Impact of Nonnative Interactions on the Binding Kinetics of Intrinsically Disordered p53 with MDM2: Insights from All-Atom Simulation and Markov State Model Analysis.

Intrinsically disordered proteins (IDPs) lack a well-defined tertiary structure but are essential players in various biological processes. Their ability to undergo a disorder-to-order transition upon binding to their partners, known as the folding-upon-binding process, is crucial for their function. One classical example is the intrinsically disordered transactivation domain (TAD) of the tumor suppressor protein p53, which quickly forms a structured α-helix after binding to its partner MDM2, with clinical significance for cancer treatment. However, the contribution of nonnative interactions between the IDP and its partner to the rapid binding kinetics, as well as their interplay with native interactions, is not well understood at the atomic level. Here, we used molecular dynamics simulation and Markov state model (MSM) analysis to study the folding-upon-binding mechanism between p53-TAD and MDM2. Our results suggest that the system progresses from the nascent encounter complex to the well-structured encounter complex and finally reaches the native complex, following an induced-fit mechanism. We found that nonnative hydrophobic and hydrogen bond interactions, combined with native interactions, effectively stabilize the nascent and well-structured encounter complexes. Among the nonnative interactions, Leu25p53-Leu54MDM2 and Leu25p53-Phe55MDM2 are particularly noteworthy, as their interaction strength is close to the optimum. Evidently, strengthening or weakening these interactions could both adversely affect the binding kinetics. Overall, our findings suggest that nonnative interactions are evolutionarily optimized to accelerate the binding kinetics of IDPs in conjunction with native interactions.

Assessment of Disordered Linker Predictions in the CAID2 Experiment.

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD's predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios.

Folding-upon-binding pathways of an intrinsically disordered protein from a deep Markov state model.

A central challenge in the study of intrinsically disordered proteins is the characterization of the mechanisms by which they bind their physiological interaction partners. Here, we utilize a deep learning-based Markov state modeling approach to characterize the folding-upon-binding pathways observed in a long timescale molecular dynamics simulation of a disordered region of the measles virus nucleoprotein NTAIL reversibly binding the X domain of the measles virus phosphoprotein complex. We find that folding-upon-binding predominantly occurs via two distinct encounter complexes that are differentiated by the binding orientation, helical content, and conformational heterogeneity of NTAIL. We observe that folding-upon-binding predominantly proceeds through a multi-step induced fit mechanism with several intermediates and do not find evidence for the existence of canonical conformational selection pathways. We observe four kinetically separated native-like bound states that interconvert on timescales of eighty to five hundred nanoseconds. These bound states share a core set of native intermolecular contacts and stable NTAIL helices and are differentiated by a sequential formation of native and non-native contacts and additional helical turns. Our analyses provide an atomic resolution structural description of intermediate states in a folding-upon-binding pathway and elucidate the nature of the kinetic barriers between metastable states in a dynamic and heterogenous, or "fuzzy", protein complex.

Critical assessment of protein intrinsic disorder prediction (CAID) - Results of round 2.

Protein intrinsic disorder (ID) is a complex and context-dependent phenomenon that covers a continuum between fully disordered states and folded states with long dynamic regions. The lack of a ground truth that fits all ID flavors and the potential for order-to-disorder transitions depending on specific conditions makes ID prediction challenging. The CAID2 challenge aimed to evaluate the performance of different prediction methods across different benchmarks, leveraging the annotation provided by the DisProt database, which stores the coordinates of ID regions when there is experimental evidence in the literature. The CAID2 challenge demonstrated varying performance of different prediction methods across different benchmarks, highlighting the need for continued development of more versatile and efficient prediction software. Depending on the application, researchers may need to balance performance with execution time when selecting a predictor. Methods based on AlphaFold2 seem to be good ID predictors but they are better at detecting absence of order rather than ID regions as defined in DisProt. The CAID2 predictors can be freely used through the CAID Prediction Portal, and CAID has been integrated into OpenEBench, which will become the official platform for running future CAID challenges.

Caregiver-based perception of disease burden in Schaaf-Yang syndrome.

BACKGROUND: Schaaf-Yang syndrome (SYS) is a neurodevelopmental disorder caused by truncating variants in the paternally expressed MAGEL2 gene in the Prader-Willi syndrome-region on chromosome 15q. In addition to hypotonia and intellectual disability, individuals with SYS are frequently affected by neonatal contractures and autism spectrum disorder. In this study, we focus on the burden of disease on patients and their families for the first time. METHODS: Based on the online SYS Patient Voices Survey the perspective of 81 primary caregivers on SYS was assessed. RESULTS: The perceived severity of muscular and developmental manifestations dominated the evaluation of the phenotype in early childhood, while behavioral issues were considered more impactful later in life. Importantly, an apprehension toward symptoms with a later onset was observed in caregivers of younger children. Available therapeutic options, while mostly effective, did not sufficiently alleviate the total burden of disease. Overall, parents stated that caring for an individual with SYS was very challenging, affecting their daily lives and long-term planning. CONCLUSION: Our study demonstrates the necessity for treatments that, adapted to age and in accordance with the caregivers' prioritization, improve the patients' medical condition and thus facilitate their and their families' social participation.

Critical Assessment of Self-Consistency Checks in the All-Atom Molecular Dynamics Simulation of Intrinsically Disordered Proteins.

All atom simulations can be used to quantify conformational properties of Intrinsically Disordered Proteins (IDP). However, simulations must satisfy convergence checks to ensure observables computed from simulation are reliable and reproducible. While absolute convergence is purely a theoretical concept requiring infinitely long simulation, a more practical, yet rigorous, approach is to impose Self Consistency Checks (SCCs) to gain confidence in the simulated data. Currently there is no study of SCCs in IDPs, unlike their folded counterparts. In this paper, we introduce different criteria for self-consistency checks for IDPs. Next, we impose these SCCs to critically assess the performance of different simulation protocols using the N terminal domain of HIV Integrase and the linker region of SARS-CoV-2 Nucleoprotein as two model IDPs. All simulation protocols begin with all-atom implicit solvent Monte Carlo (MC) simulation and subsequent clustering of MC generated conformations to create the representative structures of the IDPs. These representative structures serve as the initial structure for subsequent molecular dynamics (MD) runs with explicit solvent. We conclude that generating multiple short (∼3 µs) MD simulation trajectories─all starting from the most representative MC generated conformation─and merging them is the protocol of choice due to (i) its ability to satisfy multiple SCCs, (ii) consistently reproducing experimental data, and (iii) the efficiency of running independent trajectories in parallel by harnessing multiple cores available in modern GPU clusters. Running one long trajectory (greater than 20 µs) can also satisfy the first two criteria but is less desirable due to prohibitive computation time. These findings help resolve the challenge of identifying a usable starting configuration, provide an objective measure of SCC, and establish rigorous criteria to determine the minimum length (for one long simulation) or number of trajectories needed in all-atom simulation of IDPs.

Structural assessment of the full-length wild-type tumor suppressor protein p53 by mass spectrometry-guided computational modeling.

The tetrameric tumor suppressor p53 represents a great challenge for 3D-structural analysis due to its high degree of intrinsic disorder (ca. 40%). We aim to shed light on the structural and functional roles of p53's C-terminal region in full-length, wild-type human p53 tetramer and their importance for DNA binding. For this, we employed complementary techniques of structural mass spectrometry (MS) in an integrated approach with computational modeling. Our results show no major conformational differences in p53 between DNA-bound and DNA-free states, but reveal a substantial compaction of p53's C-terminal region. This supports the proposed mechanism of unspecific DNA binding to the C-terminal region of p53 prior to transcription initiation by specific DNA binding to the core domain of p53. The synergies between complementary structural MS techniques and computational modeling as pursued in our integrative approach is envisioned to serve as general strategy for studying intrinsically disordered proteins (IDPs) and intrinsically disordered region (IDRs).

Exploring the aggregation of amyloid-ß 42 through Monte Carlo simulations.

Coarse-grained Monte Carlo simulations are performed for a disordered protein, amyloid-ß 42 to identify the interactions and understand the mechanism of its aggregation. A statistical potential is developed from a selected dataset of intrinsically disordered proteins, which accounts for the respective contributions of the bonded and non-bonded potentials. While, the bonded potential comprises the bond, bend, and dihedral constraints, the nonbonded interactions include van der Waals interactions, hydrogen bonds, and the two-body potential. The two-body potential captures the features of both hydrophobic and electrostatic interactions that brings the chains at a contact distance, while the repulsive van der Waals interactions prevent them from a collapse. Increased two-body hydrophobic interactions facilitate the formation of amorphous aggregates rather than the fibrillar ones. The formation of aggregates is validated from the interchain distances, and the total energy of the system. The aggregate is structurally characterized by the root-mean-square deviation, root-mean-square fluctuation and the radius of gyration. The aggregates are characterized by a decrease in SASA, an increase in the non-local interactions and a distinct free energy minimum relative to that of the monomeric state of amyloid-ß 42. The hydrophobic residues help in nucleation, while the charged residues help in oligomerization and aggregation.

Assessment of models for calculating the hydrodynamic radius of intrinsically disordered proteins.

Diffusion measurements by pulsed-field gradient NMR and fluorescence correlation spectroscopy can be used to probe the hydrodynamic radius of proteins, which contains information about the overall dimension of a protein in solution. The comparison of this value with structural models of intrinsically disordered proteins is nonetheless impaired by the uncertainty of the accuracy of the methods for computing the hydrodynamic radius from atomic coordinates. To tackle this issue, we here build conformational ensembles of 11 intrinsically disordered proteins that we ensure are in agreement with measurements of compaction by small-angle x-ray scattering. We then use these ensembles to identify the forward model that more closely fits the radii derived from pulsed-field gradient NMR diffusion experiments. Of the models we examined, we find that the Kirkwood-Riseman equation provides the best description of the hydrodynamic radius probed by pulsed-field gradient NMR experiments. While some minor discrepancies remain, our results enable better use of measurements of the hydrodynamic radius in integrative modeling and for force field benchmarking and parameterization.

Pan-cancer assessment of mutational landscape in intrinsically disordered hotspots reveals potential driver genes.

Large-scale cancer genome sequencing has enabled the catalogs of somatic mutations; however, the mutational impact on intrinsically disordered protein regions (IDRs) has not been systematically investigated to date. Here, we comprehensively characterized the mutational landscapes of IDRs and found that IDRs have higher mutation frequencies across diverse cancers. We thus developed a computational method, ROI-Driver, to identify putative driver genes enriching IDR and domain hotspots in cancer. Numerous well-known cancer-related oncogenes or tumor suppressors that play important roles in cancer signaling regulation, development and immune response were identified at a higher resolution. In particular, the incorporation of IDR structures helps in the identification of novel potential driver genes that play central roles in human protein-protein interaction networks. Interestingly, we found that the putative driver genes with IDR hotspots were significantly enriched with predicted phase separation propensities, suggesting that IDR mutations disrupt phase separation in key cellular pathways. We also identified an appreciable number of clinically relevant genes enriching IDR mutational hotspots that exhibited differential expression patterns and are associated with cancer patient survival. In summary, combinations of mutational effects on IDRs significantly increase the sensitivity of driver detection and are likely to open new therapeutic avenues for various cancers.

Eficacia y seguridad de sofosbuvir / velpatasvir en pacientes adultos postrasplante de medula ósea con infección crónica por el virus de la hepatitis C, con grado de fibrosis hepática F0 y sin tratamiento previo / Efficacy and safety of sofosbuvir/velpatasvir in adult post-bone marrow transplant patients with chronic hepatitis C virus infection, with liver fibrosis grade F0 and without prior treatment

ANTECEDENTES: En el marco de la metodología ad hoc para evaluar solicitudes de tecnologías sanitarias, aprobada mediante Resolución de Institución de Evaluación de Tecnologías en Salud e Investigación N° 111-IETSI-ESSALUD-2021, se ha elaborado el presente dictamen, el que expone la evaluación de la eficacia y seguridad de sofosbuvir/velpatasvir (SOF/VEL) en pacientes adultos postrasplante de medula ósea con infección crónica por el virus de la hepatitis C (VHC), con grado de fibrosis hepática FO y sin tratamiento previo. Así, la Dra. Estefanía Liza Baca especialista en Gastroenterología del Hospital Nacional Edgardo Rebagliati Martins, siguiendo la Directiva N° 003-IETSI-ESSALUD-2016, envió al Instituto de Evaluación de Tecnologías en Salud e Investigación - IETSI la solicitud de uso fuera del petitorio del producto SOFNEL. ASPECTOS GENERALES: La infección crónica por el virus de la hepatitis C (VHC) sigue siendo un problema de salud pública, a nivel mundial, con 71 millones de personas viviendo con el VHC. De ellos, aproximadamente, de 290 000 a 399 000 pacientes por año fallecen por complicaciones asociadas, incluyendo cirrosis hepática, carcinoma hepatocelular y falla hepática (OMS 2021; 2017). En el Perú, la prevalencia de infección crónica por el VHC no se conoce con exactitud; sin embargo, de acuerdo a algunos estudios seroepidemiológicos realizados en el país, se ha estimado entre 0.25 % a 1 % aproximadamente, con una tasa de mortalidad por el VHC de 0.04 por 100 000 habitantes (Colichon Yerosh et al. 2004; Dávalos Moscol 2009; Farfán y Cabezas 2003; Sanchez et al. 2000). Algunos pacientes con infección crónica por el VHC presentan comorbilidades u otras condiciones (i.e. trasplante de células madre) que pueden acelerar la progresión de la enfermedad a problemas hepáticos graves como: cirrosis, cáncer hepático y necesidad de trasplante hepático. El objetivo de la terapia antiviral en pacientes con infección crónica por el VHC es disminuir el ARN del VHC a niveles indetectables (AASLD 2021; EASL 2020), definido mediante el logro de una respuesta viral sostenida a las 12 semanas después del tratamiento (RVS12) (Chopra 2020). La ribavirina y el interferón pegilado han sido tradicionalmente los \ tratamientos para la infección crónica por el VHC; sin embargo, en los últimos años, el """" " " I desarrollo de los agentes antivirales de acción directa (AAD) y AAD pangenotípicos han ido 1 desplazando su uso, debido a mejores tasas de RVS (e.g. SOFNEL) con mejores perfiles de seguridade. METODOLOGÍA: Se llevó a cabo una búsqueda bibliográfica exhaustiva con el objetivo de identificar la mejor evidencia sobre la eficacia y seguridad de SOFNEL en pacientes adultos postrasplante de medula ósea con infección crónica por el VHC, con grado de fibrosis hepática FO y sin tratamiento previo. La búsqueda bibliográfica se realizó en las bases de datos PubMed, The Cochrane Library y LILACS. Asimismo, se realizó una búsqueda manual dentro de las páginas web pertenecientes a grupos que realizan evaluación de tecnologías sanitarias (ETS) y guías de práctica clínica (GPC) incluyendo el National Institute for Health and Care Excellence (NICE), la Canadian Agency for Drugs and Technologies in Health (CADTH), el Scottish Medicines Consortium (SMC), el Scottish Intercollegiate Guidelines Network (SIGN), el Institute for Quality and Efficiency in Healthcare (IQWiG por sus siglas en alemán), la International Database of GRADE Guideline, el Centro Nacional de Excelencia Tecnológica en Salud (CENETEC), la Guidelines International Network (GIN), National Health and Medical Research Council (NHMRC), la Cancer Guidelines Database, el New Zealand Guidelines Group (NZGG), el Instituto de Evaluación Tecnológica en Salud (IETS), el Instituto de Efectividad Clínica y Sanitaria (IECS), la Base Regional de Informes de Evaluación de Tecnologías en Salud de las Américas (BRISA), la OMS, el Ministerio de Salud del Perú (MINSA) y el Instituto de Evaluación de Tecnologías en Salud e Investigación (IETSI). Además, se realizó una búsqueda de GPC de las principales sociedades o instituciones especializadas en estudios del hígado, infectología y trasplante de medula ósea, tales como: la American Association for the Study of Liver Disease (AASLD), la European Association for the Study of the Liver (EASL), la Infectious Diseases Society of America (IDSA), American Society for Blood and Marrow Transplantation, Finalmente, se realizó una búsqueda en la página web de registro de ensayos clínicos (EC) www.clinicaltrials.gov, para identificar EC en curso o que no hayan sido publicados aún. RESULTADOS: Luego de la búsqueda bibliográfica hasta diciembre de 2021, se identificó una GPC elaborada por la European Association for the Study of the Liver en el 2020 (EASL 2020). No se identificaron ETS, ECA o RS de ECA o estudios observacionales comparativos que respondieran a la pregunta PICO de interés del presente dictamen. En tal sentido se optó por incluir el ECA que sirvió de base para la aprobación de SOFNEL ante la Food and Drug Administration y la European Medicine Agency. Así, se incluyó al ECA ASTRAL-1 publicado por Feld et al. en el 2015. CONCLUSIÓN: Por lo expuesto, el Instituto de Evaluación de Tecnologías en Salud e Investigación aprueba el uso de SOFNEL para pacientes adultos postrasplante de medula ósea con infección crónica por el VHC, con grado de fibrosis hepática FO y sin tratamiento previo, como producto farmacéutico no incluido en el Petitorio Farmacológico de EsSalud, según lo establecido en el Anexo N° 1. La vigencia del presente dictamen preliminar es de un año a partir de la fecha de publicación. Así, la continuación de dicha aprobación estará sujeta a la evaluación de los resultados obtenidos y de mayor evidencia que pueda surgir en el tiempo.

Phase separation vs aggregation behavior for model disordered proteins.

Liquid-liquid phase separation (LLPS) is widely utilized by the cell to organize and regulate various biochemical processes. Although the LLPS of proteins is known to occur in a sequence-dependent manner, it is unclear how sequence properties dictate the nature of the phase transition and thereby influence condensed phase morphology. In this work, we have utilized grand canonical Monte Carlo simulations for a simple coarse-grained model of disordered proteins to systematically investigate how sequence distribution, sticker fraction, and chain length impact the formation of finite-size aggregates, which can preempt macroscopic phase separation for some sequences. We demonstrate that a normalized sequence charge decoration (SCD) parameter establishes a "soft" predictive criterion for distinguishing when a model protein undergoes macroscopic phase separation vs finite aggregation. Additionally, we find that this order parameter is strongly correlated with the critical density for phase separation, highlighting an unambiguous connection between sequence distribution and condensed phase density. Results obtained from an analysis of the order parameter reveal that at sufficiently long chain lengths, the vast majority of sequences are likely to phase separate. Our results suggest that classical LLPS should be the primary phase transition for disordered proteins when short-ranged attractive interactions dominate and suggest a possible reason behind recent findings of widespread phase separation throughout living cells.

Assessment of Amyloid Forming Tendency of Peptide Sequences from Amyloid Beta and Tau Proteins Using Force-Field, Semi-Empirical, and Density Functional Theory Calculations.

A wide variety of neurodegenerative diseases are characterized by the accumulation of protein aggregates in intraneuronal or extraneuronal brain regions. In Alzheimer's disease (AD), the extracellular aggregates originate from amyloid-ß proteins, while the intracellular aggregates are formed from microtubule-binding tau proteins. The amyloid forming peptide sequences in the amyloid-ß peptides and tau proteins are responsible for aggregate formation. Experimental studies have until the date reported many of such amyloid forming peptide sequences in different proteins, however, there is still limited molecular level understanding about their tendency to form aggregates. In this study, we employed umbrella sampling simulations and subsequent electronic structure theory calculations in order to estimate the energy profiles for interconversion of the helix to ß-sheet like secondary structures of sequences from amyloid-ß protein (KLVFFA) and tau protein (QVEVKSEKLD and VQIVYKPVD). The study also included a poly-alanine sequence as a reference system. The calculated force-field based free energy profiles predicted a flat minimum for monomers of sequences from amyloid and tau proteins corresponding to an α-helix like secondary structure. For the parallel and anti-parallel dimer of KLVFFA, double well potentials were obtained with the minima corresponding to α-helix and ß-sheet like secondary structures. A similar double well-like potential has been found for dimeric forms for the sequences from tau fibril. Complementary semi-empirical and density functional theory calculations displayed similar trends, validating the force-field based free energy profiles obtained for these systems.

Critical assessment of protein intrinsic disorder prediction.

Intrinsically disordered proteins, defying the traditional protein structure-function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.

Influence of macromolecular crowding on the charge regulation of intrinsically disordered proteins.

In this work we study the coupling between ionization and conformational properties of two IDPs, histatin-5 and ß-amyloid 42, in the presence of neutral and charged crowders. The latter is modeled to resemble bovine serum albumin (BSA). With this aim, semi-grand canonical Monte Carlo simulations are performed, so that the IDP charge is a dynamic property, undergoing protonation/deprotonation processes. Both ionization properties (global and specific amino acid charge and binding capacitance) and radius of gyration are analyzed in a large range of pH values and salt concentrations. Without crowder agents, the titration curve of histatin-5, a polycation, is salt-dependent while that of ß-amyloid 42, a polyampholyte, is almost unaffected. The salt concentration is found to be particularly relevant at pH values where the protein binding capacitance (directly linked with charge fluctuation) is larger. Upon addition of neutral crowders, charge regulation is observed in histatin-5, while for ß-amyloid 42 this effect is very small. The main mechanism for charge regulation is found to be the effective increase in the ionic strength due to the excluded volume. In the presence of charged crowders, a significant increase in the charge of both IDPs is observed in almost all the pH range. In this case, the IDP charge is altered not only by the increase in the effective ionic strength but also by its direct electrostatic interaction with the charged crowders.

Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins.

With over 60 disorder predictors, users need help navigating the predictor selection task. We review 28 surveys of disorder predictors, showing that only 11 include assessment of predictive performance. We identify and address a few drawbacks of these past surveys. To this end, we release a novel benchmark dataset with reduced similarity to the training sets of the considered predictors. We use this dataset to perform a first-of-its-kind comparative analysis that targets two large functional families of disordered proteins that interact with proteins and with nucleic acids. We show that limiting sequence similarity between the benchmark and the training datasets has a substantial impact on predictive performance. We also demonstrate that predictive quality is sensitive to the use of the well-annotated order and inclusion of the fully structured proteins in the benchmark datasets, both of which should be considered in future assessments. We identify three predictors that provide favorable results using the new benchmark set. While we find that VSL2B offers the most accurate and robust results overall, ESpritz-DisProt and SPOT-Disorder perform particularly well for disordered proteins. Moreover, we find that predictions for the disordered protein-binding proteins suffer low predictive quality compared to generic disordered proteins and the disordered nucleic acids-binding proteins. This can be explained by the high disorder content of the disordered protein-binding proteins, which makes it difficult for the current methods to accurately identify ordered regions in these proteins. This finding motivates the development of a new generation of methods that would target these difficult-to-predict disordered proteins. We also discuss resources that support users in collecting and identifying high-quality disorder predictions.

The Effects of Chain Length on the Structural Properties of Intrinsically Disordered Proteins in Concentrated Solutions.

Intrinsically disordered proteins (IDP) are proteins that sample a heterogeneous ensemble of conformers in solution. An estimated 25-30% of all eukaryotic proteins belong to this class. In vivo, IDPs function under conditions that are highly crowded by other biological macromolecules. Previous research has highlighted that the presence of crowding agents can influence the conformational ensemble sampled by IDPs, resulting in either compaction or expansion. The effects of self-crowding of the disordered protein Histatin 5 has, in an earlier study, been found to have limited influence on the conformational ensemble. In this study, it is examined whether the short chain length of Histatin 5 can explain the limited effects of crowding observed, by introducing (Histatin 5)2, a tandem repeat of Histatin 5. By utilizing small-angle X-ray scattering, it is shown that the conformational ensemble is conserved at high protein concentrations, in resemblance with Histatin 5, although with a lowered protein concentration at which aggregation arises. Under dilute conditions, atomistic molecular dynamics and coarse-grained Monte Carlo simulations, as well as an established scaling law, predicted more extended conformations than indicated by experimental data, hence implying that (Histatin 5)2 does not behave as a self-avoiding random walk.

Quantitative Protein Disorder Assessment Using NMR Chemical Shifts.

Disorder is vital for the biological function of many proteins. The huge diversity found in disorder composition and amplitude reflects the complexity and pluripotency of intrinsically disordered proteins (IDPs). The first step toward a better understanding of IDPs is a quantitative and position-specific experimental characterization, and nuclear magnetic resonance (NMR) spectroscopy has emerged as the method of first choice. Here, we describe how to quantitatively assess the local balance between order and disorder in proteins by utilizing the Chemical shift Z-score for assessing Order/Disorder (CheZOD Z-score). This order/disorder metric is computed from the difference between experimentally determined NMR chemical shifts and computed random coil reference values. We explain in detail how CheZOD Z-scores are calculated fast and easily, either by using a python executable or by data submission to a server.

Glutenin and Gliadin, a Piece in the Puzzle of their Structural Properties in the Cell Described through Monte Carlo Simulations.

Gluten protein crosslinking is a predetermined process where specific intra- and intermolecular disulfide bonds differ depending on the protein and cysteine motif. In this article, all-atom Monte Carlo simulations were used to understand the formation of disulfide bonds in gliadins and low molecular weight glutenin subunits (LMW-GS). The two intrinsically disordered proteins appeared to contain mostly turns and loops and showed "self-avoiding walk" behavior in water. Cysteine residues involved in intramolecular disulfide bonds were located next to hydrophobic peptide sections in the primary sequence. Hydrophobicity of neighboring peptide sections, synthesis chronology, and amino acid chain flexibility were identified as important factors in securing the specificity of intramolecular disulfide bonds formed directly after synthesis. The two LMW-GS cysteine residues that form intermolecular disulfide bonds were positioned next to peptide sections of lower hydrophobicity, and these cysteine residues are more exposed to the cytosolic conditions, which influence the crosslinking behavior. In addition, coarse-grained Monte Carlo simulations revealed that the protein folding is independent of ionic strength. The potential molecular behavior associated with disulfide bonds, as reported here, increases the biological understanding of seed storage protein function and provides opportunities to tailor their functional properties for different applications.

Prediction of Intrinsic Disorder with Quality Assessment Using QUARTER.

Intrinsically disordered regions (IDRs) are estimated to be highly abundant in nature. While only several thousand proteins are annotated with experimentally derived IDRs, computational methods can be used to predict IDRs for the millions of currently uncharacterized protein chains. Several dozen disorder predictors were developed over the last few decades. While some of these methods provide accurate predictions, unavoidably they also make some mistakes. Consequently, one of the challenges facing users of these methods is how to decide which predictions can be trusted and which are likely incorrect. This practical problem can be solved using quality assessment (QA) scores that predict correctness of the underlying (disorder) predictions at a residue level. We motivate and describe a first-of-its-kind toolbox of QA methods, QUARTER (QUality Assessment for pRotein inTrinsic disordEr pRedictions), which provides the scores for a diverse set of ten disorder predictors. QUARTER is available to the end users as a free and convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTER/ . We briefly describe the predictive architecture of QUARTER and provide detailed instructions on how to use the webserver. We also explain how to interpret results produced by QUARTER with the help of a case study.