Interaction Networks in Protein Folding via Atomic-Resolution Experiments and Long-Time-Scale Molecular Dynamics Simulations

Physics-Based Protein Networks Might Recover Effectful Mutations─a Case Study on Cathepsin G

Molecular dynamics simulations have been remarkably effective for observing and analyzing structures and dynamics of proteins, with longer trajectories being computed every day. Still, often, relevant time scales are not observed. Adequately analyzing the generated trajectories can highlight the interesting areas within a protein such as mutation sites or allosteric hotspots, which might foreshadow dynamics untouched by the simulations. We employ a physics-based protein network and propose that such a network can adequately analyze the protein dynamics. The analysis is conducted on simulations of cathepsin G and neutrophil elastase, which are remarkably similar but with different specificities. However, a single mutation in cathepsin G recovers the specificity of neutrophil elastase. The physics-based network built on the interactions between residues instead of the distances can pinpoint the active triad in the proteins studied. Overall, the network seems to capture the structural behavior better than purely distance-based networks.

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau protein is a promising avenue for tauopathies therapy. To dissect the underlying aggregation mechanisms on Tau, we employ a set of molecular simulations and the Markov state model to determine the kinetics of ensemble of K18. K18 is the microtubule-binding domain of Tau protein and plays a vital role in the microtubule assembly, recycling processes, and amyloid fibril formation. Here, we efficiently explore the conformation of K18 with about 150 μs lifetimes in silico. Our results observe that all four repeat regions (R1-R4) are very dynamic, featuring frequent conformational conversion and lacking stable conformations, and the R2 region is more flexible than the R1, R3, and R4 regions. Additionally, it is worth noting that residues 300-310 in R2-R3 and residues 319-336 in R3 tend to form sheet structures, indicating that K18 has a broader functional role than individual repeat monomers. Finally, the simulations combined with Markov state models and deep learning reveal 5 key conformational states along the transition pathway and provide the information on the microsecond time scale interstate transition rates. Overall, this study offers significant insights into the molecular mechanism of Tau pathological aggregation and develops novel strategies for both securing tauopathies and advancing drug discovery.

Bovine serum albumin prevents human hemoglobin aggregation and retains its chaperone-like activity

This study investigates the ability of bovine serum albumin (BSA) to act as an extracellular chaperone (EC) on human hemoglobin (Hb) at a pH of 7.4. The best temperature for studying this behavior was determined by analyzing Hb's aggregation kinetics at multiple temperatures. 55 °C was chosen as the optimal temperature for forming Hb amyloids. BSA was then tested at various concentrations (20-100 μM) to assess its chaperone-like activity on Hb at 55 °C. At a concentration of 100 μM, BSA exhibits chaperone-like activity with a client protein:BSA ratio of 1:10. The high ratio implies that the chaperone activity of BSA is favored by the effects of macromolecular crowding. The results showed that BSA has the potential to inhibit Hb's dissociation into alpha and beta subunits and protein aggregation by inhibiting secondary nucleation. BSA also causes the depolymerization of fibrils over time. The results were validated using molecular docking and all-atom molecular dynamics simulations. MD analysis such as RMSD, RMSF, Rg, SASA, Hydrogen bond, PCA, Free energy landscape (FEL) revealed that the stability of hemoglobin is greater when it is bound to BSA compared to unbound state. The study suggests that BSA can potentially bind to Hb dimers and reduce excitonic interactions, which reduces Hb aggregation. These results are consistent with the aggregation kinetics experiments.Communicated by Ramaswamy H. Sarma.

Natural and Engineered Isoforms of the Inflammasome Adaptor ASC Form Noncovalent, pH-Responsive Hydrogels

The protein ASC polymerizes into intricate filament networks to assemble the inflammasome, a filamentous multiprotein complex that triggers the inflammatory response. ASC carries two Death Domains integrally involved in protein self-association for filament assembly. We have leveraged this behavior to create noncovalent, pH-responsive hydrogels of full-length, folded ASC by carefully controlling the pH as a critical factor in the polymerization process. We show that natural variants of ASC (ASC isoforms) involved in inflammasome regulation also undergo hydrogelation. To further demonstrate this general capability, we engineered proteins inspired by the ASC structure that also form hydrogels. We analyzed the structural network of the natural and engineered protein hydrogels using transmission and scanning electron microscopy and studied their viscoelastic behavior using shear rheology. Our results reveal one of the very few examples of hydrogels created by the self-assembly of globular proteins and domains in their native conformation and show that Death Domains can be used alone or as building blocks to engineer bioinspired hydrogels.

Residue-Dependent Transition Temperatures and Denaturant Midpoints in the Folding of a Multidomain Protein

As a consequence of the finite size of globular proteins, it is expected that there should be dispersions in the global melting temperature (T_m) and the denaturation midpoint (C_m). Thermodynamic considerations dictate that the dispersions, ΔT_m in T_m, and ΔC_m in C_m, should decrease with N, the number of residues in the protein. We performed coarse-grained simulations of the self-organized polymer (SOP) model of the multidomain protein adenylate kinase (ADK) with N = 214 in order to calculate thermal and denaturation unfolding titration curves. The results show that ΔT_m/T_m and ΔC_m/C_m are nonzero and follow the previously established ( Phys. Rev. Lett. 2004, 93, 268107) thermodynamic 1/N scaling for proteins accurately. For ADK, the dispersions are small (≈0.004), which implies that the melting temperature is more or less unique, which is unlike in BBL (N = 40) where ΔT_m/T_m ≈ 0.03.

Markov state models elucidate the stability of DNA influenced by the chiral 5S-Tg base

The static and dynamic structures of DNA duplexes affected by 5S-Tg (Tg, Thymine glycol) epimers were studied using MD simulations and Markov State Models (MSMs) analysis. The results show that the 5S,6S-Tg base caused little perturbation to the helix, and the base-flipping barrier was determined to be 4.4 kcal mol-1 through the use of enhanced sampling meta-eABF calculations, comparable to 5.4 kcal mol-1 of the corresponding thymine flipping. Two conformations with the different hydrogen bond structures between 5S,6R-Tg and A19 were identified in several independent MD trajectories. The 5S,6R-Tg:O6HO6•••N1:A19 hydrogen bond is present in the high-energy conformation displaying a clear helical distortion, and near barrier-free Tg base flipping. The low-energy conformation always maintains Watson-Crick base pairing between 5S,6R-Tg and A19, and 5S-Tg base flipping is accompanied by a small barrier of ca. 2.0 KBT (T = 298 K). The same conformations are observed in the MSMs analysis. Moreover, the transition path and metastable structures of the damaged base flipping are for the first time verified through MSMs analysis. The data clearly show that the epimers have completely different influence on the stability of the DNA duplex, thus implying different enzymatic mechanisms for DNA repair.

A modular approach to map out the conformational landscapes of unbound intrinsically disordered proteins

SignificanceIntrinsically disordered proteins have the unique ability to morph in response to multiple partners and thereby process sophisticated inputs and outputs. It is, however, a mystery whether their response is passive, that is, entirely determined by the partner, or controlled via an internal, yet unknown, folding mechanism. Here we introduce an approach to examine this key question and demonstrate its potential by dissecting the conformational properties of the partially disordered protein NCBD and obtaining important clues about how it performs its biological function.

Heterogeneity in Protein Folding and Unfolding Reactions

Proteins have dynamic structures that undergo chain motions on time scales spanning from picoseconds to seconds. Resolving the resultant conformational heterogeneity is essential for gaining accurate insight into fundamental mechanistic aspects of the protein folding reaction. The use of high-resolution structural probes, sensitive to population distributions, has begun to enable the resolution of site-specific conformational heterogeneity at different stages of the folding reaction. Different states populated during protein folding, including the unfolded state, collapsed intermediate states, and even the native state, are found to possess significant conformational heterogeneity. Heterogeneity in protein folding and unfolding reactions originates from the reduced cooperativity of various kinds of physicochemical interactions between various structural elements of a protein, and between a protein and solvent. Heterogeneity may arise because of functional or evolutionary constraints. Conformational substates within the unfolded state and the collapsed intermediates that exchange at rates slower than the subsequent folding steps give rise to heterogeneity on the protein folding pathways. Multiple folding pathways are likely to represent distinct sequences of structure formation. Insight into the nature of the energy barriers separating different conformational states populated during (un)folding can also be obtained by resolving heterogeneity.

NMR Analysis of Protein Folding Interaction Networks

Theory and experimental evidence unequivocally indicate that protein folding is far more complex than the two-state (all-or-none) model that is usually assumed in the analysis of folding experiments. Proteins tend to fold hierarchically by forming secondary structure elements, followed by supersecondary arrangements, and other intermediate states that ultimately adopt the native tertiary fold as a result of a delicate balance between interatomic interactions and entropic contributions. However, small proteins with simple folds typically follow downhill folding, characterized by very small energetic barriers (<3 RT) that allow multiple protein conformations to be populated along the folding path down the free energy landscape, reaching the native fold at the lowest energy level.Here we describe the use of solution-state nuclear magnetic resonance (NMR) for the analysis of protein folding interaction networks at atomic resolution. The assignment of NMR spectra acquired at different unfolding conditions provides hundreds of atomic unfolding curves that are analyzed to infer the network of folding interactions. The method is particularly useful to study small proteins that fold autonomously in the sub-millisecond timescale. The information obtained from the application of this method can potentially unveil the basic relationships between protein structure and folding.

Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations

We develop an approach to characterize the effects of gating by a multiconformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterize them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multistate gating theory from which we derive a gating factor γ that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open, and wide-open) and two inaccessible (closed and a newly identified, "parted") macrostate conformations. The parted conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because γ = 0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease─having a wide range of experimental association constants (∼10⁴-10¹⁰ M^-1 s^-1)─yields gated rate constants in the range of ∼0.5-5.7 × 10⁸ M^-1 s^-1. This indicates that, whereas the association rate of some inhibitors could be described by the model, for many inhibitors either subsequent conformational transitions or alternate binding mechanisms may be rate-limiting. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands.

The anatomy of unfolding of Yfh1 is revealed by site-specific fold stability analysis measured by 2D NMR spectroscopy

Most techniques allow detection of protein unfolding either by following the behaviour of single reporters or as an averaged all-or-none process. We recently added 2D NMR spectroscopy to the well-established techniques able to obtain information on the process of unfolding using resonances of residues in the hydrophobic core of a protein. Here, we questioned whether an analysis of the individual stability curves from each resonance could provide additional site-specific information. We used the Yfh1 protein that has the unique feature to undergo both cold and heat denaturation at temperatures above water freezing at low ionic strength. We show that stability curves inconsistent with the average NMR curve from hydrophobic core residues mainly comprise exposed outliers that do nevertheless provide precious information. By monitoring both cold and heat denaturation of individual residues we gain knowledge on the process of cold denaturation and convincingly demonstrate that the two unfolding processes are intrinsically different.

Investigating the trade-off between folding and function in a multidomain Y-family DNA polymerase

The way in which multidomain proteins fold has been a puzzling question for decades. Until now, the mechanisms and functions of domain interactions involved in multidomain protein folding have been obscure. Here, we develop structure-based models to investigate the folding and DNA-binding processes of the multidomain Y-family DNA polymerase IV (DPO4). We uncover shifts in the folding mechanism among ordered domain-wise folding, backtracking folding, and cooperative folding, modulated by interdomain interactions. These lead to ‘U-shaped’ DPO4 folding kinetics. We characterize the effects of interdomain flexibility on the promotion of DPO4–DNA (un)binding, which probably contributes to the ability of DPO4 to bypass DNA lesions, which is a known biological role of Y-family polymerases. We suggest that the native topology of DPO4 leads to a trade-off between fast, stable folding and tight functional DNA binding. Our approach provides an effective way to quantitatively correlate the roles of protein interactions in conformational dynamics at the multidomain level.

Lessons about Protein Folding and Binding from Archetypal Folds

The function of proteins as biological nanomachines relies on their ability to fold into complex 3D structures, bind selectively to partners, and undergo conformational changes on cue. The native functional structures, and the rates of interconversion between conformational states (folded-unfolded, bound-free), are all encoded in the physical chemistry of their amino acid sequence. However, despite extensive research over decades, this code has proven difficult to fully crack, in terms of both prediction and understanding the molecular mechanisms at play.Earlier work on single-domain proteins reported a commonality of slow rates (10^-2-10² s^-1) and simple behavior in both kinetic and thermodynamic unfolding experiments, which suggested the process was all-or-none and thereby analogous to a chemical reaction (e.g., A ⇄ B). In the absence of a first-principles pre-exponential factor for protein (un)folding dynamics, the rates could only be interpreted in relative terms, e.g., the changes induced by mutation, and hence, neither the height of nor the entropic contribution to the free energy barriers was known. The rates were also many orders of magnitude too slow for direct atomistic simulations, and the computational focus was on predicting rate changes induced by mutation via coarse grained simulations. However, even the effects of mutation proved to be strikingly homogeneous with all experimental data clustering at ∼1/3 of the free energy perturbation recovered on folding and ∼2/3 on unfolding.The implementation of ultrafast kinetic methods turned the field upside down because they allowed researchers to measure the time scales of elementary (un)folding motions, which set the pre-exponential factor for protein conformational transitions at ∼1 μs. In parallel, we and others set out to investigate the simplest possible protein structures capable of autonomous folding, which we defined as archetypal folds. The rationale was to recapitulate the hierarchical organization of protein structure, starting from the bottom up. The study of fold archetypes ended up opening new research avenues in protein (un)folding, but also making unexpected connections with the folding upon binding of intrinsically disordered proteins and suggesting their functioning as conformational rheostats.This Account describes our work on the kinetic, thermodynamic, mechanistic, and functional analysis of fold archetypes. We first discuss the kinetic studies, emphasizing their impact on our understanding of (un)folding rates, of barrierless (downhill) folding, and as benchmarks for atomistic simulations. We continue with the thermodynamic analysis, introducing the differential scanning calorimetry, multiprobe, and NMR approaches that we developed to dissect their gradual, minimally cooperative (un)folding transitions and to probe the underlying mechanisms with unprecedented detail. The last two sections cover single-molecule analyses and some recent, mostly computational, results on the exploration of possible biological and technological roles for the gradual conformational transitions of fold archetypes.

Downhill (Un)Folding Coupled to Binding as a Mechanism for Engineering Broadband Protein Conformational Transducers

Canonical proteins fold and function as conformational switches that toggle between their folded (on) and unfolded (off) states, a mechanism that also provides the basis for engineering transducers for biosensor applications. One of the limitations of such transducers, however, is their relatively narrow operational range, limited to ligand concentrations 20-fold below or above their C₅₀. Previously, we discovered that certain fast-folding proteins lose/gain structure gradually (downhill folding), which led us to postulate their operation as conformational rheostats capable of processing inputs/outputs in analog fashion. Conformational rheostats could make transducers with extended sensitivity. Here we investigate this hypothesis by engineering pH transducing into the naturally pH insensitive, downhill folding protein gpW. Particularly, we engineered histidine grafts into its hydrophobic core to induce unfolding via histidine ionization. We designed and tested the effects of ionization via computational modeling and studied experimentally the four most promising single grafts and two double grafts. All tested mutants become reversible pH transducers in the 4-9 range, and their response increases proportionally to how buried the histidine graft is. Importantly, the pH-dependent reversible (un)folding occurs in rheostatic fashion, so the engineered transducers can detect up to 6 orders of magnitude in [H⁺] for single grafts, and even more for double grafts. Our results demonstrate that downhill (un)folding coupled to binding produces the gradual, analog responses to the ligand (here H⁺) that are expected of conformational rheostats, and which make them a powerful mechanism for engineering transducers with sensitivity over many orders of magnitude in ligand concentration (broadband).

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.

Curvature and Torsion of Protein Main Chain as Local Order Parameters of Protein Unfolding

Thermal protein unfolding resembles a global (two-state) phase transition. At the local scale, protein unfolding is, however, heterogeneous and probe dependent. Here, we consider local order parameters defined by the local curvature and torsion of the protein main chain. Because chemical shifts (CS's) measured by NMR spectroscopy are extremely sensitive to the local atomic environment, CS has served as a local probe of thermal unfolding of proteins by varying the position of the atomic isotope along the amino acid sequence. The variation of the CS of each C^α atom along the sequence as a function of the temperature defines a local heat-induced denaturation curve. We demonstrate that these local heat-induced denaturation curves mirror the local protein nativeness defined by the free energy landscape of the local curvature and torsion of the protein main chain described by the C^α-C^α virtual bonds. Comparison between molecular dynamics simulations and CS data of the gpW protein demonstrates that some local native states defined by the local curvature and torsion of the main chain, mainly located in secondary structures, are coupled to each other whereas others, mainly located in flexible protein segments, are not. Consequently, CS's of some residues are faithful reporters of global protein unfolding, with heat-induced denaturation curves similar to the average global one, whereas other residues remain silent about the protein unfolded state. For the latter, the local deformation of the protein main chain, characterized by its local curvature and torsion, is not cooperatively coupled to global unfolding.

Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination

Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library.

Amyloid assembly is dominated by misregistered kinetic traps on an unbiased energy landscape

Atomistic description of protein fibril formation has been elusive due to the complexity and long time scales of the conformational search. Here, we develop a multiscale approach combining numerous atomistic simulations in explicit solvent to construct Markov State Models (MSMs) of fibril growth. The search for the in-register fully bound fibril state is modeled as a random walk on a rugged two-dimensional energy landscape defined by β-sheet alignment and hydrogen-bonding states, whereas transitions involving states without hydrogen bonds are derived from kinetic clustering. The reversible association/dissociation of an incoming peptide and overall growth kinetics are then computed from MSM simulations. This approach is applied to derive a parameter-free, comprehensive description of fibril elongation of Aβ_16-22 and how it is modulated by phenylalanine-to-cyclohexylalanine (CHA) mutations. The trajectories show an aggregation mechanism in which the peptide spends most of its time trapped in misregistered β-sheet states connected by weakly bound states twith short lifetimes. Our results recapitulate the experimental observation that mutants CHA19 and CHA1920 accelerate fibril elongation but have a relatively minor effect on the critical concentration for fibril growth. Importantly, the kinetic consequences of mutations arise from cumulative effects of perturbing the network of productive and nonproductive pathways of fibril growth. This is consistent with the expectation that nonfunctional states will not have evolved efficient folding pathways and, therefore, will require a random search of configuration space. This study highlights the importance of describing the complete energy landscape when studying the elongation mechanism and kinetics of protein fibrils.

Development of a Force Field for the Simulation of Single-Chain Proteins and Protein-Protein Complexes

The accuracy of atomistic physics-based force fields for the simulation of biological macromolecules has typically been benchmarked experimentally using biophysical data from simple, often single-chain systems. In the case of proteins, the careful refinement of force field parameters associated with torsion-angle potentials and the use of improved water models have enabled a great deal of progress toward the highly accurate simulation of such monomeric systems in both folded and, more recently, disordered states. In living organisms, however, proteins constantly interact with other macromolecules, such as proteins and nucleic acids, and these interactions are often essential for proper biological function. Here, we show that state-of-the-art force fields tuned to provide an accurate description of both ordered and disordered proteins can be limited in their ability to accurately describe protein-protein complexes. This observation prompted us to perform an extensive reparameterization of one variant of the Amber protein force field. Our objective involved refitting not only the parameters associated with torsion-angle potentials but also the parameters used to model nonbonded interactions, the specification of which is expected to be central to the accurate description of multicomponent systems. The resulting force field, which we call DES-Amber, allows for more accurate simulations of protein-protein complexes, while still providing a state-of-the-art description of both ordered and disordered single-chain proteins. Despite the improvements, calculated protein-protein association free energies still appear to deviate substantially from experiment, a result suggesting that more fundamental changes to the force field, such as the explicit treatment of polarization effects, may simultaneously further improve the modeling of single-chain proteins and protein-protein complexes.

Mosaic Cooperativity in Slow Polypeptide Topological Isomerization Revealed by Residue-Specific NMR Thermodynamic Analysis

Slow polypeptide conformational changes on time scales of >1 s are generally assumed to be highly cooperative two-state transitions, reflecting the high energy barrier. However, few experimental characterizations have tested the validity of this assumption. We performed residue-specific NMR thermodynamic analysis of the 27-residue lantibiotic peptide, nukacin ISK-1, to characterize the isomerization between two topological states on the second time scale. Unexpectedly, the thermal transition behaviors were distinct among peptide regions, indicating that the topological isomerization process is a mosaic of different degrees of cooperativity. The conformational change path between the two NMR structures was deduced by a targeted molecular dynamics simulation. The unique side-chain threading motions through the monosulfide rings are the structural basis of the high energy barrier, and the nonlocal interactions in the hydrophobic core are the structural basis of the cooperativity. Taken together, we provide an energetic description of the topological isomerization of nukacin ISK-1.

Confinement and Crowding Effects on Folding of a Multidomain Y-Family DNA Polymerase

Proteins in vivo endure highly various interactions from the luxuriant surrounding macromolecular cosolutes. Confinement and macromolecular crowding are the two major effects that should be considered while comparing the results of protein dynamics from in vitro to in vivo. However, efforts have been largely focused on single domain protein folding up to now, and the quantifications of the in vivo effects in terms of confinements and crowders on modulating the structure and dynamics as well as the physical understanding of the underlying mechanisms on multidomain protein folding are still challenging. Here we developed a topology-based model to investigate folding of a multidomain Y-family DNA polymerase (DPO4) within spherical confined space and in the presence of repulsive and attractive crowders. We uncovered that the entropic component of the thermodynamic driving force led by confinements and repulsive crowders increases the stability of folded states relative to the folding intermediates and unfolded states, while the enthalpic component of the thermodynamic driving force led by attractive crowders gives rise to the opposite effects with less stability. We found that the shapes of DPO4 conformations influenced by the confinements and the crowders are quite different even when only the entropic component of the thermodynamic driving force is considered. We uncovered that under all in vivo conditions, the folding cooperativity of DPO4 decreases compared to that in bulk. We showed that the loss of folding cooperativity can promote the sequential domain-wise folding, which was widely found in cotranslational multidomain protein folding, and effectively prohibit the backtracking led by topological frustrations during multidomain protein folding processes.

Dissecting C-H∙∙∙π and N-H∙∙∙π Interactions in Two Proteins Using a Combined Experimental and Computational Approach

C−H∙∙∙π and N−H∙∙∙π interactions can have an important contribution for protein stability. However, direct measurements of these interactions in proteins are rarely reported. In this work, we combined the mutant cycle experiments and molecular dynamics (MD) simulations to characterize C−H∙∙∙π and N−H∙∙∙π interactions and their cooperativity in two model proteins. It is shown that the average C−H∙∙∙π interaction per residue pair is ~ −0.5 kcal/mol while the N−H∙∙∙π interaction is slightly stronger. The triple mutant box measurement indicates that N−H∙∙∙π∙∙∙C−H∙∙∙π and C−H∙∙∙π∙∙∙C−H∙∙∙π can have a positive or negative cooperativity. MD simulations suggest that the cooperativity, depending on the local environment of the interactions, mainly arises from the geometric rearrangement when the nearby interaction is perturbed.

Learning dynamical information from static protein and sequencing data

Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. Although efficient algorithms for cluster detection in high-dimensional spaces have been developed over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein-folding transitions, gene-regulatory network motifs, and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations, and phylogenetic trees, respectively. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein-sequencing datasets, and future cryo-electron microscopy (cryo-EM) data.

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.

Dihedral Angle Calculations To Elucidate the Folding of Peptides through Its Main Mechanical Forces

This study reports a general method to calculate dihedral angles (φ and ψ) of a given amino acid sequence, focusing on potential energy and torque moment concepts. By defining these physical measures in relation to the chemical interactions that occur on each single amino acid residue within a peptide, we analyze the folding process as the result of main mechanical forces (MMFs) exerted in the specific amino acid chain of interest. As a proof of concept, Leu-enkephalin was initially used as a model peptide to carry out the theoretical study. Our data show agreement between calculated Leu-enkephalin backbone dihedral angles and the corresponding experimentally determined X-ray values. Hence, we used calcitonin to validate our MMF-based method on a larger peptide, i.e., 32 amino acid residues forming an α-helix. Through a similar approach (although simplified with regard to electrostatic interactions), the calculations for calcitonin also demonstrate a good agreement with experimental values. This study offers new opportunities to analyze peptides' amino acid sequences and to help in the prediction of how they must fold, assisting in the development of new computational techniques in the field.

Using correlated motions to determine sufficient sampling times for molecular dynamics

Here we present a time-dependent correlation method that provides insight into how long a system takes to grow into its equal-time (Pearson) correlation. We also show a usage of an extant time-lagged correlation method that indicates the time for parts of a system to become decorrelated, relative to equal-time correlation. Given a completed simulation (or set of simulations), these tools estimate (i) how long of a simulation of the same system would be sufficient to observe the same correlated motions, (ii) if patterns of observed correlated motions indicate events beyond the timescale of the simulation, and (iii) how long of a simulation is needed to observe these longer timescale events. We view this method as a decision-support tool that will aid researchers in determining necessary sampling times. In principle, this tool is extendable to any multidimensional time series data with a notion of correlated fluctuations; however, here we limit our discussion to data from molecular-dynamics simulations.

Protein Folding Cooperativity and Thermodynamic Barriers of the Simplest β-Sheet Fold: A Survey of WW Domains

Theory and experiments have shown that microsecond folding proteins exhibit characteristic thermodynamic properties that reflect the limited cooperativity of folding over marginal barriers (downhill folding). Those studies have mostly focused on proteins with large α-helical contents and small size, which tend to be the fastest folders. A key open question is whether such properties are also present in the fastest all-β proteins. We address this issue by investigating the unfolding thermodynamics of a collection of WW domains as representatives of the simplest β-sheet fold. WW domains are small microsecond folders, although they do not fold as fast as their α-helical counterparts. In previous work on the NEDD4-WW4 domain, we reported deviations from two-state thermodynamics that were less apparent and thus suggestive of an incipient downhill scenario. Here we investigate the unfolding thermodynamics of four other WW domains (NEDD4-WW3, YAP65-WW1(L30K), FBP11-WW1, and FBP11-WW2) by performing all of the thermodynamic tests for downhill folding that have been previously developed on α-helical proteins. This set of five WW domains shares low sequence identity and include examples from two specificity classes, thus providing a comprehensive survey. Thermodynamic analysis of the four new WW domains consistently reveals all of the properties of downhill folding equilibria, which are in all cases more marked than what we found before in NEDD4-WW4. Our results show that fast-folding all-β proteins do share limited cooperativity and gradual unfolding thermodynamics with fast α-helical proteins and suggest that the free energy barrier to folding of natural proteins is mostly determined by size and fold topology and much less by the specific amino acid sequence.

Molecular Modeling for Structural Insights Concerning the Activation Mechanisms of F1174L and R1275Q Mutations on Anaplastic Lymphoma Kinase

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase involved in various cancers. In its basal state, the structure of ALK is in an autoinhibitory form stabilized by its A-loop, which runs from the N-lobe to the C-lobe of the kinase. Specifically, the A-loop adopts an inhibitory pose with its proximal A-loop helix (αAL-helix) to anchor the αC-helix orientation in an inactive form in the N-lobe; the distal portion of the A-loop is packed against the C-lobe to block the peptide substrate from binding. Upon phosphorylation of the first A-loop tyrosine (Y1278), the αAL-helix unfolds; the distal A-loop detaches from the C-lobe and reveals the P+1 pocket that accommodates the residues immediately after their phosphorylation, and ALK is activated accordingly. Recently, two neuroblastoma mutants, F1174L and R1275Q, have been determined to cause ALK activation without phosphorylation on Y1278. Notably, F1174 is located on the C-terminus of the αC-helix and away from the A-loop, whereas R1275 sits on the αAL-helix. In this molecular modeling study, we investigated the structural impacts of F1174L and R1275Q that lead to the gain-of-function event. Wild-type ALK and ALK with phosphorylated Y1278 were also modeled for comparison. Our modeling suggests that the replacement of F1174 with a smaller residue, namely leucine, moves the αC-helix and αAL-helix into closer contact and further distorts the distal portion of the A-loop. In wild-type ALK, R1275 assumes the dual role of maintaining the αAL-helix–αC-helix interaction in an inactive form and securing αAL-helix conformation through the D1276–R1275 interaction. Accordingly, mutating R1275 to a glutamine reorients the αC-helix to an active form and deforms the entire A-loop. In both F1174L and R1275Q mutants, the A-loop rearranges itself to expose the P+1 pocket, and kinase activity resumes.

A critical comparison of coarse-grained structure-based approaches and atomic models of protein folding

Structure-based coarse-grained Gō-like models have been used extensively in deciphering protein folding mechanisms because of their simplicity and tractability. Meanwhile, explicit-solvent molecular dynamics (MD) simulations with physics-based all-atom force fields have been applied successfully to simulate folding/unfolding transitions for several small, fast-folding proteins. To explore the degree to which coarse-grained Gō-like models and their extensions to incorporate nonnative interactions are capable of producing folding processes similar to those in all-atom MD simulations, here we systematically compare the computed unfolded states, transition states, and transition paths obtained using coarse-grained models and all-atom explicit-solvent MD simulations. The conformations in the unfolded state in common Gō models are more extended, and are thus more in line with experiment, than those from all-atom MD simulations. Nevertheless, the structural features of transition states obtained by the two types of models are largely similar. In contrast, the folding transition paths are significantly more sensitive to modeling details. In particular, when common Gō-like models are augmented with nonnative interactions, the predicted dimensions of the unfolded conformations become similar to those computed using all-atom MD. With this connection, the large deviations of all-atom MD from simple diffusion theory are likely caused in part by the presence of significant nonnative effects in folding processes modelled by current atomic force fields. The ramifications of our findings to the application of coarse-grained modeling to more complex biomolecular systems are discussed.

Statistical Model To Decipher Protein Folding/Unfolding at a Local Scale

Protein folding/unfolding can be analyzed experimentally at a local scale by monitoring the physical properties of local probes as a function of the temperature, for example, the distance between fluorophores or the values of chemical shifts of backbone atoms. Here, the analytical Lifson-Roig model for the helix-coil transition is modified to analyze local thermal unfolding of the fast-folder W protein of bacteriophage lambda (gpW) simulated by all-atom molecular dynamics (MD) simulations in explicit solvent at 15 different temperatures. The protein structure is described by the coarse-grained dihedral angles (γ) and bond angles (θ) built between successive C^α-C^α virtual bonds. Each (γ,θ) pair serves as a local probe of protein unfolding. Local native/non-native states are defined for each pair of (γ,θ) angles by analyzing the free-energy landscapes Δ G(γ,θ) computed from MD trajectories. The three local elementary equilibrium constants of the model are extracted for each (γ,θ) pair along the sequence from MD simulations, and the model predictions are compared to the MD data. Using only the local equilibrium constants as an input, we show that the local denaturation curves computed from the model partition function fit their MD simulated counterparts in a satisfying manner without any adjustment. In the model and MD simulations, gpW unfolds gradually between 320 and 340 K, with an average native percentage decreasing from 0.8 (320 K) to 0.2 (340 K). In the prism of the model, there is no stable structure at the local scale in this 20 K unfolding temperature range. The enthalpy change upon local unfolding computed from the model and from MD trajectories suggests that the unfolded state between 320 and 340 K corresponds to a dynamical equilibrium between a large ensemble of constantly changing structures. The present results confirm the downhill unfolding of gpW, which does not obey a two-state global folding/unfolding model, and shed light on the interpretation of local denaturation curves.

Simultaneous determination of fast and slow dynamics in molecules using extreme CPMG relaxation dispersion experiments

Molecular dynamics play a significant role in how molecules perform their function. A critical method that provides information on dynamics, at the atomic level, is NMR-based relaxation dispersion (RD) experiments. RD experiments have been utilized for understanding multiple biological processes occurring at micro-to-millisecond time, such as enzyme catalysis, molecular recognition, ligand binding and protein folding. Here, we applied the recently developed high-power RD concept to the Carr-Purcell-Meiboom-Gill sequence (extreme CPMG; E-CPMG) for the simultaneous detection of fast and slow dynamics. Using a fast folding protein, gpW, we have shown that previously inaccessible kinetics can be accessed with the improved precision and efficiency of the measurement by using this experiment.

Site-specific time-resolved FRET reveals local variations in the unfolding mechanism in an apparently two-state protein unfolding transition

Using multi-site time-resolved FRET, it is shown that equilibrium unfolding of monellin is not only heterogeneous, but that the degree of non-cooperativity differs between the sole α-helix and different parts of the β-sheet.

Simple MD-based model for oxidative folding of peptides and proteins

Significant strides have been recently made to fold peptides and small proteins in silico using MD simulations. However, facilities are currently lacking to include disulfide bonding in the MD models of protein folding. To address this problem, we have developed a simple empirical protocol to model formation of disulfides, which is perturbation-free, retains the same speed as conventional MD simulations and allows one to control the reaction rate. The new protocol has been tested on 15-aminoacid peptide guanylin containing four cysteine residues; the net simulation time using Amber ff14SB force field was 61 μs. The resulting isomer distribution is in qualitative agreement with experiment, suggesting that oxidative folding of guanylin in vitro occurs under kinetic control. The highly stable conformation of the so-called isomer 2(B) has been obtained for full-length guanylin, which is significantly different from the poorly ordered structure of the truncated peptide PDB ID 1GNB. In addition, we have simulated oxidative folding of guanylin within the 94-aminoacid prohormone proguanylin. The obtained structure is in good agreement with the NMR coordinates 1O8R. The proposed modeling strategy can help to explore certain fundamental aspects of protein folding and is potentially relevant for manufacturing of synthetic peptides and recombinant proteins.

Selective inhibition mechanism of RVX-208 to the second bromodomain of bromo and extraterminal proteins: insight from microsecond molecular dynamics simulations

RVX-208 is a recently reported inhibitor of bromo and extraterminal (BET) family proteins (including BRD2-4 and BRDT) with selectivity for the second bromodomain (BD2), currently in phase III clinical trials. Despite of its promising antitumor activity, due to the conserved folds of the first and second bromodomains (BD1 and BD2), the detailed selectivity mechanism of RVX-208 towards BD2 over BD1 is still unknown. To elucidate selective inhibition mechanism of RVX-208 to BD2, microsecond molecular dynamics simulations were performed in this study for BRD2-BD1, BRD2-BD2 and BRD4-BD1 with and without RVX-208, respectively. Binding free energy calculations show that there exists strongest interaction between RVX-208 and BRD2-BD2. Leu383 and Asn429 are two most important residues of BRD2-BD2 for binding to RVX-208. Structural network analysis reveals that RVX-208 can shorten the communication path of ZA and BC loops in BRD2-BD2 pocket, making pocket more suitable to accommodate RVX-208. Additionally, different behaviors of His433 (Asp160 in BRD2-BD1) and Val435 (Ile162 in BRD2-BD1) in BRD2-BD2 are key factors responsible for selective binding of RVX-208 to BRD2-BD2. The proposed selective inhibition mechanism of RVX-208 to BRD2-BD2 can be helpful for rational design of novel selective inhibitors of the second bromodomain of BET family proteins.

Combining experimental and simulation data of molecular processes via augmented Markov models

Accurate mechanistic description of structural changes in biomolecules is an increasingly important topic in structural and chemical biology. Markov models have emerged as a powerful way to approximate the molecular kinetics of large biomolecules while keeping full structural resolution in a divide-and-conquer fashion. However, the accuracy of these models is limited by that of the force fields used to generate the underlying molecular dynamics (MD) simulation data. Whereas the quality of classical MD force fields has improved significantly in recent years, remaining errors in the Boltzmann weights are still on the order of a few [Formula: see text], which may lead to significant discrepancies when comparing to experimentally measured rates or state populations. Here we take the view that simulations using a sufficiently good force-field sample conformations that are valid but have inaccurate weights, yet these weights may be made accurate by incorporating experimental data a posteriori. To do so, we propose augmented Markov models (AMMs), an approach that combines concepts from probability theory and information theory to consistently treat systematic force-field error and statistical errors in simulation and experiment. Our results demonstrate that AMMs can reconcile conflicting results for protein mechanisms obtained by different force fields and correct for a wide range of stationary and dynamical observables even when only equilibrium measurements are incorporated into the estimation process. This approach constitutes a unique avenue to combine experiment and computation into integrative models of biomolecular structure and dynamics.

A self-consistent structural perturbation approach for determining the magnitude and extent of allosteric coupling in proteins

Elucidating the extent of energetic coupling between residues in single-domain proteins, which is a fundamental determinant of allostery, information transfer and folding cooperativity, has remained a grand challenge. While several sequence- and structure-based approaches have been proposed, a self-consistent description that is simultaneously compatible with unfolding thermodynamics is lacking. We recently developed a simple structural perturbation protocol that captures the changes in thermodynamic stabilities induced by point mutations within the protein interior. Here, we show that a fundamental residue-specific component of this perturbation approach, the coupling distance, is uniquely sensitive to the environment of a residue in the protein to a distance of ∼15 Å. With just the protein contact map as an input, we reproduce the extent of percolation of perturbations within the structure as observed in network analysis of intra-protein interactions, molecular dynamics simulations and NMR-observed changes in chemical shifts. Using this rapid protocol that relies on a single structure, we explain the results of statistical coupling analysis (SCA) that requires hundreds of sequences to identify functionally critical sectors, the propagation and dissipation of perturbations within proteins and the higher-order couplings deduced from detailed NMR experiments. Our results thus shed light on the possible mechanistic origins of signaling through the interaction network within proteins, the likely distance dependence of perturbations induced by ligands and post-translational modifications and the origins of folding cooperativity through many-body interactions.

When fast is better: protein folding fundamentals and mechanisms from ultrafast approaches

Protein folding research stalled for decades because conventional experiments indicated that proteins fold slowly and in single strokes, whereas theory predicted a complex interplay between dynamics and energetics resulting in myriad microscopic pathways. Ultrafast kinetic methods turned the field upside down by providing the means to probe fundamental aspects of folding, test theoretical predictions and benchmark simulations. Accordingly, experimentalists could measure the timescales for all relevant folding motions, determine the folding speed limit and confirm that folding barriers are entropic bottlenecks. Moreover, a catalogue of proteins that fold extremely fast (microseconds) could be identified. Such fast-folding proteins cross shallow free energy barriers or fold downhill, and thus unfold with minimal co-operativity (gradually). A new generation of thermodynamic methods has exploited this property to map folding landscapes, interaction networks and mechanisms at nearly atomic resolution. In parallel, modern molecular dynamics simulations have finally reached the timescales required to watch fast-folding proteins fold and unfold in silico. All of these findings have buttressed the fundamentals of protein folding predicted by theory, and are now offering the first glimpses at the underlying mechanisms. Fast folding appears to also have functional implications as recent results connect downhill folding with intrinsically disordered proteins, their complex binding modes and ability to moonlight. These connections suggest that the coupling between downhill (un)folding and binding enables such protein domains to operate analogically as conformational rheostats.

Accelerating physical simulations of proteins by leveraging external knowledge

It is challenging to compute structure-function relationships of proteins using molecular physics. The problem arises from the exponential scaling of the computational searching and sampling of large conformational spaces. This scaling challenge is not met by today's methods, such as Monte Carlo, simulated annealing, genetic algorithms, or molecular dynamics (MD) or its variants such as replica exchange. Such methods of searching for optimal states on complex probabalistic landscapes are referred to more broadly as Explore-and-Exploit (EE), including in contexts such as computational learning, games, industrial planning and modeling military strategies. Here we describe a Bayesian method, called MELD, that 'melds' together explore-and-exploit approaches with externally added information that can be vague, combinatoric, noisy, intuitive, heuristic, or from experimental data. MELD is shown to accelerate physical MD simulations when using experimental data to determine protein structures; for predicting protein structures by using heuristic directives; and when predicting binding affinities of proteins from limited information about the binding site. Such Guided Explore-and-Exploit approaches might also be useful beyond proteins and beyond molecular science.

Effectively explore metastable states of proteins by adaptive nonequilibrium driving simulations

Nonequilibrium drivings applied in molecular dynamics (MD) simulations can efficiently extend the visiting range of protein conformations, but might compel systems to go far away from equilibrium and thus mainly explore irrelevant conformations. Here we propose a general method, called adaptive nonequilibrium simulation (ANES), to automatically adjust the external driving on the fly, based on the feedback of the short-time average response of system. Thus, the ANES approximately keeps the local equilibrium but efficiently accelerates the global motion. We illustrate the capability of the ANES in highly efficiently exploring metastable conformations in the deca-alanine peptide and find that the 0.2-μs ANES approximately captures the important states and folding and unfolding pathways in the HP35 solution by comparing with the result of the recent 398-μs equilibrium MD simulation on Anton [S. Piana et al., Proc. Natl. Acad. Sci. USA 109, 17845 (2012)PNASA60027-842410.1073/pnas.1201811109].

Interplay between the folding mechanism and binding modes in folding coupled to binding processes

Proteins that fold upon binding to their partners exhibit complex binding behavior such as induced-fit. Using molecular simulations we find that the ability to bind via induced-fit requires the disordered protein to fold downhill. Thus induced-fit appears as a manifestation of a conformational rheostat.

Toward a quantitative description of microscopic pathway heterogeneity in protein folding

Experimentally consistent statistical modeling of protein folding thermodynamics reveals unprecedented complexity with numerous parallel folding routes in five different proteins.

Hierarchical Time-Lagged Independent Component Analysis: Computing Slow Modes and Reaction Coordinates for Large Molecular Systems

Analysis of molecular dynamics, for example using Markov models, often requires the identification of order parameters that are good indicators of the rare events, i.e. good reaction coordinates. Recently, it has been shown that the time-lagged independent component analysis (TICA) finds the linear combinations of input coordinates that optimally represent the slow kinetic modes and may serve in order to define reaction coordinates between the metastable states of the molecular system. A limitation of the method is that both computing time and memory requirements scale with the square of the number of input features. For large protein systems, this exacerbates the use of extensive feature sets such as the distances between all pairs of residues or even heavy atoms. Here we derive a hierarchical TICA (hTICA) method that approximates the full TICA solution by a hierarchical, divide-and-conquer calculation. By using hTICA on distances between heavy atoms we identify previously unknown relaxation processes in the bovine pancreatic trypsin inhibitor.

How cooperative are protein folding and unfolding transitions?

A thermodynamically and kinetically simple picture of protein folding envisages only two states, native (N) and unfolded (U), separated by a single activation free energy barrier, and interconverting by cooperative two-state transitions. The folding/unfolding transitions of many proteins occur, however, in multiple discrete steps associated with the formation of intermediates, which is indicative of reduced cooperativity. Furthermore, much advancement in experimental and computational approaches has demonstrated entirely non-cooperative (gradual) transitions via a continuum of states and a multitude of small energetic barriers between the N and U states of some proteins. These findings have been instrumental towards providing a structural rationale for cooperative versus noncooperative transitions, based on the coupling between interaction networks in proteins. The cooperativity inherent in a folding/unfolding reaction appears to be context dependent, and can be tuned via experimental conditions which change the stabilities of N and U. The evolution of cooperativity in protein folding transitions is linked closely to the evolution of function as well as the aggregation propensity of the protein. A large activation energy barrier in a fully cooperative transition can provide the kinetic control required to prevent the accumulation of partially unfolded forms, which may promote aggregation. Nevertheless, increasing evidence for barrier-less "downhill" folding, as well as for continuous "uphill" unfolding transitions, indicate that gradual non-cooperative processes may be ubiquitous features on the free energy landscape of protein folding.

Protein States as Symmetry Transitions in the Correlation Matrices

Over the last few years, there has been significant progress in the knowledge on protein folding. However, some aspects of protein folding still need further attention. One of these is the exact relationship between the folded and unfolded states and the differences between them. Whereas the folded state is well known, at least from a structural point of view (just think of the thousands of structures in online databases), the unfolded state is more elusive. Also, these are dynamic states of matter, and this aspect cannot be overlooked. Molecular dynamics-derived correlation matrices are an invaluable source of information on the protein dynamics. Here, bulk eigenvalue spectra of the correlation matrices obtained from the Trp-cage dynamics in the folded and unfolded states have been analyzed. The associated modes represent localized vibrations and are significantly affected by the fine details of the structure and interactions. Therefore, these bulk modes can be used as probes of the protein local dynamics in different states. The results of these analyses show that the correlation matrices describing the folded and unfolded dynamics belong to different symmetry classes. This finding provides new support to the phase-transition models of protein folding.

The Important Role of Halogen Bond in Substrate Selectivity of Enzymatic Catalysis

The use of halogen bond is widespread in drug discovery, design, and clinical trials, but is overlooked in drug biosynthesis. Here, the role of halogen bond in the nitrilase-catalyzed synthesis of ortho-, meta-, and para-chlorophenylacetic acid was investigated. Different distributions of halogen bond induced changes of substrate binding conformation and affected substrate selectivity. By engineering the halogen interaction, the substrate selectivity of the enzyme changed, with the implication that halogen bond plays an important role in biosynthesis and should be used as an efficient and reliable tool in enzymatic drug synthesis.

More than a look into a crystal ball: protein structure elucidation guided by molecular dynamics simulations

The 'form follows function' principle implies that a structural determination of protein structures is indispensable to understand proteins in their biological roles. However, experimental methods still show shortcomings in the description of the dynamic properties of proteins. Therefore, molecular dynamics (MD) simulations represent an essential tool for structural biology to investigate proteins as flexible and dynamic entities. Here, we will give an overview on the impact of MD simulations on structural investigations, including studies that aim at a prediction of protein-folding pathways, protein-assembly processes and the sampling of conformational space by computational means.