PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality

The evolutionary conserved TLDc domain defines a new class of (H+)V-ATPase interacting proteins

We recently found that nuclear receptor coactivator 7 (Ncoa7) and Oxr1 interact with the proton-pumping V-ATPase. Ncoa7 and Oxr1 belong to a group of proteins playing a role in the oxidative stress response, that contain the conserved “TLDc” domain. Here we asked if the three other proteins in this family, i.e., Tbc1d24, Tldc1 and Tldc2 also interact with the V-ATPase and if the TLDc domains are involved in all these interactions. By co-immunoprecipitation, endogenous kidney Tbc1d24 (and Ncoa7 and Oxr1) and overexpressed Tldc1 and Tldc2, all interacted with the V-ATPase. In addition, purified TLDc domains of Ncoa7, Oxr1 and Tldc2 (but not Tbc1d24 or Tldc1) interacted with V-ATPase in GST pull-downs. At the amino acid level, point mutations G815A, G845A and G896A in conserved regions of the Ncoa7 TLDc domain abolished interaction with the V-ATPase, and S817A, L926A and E938A mutations resulted in decreased interaction. Furthermore, poly-E motifs upstream of the TLDc domain in Ncoa7 and Tldc2 show a (nonsignificant) trend towards enhancing the interaction with V-ATPase. Our principal finding is that all five members of the TLDc family of proteins interact with the V-ATPase. We conclude that the TLDc motif defines a new class of V-ATPase interacting regulatory proteins.

Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape

The ongoing COVID-19 pandemic is a global public health emergency requiring urgent development of efficacious vaccines. While concentrated research efforts have focused primarily on antibody-based vaccines that neutralize SARS-CoV-2, and several first-generation vaccines have either been approved or received emergency use authorization, it is forecasted that COVID-19 will become an endemic disease requiring updated second-generation vaccines. The SARS-CoV-2 surface spike (S) glycoprotein represents a prime target for vaccine development because antibodies that block viral attachment and entry, i.e., neutralizing antibodies, bind almost exclusively to the receptor-binding domain. Here, we develop computational models for a large subset of S proteins associated with SARS-CoV-2, implemented through coarse-grained elastic network models and normal mode analysis. We then analyze local protein domain dynamics of the S protein systems and their thermal stability to characterize structural and dynamical variability among them. These results are compared against existing experimental data and used to elucidate the impact and mechanisms of SARS-CoV-2 S protein mutations and their associated antibody binding behavior. We construct a SARS-CoV-2 antigenic map and offer predictions about the neutralization capabilities of antibody and S mutant combinations based on protein dynamic signatures. We then compare SARS-CoV-2 S protein dynamics to SARS-CoV and MERS-CoV S proteins to investigate differing antibody binding and cellular fusion mechanisms that may explain the high transmissibility of SARS-CoV-2. The outbreaks associated with SARS-CoV, MERS-CoV, and SARS-CoV-2 over the last two decades suggest that the threat presented by coronaviruses is ever-changing and long term. Our results provide insights into the dynamics-driven mechanisms of immunogenicity associated with coronavirus S proteins and present a new, to our knowledge, approach to characterize and screen potential mutant candidates for immunogen design, as well as to characterize emerging natural variants that may escape vaccine-induced antibody responses.

Computational Resources for Bioscience Education

With the ongoing laboratory restrictions, it is often challenging for bioscience students to make satisfactory progress in their projects. A long-standing practice in multi-disciplinary research is to use computational and theoretical method to corroborate with experiment findings. In line with the lack of opportunity to access laboratory instruments, the pandemic situation is a win-win scenario for scholars to focus on computational methods. This communication outline some of the standalone tools and webservers that bioscience students can successfully learn and adopt to obtain in-depth insights into biochemistry, biophysics, biotechnology, and bioengineering research work.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new S^transitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Regional TMPRSS2 V197M Allele Frequencies Are Correlated with COVID-19 Case Fatality Rates

Coronavirus disease, COVID-19 (coronavirus disease 2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has a higher case fatality rate in European countries than in others, especially East Asian ones. One potential explanation for this regional difference is the diversity of the viral infection efficiency. Here, we analyzed the allele frequencies of a nonsynonymous variant rs12329760 (V197M) in the TMPRSS2 gene, a key enzyme essential for viral infection and found a significant association between the COVID-19 case fatality rate and the V197M allele frequencies, using over 200,000 present-day and ancient genomic samples. East Asian countries have higher V197M allele frequencies than other regions, including European countries which correlates to their lower case fatality rates. Structural and energy calculation analysis of the V197M amino acid change showed that it destabilizes the TMPRSS2 protein, possibly negatively affecting its ACE2 and viral spike protein processing.

Improving the alkaline stability of pepsin through rational protein design using renin, an alkaline-stable aspartic protease, as a structural and functional reference

The present study sought to identify the structural determinants of aspartic protease structural stability and activity at elevated pH. Various hypotheses have been published regarding the features responsible for the unusual alkaline structural stability of renin, however, few structure-function studies have verified these claims. Using pepsin as a model system, and renin as a template for functional and structural alkaline stability, a rational re-design of pepsin was undertaken to identify residues contributing to the alkaline instability of pepsin-like aspartic proteases in regards to both structure and function. We constructed 13 mutants based on this strategy. Among them, mutants D159 L and D60A led to an increase in activity at elevated pH levels (p ≤ 0.05) and E4V and H53F were shown to retain native-like structure at elevated pH (p ≤ 0.05). Previously suggested carboxyl groups Asp¹¹, Asp¹¹⁸, and Glu¹³ were individually shown not to be responsible for the structural instability or lack of activity at neutral pH in pepsin. The importance of the β-barrel to structural stability was highlighted as the majority of the stabilizing residues identified, and 39% of the weakly conserved residues in the N-terminal lobe, were located in β-sheet strands of the barrel. The results of the present study indicate that alkaline stabilization of pepsin will require reduction of electrostatic repulsions and an improved understanding of the role of the hydrogen bonding network of the characteristic β-barrel.

Aphid Odorant-Binding Protein 9 Is Narrowly Tuned to Linear Alcohols and Aldehydes of Sixteen Carbon Atoms

Aphid odorant-binding protein 9 is almost exclusively expressed in antennae and is well conserved between different aphid species. In order to investigate its function, we have expressed this protein and measured ligand-binding affinities to a number of common natural compounds. The best ligands are long-chain aldehydes and alcohols, in particular Z9-hexadecenal and Z11-hexadecenal, as well as 1-hexadecanol and Z11-1-hexadecenol. A model of this protein indicated Lys37 as the residue that is likely to establish strong interactions with the ligands, probably a Schiff base with aldehydes and a hydrogen bond with alcohols. Indeed, when we replaced this lysine with a leucine, the mutated protein lost its affinity to both long aldehydes and alcohols, while the binding of other volatiles was unaffected. Long-chain linear alcohols are common products of molds and have been reported as aphid antifeedants. Corresponding aldehydes, instead, are major components of sex pheromones for several species of Lepidoptera. We speculate that aphids might use OBP9 to avoid mold-contaminated plants as well as competition with lepidopteran larvae.

Molecular Dynamics Simulations in Designing DARPins as Phosphorylation-Specific Protein Binders of ERK2

Extracellular signal-regulated kinases 1 and 2 (ERK1/2) play key roles in promoting cell survival and proliferation through the phosphorylation of various substrates. Remarkable antitumour activity is found in many inhibitors that act upstream of the ERK pathway. However, drug-resistant tumour cells invariably emerge after their use due to the reactivation of ERK1/2 signalling. ERK1/2 inhibitors have shown clinical efficacy as a therapeutic strategy for the treatment of tumours with mitogen-activated protein kinase (MAPK) upstream target mutations. These inhibitors may be used as a possible strategy to overcome acquired resistance to MAPK inhibitors. Here, we report a class of repeat proteins-designed ankyrin repeat protein (DARPin) macromolecules targeting ERK2 as inhibitors. The structural basis of ERK2-DARPin interactions based on molecular dynamics (MD) simulations was studied. The information was then used to predict stabilizing mutations employing a web-based algorithm, MAESTRO. To evaluate whether these design strategies were successfully deployed, we performed all-atom, explicit-solvent molecular dynamics (MD) simulations. Two mutations, Ala → Asp and Ser → Leu, were found to perform better than the original sequence (DARPin E40) based on the associated energy and key residues involved in protein-protein interaction. MD simulations and analysis of the data obtained on these mutations supported our predictions.

Directed Evolution: Methodologies and Applications

Directed evolution aims to expedite the natural evolution process of biological molecules and systems in a test tube through iterative rounds of gene diversifications and library screening/selection. It has become one of the most powerful and widespread tools for engineering improved or novel functions in proteins, metabolic pathways, and even whole genomes. This review describes the commonly used gene diversification strategies, screening/selection methods, and recently developed continuous evolution strategies for directed evolution. Moreover, we highlight some representative applications of directed evolution in engineering nucleic acids, proteins, pathways, genetic circuits, viruses, and whole cells. Finally, we discuss the challenges and future perspectives in directed evolution.

Predicting mutant outcome by combining deep mutational scanning and machine learning

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted "targets": (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment-the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.

Computational and synthetic biology approaches for the biosynthesis of antiviral and anticancer terpenoids from Bacillus subtilis

Recent advancements in medicinal research have identified several antiviral and anticancer terpenoids that are usually deployed as a source of flavor, fragrances and pharmaceuticals. Under the current COVID-19 pandemic conditions, natural therapeutics with least side effects are the need of the hour to save the patients, especially, which are pre-affected with other medical complications. Although, plants are the major sources of terpenoids; however, for the environmental concerns, the global interest has shifted to the biocatalytic production of molecules from microbial sources. The gram-positive bacterium Bacillus subtilis is a suitable host in this regard due to its GRAS (generally regarded as safe) status, ease in genetic manipulations and wide industrial acceptability. The B. subtilis synthesizes its terpenoid molecules from 1-deoxy-d-xylulose-5-phosphate (DXP) pathway, a common route in almost all microbial strains. Here, we summarize the computational and synthetic biology approaches to improve the production of terpenoid-based therapeutics from B. subtilis by utilizing DXP pathway. We focus on the in-silico approaches for screening the functionally improved enzyme-variants of the two crucial enzymes namely, the DXP synthase (DXS) and farnesyl pyrophosphate synthase (FPPS). The approaches for engineering the active sites are subsequently explained. It will be helpful to construct the functionally improved enzymes for the high-yield production of terpenoid-based anticancer and antiviral metabolites, which would help to reduce the cost and improve the availability of such therapeutics for the humankind.

Large-scale genome mining allows identification of neutral polymorphisms and novel resistance mutations in genes involved in Candida albicans resistance to azoles and echinocandins

BACKGROUND

The genome of Candida albicans displays significant polymorphism. Point mutations in genes involved in resistance to antifungals may either confer phenotypic resistance or be devoid of phenotypic consequences.

OBJECTIVES

To catalogue polymorphisms in azole and echinocandin resistance genes occurring in susceptible strains in order to rapidly pinpoint relevant mutations in resistant strains.

METHODS

Genome sequences from 151 unrelated C. albicans strains susceptible to fluconazole and caspofungin were used to create a catalogue of non-synonymous polymorphisms in genes involved in resistance to azoles (ERG11, TAC1, MRR1 and UPC2) or echinocandins (FKS1). The potential of this catalogue to reveal putative resistance mutations was tested in 10 azole-resistant isolates, including 1 intermediate to caspofungin. Selected mutations were analysed by mutagenesis experiments or mutational prediction effect.

RESULTS

In the susceptible strains, we identified 126 amino acid substitutions constituting the catalogue of phenotypically neutral polymorphisms. By excluding these neutral substitutions, we identified 22 additional substitutions in the 10 resistant strains. Among these substitutions, 10 had already been associated with resistance. The remaining 12 were in Tac1p (n = 6), Upc2p (n = 2) and Erg11p (n = 4). Four out of the six homozygous substitutions in Tac1p (H263Y, A790V, H839Y and P971S) conferred increases in azole MICs, while no effects were observed for those in Upc2p. Additionally, two homozygous substitutions (Y64H and P236S) had a predicted conformation effect on Erg11p.

CONCLUSIONS

By establishing a catalogue of neutral polymorphisms occurring in genes involved in resistance to antifungal drugs, we provide a useful resource for rapid identification of mutations possibly responsible for phenotypic resistance in C. albicans.

Inhibition of aquaporin-1 prevents myocardial remodeling by blocking the transmembrane transport of hydrogen peroxide

Pathological remodeling of the myocardium has long been known to involve oxidant signaling, but strategies using systemic antioxidants have generally failed to prevent it. We sought to identify key regulators of oxidant-mediated cardiac hypertrophy amenable to targeted pharmacological therapy. Specific isoforms of the aquaporin water channels have been implicated in oxidant sensing, but their role in heart muscle is unknown. RNA sequencing from human cardiac myocytes revealed that the archetypal AQP1 is a major isoform. AQP1 expression correlates with the severity of hypertrophic remodeling in patients with aortic stenosis. The AQP1 channel was detected at the plasma membrane of human and mouse cardiac myocytes from hypertrophic hearts, where it colocalized with NADPH oxidase-2 and caveolin-3. We show that hydrogen peroxide (H₂O₂), produced extracellularly, is necessary for the hypertrophic response of isolated cardiac myocytes and that AQP1 facilitates the transmembrane transport of H₂O₂ through its water pore, resulting in activation of oxidant-sensitive kinases in cardiac myocytes. Structural analysis of the amino acid residues lining the water pore of AQP1 supports its permeation by H₂O₂ Deletion of Aqp1 or selective blockade of the AQP1 intrasubunit pore inhibited H₂O₂ transport in mouse and human cells and rescued the myocyte hypertrophy in human induced pluripotent stem cell-derived engineered heart muscle. Treatment of mice with a clinically approved AQP1 inhibitor, Bacopaside, attenuated cardiac hypertrophy. We conclude that cardiac hypertrophy is mediated by the transmembrane transport of H₂O₂ by the water channel AQP1 and that inhibitors of AQP1 represent new possibilities for treating hypertrophic cardiomyopathies.

A Deep-Learning Sequence-Based Method to Predict Protein Stability Changes Upon Genetic Variations

Several studies have linked disruptions of protein stability and its normal functions to disease. Therefore, during the last few decades, many tools have been developed to predict the free energy changes upon protein residue variations. Most of these methods require both sequence and structure information to obtain reliable predictions. However, the lower number of protein structures available with respect to their sequences, due to experimental issues, drastically limits the application of these tools. In addition, current methodologies ignore the antisymmetric property characterizing the thermodynamics of the protein stability: a variation from wild-type to a mutated form of the protein structure (XW→XM) and its reverse process (XM→XW) must have opposite values of the free energy difference (ΔΔGWM=−ΔΔGMW). Here we propose ACDC-NN-Seq, a deep neural network system that exploits the sequence information and is able to incorporate into its architecture the antisymmetry property. To our knowledge, this is the first convolutional neural network to predict protein stability changes relying solely on the protein sequence. We show that ACDC-NN-Seq compares favorably with the existing sequence-based methods.

PU.1 Mimic Synthetic Peptides Selectively Bind with GATA-1 and Allow c-Jun PU.1 Binding to Enhance Myelopoiesis

BACKGROUND

Hematopoietic stem cells' commitment to myelopoiesis builds immunity to prevent infection. This process is controlled through transcription factor, especially Purine rich box 1 (PU.1) protein, which plays a central role in regulating myelopoiesis. The β3/β4 region of PU.1 accommodates a coactivator transcription factor, c-Jun, to activate myelopoiesis. However, an erythroid transcription factor, GATA-1, competes with c-Jun for the β3/β4 region, abolishing myelopoiesis and promoting erythropoiesis. This competitive regulation decides the hematopoietic stem cells' commitment towards either erythroid or myeloid lineage.

METHODS

Therefore, this study investigated the in vitro and in vivo effect of novel synthetic PU.1 β3/β4 mimic peptide analogs and peptide-loaded hydrophilic poly(D,L-lactide-co-glycolide) (PLGA) nanoparticles.

RESULTS

The designed peptides significantly increase the expression of corresponding myeloid markers, specifically CD33 in vitro. However, the in vivo delivery of peptide-loaded PLGA nanoparticles, which have sustained release effect of peptides, increases 10.8% of granulocytes as compared to control.

CONCLUSION

The observations showed that the fabricated nanoparticles protected the loaded peptides from the harsh intracellular environment for a longer duration without causing any toxicity. These findings highlight the possibility to use these peptides and peptide-loaded nanoparticles to increase hematopoietic stem cell commitment to myeloid cells in case of opportunistic infection.

Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations

Understanding how a mutation might affect protein stability is of significant importance to protein engineering and for understanding protein evolution genetic diseases. While a number of computational tools have been developed to predict the effect of missense mutations on protein stability protein stability upon mutations, they are known to exhibit large biases imparted in part by the data used to train and evaluate them. Here, we provide a comprehensive overview of predictive tools, which has provided an evolving insight into the importance and relevance of features that can discern the effects of mutations on protein stability. A diverse selection of these freely available tools was benchmarked using a large mutation-level blind dataset of 1342 experimentally characterised mutations across 130 proteins from ThermoMutDB, a second test dataset encompassing 630 experimentally characterised mutations across 39 proteins from iStable2.0 and a third blind test dataset consisting of 268 mutations in 27 proteins from the newly published ProThermDB. The performance of the methods was further evaluated with respect to the site of mutation, type of mutant residue and by ranging the pH and temperature. Additionally, the classification performance was also evaluated by classifying the mutations as stabilizing (∆∆G ≥ 0) or destabilizing (∆∆G < 0). The results reveal that the performance of the predictors is affected by the site of mutation and the type of mutant residue. Further, the results show very low performance for pH values 6-8 and temperature higher than 65 for all predictors except iStable2.0 on the S630 dataset. To illustrate how stability and structure change upon single point mutation, we considered four stabilizing, two destabilizing and two stabilizing mutations from two proteins, namely the toxin protein and bovine liver cytochrome. Overall, the results on S268, S630 and S1342 datasets show that the performance of the integrated predictors is better than the mechanistic or individual machine learning predictors. We expect that this paper will provide useful guidance for the design and development of next-generation bioinformatic tools for predicting protein stability changes upon mutations.

Structural effects driven by rare point mutations in amylin hormone, the type II diabetes-associated peptide

BACKGROUND

Amylin is a 37-amino-acid peptide hormone co-secreted with insulin, which participates in glucose homeostasis. This hormone is able to aggregate in a β-sheet conformation and deposit in islet amyloids, a hallmark in type II diabetes. Since amylin is a gene-encoded hormone, this peptide has variants caused by point mutations that can impact its functions.

METHODS

Here, we analyzed the structural effects caused by S20G and G33R point mutations which, according to the 1000 Genomes Project, have frequency in East Asian and European populations, respectively. The analyses were performed by means of aggrescan server, SNP functional effect predictors, and molecular dynamics.

RESULTS

We found that both mutations have aggregation potential and cause changes in the monomeric forms when compared with wild-type amylin. Furthermore, comparative analyses with pramlintide, an amylin drug analogue, allowed us to infer that second α-helix maintenance may be related to the aggregation potential.

CONCLUSIONS

The S20G mutation has been described as pathologically related, which is in agreement with our findings. In addition, our data suggest that the G33R mutation might have a deleterious effect. The data presented here also provide new therapy opportunities, whether for creating more effective drugs for diabetes or implementing specific treatment for patients with these mutations.

GENERAL SIGNIFICANCE

Our data could help to better understand the impact of mutations on the wild-type amylin sequence, as a starting point for the evaluation and characterization of other variations. Moreover, these findings could improve the health of patients with type II diabetes.

KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability

We have performed 280 μs of unbiased molecular dynamics (MD) simulations to investigate the effects of 12 different cancer mutations on Kelch-like ECH-associated protein 1 (KEAP1) (G333C, G350S, G364C, G379D, R413L, R415G, A427V, G430C, R470C, R470H, R470S and G476R), one of the frequently mutated proteins in lung cancer. The aim was to provide structural insight into the effects of these mutants, including a new class of ANCHOR (additionally NRF2-complexed hypomorph) mutant variants. Our work provides additional insight into the structural dynamics of mutants that could not be analyzed experimentally, painting a more complete picture of their mutagenic effects. Notably, blade-wise analysis of the Kelch domain points to stability as a possible target of cancer in KEAP1. Interestingly, structural analysis of the R470C ANCHOR mutant, the most prevalent missense mutation in KEAP1, revealed no significant change in structural stability or NRF2 binding site dynamics, possibly indicating an covalent modification as this mutant's mode of action.

Rational design and structure-based engineering of alkaline pectate lyase from Paenibacillus sp. 0602 to improve thermostability

BACKGROUND

Ramie degumming is often carried out at high temperatures; therefore, thermostable alkaline pectate lyase (PL) is beneficial for ramie degumming for industrial applications. Thermostable PLs are usually obtained by exploring new enzymes or reconstructing existing enzyme by rational design. Here, we improved the thermostability of an alkaline pectate lyase (PelN) from Paenibacillus sp. 0602 with rational design and structure-based engineering.

RESULTS

From 26 mutants, two mutants of G241A and G241V showed a higher thermostability compared with the wild-type PL. The mutant K93I showed increasing specific activity at 45 °C. Subsequently, we obtained combinational mutations (K93I/G241A) and found that their thermostability and specific activity improved simultaneously. The K93I/G241A mutant showed a half-life time of 15.9 min longer at 60 °C and a melting temperature of 1.6 °C higher than those of the wild PL. The optimum temperature decreased remarkably from 67.5 °C to 60 °C, accompanied by a 57% decrease in Km compared with the Km value of the wild-type strain. Finally, we found that the intramolecular interaction in PelN was the source in the improvements of molecular properties by comparing the model structures. Rational design of PelN was performed by stabilizing the α-helices with high conservation and increasing the stability of the overall structure of the protein. Two engineering strategies were applied by decreasing the mutation energy calculated by Discovery Studio and predicting the free energy in the process of protein folding by the PoPMuSiC algorithm.

CONCLUSIONS

The results demonstrated that the K93I/G241A mutant was more suitable for industrial production than the wild-type enzyme. Furthermore, the two forementioned strategies could be extended to reveal engineering of other kinds of industrial enzymes.

A meta-analysis of the activity, stability, and mutational characteristics of temperature-adapted enzymes

Understanding the characteristics that define temperature-adapted enzymes has been a major goal of extremophile enzymology in recent decades. In the present study, we explore these characteristics by comparing psychrophilic, mesophilic, and thermophilic enzymes. Through a meta-analysis of existing data, we show that psychrophilic enzymes exhibit a significantly larger gap (T_g) between their optimum and melting temperatures compared with mesophilic and thermophilic enzymes. These results suggest that T_g may be a useful indicator as to whether an enzyme is psychrophilic or not and that models of psychrophilic enzyme catalysis need to account for this gap. Additionally, by using predictive protein stability software, HoTMuSiC and PoPMuSiC, we show that the deleterious nature of amino acid substitutions to protein stability increases from psychrophiles to thermophiles. How this ultimately affects the mutational tolerance and evolutionary rate of temperature adapted organisms is currently unknown.

Stability Prediction for Mutations in the Cytosolic Domains of Cystic Fibrosis Transmembrane Conductance Regulator

Cystic Fibrosis (CF) is caused by mutations to the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) chloride channel. CFTR is composed of two membrane spanning domains, two cytosolic nucleotide-binding domains (NBD1 and NBD2) and a largely unstructured R-domain. Multiple CF-causing mutations reside in the NBDs and some are known to compromise the stability of these domains. The ability to predict the effect of mutations on the stability of the cytosolic domains of CFTR and to shed light on the mechanisms by which they exert their effect is therefore important in CF research. With this in mind, we have predicted the effect on domain stability of 59 mutations in NBD1 and NBD2 using 15 different algorithms and evaluated their performances via comparison to experimental data using several metrics including the correct classification rate (CCR), and the squared Pearson correlation (R²) and Spearman's correlation (ρ) calculated between the experimental ΔT_m values and the computationally predicted ΔΔG values. Overall, the best results were obtained with FoldX and Rosetta. For NBD1 (35 mutations), FoldX provided R² and ρ values of 0.64 and -0.71, respectively, with an 86% correct classification rate (CCR). For NBD2 (24 mutations), FoldX R², ρ, and CCR were 0.51, -0.73, and 75%, respectively. Application of the Rosetta high-resolution protocol (Rosetta_hrp) to NBD1 yielded R², ρ, and CCR of 0.64, -0.75, and 69%, respectively, and for NBD2 yielded R², ρ, and CCR of 0.29, -0.27, and 50%, respectively. The corresponding numbers for the Rosetta's low-resolution protocol (Rosetta_lrp) were R² = 0.47, ρ = -0.69, and CCR = 69% for NBD1 and R² = 0.27, ρ = -0.24, and CCR = 63% for NBD2. For NBD1, both algorithms suggest that destabilizing mutations suffer from destabilizing vdW clashes, whereas stabilizing mutations benefit from favorable H-bond interactions. Two triple consensus approaches based on FoldX, Rosetta_lpr, and Rosetta_hpr were attempted using either "majority-voting" or "all-voting". The all-voting consensus outperformed the individual predictors, albeit on a smaller data set. In summary, our results suggest that the effect of mutations on the stability of CFTR's NBDs could be largely predicted. Since NBDs are common to all ABC transporters, these results may find use in predicting the effect and mechanism of the action of multiple disease-causing mutations in other proteins.

In Silico Analysis of the Molecular-Level Impact of SMPD1 Variants on Niemann-Pick Disease Severity

Sphingomyelin phosphodiesterase (SMPD1) is a key enzyme in the sphingolipid metabolism. Genetic SMPD1 variants have been related to the Niemann-Pick lysosomal storage disorder, which has different degrees of phenotypic severity ranging from severe symptomatology involving the central nervous system (type A) to milder ones (type B). They have also been linked to neurodegenerative disorders such as Parkinson and Alzheimer. In this paper, we leveraged structural, evolutionary and stability information on SMPD1 to predict and analyze the impact of variants at the molecular level. We developed the SMPD1-ZooM algorithm, which is able to predict with good accuracy whether variants cause Niemann-Pick disease and its phenotypic severity; the predictor is freely available for download. We performed a large-scale analysis of all possible SMPD1 variants, which led us to identify protein regions that are either robust or fragile with respect to amino acid variations, and show the importance of aromatic-involving interactions in SMPD1 function and stability. Our study also revealed a good correlation between SMPD1-ZooM scores and in vitro loss of SMPD1 activity. The understanding of the molecular effects of SMPD1 variants is of crucial importance to improve genetic screening of SMPD1-related disorders and to develop personalized treatments that restore SMPD1 functionality.

Three Simple Properties Explain Protein Stability Change upon Mutation

Accurate prediction of protein stability upon mutation enables rational engineering of new proteins and insights into protein evolution and monogenetic diseases caused by single-point amino acid substitutions. Many tools have been developed to this aim, ranging from energy-based models to machine-learning methods that use large amounts of experimental data. However, as the methods become more complex, the interpretation of the chemistry underlying the protein stability effects becomes obscure. It is thus of interest to identify the simplest prediction model that retains complete amino acid specific interpretation; for a given number of input descriptors, we expect such a model to be almost universal. In this study, we identify such a limiting model, SimBa, a simple multilinear regression model trained on a substitution-type-balanced experimental data set. The model accounts only for the solvent accessibility of the site, volume difference, and polarity difference caused by mutation. Our results show that this very simple and directly applicable model performs comparably to other much more complex, widely used protein stability prediction methods. This suggests that a hard limit of ∼1 kcal/mol numerical accuracy and an R ∼ 0.5 trend accuracy exists and that new features, such as account of unfolded states, water colocalization, and amino acid correlations, are required to improve accuracy to, e.g., 1/2 kcal/mol.

A multidimensional computational exploration of congenital myasthenic syndrome causing mutations in human choline acetyltransferase

Missense mutations of human choline acetyltransferase (CHAT) are mainly associated with congenital myasthenic syndrome (CMS). To date, several pathogenic mutations have been reported, but due to the rarity and genetic complexity of CMS and difficult genotype-phenotype correlations, the CHAT mutations, and their consequences are underexplored. In this study, we systematically sift through the available genetic data in search of previously unreported pathogenic mutations and use a dynamic in silico model to provide structural explanations for the pathogenicity of the reported deleterious and undetermined variants. Through rigorous multiparameter analyses, we conclude that mutations can affect CHAT through a variety of different mechanisms: by disrupting the secondary structure, by perturbing the P-loop through long-range allosteric interactions, by disrupting the domain connecting loop, and by affecting the phosphorylation process. This study provides the first dynamic look at how mutations affect the structure and catalytic activity in CHAT and highlights the need for further genomic research to better understand the pathology of CHAT.

A base measure of precision for protein stability predictors: structural sensitivity

BACKGROUND

Prediction of the change in fold stability (ΔΔG) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict ΔΔG. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods.

RESULTS

We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity of ~ 0.6 to 0.8 kcal/mol, whereas machine-learning methods display much lower sensitivity (~ 0.1 kcal/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts.

CONCLUSIONS

The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that ΔΔG values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure.

Structural insight into mutations at 155 position of valosin containing protein (VCP) linked to inclusion body myopathy with Paget disease of bone and frontotemporal Dementia

Mutations in Valosin-containing protein (VCP) have been implicated in the pathology linked to inclusion body myopathy, paget disease of bone and frontotemporal dementia (IBMPFD). VCP is an essential component of AAA-ATPase superfamily involved in various cellular functions. Advanced In-silico analysis was performed using prediction based servers to determine the most deleterious mutation. Molecular dynamics simulation was used to study the protein dynamics at atomic level. Molecular docking was used to study the effect of mutation on ATP/ADP transition in the kinase domain. This ATPase of 806 amino acids has four domains: N-terminal domain, C-terminal domain and two ATPase domains D1 and D2 and each of these domains have a distinct role in its functioning. The mutations in VCP protein are distributed among regions known as hotspots, one such hotspot is codon 155. Three missense mutations reported in this hotspot are R155C, R155H and R155P. Potentiality of the deleteriousness calculated using server based prediction models reveal R155C mutation to be the most deleterious. The atomic insight into the effect of mutation by molecular dynamics simulation revealed major conformational changes in R155C variants ATP binding site in D1 domain. The nucleotide-binding mode at the catalytic pocket of VCP and its three variants at codon 155 showed change in the structure, which affects the ATP-ADP transition kinetics in all the three variants.

Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes

Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Structural Insights into the Mechanisms Underlying the Kinetic Stability of GH28 Endo-Polygalacturonase

Thermostability is a key property of industrial enzymes. Endo-polygalacturonases of the glycoside hydrolase family 28 have many practical applications, but only few of their structures have been determined, and the reasons for their stability remain unclear. We identified and characterized the Talaromyces leycettanus JCM12802 endo-polygalacturonase TlPGA, which differs from other GH28 family members because of its high catalytic activity, with an optimum temperature of 70 °C. Distinctive features were revealed by comparison of thermophilic TlPGA and all known structures of fungal endo-polygalacturonases, including a relatively large exposed polar accessible surface area in thermophilic TlPGA. By mutating potentially important residues in thermophilic TlPGA, we identified Thr284 as a critical residue. Mutant T284A was comparable to thermophilic TlPGA in melting temperature but exhibited a significantly lower half-life and half-inactivation temperature, implicating residue Thr284 in the kinetic stability of thermophilic TlPGA. Structure analysis of thermophilic TlPGA and mutant T284A revealed that a carbon-oxygen hydrogen bond between the hydroxyl group of Thr284 and the Cα atom of Gln255, and the stable conformation adopted by Gln255, contribute to its kinetic stability. Our results clarify the mechanism underlying the kinetic stability of GH28 endo-polygalacturonases and may guide the engineering of thermostable enzymes for industrial applications.

SWOTein: a structure-based approach to predict stability Strengths and Weaknesses of prOTEINs

MOTIVATION

Although structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes.

RESULTS

We developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale analysis of the different types of strengths and weaknesses demonstrated their complementarity and the enhancement of the information they provide. Moreover, a good average correlation was observed between predicted and experimental strengths and weaknesses obtained from native hydrogen exchange data. SWOTein application to three test cases further showed its suitability to predict and interpret strong and weak residues in the context of folding, conformational changes and protein-protein binding. In summary, SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes.

AVAILABILITY

The SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein/.

ELASPIC2 (EL2): Combining Contextualized Language Models and Graph Neural Networks to Predict Effects of Mutations

The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org.

SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability

Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers’ code.

ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years

ProThermDB is an updated version of the thermodynamic database for proteins and mutants (ProTherm), which has ∼31 500 data on protein stability, an increase of 84% from the previous version. It contains several thermodynamic parameters such as melting temperature, free energy obtained with thermal and denaturant denaturation, enthalpy change and heat capacity change along with experimental methods and conditions, sequence, structure and literature information. Besides, the current version of the database includes about 120 000 thermodynamic data obtained for different organisms and cell lines, which are determined by recent high throughput proteomics techniques using whole-cell approaches. In addition, we provided a graphical interface for visualization of mutations at sequence and structure levels. ProThermDB is cross-linked with other relevant databases, PDB, UniProt, PubMed etc. It is freely available at https://web.iitm.ac.in/bioinfo2/prothermdb/index.html without any login requirements. It is implemented in Python, HTML and JavaScript, and supports the latest versions of major browsers, such as Firefox, Chrome and Safari.

Can constraint network analysis guide the identification phase of KnowVolution? A case study on improved thermostability of an endo-β-glucanase

Cellulases are industrially important enzymes, e.g., in the production of bioethanol, in pulp and paper industry, feedstock, and textile. Thermostability is often a prerequisite for high process stability and improving thermostability without affecting specific activities at lower temperatures is challenging and often time-consuming. Protein engineering strategies that combine experimental and computational are emerging in order to reduce experimental screening efforts and speed up enzyme engineering campaigns. Constraint Network Analysis (CNA) is a promising computational method that identifies beneficial positions in enzymes to improve thermostability. In this study, we compare CNA and directed evolution in the identification of beneficial positions in order to evaluate the potential of CNA in protein engineering campaigns (e.g., in the identification phase of KnowVolution). We engineered the industrially relevant endoglucanase EGLII from Penicillium verruculosum towards increased thermostability. From the CNA approach, six variants were obtained with an up to 2-fold improvement in thermostability. The overall experimental burden was reduced to 40% utilizing the CNA method in comparison to directed evolution. On a variant level, the success rate was similar for both strategies, with 0.27% and 0.18% improved variants in the epPCR and CNA-guided library, respectively. In essence, CNA is an effective method for identification of positions that improve thermostability.

Construction of the R17L mutant of MtC1LPMO for improved lignocellulosic biomass conversion by rational point mutation and investigation of the mechanism by molecular dynamics simulations

To enhance the biomass conversion efficiency, the R17L mutant of the lytic polysaccharide monooxygenase (LPMO) MtC1LPMO with improved catalytic efficiency was constructed via rational point mutation based on the HotSpot Wizard 3.0 and dezyme web servers. Compared with the wild-type (WT) MtC1LPMO, R17L exhibited a 1.8-fold increase of specific activity and 1.92-fold increase of catalytic efficiency (k_cat/K_m). The degree of increase of the reducing sugar yield from microcrystalline cellulose and three plant biomass materials during synergistic hydrolysis using cellulase in combination with R17L was about 2 times higher than with the WT. Molecular dynamics simulations revealed that the R17L mutation reduced the stability of the region R18-I36, which then weakened the direct interactions between region N24-V31 and the substrate cellohexaose. Consequently, the deflection time of the cellohexaose conformation in R17L was prolonged compared to the WT, which enhanced its catalytic efficiency.

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.

Excavating the functionally crucial active-site residues of the DXS protein of Bacillus subtilis by exploring its closest homologues

Background

To achieve a high yield of terpenoid-based therapeutics, 1-deoxy-d-xylulose-5-phosphate (DXP) pathway has been significantly exploited for the production of downstream enzymes. The DXP synthase (DXS) enzyme, the initiator of this pathway, is pivotal for the convergence of carbon flux, and is computationally studied well for the industrially utilized generally regarded as safe (GRAS) bacterium Bacillus subtilis to decode its vital regions for aiding the construction of a functionally improved mutant library.

Results

For the 546 sequence dataset of DXS sequences, a representative set of 108 sequences is created, and it shows a significant evolutionary divergence across different species clubbed into 37 clades, whereas three clades are observed for the 76 sequence dataset of Bacillus subtilis. The DXS enzyme, sharing a statistically significant homology to transketolase, is shown to be evolutionarily too distant. By the mutual information-based co-evolutionary network and hotspot analysis, the most crucial loci within the active site are deciphered. The 650-residue representative structure displays a complete conservation of 114 loci, and only two co-evolving residues ASP154 and ILE371 are found to be the conserved ones. Lastly, P318D is predicted to be the top-ranked mutation causing the increase in the thermodynamic stability of 6OUW.

Conclusion

The study excavates the vital functional, phylogenetic, and conserved residues across the active site of the DXS protein, the key rate-limiting controller of the entire pathway. It would aid to computationally understand the evolutionary landscape of this industrially useful enzyme and would allow us to widen its substrate repertoire to increase the enzymatic yield of unnatural molecules for in vivo and in vitro applications.

Mutations Causing Mild or No Structural Damage in Interfaces of Multimerization of the Fibrinogen γ-Module More Likely Confer Negative Dominant Behaviors

Different pathogenic variants in the same protein or even within the same domain of a protein may differ in their patterns of disease inheritance, with some of the variants behaving as negative dominant and others as autosomal recessive mutations. Here is presented a structural analysis and comparison of the molecular characteristics of the sites in fibrinogen γ-module, a fibrinogen component critical in multimerization processes, targeted by pathogenic variants (HGMD database) and by variants found in the healthy population (gnomAD database). The main result of this study is the identification of the molecular pathogenic mechanisms defining which pattern of disease inheritance is selected by mutations at the crossroad of autosomal recessive and negative dominant modalities. The observations in this analysis also warn about the possibility that several variants reported in the non-pathogenic gnomAD database might indeed be a hidden source of diseases with autosomal recessive inheritance or requiring a combination with other disease-causing mutations. Disease presentation might remain mostly unrevealed simply because the very low variant frequency rarely results in biallelic pathogenic mutations or the coupling with mutations in other genes contributing to the same disease. The results here presented provide hints for a deeper search of pathogenic mechanisms and modalities of disease inheritance for protein mutants participating in multimerization phenomena.

Variation benchmark datasets: update, criteria, quality and applications

Development of new computational methods and testing their performance has to be carried out using experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets used mainly for variation interpretation. They have been used for training and benchmarking predictors for various types of variations and their effects. VariBench was updated with 419 new datasets from 109 papers containing altogether 329 014 152 variants; however, there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performances to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and show that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data.

Database URL: http://structure.bmc.lu.se/VariBench

A sequence embedding method for enzyme optimal condition analysis

Background

An enzyme activity is influenced by the external environment. It is important to have an enzyme remain high activity in a specific condition. A usual way is to first determine the optimal condition of an enzyme by either the gradient test or by tertiary structure, and then to use protein engineering to mutate a wild type enzyme for a higher activity in an expected condition.

Results

In this paper, we investigate the optimal condition of an enzyme by directly analyzing the sequence. We propose an embedding method to represent the amino acids and the structural information as vectors in the latent space. These vectors contain information about the correlations between amino acids and sites in the aligned amino acid sequences, as well as the correlation with the optimal condition. We crawled and processed the amino acid sequences in the glycoside hydrolase GH11 family, and got 125 amino acid sequences with optimal pH condition. We used probabilistic approximation method to implement the embedding learning method on these samples. Based on these embedding vectors, we design a computational score to determine which one has a better optimal condition for two given amino acid sequences and achieves the accuracy 80% on the test proteins in the same family. We also give the mutation suggestion such that it has a higher activity in an expected environment, which is consistent with the previously professional wet experiments and analysis.

Conclusion

A new computational method is proposed for the sequence based on the enzyme optimal condition analysis. Compared with the traditional process that involves a lot of wet experiments and requires multiple mutations, this method can give recommendations on the direction and location of amino acid substitution with reference significance for an expected condition in an efficient and effective way.

Naturally-Occurring Rare Mutations Cause Mild to Catastrophic Effects in the Multifunctional and Cancer-Associated NQO1 Protein

The functional and pathological implications of the enormous genetic diversity of the human genome are mostly unknown, primarily due to our unability to predict pathogenicity in a high-throughput manner. In this work, we characterized the phenotypic consequences of eight naturally-occurring missense variants on the multifunctional and disease-associated NQO1 protein using biophysical and structural analyses on several protein traits. Mutations found in both exome-sequencing initiatives and in cancer cell lines cause mild to catastrophic effects on NQO1 stability and function. Importantly, some mutations perturb functional features located structurally far from the mutated site. These effects are well rationalized by considering the nature of the mutation, its location in protein structure and the local stability of its environment. Using a set of 22 experimentally characterized mutations in NQO1, we generated experimental scores for pathogenicity that correlate reasonably well with bioinformatic scores derived from a set of commonly used algorithms, although the latter fail to semiquantitatively predict the phenotypic alterations caused by a significant fraction of mutations individually. These results provide insight into the propagation of mutational effects on multifunctional proteins, the implementation of in silico approaches for establishing genotype-phenotype correlations and the molecular determinants underlying loss-of-function in genetic diseases.

Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.

Combining structure and genomics to understand antimicrobial resistance

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

NGS Panel Testing of Triple-Negative Breast Cancer Patients in Cyprus: A Study of BRCA-Negative Cases

Simple Summary

In Cyprus, approximately 9% of triple-negative (negative in common breast cancer receptors—estrogen, progesterone, and human epidermal growth factor receptor 2 (HER2) receptors) breast cancer (TNBC) patients carry inherited mutations in the BRCA1/2 breast cancer (BC) susceptibility genes. These mutations increase the risk of BC. However, the contribution of other BC susceptibility genes has not yet been determined. To this end, we aimed to investigate the prevalence of mutations in BRCA1/2-negative TNBC patients in Cyprus. Ninety-five cancer susceptibility genes were sequenced for 163 TNBC patients. The frequency of non-BRCA mutations and especially PALB2 in TNBC patients in Cyprus appears to be higher compared to other populations, and half of the mutation-positive patients were diagnosed over the age of 60 years. Based on these results, we believe that PALB2 and TP53 along with BRCA1/2 genetic testing could be beneficial for a large proportion of TNBC patients in Cyprus, irrespective of their age of diagnosis.

Abstract

In Cyprus, approximately 9% of triple-negative (estrogen receptor-negative, progesterone receptor-negative, and human epidermal growth factor receptor 2-negative) breast cancer (TNBC) patients are positive for germline pathogenic variants (PVs) in BRCA1/2. However, the contribution of other genes has not yet been determined. To this end, we aimed to investigate the prevalence of germline PVs in BRCA1/2-negative TNBC patients in Cyprus, unselected for family history of cancer or age of diagnosis. A comprehensive 94-cancer-gene panel was implemented for 163 germline DNA samples, extracted from the peripheral blood of TNBC patients. Identified variants of uncertain clinical significance were evaluated, using extensive in silico investigation. Eight PVs (4.9%) were identified in two high-penetrance TNBC susceptibility genes. Of these, seven occurred in PALB2 (87.5%) and one occurred in TP53 (12.5%). Interestingly, 50% of the patients carrying PVs were diagnosed over the age of 60 years. The frequency of non-BRCA PVs (4.9%) and especially PALB2 PVs (4.3%) in TNBC patients in Cyprus appears to be higher compared to other populations. Based on these results, we believe that PALB2 and TP53 along with BRCA1/2 genetic testing could be beneficial for a large proportion of TNBC patients in Cyprus, irrespective of their age of diagnosis.

In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review

Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.

Identification of pathogenic missense mutations using protein stability predictors

Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

Accurate and efficient structure-based computational mutagenesis for modeling fluorescence levels of Aequorea victoria green fluorescent protein mutants

A computational mutagenesis technique was used to characterize the structural effects associated with over 46 000 single and multiple amino acid variants of Aequorea victoria green fluorescent protein (GFP), whose functional effects (fluorescence levels) were recently measured by experimental researchers. For each GFP mutant, the approach generated a single score reflecting the overall change in sequence-structure compatibility relative to native GFP, as well as a vector of environmental perturbation (EP) scores characterizing the impact at all GFP residue positions. A significant GFP structure–function relationship (P &lt; 0.0001) was elucidated by comparing the sequence-structure compatibility scores with the functional data. Next, the computed vectors for GFP mutants were used to train predictive models of fluorescence by implementing random forest (RF) classification and tree regression machine learning algorithms. Classification performance reached 0.93 for sensitivity, 0.91 for precision and 0.90 for balanced accuracy, and regression models led to Pearson’s correlation as high as r = 0.83 between experimental and predicted GFP mutant fluorescence. An RF model trained on a subset of over 1000 experimental single residue GFP mutants with measured fluorescence was used for predicting the 3300 remaining unstudied single residue mutants, with results complementing known GFP biochemical and biophysical properties. In addition, models trained on the subset of experimental GFP mutants harboring multiple residue replacements successfully predicted fluorescence of the single residue GFP mutants. The models developed for this study were accurate and efficient, and their predictions outperformed those of several related state-of-the-art methods.