May Measurement Month 2021: an analysis of blood pressure screening campaign results from India.

Raised blood pressure (BP) is the leading preventable risk factor for cardiovascular diseases that makes a major impact on early mortality and morbidity. Recognizing hypertension in the community, educating people about routine BP monitoring, and improving medication compliance are all important steps in detecting, controlling, and managing hypertension. During the course of 5 months, members of the Indian Society of Hypertension organized unique medical indoor and outdoor camps at 100 screening locations around India for the May Measurement Month (MMM) 2021 study. At every location, BP was measured three times, and a questionnaire was completed. Participants known to have hypertension before the study whether taking or not taking treatment were not included (not a normal pre-requisite for exclusion in MMM). The analysis included 15 045 participants in total. After calculating the average of the second and third BP measurements, 16.4% of participants were found to have hypertension based on ≥140/90 mmHg thresholds (2461 out of 15 045). 14.0% of females and 16.4% of males had hypertension. 16.4% of participants had undiagnosed hypertension and were not receiving treatment. The MMM screening campaign has the potential for identifying large numbers of people with undiagnosed hypertension and raising awareness of the importance of raised BP among the general public, medical professionals, policymakers, the government, and the media. Future BP screening campaigns should be larger in scope and involve follow-ups with past participants.

New-onset diabetes mellitus with COVID-19: Coincidence or cause.

Diabetes mellitus (DM) was noted as the commonest comorbidity in the coronavirus disease 2019 (COVID-19) which contributed to worse prognosis in these patients. In some cases, we also noted new-onset DM detected during hospitalization for symptomatic COVID disease. We describe three such cases, where the patients presented with severe symptomatic hyperglycemia and ketoacidosis in two and hyperosmolality in one of them. Antibody to GAD-65 was negative and varying degrees of C-peptide secretion was noted after recovery in them. There was no clinical or biochemical evidence of exocrine pancreatic involvement noted during acute presentation or after the recovery. This interesting phenomenon of coexisting DM in symptomatic COVID-19 requires further studies to differentiate between coincidence or causation.

Differences between cotranscriptional and free riboswitch folding.

Riboswitches are part of noncoding regions of messenger RNA (mRNA) that act as RNA sensors regulating gene expression of the downstream gene. Typically, one out of two distinct conformations is formed depending on ligand binding when the transcript leaves RNA polymerase (RNAP). Elongation of the RNA chain by RNAP, folding and binding all occurs simultaneously and interdependently on the seconds' timescale. To investigate the effect of transcript elongation velocity on folding for the S-adenosylmethionine (SAM)-I and adenine riboswitches we employ two complementary coarse-grained in silico techniques. Native structure-based molecular dynamics simulations provide a 3D, atomically resolved model of folding with homogenous energetics. Energetically more detailed kinetic Monte Carlo simulations give access to longer timescale by describing folding on the secondary structure level and feature the incorporation of competing aptamer conformations and a ligand-binding model. Depending on the extrusion scenarios, we observe and quantify different pathways in structure formation with robust agreements between the two techniques. In these scenarios, free-folding riboswitches exhibit different folding characteristics compared with transcription-rate limited folding. The critical transcription rate distinguishing these cases is higher than physiologically relevant rates. This result suggests that in vivo folding of the analyzed SAM-I and adenine riboswitches is transcription-rate limited.

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

The integration of atomic-resolution experimental and computational methods offers the potential for elucidating key aspects of protein folding that are not revealed by either approach alone. Here, we combine equilibrium NMR measurements of thermal unfolding and long molecular dynamics simulations to investigate the folding of gpW, a protein with two-state-like, fast folding dynamics and cooperative equilibrium unfolding behavior. Experiments and simulations expose a remarkably complex pattern of structural changes that occur at the atomic level and from which the detailed network of residue-residue couplings associated with cooperative folding emerges. Such thermodynamic residue-residue couplings appear to be linked to the order of mechanistically significant events that take place during the folding process. Our results on gpW indicate that the methods employed in this study are likely to prove broadly applicable to the fine analysis of folding mechanisms in fast folding proteins.

Revealing the global map of protein folding space by large-scale simulations.

The full characterization of protein folding is a remarkable long-standing challenge both for experiment and simulation. Working towards a complete understanding of this process, one needs to cover the full diversity of existing folds and identify the general principles driving the process. Here, we want to understand and quantify the diversity in folding routes for a large and representative set of protein topologies covering the full range from all alpha helical topologies towards beta barrels guided by the key question: Does the majority of the observed routes contribute to the folding process or only a particular route? We identified a set of two-state folders among non-homologous proteins with a sequence length of 40-120 residues. For each of these proteins, we ran native-structure based simulations both with homogeneous and heterogeneous contact potentials. For each protein, we simulated dozens of folding transitions in continuous uninterrupted simulations and constructed a large database of kinetic parameters. We investigate folding routes by tracking the formation of tertiary structure interfaces and discuss whether a single specific route exists for a topology or if all routes are equiprobable. These results permit us to characterize the complete folding space for small proteins in terms of folding barrier ΔG(‡), number of routes, and the route specificity RT.

eSBMTools 1.0: enhanced native structure-based modeling tools.

MOTIVATION: Molecular dynamics simulations provide detailed insights into the structure and function of biomolecular systems. Thus, they complement experimental measurements by giving access to experimentally inaccessible regimes. Among the different molecular dynamics techniques, native structure-based models (SBMs) are based on energy landscape theory and the principle of minimal frustration. Typically used in protein and RNA folding simulations, they coarse-grain the biomolecular system and/or simplify the Hamiltonian resulting in modest computational requirements while achieving high agreement with experimental data. eSBMTools streamlines running and evaluating SBM in a comprehensive package and offers high flexibility in adding experimental- or bioinformatics-derived restraints. RESULTS: We present a software package that allows setting up, modifying and evaluating SBM for both RNA and proteins. The implemented workflows include predicting protein complexes based on bioinformatics-derived inter-protein contact information, a standardized setup of protein folding simulations based on the common PDB format, calculating reaction coordinates and evaluating the simulation by free-energy calculations with weighted histogram analysis method or by phi-values. The modules interface with the molecular dynamics simulation program GROMACS. The package is open source and written in architecture-independent Python2. AVAILABILITY: http://sourceforge.net/projects/esbmtools/. CONTACT: alexander.schug@kit.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modulation of a pre-existing conformational equilibrium tunes adenylate kinase activity.

Structural plasticity is often required for distinct microscopic steps during enzymatic reaction cycles. Adenylate kinase from Escherichia coli (AK(eco)) populates two major conformations in solution; the open (inactive) and closed (active) state, and the overall turnover rate is inversely proportional to the lifetime of the active conformation. Therefore, structural plasticity is intimately coupled to enzymatic turnover in AK(eco). Here, we probe the open to closed conformational equilibrium in the absence of bound substrate with NMR spectroscopy and molecular dynamics simulations. The conformational equilibrium in absence of substrate and, in turn, the turnover number can be modulated with mutational- and osmolyte-driven perturbations. Removal of one hydrogen bond between the ATP and AMP binding subdomains results in a population shift toward the open conformation and a resulting increase of k(cat). Addition of the osmolyte TMAO to AK(eco) results in population shift toward the closed conformation and a significant reduction of k(cat). The Michaelis constants (K(M)) scale with the change in k(cat), which follows from the influence of the population of the closed conformation for substrate binding affinity. Hence, k(cat) and K(M) are mutually dependent, and in the case of AK(eco), any perturbation that modulates k(cat) is mirrored with a proportional response in K(M). Thus, our results demonstrate that the equilibrium constant of a pre-existing conformational equilibrium directly affects enzymatic catalysis. From an evolutionary perspective, our findings suggest that, for AK(eco), there exists ample flexibility to obtain a specificity constant (k(cat)/K(M)) that commensurate with the exerted cellular selective pressure.

A free-energy approach for all-atom protein simulation.

All-atom free-energy methods offer a promising alternative to kinetic molecular mechanics simulations of protein folding and association. Here we report an accurate, transferable all-atom biophysical force field (PFF02) that stabilizes the native conformation of a wide range of proteins as the global optimum of the free-energy landscape. For 32 proteins of the ROSETTA decoy set and six proteins that we have previously folded with PFF01, we find near-native conformations with an average backbone RMSD of 2.14 A to the native conformation and an average Z-score of -3.46 to the corresponding decoy set. We used nonequilibrium sampling techniques starting from completely extended conformations to exhaustively sample the energy surface of three nonhomologous hairpin-peptides, a three-stranded beta-sheet, the all-helical 40 amino-acid HIV accessory protein, and a zinc-finger beta beta alpha motif, and find near-native conformations for the minimal energy for each protein. Using a massively parallel evolutionary algorithm, we also obtain a near-native low-energy conformation for the 54 amino-acid engrailed homeodomain. Our force field thus stabilized near-native conformations for a total of 20 proteins of all structure classes with an average RMSD of only 3.06 A to their respective experimental conformations.

Folding path and funnel scenarios for two small disulfide-bridged proteins.

The presence of disulfide bonds leads to an interesting interplay between noncovalent intramolecular interactions and disulfide bond formation even in small proteins. Here we have investigated the folding mechanism of the 23-residue potassium channel blocker 1WQE and the 18-residue antimicrobial peptide protegrin-1 1PG1 , as two proteins containing disulfide bridges, in all-atom basin hopping simulations starting from completely extended conformations. The minimal-energy conformations deviate by only 2.1 and 1.2 A for 1WQE and 1PG1 , respectively, from their structurally conserved experimental conformations. A detailed analysis of their free energy surfaces demonstrates that the folding mechanism of disulfide-bridged proteins can vary dramatically from Levinthal's single-path scenario to a cooperative process consistent with the funnel paradigm of protein folding.

Protein structure prediction by all-atom free-energy refinement.

BACKGROUND: The reliable prediction of protein tertiary structure from the amino acid sequence remains challenging even for small proteins. We have developed an all-atom free-energy protein forcefield (PFF01) that we could use to fold several small proteins from completely extended conformations. Because the computational cost of de-novo folding studies rises steeply with system size, this approach is unsuitable for structure prediction purposes. We therefore investigate here a low-cost free-energy relaxation protocol for protein structure prediction that combines heuristic methods for model generation with all-atom free-energy relaxation in PFF01. RESULTS: We use PFF01 to rank and cluster the conformations for 32 proteins generated by ROSETTA. For 22/10 high-quality/low quality decoy sets we select near-native conformations with an average Calpha root mean square deviation of 3.03 A/6.04 A. The protocol incorporates an inherent reliability indicator that succeeds for 78% of the decoy sets. In over 90% of these cases near-native conformations are selected from the decoy set. This success rate is rationalized by the quality of the decoys and the selectivity of the PFF01 forcefield, which ranks near-native conformations an average 3.06 standard deviations below that of the relaxed decoys (Z-score). CONCLUSION: All-atom free-energy relaxation with PFF01 emerges as a powerful low-cost approach toward generic de-novo protein structure prediction. The approach can be applied to large all-atom decoy sets of any origin and requires no preexisting structural information to identify the native conformation. The study provides evidence that a large class of proteins may be foldable by PFF01.

Protein folding at atomic resolution: analysis of autonomously folding supersecondary structure motifs by nuclear magnetic resonance.

The study of protein folding has been conventionally hampered by the assumption that all single-domain proteins fold by an all-or-none process (two-state folding) that makes it impossible to resolve folding mechanisms experimentally. Here we describe an experimental method for the thermodynamic analysis of protein folding at atomic resolution using nuclear magnetic resonance (NMR). The method is specifically developed for the study of small proteins that fold autonomously into basic supersecondary structure motifs, and that do so in the sub-millisecond timescale (folding archetypes). From the NMR experiments we obtain hundreds of atomic unfolding curves that are subsequently analyzed leading to the determination of the characteristic network of folding interactions. The application of this approach to a comprehensive catalog of elementary folding archetypes holds the promise of becoming the first experimental approach capable of unraveling the basic rules connecting protein structure and folding mechanism.

Mirror images as naturally competing conformations in protein folding.

Evolution has selected a protein's sequence to be consistent with the native state geometry, as this configuration must be both thermodynamically stable and kinetically accessible to prevent misfolding and loss of function. In simple protein geometries, such as coiled-coil helical bundles, symmetry produces a competing, globally different, near mirror image with identical secondary structure and similar native contact interactions. Experimental techniques such as circular dichroism, which rely on probing secondary structure content, cannot readily distinguish these folds. Here, we want to clarify whether the native fold and mirror image are energetically competitive by investigating the free energy landscape of three-helix bundles. To prevent a bias from a specific computational approach, the present study employs the structure prediction forcefield PFF01/02, explicit solvent replica exchange molecular dynamics (REMD) with the Amber94 forcefield, and structure-based simulations based on energy landscape theory. We observe that the native fold and its mirror image have a similar enthalpic stability and are thermodynamically competitive. There is evidence that the mirror fold has faster folding kinetics and could function as a kinetic trap. All together, our simulations suggest that mirror images might not just be a computational annoyance but are competing folds that might switch depending on environmental conditions or functional considerations.

Performance assessment of different constraining potentials in computational structure prediction for disulfide-bridged proteins.

The presence of disulfide bonds in proteins has very important implications on the three-dimensional structure and folding of proteins. An adequate treatment of disulfide bonds in de-novo protein simulations is therefore very important. Here we present a computational study of a set of small disulfide-bridged proteins using an all-atom stochastic search approach and including various constraining potentials to describe the disulfide bonds. The proposed potentials can easily be implemented in any code based on all-atom force fields and employed in simulations to achieve an improved prediction of protein structure. Exploring different potential parameters and comparing the structures to those from unconstrained simulations and to experimental structures by means of a scoring function we demonstrate that the inclusion of constraining potentials improves the quality of final structures significantly. For some proteins (1KVG and 1PG1) the native conformation is visited only in simulations in presence of constraints. Overall, we found that the Morse potential has optimal performance, in particular for the ß-sheet proteins.

Revisiting the NMR structure of the ultrafast downhill folding protein gpW from bacteriophage λ.

GpW is a 68-residue protein from bacteriophage λ that participates in virus head morphogenesis. Previous NMR studies revealed a novel α+ß fold for this protein. Recent experiments have shown that gpW folds in microseconds by crossing a marginal free energy barrier (i.e., downhill folding). These features make gpW a highly desirable target for further experimental and computational folding studies. As a step in that direction, we have re-determined the high-resolution structure of gpW by multidimensional NMR on a construct that eliminates the purification tags and unstructured C-terminal tail present in the prior study. In contrast to the previous work, we have obtained a full manual assignment and calculated the structure using only unambiguous distance restraints. This new structure confirms the α+ß topology, but reveals important differences in tertiary packing. Namely, the two α-helices are rotated along their main axis to form a leucine zipper. The ß-hairpin is orthogonal to the helical interface rather than parallel, displaying most tertiary contacts through strand 1. There also are differences in secondary structure: longer and less curved helices and a hairpin that now shows the typical right-hand twist. Molecular dynamics simulations starting from both gpW structures, and calculations with CS-Rosetta, all converge to our gpW structure. This confirms that the original structure has strange tertiary packing and strained secondary structure. A comparison of NMR datasets suggests that the problems were mainly caused by incomplete chemical shift assignments, mistakes in NOE assignment and the inclusion of ambiguous distance restraints during the automated procedure used in the original study. The new gpW corrects these problems, providing the appropriate structural reference for future work. Furthermore, our results are a cautionary tale against the inclusion of ambiguous experimental information in the determination of protein structures.

Conformational landscape of the HIV-V3 hairpin loop from all-atom free-energy simulations.

Small beta hairpins have many distinct biological functions, including their involvement in chemokine and viral receptor recognition. The relevance of structural similarities between different hairpin loops with near homologous sequences is not yet understood, calling for the development of methods for de novo hairpin structure prediction and simulation. De novo folding of beta strands is more difficult than that of helical proteins because of nonlocal hydrogen bonding patterns that connect amino acids that are distant in the amino acid sequence and there is a large variety of possible hydrogen bond patterns. Here we use a greedy version of the basin hopping technique with our free-energy forcefield PFF02 to reproducibly and predictively fold the hairpin structure of a HIV-V3 loop. We performed 20 independent basin hopping runs for 500 cycles corresponding to 7.4 x 10(7) energy evaluations each. The lowest energy structure found in the simulation has a backbone root mean square deviation (bRMSD) of only 2.04 A to the native conformation. The lowest 9 out of the 20 simulations converged to conformations deviating less than 2.5 A bRMSD from native.

All-atom de novo protein folding with a scalable evolutionary algorithm.

The search for efficient and predictive methods to describe the protein folding process at the all-atom level remains an important grand-computational challenge. The development of multi-teraflop architectures, such as the IBM BlueGene used in this study, has been motivated in part by the large computational requirements of such studies. Here we report the predictive all-atom folding of the forty-amino acid HIV accessory protein using an evolutionary stochastic optimization technique. We implemented the optimization method as a master-client model on an IBM BlueGene, where the algorithm scales near perfectly from 64 to 4096 processors in virtual processor mode. Starting from a completely extended conformation, we optimize a population of 64 conformations of the protein in our all-atom free-energy model PFF01. Using 2048 processors the algorithm predictively folds the protein to a near-native conformation with an RMS deviation of 3.43 A in < 24 h.

Comparison of stochastic optimization methods for all-atom folding of the Trp-Cage protein.

The performances of three different stochastic optimization methods for all-atom protein structure prediction are investigated and compared. We use the recently developed all-atom free-energy force field (PFF01), which was demonstrated to correctly predict the native conformation of several proteins as the global optimum of the free energy surface. The trp-cage protein (PDB-code 1L2Y) is folded with the stochastic tunneling method, a modified parallel tempering method, and the basin-hopping technique. All the methods correctly identify the native conformation, and their relative efficiency is discussed.