behavior of the NPs. The formation of the PC can eventually favor the rapid clearance of the NPs from the body before fulfilling the desired objective or lead to increased cytotoxicity. The PC nature varies as a function of the different repulsive and attractive forces that govern the NP-protein interaction and their colloidal stability. This review focuses on the phenomenon of PC formation on NPs from a physicochemical perspective, aiming to provide a general overview of this critical process. Main issues related to NP toxicity and clearance from the body as a result of protein adsorption are covered, including the most promising strategies to control PC formation and, thereby, ensure the successful application of NPs in nanomedicine.

An Evolutionary Profile Guided Greedy Parallel Replica-Exchange Monte Carlo Search Algorithm for Rapid Convergence in Protein Design

Protein design, also known as the inverse protein folding problem, is the identification of a protein sequence that folds into a target protein structure. Protein design is proved as an NP-hard problem. While researchers are working on designing heuristics with an emphasis on new scoring functions, we propose a replica-exchange Monte Carlo (REMC) search algorithm that ensures faster convergence using a greedy strategy. Using biological insights, we construct an evolutionary profile to encode the amino acid variability in different positions of the target protein from its structural homologs. The evolutionary profile guides the REMC search, and the greedy approach confirms appreciable exploration and exploitation of the sequence-structure fitness surface. We allow termination of a simulation trajectory once stagnant situation is detected. A series of sequence and structure level validations establish the goodness of our design. On a benchmark dataset, our algorithm reports an average root-mean-square deviation of 1.21Å between the target and the design proteins when modeled with an existing protein folding software. Besides, our algorithm assures 6.16 times overall speedup. In Molecular Dynamics simulations, we observe that four out of selected five design proteins report better to comparable stability to the corresponding target proteins.

Statistical Evidence for a Helical Nascent Chain

We investigate the hypothesis that protein folding is a kinetic, non-equilibrium process, in which the structure of the nascent chain is crucial. We compare actual amino acid frequencies in loops, α-helices and β-sheets with the frequencies that would arise in the absence of any amino acid bias for those secondary structures. The novel analysis suggests that while specific amino acids exist to drive the formation of loops and sheets, none stand out as drivers for α-helices. This favours the idea that the α-helix is the initial structure of most proteins before the folding process begins.

The concept of protein folding/unfolding and its impacts on human health

Proteins have evolved in specific 3D structures and play different functions in cells and determine various reactions and pathways. The newly synthesized amino acid chains once depart ribosome must crumple into three-dimensional structures so can be biologically active. This process of protein that makes a functional molecule is called protein folding. The protein folding is both a biological and a physicochemical process that depends on the sequence of it. In fact, this process occurs more complicated and in some cases and in exposure to some molecules like glucose (glycation), mistaken folding leads to amyloid structures and fatal disorders called conformational diseases. Such conditions are detected by the quality control system of the cell and these abnormal proteins undergo renovation or degradation. This scenario takes place by the chaperones, chaperonins, and Ubiquitin-proteasome complex. Understanding of protein folding mechanisms from different views including experimental and computational approaches has revealed some intermediate ensembles such as molten globule and has been subjected to biophysical and molecular biology attempts to know more about prevalent conformational diseases.

Binding Dynamics of Disordered Linker Histone H1 with a Nucleosomal Particle

Linker histone H1 is an essential regulatory protein for many critical biological processes, such as eukaryotic chromatin packaging and gene expression. Mis-regulation of H1s is commonly observed in tumor cells, where the balance between different H1 subtypes has been shown to alter the cancer phenotype. Consisting of a rigid globular domain and two highly charged terminal domains, H1 can bind to multiple sites on a nucleosomal particle to alter chromatin hierarchical condensation levels. In particular, the disordered H1 amino- and carboxyl-terminal domains (NTD/CTD) are believed to enhance this binding affinity, but their detailed dynamics and functions remain unclear. In this work, we used a coarse-grained computational model, AWSEM-DNA, to simulate the H1.0b-nucleosome complex, namely chromatosome. Our results demonstrate that H1 disordered domains restrict the dynamics and conformation of both globular H1 and linker DNA arms, resulting in a more compact and rigid chromatosome particle. Furthermore, we identified regions of H1 disordered domains that are tightly tethered to DNA near the entry-exit site. Overall, our study elucidates at near-atomic resolution the way the disordered linker histone H1 modulates nucleosome's structural preferences and conformational dynamics.

Protecting thermodynamic stability of protein: The basic paradigm against stress and unfolded protein response by osmolytes

Organic osmolytes are known to play important role in stress protection by stabilizing macromolecules and suppressing harmful effects on functional activity. There is existence of several reports in the literature regarding their effects on structural, functional and thermodynamic aspects of many enzymes and the interaction parameters with proteins have been explored. Osmolytes are compatible with enzyme function and therefore, can be accumulated up to several millimolar concentrations. From the thermodynamic point of view, osmolyte raises mid-point of thermal denaturation (T_m) of proteins while having no significant effect on ΔG_D° (free energy change at physiological condition). Unfavorable interaction with the peptide backbone due to preferential hydration is the major driving force for folding of unfolded polypeptide in presence of osmolyte. However, the thermodynamic basis of stress protection and origin of compatibility paradigm has been a debatable issue. In the present manuscript, we attempt to elaborate the origin of stress protection and compatibility paradigm of osmolytes based on the effect on thermodynamic stability of proteins. We also infer that protective effects of osmolytes on ΔG_D° (of proteins) could also indicate its potential involvement in unfolded protein response and overall stress biology on macromolecular level.

Investigating the folding mechanism of the N-terminal domain of ribosomal protein L9

Protein folding is a popular topic in the life science. However, due to the limited sampling ability of experiments and simulations, the general folding mechanism is not yet clear to us. In this work, we study the folding of the N-terminal domain of ribosomal protein L9 (NTL9) in detail by a mixing replica exchange molecular dynamics method. The simulation results are close to previous experimental observations. According to the Markov state model, the folding of the protein follows a nucleation-condensation path. Moreover, after the comparison to its 39-residue β-α-β motif, we find that the helix at the C-terminal has a great influence on the folding process of the intact protein, including the nucleation of the key residues in the transition state ensemble and the packing of the hydrophobic residues in the native state.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.

SWOTein: a structure-based approach to predict stability Strengths and Weaknesses of prOTEINs

MOTIVATION

Although structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes.

RESULTS

We developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale analysis of the different types of strengths and weaknesses demonstrated their complementarity and the enhancement of the information they provide. Moreover, a good average correlation was observed between predicted and experimental strengths and weaknesses obtained from native hydrogen exchange data. SWOTein application to three test cases further showed its suitability to predict and interpret strong and weak residues in the context of folding, conformational changes and protein-protein binding. In summary, SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes.

AVAILABILITY

The SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein/.

On topology and knotty entanglement in protein folding

We investigate aspects of topology in protein folding. For this we numerically simulate the temperature driven folding and unfolding of the slipknotted archaeal virus protein AFV3-109. Due to knottiness the (un)folding is a topological process, it engages the entire backbone in a collective fashion. Accordingly we introduce a topological approach to model the process. Our simulations reveal that the (un)folding of AFV3-109 slipknot proceeds through a folding intermediate that has the topology of a trefoil knot. We observe that the final slipknot causes a slight swelling of the folded AFV3-109 structure. We disclose the relative stability of the strands and helices during both the folding and unfolding processes. We confirm results from previous studies that pointed out that it can be very demanding to simulate the formation of knotty self-entanglement, and we explain how the problems are circumvented: The slipknotted AFV3-109 protein is a very slow folder with a topologically demanding pathway, which needs to be properly accounted for in a simulation description. When we either increase the relative stiffness of bending, or when we decrease the speed of ambient cooling, the rate of slipknot formation rapidly increases.