iPTREE-STAB: interpretable decision tree based method for predicting protein stability changes upon mutations

Protein stability prediction by fine-tuning a protein language model on a mega-scale dataset

Protein stability plays a crucial role in a variety of applications, such as food processing, therapeutics, and the identification of pathogenic mutations. Engineering campaigns commonly seek to improve protein stability, and there is a strong interest in streamlining these processes to enable rapid optimization of highly stabilized proteins with fewer iterations. In this work, we explore utilizing a mega-scale dataset to develop a protein language model optimized for stability prediction. ESMtherm is trained on the folding stability of 528k natural and de novo sequences derived from 461 protein domains and can accommodate deletions, insertions, and multiple-point mutations. We show that a protein language model can be fine-tuned to predict folding stability. ESMtherm performs reasonably on small protein domains and generalizes to sequences distal from the training set. Lastly, we discuss our model's limitations compared to other state-of-the-art methods in generalizing to larger protein scaffolds. Our results highlight the need for large-scale stability measurements on a diverse dataset that mirrors the distribution of sequence lengths commonly observed in nature.

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

Current status and emerging frontiers in enzyme engineering: An industrial perspective

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

Computational identification and analysis of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene: a structural and functional impact

Cytochrome P450 oxidoreductase (POR) protein is essential for steroidogenesis, and POR gene mutations are frequently associated with P450 Oxidoreductase Deficiency (PORD), a disorder of hormone production. To our knowledge, no previous attempt has been made to identify and analyze the deleterious/pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene through an extensive computational approach. Computational algorithms and tools were employed to identify, characterize, and validate the pathogenic SNPs associated with certain diseases. To begin with, all the high-confidence SNPs were collected, and their structural and functional impacts on the protein structures were explored. The results of various in silico analyses affirm that the A287P and R457H variants of POR could destabilize the interactions between the amino acids and the hydrogen bond networks, resulting in functional deviations of POR. The literature study further confirms that the pathogenic mutations (A287P and R457H) are associated with the onset of PORD. Molecular dynamics simulations (MDS) and essential dynamics (ED) studies characterized the structural consequences of prioritized deleterious mutations, representing the structural destabilization that might disrupt POR biological function. The identified deleterious mutations at the cofactor's binding domains might interfere with the essential interactions between the protein and cofactors, thus inhibiting POR catalytic activity. The consolidated insights from the computational analyses can be used to predict potential deleterious mutants and understand the disease's pathological basis and the molecular mechanism of drug metabolism for the application of personalized medication. HIGHLIGHTSNADPH cytochrome P450 oxidoreductase (POR) mutations are associated with a broad spectrum of human diseasesIdentified and analyzed the most deleterious nsSNPs of POR through the sequence and structure-based prediction toolsInvestigated the structural and functional impacts of the most significant mutations (A287P and R457H) associated with PORDMolecular dynamics and PCA-based FEL analysis were utilized to probe the mutation-induced structural alterations in PORCommunicated by Ramaswamy H. Sarma.

Identification of the SIRT1 gene's most harmful non-synonymous SNPs and their effects on functional and structural features-an in silico analysis

Introduction

The sirtuin (Silent mating type information regulation 2 homolog)1(SIRT1) protein plays a vital role in many disorders such as diabetes, cancer, obesity, inflammation, and neurodegenerative and cardiovascular diseases. The objective of this in silico analysis of SIRT1's functional single nucleotide polymorphisms (SNPs) was to gain valuable insight into the harmful effects of non-synonymous SNPs (nsSNPs) on the protein. The objective of the study was to use bioinformatics methods to investigate the genetic variations and modifications that may have an impact on the SIRT1 gene's expression and function.

Methods

nsSNPs of SIRT1 protein were collected from the dbSNP site, from its three (3) different protein accession IDs. These were then fed to various bioinformatic tools such as SIFT, Provean, and I- Mutant to find the most deleterious ones. Functional and structural effects were examined using the HOPE server and I-Tasser. Gene interactions were predicted by STRING software. The SIFT, Provean, and I-Mutant tools detected the most deleterious three nsSNPs (rs769519031, rs778184510, and rs199983221).

Results

Out of 252 nsSNPs, SIFT analysis showed that 94 were deleterious, Provean listed 67 dangerous, and I-Mutant found 58 nsSNPs resulting in lowered stability of proteins. HOPE modelling of rs199983221 and rs769519031 suggested reduced hydrophobicity due to Ile 4Thr and Ile223Ser resulting in decreased hydrophobic interactions. In contrast, on modelling rs778184510, the mutant protein had a higher hydrophobicity than the wild type.

Conclusions

Our study reports that three nsSNPs (D357A, I223S, I4T) are the most damaging mutations of the SIRT1 gene. Mutations may result in altered protein structure and functions. Such altered protein may be the basis for various disorders. Our findings may be a crucial guide in establishing the pathogenesis of various disorders.

Prediction of Thermostability of Enzymes Based on the Amino Acid Index (AAindex) Database and Machine Learning

The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we used an innovative sequence-activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this paper, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability mutants than those predicted using the existing data. We successfully improved the coefficient of determination (R2) of the model from 0.66 to 0.92. In addition, root-mean-squared deviation (RMSD), root-mean-squared fluctuation (RMSF), solvent accessible surface area (SASA), hydrogen bonds, and the radius of gyration were estimated based on molecular dynamics simulations, and the differences between the predicted mutants and the wild-type (WT) were analyzed. The successful application of the innov'SAR algorithm in improving the thermostability of AT-ATA may help in directed evolutionary screening and open up new avenues for protein engineering.

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks

Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exhibit anti-symmetric bias toward destabilizing situations, whereas others struggle with generalization to unseen examples. To address these issues, we propose a gated graph neural network-based approach to predict changes in protein stability upon mutation. The model uses message passing to encode the links between the molecular structure and property after eliminating the non-mutant structure and creating input feature vectors. While doing so, it also incorporates the coordinates of the raw atoms to provide spatial insights into the chemical systems. We test the model on the Ssym, Myoglobin, Broom, and p53 datasets to demonstrate the generalization performance. Compared to existing approaches, our proposed method achieves improved linearity with symmetry in less time. The code for this study is available at: https://github.com/HongzhouTang/Pros-GNN.

FireProt 2.0: web-based platform for the fully automated design of thermostable proteins

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning

Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.

Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes

Prematurity is the foremost cause of death in children under 5 years of age. Genetics contributes to 25-40% of all preterm births (PTB) yet we still need to identify specific targets for intervention based on genetic pathways. This study involved the effect of region-specific non-synonymous variations and their transcript level mutational impact on protein functioning and stability by various in-silico tools. This investigation identifies potential therapeutic targets to manage the challenge of PTB, corresponding protein cavities and explores their binding interactions with intervening compounds. We searched 20 genes coding 55 PTB proteins from NCBI. Single Nucleotide Polymorphisms (SNPs) of concerned genes were extracted from ENSEMBL, and filtration of exonic variants (non-synonymous) was performed. Several in-silico downstream protein functional effect prediction tools were used to identify damaging variants. Rare coding variants were selected with an allele frequency of ≤1% in 1KGD, further supported by South Asian ALFA frequencies and GTEx gene/tissue expression database. CNN1, COL24A1, IQGAP2 and SLIT2 were identified with 7 rare pathogenic variants found in 17 transcript sequences. The functional impact analyses of rs532147352 (R>H) of CNN1 computed through PhD-SNP, PROVEAN, SNP&GO, PMut and MutPred2 algorithms showed impending deleterious effects, and the presence of this pathogenic mutation in CNN1 resulted in large decrease in protein structural stability (ΔΔG (kcal/mol). After structural protein identification, homology modelling of CNN1, which has been previously reported as a biomarker for the prediction of PTB, was performed, followed by the stereochemical quality checks of the 3D model. Blind docking approach were used to search the binding cavities and molecular interactions with progesterone, ranked with energetic estimations. Molecular interactions of CNN1 with progesterone were investigated through LigPlot 2D. Further, molecular docking experimentation of CNN1 showed the significant interactions at S102, L105, A106, K123, Y124 with five selected PTB-drugs, Allylestrenol (-7.56 kcal/mol), Hydroxyprogesterone caproate (-8.19 kcal/mol), Retosiban (-9.43 kcal/mol), Ritodrine (-7.39 kcal/mol) and Terbutaline (-6.87 kcal/mol). Calponin-1 gene and its molecular interaction analysis could serve as an intervention target for the prevention of PTB.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Prediction of protein stability changes upon single-point variant using 3D structure profile

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.

Insights From Long-term Follow-up of a Girl With Adrenal Insufficiency and Sphingosine-1-Phosphate Lyase Deficiency

Introduction

Sphingosine-1-phosphate lyase (SGPL1) insufficiency syndrome (SPLIS) is a multisystemic disorder which, in the main, incorporates steroid-resistant nephrotic syndrome and primary adrenal insufficiency (PAI).

Case Presentation

We present a young girl with a novel homozygous variant in SGPL1, p.D350G, with PAI in the absence of nephrotic syndrome. In the course of 15 years of follow-up she has further developed primary hypothyroidism and while she has progressed through puberty appropriately, ovarian calcifications were noted on imaging. The p.D350G variant results in reduced protein expression of SGPL1. We demonstrate that CRISPR engineered knockout of SGPL1 in human adrenocortical (H295R) cells abrogates cortisol production. Furthermore, while wild-type SGPL1 is able to rescue cortisol production in this in vitro model of adrenal disease, this is not observed with the p.D350G mutant.

Conclusion

SGPL1 deficiency should be considered in the differential diagnosis of PAI with close attention paid to evolving disease on follow-up.

Structural Consequence of Non-Synonymous Single-Nucleotide Variants in the N-Terminal Domain of LIS1

Disruptive neuronal migration during early brain development causes severe brain malformation. Characterized by mislocalization of cortical neurons, this condition is a result of the loss of function of migration regulating genes. One known neuronal migration disorder is lissencephaly (LIS), which is caused by deletions or mutations of the LIS1 (PAFAH1B1) gene that has been implicated in regulating the microtubule motor protein cytoplasmic dynein. Although this class of diseases has recently received considerable attention, the roles of non-synonymous polymorphisms (nsSNPs) in LIS1 on lissencephaly progression remain elusive. Therefore, the present study employed combined bioinformatics and molecular modeling approach to identify potential damaging nsSNPs in the LIS1 gene and provide atomic insight into their roles in LIS1 loss of function. Using this approach, we identified three high-risk nsSNPs, including rs121434486 (F31S), rs587784254 (W55R), and rs757993270 (W55L) in the LIS1 gene, which are located on the N-terminal domain of LIS1. Molecular dynamics simulation highlighted that all variants decreased helical conformation, increased the intermonomeric distance, and thus disrupted intermonomeric contacts in the LIS1 dimer. Furthermore, the presence of variants also caused a loss of positive electrostatic potential and reduced dimer binding potential. Since self-dimerization is an essential aspect of LIS1 to recruit interacting partners, thus these variants are associated with the loss of LIS1 functions. As a corollary, these findings may further provide critical insights on the roles of LIS1 variants in brain malformation.

An in-silico analysis to identify structural, functional and regulatory role of SNPs in hMRE11

Meiotic recombination 11 (MRE11) is a component of the tri-molecular MRE11-RAD50-NBS1 (MRN) complex, which functions as an exonuclease and endonuclease which is involved in identifying, signalling, protecting and repairing double-strand breaks in DNA (DSBs). Ataxia-telangiectasia-like disorder (ATLD) 1 and Nijmegen breakage syndrome (NBS)-like disorder are MRE11 associated diseases. In the present study, we used an integrated computational approach to identify the most deleterious SNPs and their structural and functional impact on human MRE11. Five of the 68 observed non-synonymous SNP (nsSNPs; I162T, S273C, W210C, D311Y and R364L) should be worked on due to their strong possible pathogenicity and the risk of changing protein properties. All the nsSNPs were highly conserved and decrease the protein stability located in the MRE11 nuclease and MRE11 DNA binding presumed domain. R364L and I162T were predicted to be involved in post-translational modification (PTM) sites. Furthermore, we also analysed the regulatory effect of noncoding SNPs on MRE11 gene regulation in which 6 SNPs were found to affect gene regulation. All six noncoding SNPs predicted chromatin interactive site whereas only one SNP was noted its association with miRNA binding site which disrupts 5 miRNA conserved site. These findings help future studies to get more insights into the role of these variants in the alteration of the MRE11 function. Communicated by Ramaswamy H. Sarma.

Computational design of a cutinase for plastic biodegradation by mining molecular dynamics simulations trajectories

Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔT_m values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔT_m value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature.

In-Silico and In-Vitro Analysis of Human SOS1 Protein Causing Noonan Syndrome - A Novel Approach to Explore the Molecular Pathways

Aims

Perform in-silico analysis of human SOS1 mutations to elucidate their pathogenic role in Noonan syndrome (NS).

Background

NS is an autosomal dominant genetic disorder caused by single nucleotide mutation in PTPN11, SOS1, RAF1, and KRAS genes. NS is thought to affect approximately 1 in 1000. NS patients suffer different pathogenic effects depending on the mutations they carry. Analysis of the mutations would be a promising predictor in identifying the pathogenic effect of NS.

Methods

We performed computational analysis of the SOS1 gene to identify the pathogenic nonsynonymous single nucleotide polymorphisms (nsSNPs) th a t cause NS. SOS1 variants were retrieved from the SNP database (dbSNP) and analyzed by in-silico tools I-Mutant, iPTREESTAB, and MutPred to elucidate their structural and functional characteristics.

Results

We found that 11 nsSNPs of SOS1 that were linked to NS. 3D modeling of the wild-type and the 11 nsSNPs of SOS1 showed that SOS1 interacts with cardiac proteins GATA4, TNNT2, and ACTN2. We also found that GRB2 and HRAS act as intermediate molecules between SOS1 and cardiac proteins. Our in-silico analysis findings were further validated using induced cardiomyocytes (iCMCs) derived from NS patients carrying SOS1 gene variant c.1654A>G (NSiCMCs) and compared to control human skin fibroblast-derived iCMCs (C-iCMCs). Our in vitro data confirmed that the SOS1, GRB2 and HRAS gene expressions as well as the activated ERK protein, were significantly decreased in NS-iCMCs when compared to C-iCMCs.

Conclusion

This is the first in-silico and in vitro study demonstrating that 11 nsSNPs of SOS1 play deleterious pathogenic roles in causing NS.

In-silico analysis reveals druggable single nucleotide polymorphisms in angiotensin 1 converting enzyme involved in the onset of blood pressure

OBJECTIVE

The Angiotensin 1 converting enzyme (ACE1) gene plays a critical role in regulating blood pressure and thus, it has become a major therapeutic target of antihypertensives. Single nucleotide polymorphisms (SNPs) occurring within a gene most especially at the functional segment of the genes alter the structure-function relationship of that gene.

RESULTS

Our study revealed that five nsSNPs of the ACE1 gene were found to be potentially deleterious and damaging and they include rs2229839, rs14507892, rs12709442, and rs4977 at point mutations P351R, R953Q, I1018T, F1051V, and T1187M. The protein stability predictive tools revealed that all the nsSNPs decreased stability of the protein and the Consurf server which estimates the evolutionary conservation profile of a protein showed that three mutants were in the highly conserved region. In conclusion, this study predicted potential druggable deleterious mutants that can be further explored to understand the pathological basis of cardiovascular disease.

Artificial intelligence challenges for predicting the impact of mutations on protein stability

Stability is a key ingredient of protein fitness, and its modification through targeted mutations has applications in various fields, such as protein engineering, drug design, and deleterious variant interpretation. Many studies have been devoted over the past decades to build new, more effective methods for predicting the impact of mutations on protein stability based on the latest developments in artificial intelligence. We discuss their features, algorithms, computational efficiency, and accuracy estimated on an independent test set. We focus on a critical analysis of their limitations, the recurrent biases toward the training set, their generalizability, and interpretability. We found that the accuracy of the predictors has stagnated at around 1 kcal/mol for over 15 years. We conclude by discussing the challenges that need to be addressed to reach improved performance.

Understanding the impact of missense mutations on the structure and function of the EDA gene in X-linked hypohidrotic ectodermal dysplasia: A bioinformatics approach

X-linked hypohidrotic dysplasia (XLHED), caused by mutations in the EDA gene, is a rare genetic disease that affects the development and function of the teeth, hair, nails, and sweat glands. The structural and functional consequences of caused by an ectodysplasin-A (EDA) mutations on protein phenotype, stability, and posttranslational modifications (PTMs) have not been well investigated. The present investigation involves five missense mutations that cause XLHED (L56P, R155C, P220L, V251M, and V322A) in different domains of EDA (TM, furin, collagen, and tumor necrosis factor [TNF]) from previously published papers. The deleterious nature of EDA mutant variants was identified using several computational algorithm tools. The point mutations induce major drifts in the structural flexibility of EDA mutant variants and have a negative impact on their stability, according to the 3D protein modeling tool assay. Using the molecular docking technique, EDA/EDA variants were docked to 10 EDA interacting partners, retrieved from the STRING database. We found a novel biomarker CD68 by molecular docking analysis, suggesting all five EDA variants had lower affinity for EDAR, EDA2R, and CD68, implying that they would affect embryonic signaling between the ectodermal and mesodermal cell layers. In silico research such as gene ontology, subcellular localization, protein-protein interaction, and PTMs investigations indicates major functional alterations would occur in EDA variants. According to molecular simulations, EDA variants influence the structural conformation, compactness, stiffness, and function of the EDA protein. Further studies on cell line and animal models might be useful in determining their specific roles in functional annotations.

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.

Rational Design of Adenylate Kinase Thermostability through Coevolution and Sequence Divergence Analysis

Protein engineering is actively pursued in industrial and laboratory settings for high thermostability. Among the many protein engineering methods, rational design by bioinformatics provides theoretical guidance without time-consuming experimental screenings. However, most rational design methods either rely on protein tertiary structure information or have limited accuracies. We proposed a primary-sequence-based algorithm for increasing the heat resistance of a protein while maintaining its functions. Using adenylate kinase (ADK) family as a model system, this method identified a series of amino acid sites closely related to thermostability. Single- and double-point mutants constructed based on this method increase the thermal denaturation temperature of the mesophilic Escherichia coli (E. coli) ADK by 5.5 and 8.3 °C, respectively, while preserving most of the catalytic function at ambient temperatures. Additionally, the constructed mutants have improved enzymatic activity at higher temperature.

Functional Analysis of Single Nucleotide Polymorphism in ZUFSP Protein and Implication in Pathogenesis

Researches have revealed that functional non-synonymous Single Nucleotide Polymorphism (nsSNPs) present in the Zinc-finger with UFM1-Specific Peptidase domain protein (ZUFSP) may be involved in genetic instability and carcinogenesis. For the first time, we employed in-silico approach using predictive tools to identify and validate potential nsSNPs that could be pathogenic. Our result revealed that 8 nsSNPs (rs 112738382, rs 140094037, rs 201652589, rs 201847265, rs 202076827, rs 373634906, rs 375114528, rs 772591104) are pathogenic after being subjected to rigorous filtering process. The structural impact of the nsSNPs on ZUFSP structure indicated that the nsSNPs affect the stability of the protein by lowering ZUFSP protein stability. Furthermore, conservation analysis showed that rs 201652589, rs 140094037, rs 201847265, and rs 772591104 were highly conserved. Interestingly, the protein-protein affinity between ZUFSP and Ubiquitin was altered rs 201652589, rs 140094037, rs 201847265, and rs 772591104 had a binding affinity of - 0.46, - 0.83, - 1.62, and - 1.12 kcal/mol respectively. Our study has been able to identify potential nsSNPs that could be used as genetic biomarkers for some diseases arising as a result of aberration in the ZUFSP structure, however, being a predictive study, the identified nsSNPs need to be experimentally investigated.

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.

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

Computational and drug target analysis of functional single nucleotide polymorphisms associated with Haemoglobin Subunit Beta (HBB) gene

There is overwhelming evidence implicating Haemoglobin Subunit Beta (HBB) protein in the onset of beta thalassaemia. In this study for the first time, we used a combined SNP informatics and computer algorithms such as Neural network, Bayesian network, and Support Vector Machine to identify deleterious non-synonymous Single Nucleotide Polymorphisms (nsSNPs) present in the HBB gene. Our findings highlight three major mutation points (R31G, W38S, and Q128P) within the HBB gene sequence that have significant statistical and computational associations with the onset of beta thalassaemia. The dynamic simulation study revealed that R31G, W38S, and Q128P elicited high structural perturbation and instability, however, the wild type protein was considerably stable. Ten compounds with therapeutic potential against HBB were also predicted by structure-based virtual screening. Interestingly, the instability caused by the mutations was reversed upon binding to a ligand. This study has been able to predict potential deleterious mutants that can be further explored in the understanding of the pathological basis of beta thalassaemia and the design of tailored inhibitors.

Current pivotal strategies leading a difficult target protein to a sample suitable for crystallographic analysis

Crystallographic structural analysis is an essential method for the determination of protein structure. However, crystallization of a protein of interest is the most difficult process in the analysis. The process is often hampered during the sample preparation, including expression and purification. Even after a sample has been purified, not all candidate proteins crystallize. In this mini-review, the current methodologies used to overcome obstacles encountered during protein crystallization are sorted. Specifically, the strategy for an effective crystallization is compared with a pipeline where various expression hosts and constructs, purification and crystallization conditions, and crystallization chaperones as target-specific binder proteins are assessed by a precrystallization screening. These methodologies are also developed continuously to improve the process. The described methods are useful for sample preparation in crystallographic analysis and other structure determination techniques, such as cryo-electron microscopy.

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

Proteus: An algorithm for proposing stabilizing mutation pairs based on interactions observed in known protein 3D structures

Background

Protein engineering has many applications for industry, such as the development of new drugs, vaccines, treatment therapies, food, and biofuel production. A common way to engineer a protein is to perform mutations in functionally essential residues to optimize their function. However, the discovery of beneficial mutations for proteins is a complex task, with a time-consuming and high cost for experimental validation. Hence, computational approaches have been used to propose new insights for experiments narrowing the search space and reducing the costs.

Results

In this study, we developed Proteus (an acronym for Protein Engineering Supporter), a new algorithm for proposing mutation pairs in a target 3D structure. These suggestions are based on contacts observed in other known structures from Protein Data Bank (PDB). Proteus’ basic assumption is that if a non-interacting pair of amino acid residues in the target structure is exchanged to an interacting pair, this could enhance protein stability. This trade is only allowed if the main-chain conformation of the residues involved in the contact is conserved. Furthermore, no steric impediment is expected between the proposed mutations and the surrounding protein atoms. To evaluate Proteus, we performed two case studies with proteins of industrial interests. In the first case study, we evaluated if the mutations suggested by Proteus for four protein structures enhance the number of inter-residue contacts. Our results suggest that most mutations proposed by Proteus increase the number of interactions into the protein. In the second case study, we used Proteus to suggest mutations for a lysozyme protein. Then, we compared Proteus’ outcomes to mutations with available experimental evidence reported in the ProTherm database. Four mutations, in which our results agree with the experimental data, were found. This could be initial evidence that changes in the side-chain of some residues do not cause disturbances that harm protein structure stability.

Conclusion

We believe that Proteus could be used combined with other methods to give new insights into the rational development of engineered proteins. Proteus user-friendly web-based tool is available at <http://proteus.dcc.ufmg.br>.

Impact of IL-17F 7488T/C Functional Polymorphism on Progressive Rheumatoid Arthritis: Novel Insight from the Molecular Dynamic Simulations

Resorption of bones and cartilage coupled with structural changes in the inflamed joints are the major hallmark of rheumatoid arthritis (RA). Genetic polymorphisms in pro-inflammatory interleukins (ILs) appear to play an important role in the susceptibility towards progressive RA. We therefore aimed to investigate the association of single nucleotide polymorphisms (SNP), present in the hotspot coding/promoter regions of IL-6, -17 and -18, with RA susceptibility or severity in a larger study cohort from Pakistan together with finding clues as to how a functional SNP impacts the predisposition towards RA. TaqMan SNP genotyping approach was first used to assess IL-6 (rs1800795), IL-17 F (rs763780), IL-17A (rs2275913), and IL-18 (rs1946518) polymorphisms in 310 subjects (150 RA and 160 control). Molecular dynamic simulations (MDS) of wild- and mutant-type IL-17A with corresponding receptor were thereafter performed using AMBER-16; Chimera 1.13 was used for analyses. Our results showed the association of two SNPs, namely IL-6 - 174 G/C [allelic (OR = 0.960, 95% CI = 0.929-0.992, p = .009)] and IL-17 F 7488 T/C [allelic (OR = 0.907, 95%CI = 0.861-0.954, p = .000)] with increased RA risk in Pakistani subjects. When mapped, IL-17 F 7488 T/C was found involved in His₁₆₁→Arg₁₆₁ change near the C-terminus of IL-17 F. Comparative MDS revealed enhanced stability of the mutant hence advocating a potential role of IL-17F functional SNP in RA susceptibility and/or severity. This study provides a novel structural insight for SNP-derived functional mutation and its overall impact on binding with heterotrimeric receptor complex of IL-17 receptor thereby opening new avenues for understanding the biochemical basis of the disease.

The autophagy gene ATG16L1 (T300A) variant is associated with the risk and progression of HBV infection

Autophagy pathway genes variants that play crucial roles in immune responses are involved in many diseases but their role in viral diseases is ill-defined. ATG16L1 gene plays a crucial role in the autophagy process. In this study, we have investigated the role of ATG16L1 variant T300A in the risk of HBV infection. rs2241880 (T300A) variant in 551 HBV infected patients (at various stages of infection) and 247 healthy controls were genotyped applying PCR-RFLP. Data analysis revealed that mutant allele G contributes to the risk of hepatitis B infection. Mutant alleles were significantly associated the HBV risk in allelic (OR = 1.31; 95%CI = 1.06-1.63, p = .01) and homozygous (OR = 1.87; 95%CI = 1.17-2.99, p = .009) models. On stratifying HBV infected individuals according to the stage of infection, a significant association was observed in asymptomatic (allelic; OR = 1.52; 95%CI = 1.10-2.09, p = .01 and homozygous; OR = 2.30; 95%CI = 1.22-4.36, p = .01) and chronic (allelic; OR = 1.36; 95%CI = 1.07-1.73, p = .01 and homozygous; OR = 2.07; 95%CI = 1.22-3.53, p = .008) stages of infection. High HBV DNA levels were associated with mutant genotype GG in asymptomatic and chronic carriers. Significantly higher ALT levels were observed in the liver cirrhosis patients with mutant genotypes. In conclusion, our data suggest that rs2241880 mutant allele carriers (allelic and homozygous models) were associated with increased risk of hepatitis B virus infection in North Indian population.

Predicting the stability of mutant proteins by computational approaches: an overview

A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naïve users in finding the best option for their needs.

Accurately Predicting Mutation-Caused Stability Changes from Protein Sequences Using Extreme Gradient Boosting

Accurately predicting the impact of point mutation on protein stability has crucial roles in protein design and engineering. In this study, we proposed a novel method (BoostDDG) to predict stability changes upon point mutations from protein sequences based on the extreme gradient boosting. We extracted features comprehensively from evolutional information and predicted structures and performed feature selection by a strategy of sequential forward selection. The features and parameters were optimized by homologue-based cross-validation to avoid overfitting. Finally, we found that 14 features from six groups led to the highest Pearson correlation coefficient (PCC) of 0.535, which is consistent with the 0.540 on an independent test. Our method was indicated to consistently outperform other sequence-based methods on three precompiled test sets, and 7363 variants on two proteins (PTEN and TPMT). These results highlighted that BoostDDG is a powerful tool for predicting stability changes upon point mutations from protein sequences.

iStable 2.0: Predicting protein thermal stability changes by integrating various characteristic modules

Protein mutations can lead to structural changes that affect protein function and result in disease occurrence. In protein engineering, drug design or and optimization industries, mutations are often used to improve protein stability or to change protein properties while maintaining stability. To provide possible candidates for novel protein design, several computational tools for predicting protein stability changes have been developed. Although many prediction tools are available, each tool employs different algorithms and features. This can produce conflicting prediction results that make it difficult for users to decide upon the correct protein design. Therefore, this study proposes an integrated prediction tool, iStable 2.0, which integrates 11 sequence-based and structure-based prediction tools by machine learning and adds protein sequence information as features. Three coding modules are designed for the system, an Online Server Module, a Stand-alone Module and a Sequence Coding Module, to improve the prediction performance of the previous version of the system. The final integrated structure-based classification model has a higher Matthews correlation coefficient than that of the single prediction tool (0.708 vs 0.547, respectively), and the Pearson correlation coefficient of the regression model likewise improves from 0.669 to 0.714. The sequence-based model not only successfully integrates off-the-shelf predictors but also improves the Matthews correlation coefficient of the best single prediction tool by at least 0.161, which is better than the individual structure-based prediction tools. In addition, both the Sequence Coding Module and the Stand-alone Module maintain performance with only a 5% decrease of the Matthews correlation coefficient when the integrated online tools are unavailable. iStable 2.0 is available at http://ncblab.nchu.edu.tw/iStable2.

Two novel cases further expand the phenotype of TOR1AIP1-associated nuclear envelopathies

Biallelic variants in TOR1AIP1, encoding the integral nuclear membrane protein LAP1 (lamina-associated polypeptide 1) with two functional isoforms LAP1B and LAP1C, have initially been linked to muscular dystrophies with variable cardiac and neurological impairment. Furthermore, a recurrent homozygous nonsense alteration, resulting in loss of both LAP1 isoforms, was identified in seven likely related individuals affected by multisystem anomalies with progeroid-like appearance and lethality within the 1st decade of life. Here, we have identified compound heterozygosity in TOR1AIP1 affecting both LAP1 isoforms in two unrelated individuals affected by congenital bilateral hearing loss, ventricular septal defect, bilateral cataracts, mild to moderate developmental delay, microcephaly, mandibular hypoplasia, short stature, progressive muscular atrophy, joint contractures and severe chronic heart failure, with much longer survival. Cellular characterization of primary fibroblasts of one affected individual revealed absence of both LAP1B and LAP1C, constitutively low lamin A/C levels, aberrant nuclear morphology including nuclear cytoplasmic channels, and premature senescence, comparable to findings in other progeroid forms of nuclear envelopathies. We additionally observed an abnormal activation of the extracellular signal-regulated kinase 1/2 (ERK 1/2). Ectopic expression of wild-type TOR1AIP1 mitigated these cellular phenotypes, providing further evidence for the causal role of identified genetic variants. Altogether, we thus further expand the TOR1AIP1-associated phenotype by identifying individuals with biallelic loss-of-function variants who survived beyond the 1st decade of life and reveal novel molecular consequences underlying the TOR1AIP1-associated disorders.

NADPH biosensor-based identification of an alcohol dehydrogenase variant with improved catalytic properties caused by a single charge reversal at the protein surface

Alcohol dehydrogenases (ADHs) are used in reductive biotransformations for the production of valuable chiral alcohols. In this study, we used a high-throughput screening approach based on the NADPH biosensor pSenSox and fluorescence-activated cell sorting (FACS) to search for variants of the NADPH-dependent ADH of Lactobacillus brevis (LbADH) with improved activity for the reduction of 2,5-hexanedione to (2R,5R)-hexanediol. In a library of approx. 1.4 × 10⁶ clones created by random mutagenesis we identified the variant LbADH^K71E. Kinetic analysis of the purified enzyme revealed that LbADH^K71E had a ~ 16% lowered K_M value and a 17% higher V_max for 2,5-hexanedione compared to the wild-type LbADH. Higher activities were also observed for the alternative substrates acetophenone, acetylpyridine, 2-hexanone, 4-hydroxy-2-butanone, and methyl acetoacetate. K71 is solvent-exposed on the surface of LbADH and not located within or close to the active site. Therefore, K71 is not an obvious target for rational protein engineering. The study demonstrates that high-throughput screening using the NADPH biosensor pSenSox represents a powerful method to find unexpected beneficial mutations in NADPH-dependent alcohol dehydrogenases that can be favorable in industrial biotransformations.

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning

Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.

Convolution Neural Network-Based Prediction of Protein Thermostability

Most natural proteins exhibit poor thermostability, which limits their industrial application. Computer-aided rational design is an efficient purpose-oriented method that can improve protein thermostability. Numerous machine-learning-based methods have been designed to predict the changes in protein thermostability induced by mutations. However, all of these methods have certain limitations due to existing mutation coding methods that overlook protein sequence features. Here we propose a method to predict protein thermostability using convolutional neural networks based on an in-depth study of thermostability-related protein properties. This method comprises a three-dimensional coding algorithm, including protein mutation information and a strategy to extract neighboring features at protein mutation sites based on multiscale convolution. The accuracies on the S1615 and S388 data sets, which are widely used for protein thermostability predictions, reached 86.4 and 87%, respectively. The Matthews correlation coefficient was nearly double those produced using other methods. Furthermore, a model was constructed to predict the thermostability of Rhizomucor miehei lipase mutants based on the S3661 data set, a single amino acid mutation data set screened from the ProTherm protein thermodynamics database. Compared with the RIF strategy, which consists of three algorithms, i.e., Rosetta ddg monomer, I Mutant 3.0, and FoldX, the accuracy of the proposed method was higher (75.0 vs 66.7%), and the negative sample resolution was simultaneously enhanced. These results indicate that our prediction method more effectively assessed the protein thermostability and distinguished its features, making it a powerful tool to devise mutations that enhance the thermostability of proteins, particularly enzymes.

Computational analysis of high-risk SNPs in human CHK2 gene responsible for hereditary breast cancer: A functional and structural impact

Nowadays CHK2 mutation is studied frequently in hereditary breast and ovarian cancer patients in addition to BRCA1/BRCA2. CHK2 is a tumor suppressor gene that encodes a serine/threonine kinase, also involved in pathways such as DNA repair, cell cycle regulation and apoptosis in response to DNA damage. CHK2 is a well-studied moderate penetrance gene that correlates with third high risk susceptibility gene with an increased risk for breast cancer. Hence before planning large population study, it is better to scrutinize putative functional SNPs of CHK2 using different computational tools. In this study, we have used various computational approaches to identify nsSNPs which are deleterious to the structure and/or function of CHK2 protein that might be causing this disease. Computational analysis was performed by different in silico tools including SIFT, Align GVGD, SNAP-2, PROVEAN, Poly-Phen-2, PANTHER, PhD-SNP, MUpro, iPTREE-STAB, Consurf, InterPro, NCBI Conserved Domain Search tool, ModPred, SPARKS-X, RAMPAGE, Verify-3D, FT Site, COACH and PyMol. Out of 78 nsSNP of human CHK2 gene, seven nsSNPs were predicted functionally most significant SNPs. Among these seven nsSNP, p.Arg160Gly, p.Gly210Arg and p.Ser415Phe are highly conserved residues with conservation score of 9 and three nsSNP were predicted to be involved in post translational modification. The p.Arg160Gly and p.Gly210Arg may interfere in phosphopeptide binding site on FHA conserved domain. The p.Ser415Phe may interfere in formation of activation loop of protein-kinase domain and might interfere in interactions of CHK2 with ligand. The study concludes that mutation of serine to phenylalanine at position 415 is a major mutation in native CHK2 protein which might contribute to its malfunction, ultimately causing disease. This is the first comprehensive study, where CHK2 gene variants are analyzed using in silico tools hence it will be of great help while considering large scale studies and also in developing precision medicines related to these polymorphisms in the era of personalized medicine.

DDGun: an untrained method for the prediction of protein stability changes upon single and multiple point variations

Background

Predicting the effect of single point variations on protein stability constitutes a crucial step toward understanding the relationship between protein structure and function. To this end, several methods have been developed to predict changes in the Gibbs free energy of unfolding (∆∆G) between wild type and variant proteins, using sequence and structure information. Most of the available methods however do not exhibit the anti-symmetric prediction property, which guarantees that the predicted ∆∆G value for a variation is the exact opposite of that predicted for the reverse variation, i.e., ∆∆G(A → B) = −∆∆G(B → A), where A and B are amino acids.

Results

Here we introduce simple anti-symmetric features, based on evolutionary information, which are combined to define an untrained method, DDGun (DDG untrained). DDGun is a simple approach based on evolutionary information that predicts the ∆∆G for single and multiple variations from sequence and structure information (DDGun3D). Our method achieves remarkable performance without any training on the experimental datasets, reaching Pearson correlation coefficients between predicted and measured ∆∆G values of ~ 0.5 and ~ 0.4 for single and multiple site variations, respectively. Surprisingly, DDGun performances are comparable with those of state of the art methods. DDGun also naturally predicts multiple site variations, thereby defining a benchmark method for both single site and multiple site predictors. DDGun is anti-symmetric by construction predicting the value of the ∆∆G of a reciprocal variation as almost equal (depending on the sequence profile) to -∆∆G of the direct variation. This is a valuable property that is missing in the majority of the methods.

Conclusions

Evolutionary information alone combined in an untrained method can achieve remarkably high performances in the prediction of ∆∆G upon protein mutation. Non-trained approaches like DDGun represent a valid benchmark both for scoring the predictive power of the individual features and for assessing the learning capability of supervised methods.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2923-1) contains supplementary material, which is available to authorized users.

SDM: a server for predicting effects of mutations on protein stability

Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth. The updated server has been extensively tested using a benchmark containing 2690 point mutations from 132 different protein structures. The revised method correlates well against the hypothetical reverse mutations, better than comparable methods built using machine-learning approaches, highlighting the strength of our knowledge-based approach for identifying stabilising mutations. Given a PDB file (a Protein Data Bank file format containing the 3D coordinates of the protein atoms), and a point mutation, the server calculates the stability difference score between the wildtype and mutant protein. The server is available at http://structure.bioc.cam.ac.uk/sdm2

A homozygous ABCB4 mutation causing an LPAC syndrome evolves into cholangiocarcinoma

Low phospholipid-associated cholelithiasis (LPAC) is characterized by the association of ABCB4 mutations and low biliary phospholipid concentration with symptomatic and recurring cholelithiasis. In the present study, we reported a case of a 63-year-old woman, who presented a biliary pain beginning at the age of 30, recurrent after cholecystectomy, along with "comet-tail shadows" revealed by ultrasonography thus, fulfilling the diagnosis of LPAC. This disease evolved into a cholangiocarcinoma. To understand the molecular basis of this phenotype, we performed the ABCB4 gene sequencing, followed by in silico analysis and Q-RT-PCR assay. The results displayed a homozygous missense sequence variation (c.140G > A, p.Arg47Gln), predicted as pathogenic according to MutPred. Accordingly, this gave rise to a decreased hepatic ABCB4 mRNA level and structural alterations of the mutated protein. Eventually, we reported, here, the first description of an ABCB4 missense mutation (p.Arg47Gln) at homozygous state in a Tunisian LPAC syndrome. An elucidation of its functional consequences was performed. Besides, this case suggests that the delayed diagnosis of LPAC syndrome and the lack of UDCA treatment may contribute in the development of complications, such as cholangiocarcinoma.

Industrial Cyber-Physical System Evolution Detection and Alert Generation

Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, etc.) or due to the anomalies detected. When an evolution happens (e.g., new devices are introduced), monitoring systems must be aware of it in order to inform the user and to provide updated and reliable information. In this article, CALENDAR is presented, a software module for a monitoring system that addresses ICPS evolutions. The solution is based on a data metamodel that captures the structure of an ICPS in different timestamps. By comparing the data model in two subsequent timestamps, CALENDAR is able to detect and effectively classify the evolution of ICPSs at runtime to finally generate alerts about the detected evolution. In order to evaluate CALENDAR with different ICPS topologies (e.g., different ICPS sizes), a scalability test was performed considering the information captured from the production lines domain.

Identification of deleterious SNPs and their effects on BCL11A, the master regulator of fetal hemoglobin expression

The B-cell lymphoma/leukemia 11A protein (encoded by BCL11A gene) is a key regulator of fetal-to-adult hemoglobin switching as seen in post-natal life. Although genetic polymorphisms like SNPs in BCL11A gene are expected to affect fetal hemoglobin (HbF) expression levels, yet their implications are poorly studied. This study utilizes a computational approach to identify the deleterious SNPs which may affect the structure and function of BCL11A protein. The study also generated a 3D structure of native and mutants. The analysis identified two SNPs in BCL11A as highly deleterious: N391K and C414S which are expected to affect structure and stability of the protein. According to conservation analysis, both residues N391 and C414 were identified as highly conserved. Additionally, post-translational modification sites were predicted at both sites. Ligand binding sites were also predicted in N391 and C414. Therefore, N391K and C414S in BCL11A can considered as important candidates to mediate HbF variation.

The HCM-causing Y235S cMyBPC mutation accelerates contractile function by altering C1 domain structure

Mutations in cardiac myosin binding protein C (cMyBPC) are a major cause of hypertrophic cardiomyopathy (HCM). In particular, a single amino acid substitution of tyrosine to serine at residue 237 in humans (residue 235 in mice) has been linked to HCM with strong disease association. Although cMyBPC truncations, deletions and insertions, and frame shift mutations have been studied, relatively little is known about the functional consequences of missense mutations in cMyBPC. In this study, we characterized the functional and structural effects of the HCM-causing Y235S mutation by performing mechanical experiments and molecular dynamics simulations (MDS). cMyBPC null mouse myocardium was virally transfected with wild-type (WT) or Y235S cMyBPC (KO^Y235S). We found that Y235S cMyBPC was properly expressed and incorporated into the cardiac sarcomere, suggesting that the mechanism of disease of the Y235S mutation is not haploinsufficiency or poison peptides. Mechanical experiments in detergent-skinned myocardium isolated from KO^Y235S hearts revealed hypercontractile behavior compared to KO^WT hearts, evidenced by accelerated cross-bridge kinetics and increased Ca²⁺ sensitivity of force generation. In addition, MDS revealed that the Y235S mutation causes alterations in important intramolecular interactions, surface conformations, and electrostatic potential of the C1 domain of cMyBPC. Our combined in vitro and in silico data suggest that the Y235S mutation directly disrupts internal and surface properties of the C1 domain of cMyBPC, which potentially alters its ligand-binding interactions. These molecular changes may underlie the mechanism for hypercontractile cross-bridge behavior, which ultimately results in the development of cardiac hypertrophy and in vivo cardiac dysfunction.

KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature Selection

Thermostability is a protein property that impacts many types of studies, including protein activity enhancement, protein structure determination, and drug development. However, most computational tools designed to predict protein thermostability require tertiary structure data as input. The few tools that are dependent only on the primary structure of a protein to predict its thermostability have one or more of the following problems: a slow execution speed, an inability to make large-scale mutation predictions, and the absence of temperature and pH as input parameters. Therefore, we developed a computational tool, named KStable, that is sequence-based, computationally rapid, and includes temperature and pH values to predict changes in the thermostability of a protein upon the introduction of a mutation at a single site. KStable was trained using basis features and minimal redundancy–maximal relevance (mRMR) features, and 58 classifiers were subsequently tested. To find the representative features, a regular-mRMR method was developed. When KStable was evaluated with an independent test set, it achieved an accuracy of 0.708.