The protein folding rate and the geometry and topology of the native state

Proteins fold in 3-dimensional conformations which are important for their function. Characterizing the global conformation of proteins rigorously and separating secondary structure effects from topological effects is a challenge. New developments in Applied Knot Theory allow to characterize the topological characteristics of proteins (knotted or not). By analyzing a small set of two-state and multi-state proteins with no knots or slipknots, our results show that 95.4% of the analyzed proteins have non-trivial topological characteristics, as reflected by the second Vassiliev measure, and that the logarithm of the experimental protein folding rate depends on both the local geometry and the topology of the protein's native state.

Current protein structure predictors do not produce meaningful folding pathways

Protein structure prediction has long been considered a gateway problem for understanding protein folding. Recent advances in deep learning have achieved unprecedented success at predicting a protein's crystal structure, but whether this achievement relates to a better modelling of the folding process remains an open question. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental folding data. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathwhay, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with parameters such as intermediate structures and the folding rate constant. These results suggest that recent advances in protein structure prediction do not yet provide an enhanced understanding of the principles underpinning protein folding.

Identification of Genotypic Variants and its Proteomic mutations of Brazilian SARS-CoV-2 Isolates

The second wave of COVID-19, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading over the world. The mechanism behind the escaping from current antivirals is still not clear, due to the occurrence of continuous variants in SARS-CoV-2 genomes. Brazil is the world’s second most COVID-19-affected country. In the present study, we identified the genomic and proteomic variants of Brazilian SARS-CoV-2 isolates. We identified 16 different genotypic variants were found among the 27 isolates. The genotypes of three isolates such as Bra/1236/2021 (G15), Bra/MASP2C844R2/2020 (G11), and Bra/RJ-DCVN5/2020 (G9) have a unique mutant in NSP4 (S184N), 2’O-Mutase (R216N), membrane protein (A2V) and Envelope protein (V5A). A mutation in RdRp of SARS-CoV-2, particularly the change of Pro to Leu at 323 resulted in the stabilization of the structure in BRA/CD1739-P4/2020. NSP4, NSP5 protein mutants are more virulent in Genotype 15 and 16. A fast protein folding rate changes the structural stability and leads to escape for current antivirals. Thus, our findings help researchers to develop the best potent antivirals based on the new mutant of Brazilian isolates.

Study on the Influence of mRNA, the Genetic Language, on Protein Folding Rates

Many works have reported that protein folding rates are influenced by the characteristics of amino acid sequences and protein structures. However, few reports on the problem of whether the corresponding mRNA sequences are related to the protein folding rates can be found. An mRNA sequence is regarded as a kind of genetic language, and its vocabulary and phraseology must provide influential information regarding the protein folding rate. In the present work, linear regressions on the parameters of the vocabulary and phraseology of mRNA sequences and the corresponding protein folding rates were analyzed. The results indicated that D2 (the adjacent base-related information redundancy) values and the GC content values of the corresponding mRNA sequences exhibit significant negative relations with the protein folding rates, but D1 (the single base information redundancy) values exhibit significant positive relations with the protein folding rates. In addition, the results show that the relationships between the parameters of the genetic language and the corresponding protein folding rates are obviously different for different protein groups. Some useful parameters that are related to protein folding rates were found. The results indicate that when predicting protein folding rates, the information from protein structures and their amino acid sequences is insufficient, and some information for regulating the protein folding rates must be derived from the mRNA sequences.

Diselenide crosslinks for enhanced and simplified oxidative protein folding

The in vitro oxidative folding of proteins has been studied for over sixty years, providing critical insight into protein folding mechanisms. Hirudin, the most potent natural inhibitor of thrombin, is a 65-residue protein with three disulfide bonds, and is viewed as a folding model for a wide range of disulfide-rich proteins. Hirudin’s folding pathway is notorious for its highly heterogeneous intermediates and scrambled isomers, limiting its folding rate and yield in vitro. Aiming to overcome these limitations, we undertake systematic investigation of diselenide bridges at native and non-native positions and investigate their effect on hirudin’s folding, structure and activity. Our studies demonstrate that, regardless of the specific positions of these substitutions, the diselenide crosslinks enhanced the folding rate and yield of the corresponding hirudin analogues, while reducing the complexity and heterogeneity of the process. Moreover, crystal structure analysis confirms that the diselenide substitutions maintained the overall three-dimensional structure of the protein and left its function virtually unchanged. The choice of hirudin as a study model has implications beyond its specific folding mechanism, demonstrating the high potential of diselenide substitutions in the design, preparation and characterization of disulfide-rich proteins.

Prediction of Protein Structural Classes: Features Extraction to Classification Algorithm

: The fast growing of protein sequencing and protein structure data has promoted the development of the protein structural class prediction. Several prediction methods have been proposed to study protein folding rate, DNA binding sites, as well as reducing the search of conformational space and realizing the prediction of tertiary structure. This paper introduces the current approaches of protein structural class prediction and emphasize their steps from information extraction to classification algorithms.

Design principles for the glycoprotein quality control pathway

Newly-translated glycoproteins in the endoplasmic reticulum (ER) often undergo cycles of chaperone binding and release in order to assist in folding. Quality control is required to distinguish between proteins that have completed native folding, those that have yet to fold, and those that have misfolded. Using quantitative modeling, we explore how the design of the quality-control pathway modulates its efficiency. Our results show that an energy-consuming cyclic quality-control process, similar to the observed physiological system, outperforms alternative designs. The kinetic parameters that optimize the performance of this system drastically change with protein production levels, while remaining relatively insensitive to the protein folding rate. Adjusting only the degradation rate, while fixing other parameters, allows the pathway to adapt across a range of protein production levels, aligning with in vivo measurements that implicate the release of degradation-associated enzymes as a rapid-response system for perturbations in protein homeostasis. The quantitative models developed here elucidate design principles for effective glycoprotein quality control in the ER, improving our mechanistic understanding of a system crucial to maintaining cellular health.

Graph Signal Processing on protein residue networks helps in studying its biophysical properties

Understanding the physical and chemical properties of proteins is vital, and many efforts have been made to study the emergent properties of the macro-molecules as a combination of long chains of amino acids. Here, we present a graph signal processing based approach to model the biophysical property of proteins. For each protein inter-residue proximity-based network is used as basis graph and the respective amino acid properties are used as node-signals. Signals on node are decomposed on network’s Laplacian eigenbasis using graph Fourier transformations. We found that the intensity in low-frequency components of graph signals of residue features could be used to model few biophysical properties of proteins. Specifically, using our approach, we could model protein folding-rate, globularity and fraction of alpha-helices and beta-sheets. Our approach also allows amalgamation of different types of chemical and graph theoretic properties of residue to be used together in a multi-variable regression model to predict biophysical properties.

Folding and Persistence Time of Intramolecular G-Quadruplexes Transiently Embedded in a DNA duplex

ABSTRACTG-quadruplex (G4) DNA structures have emerged as important regulatory elements during DNA replication, transcription or repair. While many in-vitro studies have focused on the kinetics of G4 formation within DNA single-strands, G4 are found in-vivo in double-stranded DNA regions, where their formation is challenged by pairing between the two complementary strands. Since the energy of hybridization of Watson-Crick structures dominates the energy of G4 folding, this competition should play a critical role on the persistence of G4 in vivo. To address this issue, we designed a single molecule assay allowing measuring G4 folding and persistence while the structure is periodically challenged by the complementary strand. We quantified both the folding rate and the persistence time of biologically relevant G4 structures and showed that the dynamics of G4 formation depends strongly on the genomic location. G4 are found much more stable in promoter regions and replication origins than in telomeric regions. In addition, we characterized how G4 dynamics was affected by G4 ligands and showed that both folding rate and persistence increased. Our assay opens new perspectives for the measurement of G4 dynamics, which is critical to understand their role in genetic regulation.

Topological principles of protein folding

Native topology correlates with folding rate: entangled topological relationships between protein loops facilitate folding. High numbers of topologically independent units (circuits) – normalized by size – are associated with fast folding kinetics.