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Motivation Prediction of protein stability change upon mutation (ΔΔG) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. In case, protein 3D structure is not available, one can predict the structure from protein sequence; however, the perspectives of ΔΔG predictions for predicted protein structures are unknown. The accuracy of using 3D structures of the best templates for the ΔΔG prediction is also unclear. Results To investigate these questions, we used a representative set of seven diverse and accurate publicly available tools (FoldX, Eris, Rosetta, DDGun, ACDC-NN, ThermoNet and DynaMut) for stability change prediction combined with AlphaFold or I-Tasser for protein 3D structure prediction. We found that best templates perform consistently better than (or similar to) homology models for all ΔΔG predictors. Our findings imply using the best template structure for the prediction of protein stability change upon mutation if the protein 3D structure is not available. Availability and implementation The data are available at https://github.com/ivankovlab/template-vs-model. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In the course of evolution, genes traverse the nucleotide sequence space, which translates to a trajectory of changes in the protein sequence in protein sequence space. The correspondence between regions of the nucleotide and protein sequence spaces is understood in general but not in detail. One of the unexplored questions is how many sequences a protein can reach with a certain number of nucleotide substitutions in its gene sequence. Here I propose an algorithm to calculate the volume of protein sequence space accessible to a given protein sequence as a function of the number of nucleotide substitutions made in the protein-coding sequence. The algorithm utilizes the power of the dynamic programming approach, and makes all calculations within a couple of seconds on a desktop computer. I apply the algorithm to green fluorescence protein, and get the number of sequences four times higher than estimated before. However, taking into account the astronomically huge size of the protein sequence space, the previous estimate can be considered as acceptable as an order of magnitude estimation. The proposed algorithm has practical applications in the study of evolutionary trajectories in sequence space. [ABSTRACT FROM AUTHOR]

The molecular toxicity of the uranyl ion (UO22+) in living cells is primarily determined by its high affinity to both native and potential metal-binding sites that commonly occur in the structure of biomolecules. Recent advances in computational and experimental research have shed light on the structural properties and functional impacts of uranyl binding to proteins, organic ligands, nucleic acids, and their complexes. In the present work, we report the results of the computational investigation of the uranyl-mediated loss of DNA-binding activity of PARP-1, a eukaryotic enzyme that participates in DNA repair, cell differentiation, and the induction of inflammation. The latest experimental studies have shown that the uranyl ion directly interacts with its DNA-binding subdomains, zinc fingers Zn1 and Zn2, and alters their tertiary structure. Here, we propose an atomistic mechanism underlying this process and compute the free energy change along the suggested pathway. Our Quantum Mechanics/Molecular Mechanics (QM/MM) simulations of the Zn2-UO22+ complex indicate that the uranyl ion replaces zinc in its native binding site. However, the resulting state is destroyed due to the spontaneous internal hydrolysis of the U-Cys162 coordination bond. Despite the enthalpy of hydrolysis being +2.8 kcal/mol, the overall reaction free energy change is −0.6 kcal/mol, which is attributed to the loss of domain's native tertiary structure originally maintained by a zinc ion. The subsequent reorganization of the binding site includes the association of the uranyl ion with the Glu190/Asp191 acidic cluster and significant perturbations in the domain's tertiary structure driven by a further decrease in the free energy by 6.8 kcal/mol. The disruption of the DNA-binding interface revealed in our study is consistent with previous experimental findings and explains the loss of PARP-like zinc fingers' affinity for nucleic acids. [ABSTRACT FROM AUTHOR]

The study of molecular evolution is important because it reveals how protein functions emerge and evolve. Recently, several types of studies indicated that substitutions in molecular evolution occur in a compensatory manner, whereby the occurrence of a substitution depends on the amino acid residues at other sites. However, a molecular or structural basis behind the compensation often remains obscure. Here, we review studies on the interface of structural biology and molecular evolution that revealed novel aspects of compensatory evolution. In many cases structural studies benefit from evolutionary data while structural data often add a functional dimension to the study of molecular evolution. [ABSTRACT FROM AUTHOR]

Over the last 5 years proteogenomics (using mass spectroscopy to identify proteins predicted from genomic sequences) has emerged as a promising approach to the high-throughput identification of protein N-termini, which remains a problem in genome annotation. Comparison of the experimentally determined N-termini with those predicted by sequence analysis tools allows identification of the signal peptides and therefore conclusions on the cytoplasmic or extracytoplasmic (periplasmic or extracellular) localization of the respective proteins. We present here the results of a proteogenomic study of the signal peptides in Escherichia coli K-12 and compare its results with the available experimental data and predictions by such software tools as SignalP and Phobius. A single proteogenomics experiment recovered more than a third of all signal peptides that had been experimentally determined during the past three decades and confirmed at least 31 additional signal peptides, mostly in the known exported proteins, which had been previously predicted but not validated. The filtering of putative signal peptides for the peptide length and the presence of an eight-residue hydrophobic patch and a typical signal peptidase cleavage site proved sufficient to eliminate the false-positive hits. Surprisingly, the results of this proteogenomics study, as well as a re-analysis of the E. coli genome with the latest version of SignalP program, show that the fraction of proteins containing signal peptides is only about 10%, or half of previous estimates. [ABSTRACT FROM AUTHOR]

The ability of protein chains to spontaneously form their spatial structures is a long-standing puzzle in molecular biology. Experimentally measured rates of spontaneous folding of single-domain globular proteins range from microseconds to hours: the difference (11 orders of magnitude) is akin to the difference between the life span of a mosquito and the age of the universe. Here, we show that physical theory with biological constraints outlines a "golden triangle" limiting the possible range of folding rates for single-domain globular proteins of various size and stability, and that the experimentally measured folding rates fall within this narrow triangle built without any adjustable parameters, filling it almost completely. In addition, the golden triangle predicts the maximal size of protein domains that fold under solely thermodynamic (rather than kinetic) control. It also predicts the maximal allowed size of the "foldable" protein domains, and the size of domains found in known protein structures is in a good agreement with this limit. [ABSTRACT FROM AUTHOR]

There are several important questions on the coupling between properties of the protein shape and the rate of protein folding. We have studied a series of structural descriptors intended for describing protein shapes (the radius of gyration, the radius of cross-section, and the coefficient of compactness) and their possible connection with folding behavior, either rates of folding or the emergence of folding intermediates, and compared them with classical descriptors, protein chain length and contact order. It has been found that when a descriptor is normalized to eliminate the influence of the protein size (the radius of gyration normalized to the radius of gyration of a ball of equal volume, the coefficient of compactness defined as the ratio of the accessible surface area of a protein to that of an ideal ball of equal volume, and relative contact order) it completely looses its ability to predict folding rates. On the other hand, when a descriptor correlates well with protein size (the radius of cross-section and absolute contact order in our consideration) then it correlates well with the logarithm of folding rates and separates reasonably well two-state folders from multi-state ones. The critical control for the performance of new descriptors demonstrated that the radius of cross-section has a somewhat higher predictive power (the correlation coefficient is —0.74) than size alone (the correlation coefficient is —0.65). So, we have shown that the numerical descriptors of the overall shape-geometry of protein structures are one of the important determinants of the protein-folding rate and mechanism. [ABSTRACT FROM AUTHOR]

Examines a method for the calculation of folding-unfolding rates of globular proteins. Application of the method to several proteins and peptides; Accounts on the complicated transition of the proteins; Differences in the experimental and theoretical estimates.

"How do proteins fold?" Researchers have been studying different aspects of this question for more than 50 years. The most conceptual aspect of the problem is how protein can find the global free energy minimum in a biologically reasonable time, without exhaustive enumeration of all possible conformations, the so-called "Levinthal's paradox." Less conceptual but still critical are aspects about factors defining folding times of particular proteins and about perspectives of machine learning for their prediction. We will discuss in this review the key ideas and discoveries leading to the current understanding of folding kinetics, including the solution of Levinthal's paradox, as well as the current state of the art in the prediction of protein folding times. [ABSTRACT FROM AUTHOR]

We present a method for predicting folding rates of proteins from their amino acid sequences only, or rather, from their chain lengths and their helicity predicted from their sequences. The method achieves 82% correlation with experiment over all 64 "two-state" and "multistate" proteins (including two artificial peptides) studied up to now. [ABSTRACT FROM AUTHOR]

Enhancing protein stability holds paramount significance in biotechnology, therapeutics, and the food industry. Circular permutations offer a distinctive avenue for manipulating protein stability while keeping intra-protein interactions intact. Amidst the creation of circular permutants, determining the optimal placement of the new N- and C-termini stands as a pivotal, albeit largely unexplored, endeavor. In this study, we employed PONDR-FIT's predictions of disorder propensity to guide the design of circular permutants for the GroEL apical domain (residues 191–345). Our underlying hypothesis posited that a higher predicted disorder value would correspond to reduced stability in the circular permutants, owing to the increased likelihood of fluctuations in the novel N- and C-termini. To substantiate this hypothesis, we engineered six circular permutants, positioning glycines within the loops as locations for the new N- and C-termini. We demonstrated the validity of our hypothesis along the set of the designed circular permutants, as supported by measurements of melting temperatures by circular dichroism and differential scanning microcalorimetry. Consequently, we propose a novel computational methodology that rationalizes the design of circular permutants with projected stability. 8ghBPrif1HpKfHNvAL1G2x Video Abstract [ABSTRACT FROM AUTHOR]

Mutations that prevent the production of proteins in the DMD gene cause Duchenne muscular dystrophy. Most frequently, these are deletions leading to reading-frame shift. The "reading-frame rule" states that deletions that preserve ORF result in a milder Becker muscular dystrophy. By removing several exons, new genome editing tools enable reading-frame restoration in DMD with the production of BMD-like dystrophins. However, not every truncated dystrophin with a significant internal loss functions properly. To determine the effectiveness of potential genome editing, each variant should be carefully studied in vitro or in vivo. In this study, we focused on the deletion of exons 8–50 as a potential reading-frame restoration option. Using the CRISPR-Cas9 tool, we created the novel mouse model DMDdel8-50, which has an in-frame deletion in the DMD gene. We compared DMDdel8-50 mice to C57Bl6/CBA background control mice and previously generated DMDdel8-34 KO mice. We discovered that the shortened protein was expressed and correctly localized on the sarcolemma. The truncated protein, on the other hand, was unable to function like a full-length dystrophin and prevent disease progression. On the basis of protein expression, histological examination, and physical assessment of the mice, we concluded that the deletion of exons 8–50 is an exception to the reading-frame rule. [ABSTRACT FROM AUTHOR]

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding. [ABSTRACT FROM AUTHOR]

Background: Divergence of two independently evolving sequences that originated from a common ancestor can be described by two parameters, the asymptotic level of divergence E and the rate r at which this level of divergence is approached. Constant negative selection impedes allele replacements and, therefore, is routinely assumed to decelerate sequence divergence. However, its impact on E and on r has not been formally investigated. Results: Strong selection that favors only one allele can make E arbitrarily small and r arbitrarily large. In contrast, in the case of 4 possible alleles and equal mutation rates, the lowest value of r, attained when two alleles confer equal fitnesses and the other two are strongly deleterious, is only two times lower than its value under selective neutrality. Conclusions: Constant selection can strongly constrain the level of sequence divergence, but cannot reduce substantially the rate at which this level is approached. In particular, under any constant selection the divergence of sequences that accumulated one substitution per neutral site since their origin from the common ancestor must already constitute at least one half of the asymptotic divergence at sites under such selection. Reviewers: This article was reviewed by Drs. Nicolas Galtier, Sergei Maslov, and Nick Grishin. [ABSTRACT FROM AUTHOR]

We have demonstrated here that protein compactness, which we define as the ratio of the accessible surface area of a protein to that of the ideal sphere of the same volume, is one of the factors determining the mechanism of protein folding. Proteins with multi-state kinetics, on average, are more compact (compactness is 1.49 ± 0.02 for proteins within the size range of 101–151 amino acid residues) than proteins with two-state kinetics (compactness is 1.59 ± 0.03 for proteins within the same size range of 101–151 amino acid residues). We have shown that compactness for homologous proteins can explain both the difference in folding rates and the difference in folding mechanisms. [ABSTRACT FROM AUTHOR]

Elevated plasma levels of hyaluronic acid (HA) is a disease marker in liver pathology and other inflammatory disorders. Inhibition of HA synthesis with coumarin 4-methylumbelliferone (4MU) has a beneficial effect in animal models of fibrosis, inflammation, cancer and metabolic syndrome. 4MU is an active compound of approved choleretic drug hymecromone with low bioavailability and a broad spectrum of action. New, more specific and efficient inhibitors of hyaluronan synthases (HAS) are required. We have tested several newly synthesized coumarin compounds and commercial chitin synthesis inhibitors to inhibit HA production in cell culture assay. Coumarin derivative compound VII (10′-methyl-6′-phenyl-3′H-spiro[piperidine-4,2′-pyrano[3,2-g]chromene]-4′,8′-dione) demonstrated inhibition of HA secretion by NIH3T3 cells with the half-maximal inhibitory concentration (IC50) = 1.69 ± 0.75 μΜ superior to 4MU (IC50 = 8.68 ± 1.6 μΜ). Inhibitors of chitin synthesis, etoxazole, buprofezin, triflumuron, reduced HA deposition with IC50 of 4.21 ± 3.82 μΜ, 1.24 ± 0.87 μΜ and 1.48 ± 1.44 μΜ, respectively. Etoxazole reduced HA production and prevented collagen fibre formation in the CCl4 liver fibrosis model in mice similar to 4MU. Bioinformatics analysis revealed homology between chitin synthases and HAS enzymes, particularly in the pore-forming domain, containing the proposed site for etoxazole binding. [ABSTRACT FROM AUTHOR]

Characterizing the fitness landscape, a representation of fitness for a large set of genotypes, is key to understanding how genetic information is interpreted to create functional organisms. Here we determined the evolutionarily-relevant segment of the fitness landscape of His3, a gene coding for an enzyme in the histidine synthesis pathway, focusing on combinations of amino acid states found at orthologous sites of extant species. Just 15% of amino acids found in yeast His3 orthologues were always neutral while the impact on fitness of the remaining 85% depended on the genetic background. Furthermore, at 67% of sites, amino acid replacements were under sign epistasis, having both strongly positive and negative effect in different genetic backgrounds. 46% of sites were under reciprocal sign epistasis. The fitness impact of amino acid replacements was influenced by only a few genetic backgrounds but involved interaction of multiple sites, shaping a rugged fitness landscape in which many of the shortest paths between highly fit genotypes are inaccessible. [ABSTRACT FROM AUTHOR]

Motivation Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n , fitness has to be measured for all 2 n genotypes of an n -dimensional hypercube in genotype space forming a 'combinatorially complete dataset'. So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets. Results We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199 847 053 unique combinatorially complete genotype combinations of dimensionality ranging from 2 to 12. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data. Availability and implementation https://github.com/ivankovlab/HypercubeME.git. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The prediction of protein folding rates is a necessary step towards understanding the principles of protein folding. Due to the increasing amount of experimental data, numerous protein folding models and predictors of protein folding rates have been developed in the last decade. The problem has also attracted the attention of scientists from computational fields, which led to the publication of several machine learning-based models to predict the rate of protein folding. Some of them claim to predict the logarithm of protein folding rate with an accuracy greater than 90%. However, there are reasons to believe that such claims are exaggerated due to large fluctuations and overfitting of the estimates. When we confronted three selected published models with new data, we found a much lower predictive power than reported in the original publications. Overly optimistic predictive powers appear from violations of the basic principles of machine-learning. We highlight common misconceptions in the studies claiming excessive predictive power and propose to use learning curves as a safeguard against those mistakes. As an example, we show that the current amount of experimental data is insufficient to build a linear predictor of logarithms of folding rates based on protein amino acid composition. [ABSTRACT FROM AUTHOR]