A fast method for predicting amino acid mutations that lead to unfolding

Amino acid mutation(s) that cause(s) partial or total unfolding of a protein can lead to disease states and failure to produce mutants. It is therefore very useful to be able to predict which mutations can retain the conformation of a wild-type protein and which mutations will lead to local or global unfolding of the protein. We have developed a fast and reasonably accurate method based on a backbone-dependent side-chain rotamer library to predict the (folded or unfolded) conformation of a protein upon mutation. This method has been tested on proteins whose wild-type 3D structures are known and whose mutant conformations have been experimentally characterized to be folded or unfolded. Furthermore, for the cases studied here, the predicted partially folded or denatured mutant conformation correlate with a decrease in the stability of the mutant relative to the wild-type protein. The key advantage of our method is that it is very fast and predicts locally or globally unfolded states fairly accurately. Hence, it may prove to be useful in designing site-directed mutagenesis, X-ray crystallography and drug design experiments as well as in free energy simulations by helping to ascertain whether a mutation will alter or retain the wild-type conformation.     

The role of local backrub motions in evolved and designed mutations

Amino acid substitutions in protein structures often require subtle backbone adjustments that are difficult to model in atomic detail. An improved ability to predict realistic backbone changes in response to engineered mutations would be of great utility for the blossoming field of rational protein design. One model that has recently grown in acceptance is the backrub motion, a low-energy dipeptide rotation with single-peptide counter-rotations, that is coupled to dynamic two-state sidechain rotamer jumps, as evidenced by alternate conformations in very high-resolution crystal structures. It has been speculated that backrubs may facilitate sequence changes equally well as rotamer changes. However, backrub-induced shifts and experimental uncertainty are of similar magnitude for backbone atoms in even high-resolution structures, so comparison of wildtype-vs.-mutant crystal structure pairs is not sufficient to directly link backrubs to mutations. In this study, we use two alternative approaches that bypass this limitation. First, we use a quality-filtered structure database to aggregate many examples for precisely defined motifs with single amino acid differences, and find that the effectively amplified backbone differences closely resemble backrubs. Second, we directly apply a provably-accurate, backrub-enabled protein design algorithm to idealized versions of these motifs, and discover that the lowest-energy computed models match the average-coordinate experimental structures. These results support the hypothesis that backrubs participate in natural protein evolution and validate their continued use for design of synthetic proteins.     

PremPS: Predicting the impact of missense mutations on protein stability

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.     

A novel Method for the selective recovery and purification of γ‐polyglutamic acid from Bacillus licheniformis fermentation broth

Microbially produced gamma‐polyglutamic acid (γ‐PGA) is a commercially important biopolymer with many applications in biopharmaceutical, food, cosmetic and waste‐water treatment industries. Owing to its increasing demand in various industries, production of γ‐PGA is well documented in the literature, however very few methods have been reported for its recovery. In this paper, we report a novel method for the selective recovery and purification of γ‐PGA from cell‐free fermentation broth of Bacillus licheniformis. The cell‐free fermentation broth was treated with divalent copper ions, resulting in the precipitation of γ‐PGA, which was collected as a pellet by centrifugation. The pellet was resolubilized and dialyzed against de‐ionized water to obtain the purified γ‐PGA biopolymer. The efficiency and selectivity of γ‐PGA recovery was compared with ethanol precipitation method. We found that 85% of the original γ‐PGA content in the broth was recovered by copper sulfate‐induced precipitation, compared to 82% recovery by ethanol precipitation method. Since ethanol is a commonly used solvent for protein precipitation, the purity of γ‐PGA precipitate was analyzed by measuring proteins that co‐precipitated with γ‐PGA. Of the total proteins present in the broth, 48% proteins were found to be co‐precipitated with γ‐PGA by ethanol precipitation, whereas in copper sulfate‐induced precipitation, only 3% of proteins were detected in the final purified γ‐PGA, suggesting that copper sulfate‐induced precipitation offers better selectivity than ethanol precipitation method. Total metal content analysis of the purified γ‐PGA revealed the undetectable amount of copper ions, whereas other metal ions detected were in low concentration range. The purified γ‐PGA was characterized using infrared spectroscopy. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Protein engineering study of protein L by simulation.

We examine the ability of our recently introduced minimalist protein model to reproduce experimentally measured thermodynamic and kinetic changes upon sequence mutation in the well-studied immunoglobulin-binding protein L. We have examined five different sequence mutations of protein L that are meant to mimic the same mutation type studied experimentally: two different mutations which disrupt the natural preference in the beta-hairpin #1 and beta-hairpin #2 turn regions, two different helix mutants where a surface polar residue in the alpha-helix has been mutated to a hydrophobic residue, and a final mutant to further probe the role of nonnative hydrophobic interactions in the folding process. These simulated mutations are analyzed in terms of various kinetic and thermodynamic changes with respect to wild type, but in addition we evaluate the structure-activity relationship of our model protein based on the phi-value calculated from both the kinetic and thermodynamic perspectives. We find that the simulated thermodynamic phi-values reproduce the experimental trends in the mutations studied and allow us to circumvent the difficult interpretation of the complicated kinetics of our model. Furthermore, the level of resolution of the model allows us to directly predict what experiments seek in regard to protein engineering studies of protein folding--namely the residues or portions of the polypeptide chain that contribute to the crucial step in the folding of the wild-type protein.     

Prediction of protein stability changes for single-site mutations using support vector machines

Accurate prediction of protein stability changes resulting from single amino acid mutations is important for understanding protein structures and designing new proteins. We use support vector machines to predict protein stability changes for single amino acid mutations leveraging both sequence and structural information. We evaluate our approach using cross-validation methods on a large dataset of single amino acid mutations. When only the sign of the stability changes is considered, the predictive method achieves 84% accuracy-a significant improvement over previously published results. Moreover, the experimental results show that the prediction accuracy obtained using sequence alone is close to the accuracy obtained using tertiary structure information. Because our method can accurately predict protein stability changes using primary sequence information only, it is applicable to many situations where the tertiary structure is unknown, overcoming a major limitation of previous methods which require tertiary information. The web server for predictions of protein stability changes upon mutations (MUpro), software, and datasets are available at http://www.igb.uci.edu/servers/servers.html.     

Apoptosis of human lung adenocarcinoma A549 cells induced by prodigiosin analogue obtained from an entomopathogenic bacterium Serratia marcescens

An entomopathogenic bacterial strain SCQ1 was isolated from silkworm (Bombyx mori) and identified as Serratia marcescens via 16S rRNA gene analysis. This strain produces a red pigment that causes acute septicemia of silkworm. The red pigment of strain SCQ1 was identified as prodigiosin analogue (PGA) with various reported biological activities. In this study, we found that low concentration of PGA showed significant anticancer activity in human lung adenocarcinoma A549 cells, but has little effect in human bone marrow stem cells, in vitro. By exposure to different concentrations of PGA for 24 h, morphological changes and the MTT assay showed that A549 cell line was very sensitive to PGA, with IC₅₀ value about 2.2 mg/L. Early stage of apoptosis was detected by flow cytometry while A549 cells were treated with PGA for 4 and 12 h, respectively. The proportion of dead cells was increased with treatment time or the concentrations of PGA, but it was inversely proportional to that of apoptotic cells. These results indicate that PGA obtained from strain SCQ1 induces apoptosis in A549 cells, but the molecular mechanisms of cell death are complicated, and the S. marcescens strain SCQ1 may serve as a source of the anticancer compound, PGA.     

Inhibition of microtubule assembly by poly(L-glutamic acid) and the site of its action

Poly(L-glutamic acid) (PGA) suppresses the polymerization of porcine brain microtubule proteins and induces the depolymerization in vitro in a concentration-dependent manner. The extent of inhibition increases with increasing molecular weight of the PGA tested. A 50% inhibition of the protein polymerization was observed at a PGA (molecular weight = 60,000) to microtubule protein ratio of 0.04 (w/w), and complete inhibition was obtained at a ratio of 0.07. Such an inhibition on the polymerization by PGA is greatly decreased when Mg2+ is present at a higher concentration. The addition of PGA raises the critical concentration of microtubule proteins necessary for assembly. During incubation with PGA, microtubule proteins retain the ability to assemble, i.e., substoichiometric amounts of taxol considerably relieve the inhibition of assembly by PGA. PGA interacts with microtubule-associated proteins (MAPs) preferentially, because the amount of MAPs binding to PGA-Sepharose 4B is much larger than that of tubulin. Tau proteins were observed only in adsorbed fractions, while MAP-2 was present in both unbound and adsorbed fractions.     

Toward Classification of BRCA1 Missense Variants Using a Biophysical Approach

Carriers of germ line mutations in breast cancer susceptibility gene BRCA1 have an increased risk of developing breast and ovarian cancers; missense mutations have, however, been difficult to assess for disease association. Here we have used a biophysical approach to classify these variants. We established an assay for measuring the thermodynamic stability of the BRCA1 BRCT domains and investigated the effects of 36 missense mutations. The mutations show a range of effects. Some do not change the stability, whereas others destabilize the protein by as much as 6 kcal mol⁻¹; one-third of the mutants could not be expressed in soluble form in Escherichia coli, and we conclude that these destabilize the protein by an even greater amount. We tested several computer algorithms for their ability to predict the mutant effects and found that by grouping them into two classes (destabilizing by less than or more than 2.2 kcal mol⁻¹), the algorithms could predict the stability changes. Importantly, with the exception of the few mutants located in the binding site, none showed a significant reduction in affinity for phosphorylated substrate. These results indicate that despite very large losses in stability, the integrity of the structure is not compromised by the mutations. Thus, the majority of mutations cause loss of function by reducing the proportion of BRCA1 molecules that are in the folded state and increasing the proportion of molecules that are unfolded. Consequently, small molecule stabilization of the structure could be a generally applicable preventative therapeutic strategy for rescuing many BRCA1 mutations.     

Cloning, overexpression, and characterization of a novel thermostable penicillin G acylase from Achromobacter xylosoxidans: probing the molecular basis for its high thermostability

The gene encoding a novel penicillin G acylase (PGA), designated pgaW, was cloned from Achromobacter xylosoxidans and overexpressed in Escherichia coli. The pgaW gene contains an open reading frame of 2586 nucleotides. The deduced protein sequence encoded by pgaW has about 50% amino acid identity to several well-characterized PGAs, including those of Providencia rettgeri, Kluyvera cryocrescens, and Escherichia coli. Biochemical studies showed that the optimal temperature for this novel PGA (PGA650) activity is greater than 60 degrees C and its half-life of inactivation at 55 degrees C is four times longer than that of another previously reported thermostable PGA from Alcaligenes faecalis (R. M. D. Verhaert, A. M. Riemens, J. V. R. Laan, J. V. Duin, and W. J. Quax, Appl. Environ. Microbiol. 63:3412-3418, 1997). To our knowledge, this is the most thermostable PGA ever characterized. To explore the molecular basis of the higher thermostability of PGA650, homology structural modeling and amino acid composition analyses were performed. The results suggested that the increased number of buried ion pair networks, lower N and Q contents, excessive arginine residues, and remarkably high content of proline residues in the structure of PGA650 could contribute to its high thermostability. The unique characteristic of higher thermostability of this novel PGA provides some advantages for its potential application in industry.     

Integrated prediction of the effect of mutations on multiple protein characteristics

Site-directed mutagenesis is routinely used in modern biology to elucidate the functional or biophysical roles of protein residues, and plays an important role in the field of rational protein design. Over the past decade, a number of computational tools have been developed that can predict the effect of point mutations on a protein's biophysical characteristics. However, these programs usually provide predictions for only a single characteristic. Furthermore, online versions of these tools are often impractical to use for examination of large and diverse sets of mutants. We have created a new web application, (http://enzyme.ucd.ie/PEAT_SA), that can simultaneously predict the effect of mutations on stability, ligand affinity and pK(a) values. PEAT-SA also provides an expanded feature-set with respect to other online tools which includes the ability to obtain predictions for multiple mutants in one submission. As a result, researchers who use site-directed mutagenesis can access state-of-the-art protein design methods with a fraction of the effort previously required. The results of benchmarking PEAT-SA on standard test-sets demonstrate that its accuracy for all three prediction types compares well to currently available tools. We illustrate PEAT-SA's potential by using it to investigate the influence of mutations on the activity of Subtilisin BPN'. This example demonstrates how the ability to obtain a wide range of information from one source, that can be combined to obtain deeper insight into the influence of mutations, makes PEAT-SA a valuable service to both experimental and computational biologists.     

Protein side chain conformation predictions with an MMGBSA energy function

The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an “MMGBSA” energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803–819. © 2016 Wiley Periodicals, Inc.     

预测蛋白质可结晶性研究进展

解析蛋白质的三维结构具有重要的生物学意义,更是蛋白质功能研究和理性药物设计的基础。目前解析蛋白质结构最重要的方法是X-射线衍射晶体学解析技术。但是运用该技术解析蛋白质结构的关键是获得高质量的蛋白质晶体。然而,据统计仅有42%的可溶纯化蛋白质能够得到晶体,即不同蛋白质的可结晶性表现不同。由于实验方法验证蛋白质的可结晶性耗时耗力,因此,有研究者运用计算机模拟的方法预测蛋白质的可结晶性,从而节省资源与成本并且提高实验的成功率。本文结合我们的研究工作,介绍了几种目前较为成功的蛋白质可结晶性预测方法及其研究途径。     

Assessing Predictors of Changes in Protein Stability upon Mutation Using Self-Consistency

The ability to predict the effect of mutations on protein stability is important for a wide range of tasks, from protein engineering to assessing the impact of SNPs to understanding basic protein biophysics. A number of methods have been developed that make these predictions, but assessing the accuracy of these tools is difficult given the limitations and inconsistencies of the experimental data. We evaluate four different methods based on the ability of these methods to generate consistent results for forward and back mutations, and examine how this ability varies with the nature and location of the mutation. We find that, while one method seems to outperform the others, the ability of these methods to make accurate predictions is limited.     

Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM

The accurate identification of protein structure class solely using extracted information from protein sequence is a complicated task in the current computational biology. Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by fusing the sequence information and evolution information to represent a protein sample. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark data-sets, 1189 and 25PDB with sequence similarity lower than 40 and 25%, respectively. Comparison of our results with other methods shows that the proposed method by us is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity data-sets.     

Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM

The accurate identification of protein structure class solely using extracted information from protein sequence is a complicated task in the current computational biology. Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by fusing the sequence information and evolution information to represent a protein sample. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark data-sets, 1189 and 25PDB with sequence similarity lower than 40 and 25%, respectively. Comparison of our results with other methods shows that the proposed method by us is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity data-sets.     

Rational design of temperature-sensitive alleles using computational structure prediction

Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate "top 5" list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions.     

Binding Pocket Optimization by Computational Protein Design

Engineering specific interactions between proteins and small molecules is extremely useful for biological studies, as these interactions are essential for molecular recognition. Furthermore, many biotechnological applications are made possible by such an engineering approach, ranging from biosensors to the design of custom enzyme catalysts. Here, we present a novel method for the computational design of protein-small ligand binding named PocketOptimizer. The program can be used to modify protein binding pocket residues to improve or establish binding of a small molecule. It is a modular pipeline based on a number of customizable molecular modeling tools to predict mutations that alter the affinity of a target protein to its ligand. At its heart it uses a receptor-ligand scoring function to estimate the binding free energy between protein and ligand. We compiled a benchmark set that we used to systematically assess the performance of our method. It consists of proteins for which mutational variants with different binding affinities for their ligands and experimentally determined structures exist. Within this test set PocketOptimizer correctly predicts the mutant with the higher affinity in about 69% of the cases. A detailed analysis of the results reveals that the strengths of PocketOptimizer lie in the correct introduction of stabilizing hydrogen bonds to the ligand, as well as in the improved geometric complemetarity between ligand and binding pocket. Apart from the novel method for binding pocket design we also introduce a much needed benchmark data set for the comparison of affinities of mutant binding pockets, and that we use to asses programs for in silico design of ligand binding.     

Application of bovine pituitary extract and polyglycolide/poly(lactide-co-glycolide) scaffold to the cultivation of bovine knee chondrocytes

In vitro cultivation of primary bovine knee chondrocytes (BKCs), using bovine pituitary extract (BPE) and porous scaffolds composed of polyglycolide (PGA) and 85/15 poly(lactide-co-glycolide) (PLGA), was investigated. Here, BPE was prepared from fresh bovine pituitaries, and cylindrical PGA/PLGA scaffolds with various chemical compositions were fabricated by solvent merging/particulate leaching method. Experimental results showed that in microcarrier systems, the rate of BKC growth on PGA surfaces is faster than that on PLGA surfaces, and the decrease in the medium pH value of BKCs-adsorbed PGA particles is faster than that of BKCs-adsorbed PLGA particles. After 28-day construct cultivation, the BKC amount and the content of glycosaminoglycans and collagen per construct increased with BPE protein concentration. For a constant BPE protein concentration, a higher PGA percentage in scaffold leads to a better biological environment for the growth of BKCs and the synthesis of extracellar matrices.