SDM--a server for predicting effects of mutations on protein stability and malfunction

Insights into membrane-bound presenilin 2 from all-atom molecular dynamics simulations

Presenilins 1 and 2 (PS1 or PS2) are main genetic risk factors of familial Alzheimer's disease (AD) that produce the β-amyloid (Aβ) peptides and also have important stand-alone functions related to, e.g. calcium signaling. Most work so far has focused on PS1, but humans carry both PS1 and PS2, and mutations in both cause AD. Here, we develop a computational model of PS2 in the membrane to address the question how pathogenic PS2 mutations affect the membrane-embedded protein. The models are based on cryo-electron microscopy structures of PS1 translated to PS2, augmented with missing residues and a complete all-atom membrane-water system, and equilibrated using three independent 500-ns simulations of molecular dynamics with a structure-balanced force field. We show that the nine-transmembrane channel structure is substantially controlled by major dynamics in the hydrophilic loop bridging TM6 and TM7, which functions as a 'plug' in the PS2 membrane channel. TM2, TM6, TM7 and TM9 flexibility controls the size of this channel. We find that most pathogenic PS2 mutations significantly reduce stability relative to random mutations, using a statistical ANOVA test with all possible mutations in the affected sites as a control. The associated loss of compactness may also impair calcium affinity. Remarkably, similar properties of the open state are known to impair the binding of substrates to γ-secretase, and we thus argue that the two mechanisms could be functionally related.Communicated by Ramaswamy H. Sarma.

CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation

Many computational approaches estimate the effect of coding variants, but their predictions often disagree with each other. These contradictions confound users and raise questions regarding reliability. Performance assessments can indicate the expected accuracy for each method and highlight advantages and limitations. The Critical Assessment of Genome Interpretation (CAGI) community aims to organize objective and systematic assessments: They challenge predictors on unpublished experimental and clinical data and assign independent assessors to evaluate the submissions. We participated in CAGI experiments as predictors, using the Evolutionary Action (EA) method to estimate the fitness effect of coding mutations. EA is untrained, uses homology information, and relies on a formal equation: The fitness effect equals the functional sensitivity to residue changes multiplied by the magnitude of the substitution. In previous CAGI experiments (between 2011 and 2016), our submissions aimed to predict the protein activity of single mutants. In 2018 (CAGI5), we also submitted predictions regarding clinical associations, folding stability, and matching genomic data with phenotype. For all these diverse challenges, we used EA to predict the fitness effect of variants, adjusted to specifically address each question. Our submissions had consistently good performance, suggesting that EA predicts reliably the effects of genetic variants.

Deciphering the structural basis for glucocorticoid resistance caused by missense mutations in the ligand binding domain of glucocorticoid receptor

The glucocorticoid resistance hereditary condition may emerge from the occurrence of point mutations in the glucocorticoid receptor (GR), which could impair its functionality. Because the main feature of such pathology is the resistance of the hypothalamic-pituitary-adrenal axis to the hormone cortisol, we used the GR ligand binding domain three-dimensional structure to perform computational analysis for eight variants known to cause this clinical condition (I559 N, V571A, D641V, G679S, F737L, I747 M, L753F and L773P), aiming to understand, on the atom scale, how they cause glucocorticoid resistance. We observed that the mutations generated a reduced affinity to cortisol and they alter some loop conformations, which could be a consequence from changes in protein motion, which in turn could result from the reduced stability of mutant GR structures. Therefore, the analyzed mutations compromise the GR ligand binding domain structure and cortisol binding, which could characterize the glucocorticoid resistance phenotype.

Understanding the impacts of missense mutations on structures and functions of human cancer-related genes: A preliminary computational analysis of the COSMIC Cancer Gene Census

Genomics and genome screening are proving central to the study of cancer. However, a good appreciation of the protein structures coded by cancer genes is also invaluable, especially for the understanding of functions, for assessing ligandability of potential targets, and for designing new drugs. To complement the wealth of information on the genetics of cancer in COSMIC, the most comprehensive database for cancer somatic mutations available, structural information obtained experimentally has been brought together recently in COSMIC-3D. Even where structural information is available for a gene in the Cancer Gene Census, a list of genes in COSMIC with substantial evidence supporting their impacts in cancer, this information is quite often for a single domain in a larger protein or for a single protomer in a multiprotein assembly. Here, we show that over 60% of the genes included in the Cancer Gene Census are predicted to possess multiple domains. Many are also multicomponent and membrane-associated molecular assemblies, with mutations recorded in COSMIC affecting such assemblies. However, only 469 of the gene products have a structure represented in the PDB, and of these only 87 structures have 90-100% coverage over the sequence and 69 have less than 10% coverage. As a first step to bridging gaps in our knowledge in the many cases where individual protein structures and domains are lacking, we discuss our attempts of protein structure modelling using our pipeline and investigating the effects of mutations using two of our in-house methods (SDM2 and mCSM) and identifying potential driver mutations. This allows us to begin to understand the effects of mutations not only on protein stability but also on protein-protein, protein-ligand and protein-nucleic acid interactions. In addition, we consider ways to combine the structural information with the wealth of mutation data available in COSMIC. We discuss the impacts of COSMIC missense mutations on protein structure in order to identify and assess the molecular consequences of cancer-driving mutations.

Reduced Function and Diversity of T Cell Repertoire and Distinct Clinical Course in Patients With IL7RA Mutation

The alpha subunit of IL-7 receptor (IL7R7α) is critical for the differentiation of T cells, specifically for the development and maintenance of γδT cells. Mutations in IL7RA are associated with Severe Combined Immunodeficiency (SCID). Infants with IL7RA deficiency can be identified through newborn screening program. We aimed at defining the immunological and genetic parameters that are directly affected by the IL7RA mutation on the immune system of five unrelated patients which were identified by our newborn screening program for SCID. The patients were found to have a novel identical homozygote mutation in IL7RA (n.c.120 C>G; p.F40L). Both surface expression of IL7Rα and functionality of IL-7 signaling were impaired in patients compared to controls. Structural modeling demonstrated instability of the protein structure due to the mutation. Lastly the TRG immune repertoire of the patients showed reduced diversity, increased clonality and differential CDR3 characteristics. Interestingly, the patients displayed significant different clinical outcome with two displaying severe clinical picture of immunodeficiency and three had spontaneous recovery. Our data supports that the presented IL7RA mutation affects the IL-7 signaling and shaping of the TRG repertoire, reinforcing the role of IL7RA in the immune system, while non-genetic factors may exist that attribute to the ultimate clinical presentation and disease progression.

Identification and Characterization of Genetic Determinants of Isoniazid and Rifampicin Resistance in Mycobacterium tuberculosis in Southern India

Drug-resistant tuberculosis (TB), one of the leading causes of death worldwide, arises mainly from spontaneous mutations in the genome of Mycobacterium tuberculosis. There is an urgent need to understand the mechanisms by which the mutations confer resistance in order to identify new drug targets and to design new drugs. Previous studies have reported numerous mutations that confer resistance to anti-TB drugs, but there has been little systematic analysis to understand their genetic background and the potential impacts on the drug target stability and/or interactions. Here, we report the analysis of whole-genome sequence data for 98 clinical M. tuberculosis isolates from a city in southern India. The collection was screened for phenotypic resistance and sequenced to mine the genetic mutations conferring resistance to isoniazid and rifampicin. The most frequent mutation among isoniazid and rifampicin isolates was S315T in katG and S450L in rpoB respectively. The impacts of mutations on protein stability, protein-protein interactions and protein-ligand interactions were analysed using both statistical and machine-learning approaches. Drug-resistant mutations were predicted not only to target active sites in an orthosteric manner, but also to act through allosteric mechanisms arising from distant sites, sometimes at the protein-protein interface.

Decreasing the immunogenicity of Erwinia chrysanthemi asparaginase via protein engineering: computational approach

Immunogenicity of therapeutic proteins is one of the main challenges in disease treatment. L-Asparaginase is an important enzyme in cancer treatment which sometimes leads to undesirable side effects such as immunogenic or allergic responses. Here, to decrease Erwinase (Erwinia chrysanthemiL-Asparaginase) immunogenicity, which is the main drawback of the enzyme, firstly conformational B cell epitopes of Erwinase were predicted from three-dimensional structure by three different computational methods. A few residues were defined as candidates for reducing immunogenicity of the protein by point mutation. In addition to immunogenicity and hydrophobicity, stability and binding energy of mutants were also analyzed computationally. In order to evaluate the stability of the best mutant, molecular dynamics simulation was performed. Among mutants, H240A and Q239A presented significant reduction in immunogenicity. In contrast, the immunogenicity scores of D235A slightly decreased according to two servers. Binding affinity of substrate to the active site reduced significantly in K265A and E268A. The final results of molecular dynamics simulation indicated that H240A mutation has not changed the stability, flexibility, and the total structure of desired protein. Overall, point mutation can be used for reducing immunogenicity of therapeutic proteins, in this context, in silico approaches can be used to screen suitable mutants.

Prediction of disease-associated mutations in the transmembrane regions of proteins with known 3D structure

Being able to assess the phenotypic effects of mutations is a much required capability in precision medicine. However, most of the currently available structure-based methods actually predict stability changes caused by mutations rather than their pathogenic potential. There are also no dedicated methods to predict damaging mutations specifically in transmembrane proteins. In this study we developed and applied a machine-learning approach to discriminate between disease-associated and benign point mutations in the transmembrane regions of proteins with known 3D structure. The method, called BorodaTM (BOosted RegressiOn trees for Disease-Associated mutations in TransMembrane proteins), was trained on sequence-, structure-, and energy-derived descriptors. When compared with the state-of-the-art methods, BorodaTM is superior in classifying point mutations in transmembrane regions. Using BorodaTM we have conducted a large-scale mutation analysis in the transmembrane regions of human proteins with known 3D structures. For each protein we generated structural models for all point mutations by replacing each residue to 19 possible residue types. We classified ~1.8 millions point mutations as benign or diseased-associated and made all predictions available as a Web-server at https://www.iitm.ac.in/bioinfo/MutHTP/boroda.php.

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.

Sequence Tolerance of a Single-Domain Antibody with a High Thermal Stability: Comparison of Computational and Experimental Fitness Profiles

The sequence fitness of a llama single-domain antibody with an unusually high thermal stability is explored by a combined computational and experimental study. Starting with the X-ray crystallographic structure, RosettaBackrub simulations were applied to model sequence-structure tolerance profiles and identify key substitution sites. From the model calculations, an experimental site-directed mutagenesis was used to produce a panel of mutants, and their melting temperatures were determined by thermal denaturation. The results reveal a sequence fitness of an excess stability of approximately 12 °C, a value taken from a decrease in the melting temperature of an electrostatic charge-reversal substitution in the CRD3 without a deleterious effect on the binding affinity to the antigen. The tolerance for the disruption of antigen recognition without loss in the thermal stability was demonstrated by the introduction of a proline in place of a tyrosine in the CDR2, producing a mutant that eliminated binding. To further assist the sequence design and the selection of engineered single-domain antibodies, an assessment of different computational strategies is provided of their accuracy in the detection of substitution "hot spots" in the sequence tolerance landscape.

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease

A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here, we introduce a unified population-genetic and machine-learning model, called Linear Allele-Specific Selection InferencE (LASSIE), for estimating the fitness effects of all observed and potential single-nucleotide variants, based on polymorphism data and predictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. This map is generally consistent with previous inferences of the bulk distribution of fitness effects but reveals pervasive weak negative selection against synonymous mutations. In addition, the estimated selection coefficients are highly predictive of inherited pathogenic variants and cancer driver mutations, outperforming state-of-the-art variant prioritization methods. By contrasting our estimated model with ultrahigh coverage ExAC exome-sequencing data, we identified 1118 genes under unusually strong negative selection, which tend to be exclusively expressed in the central nervous system or associated with autism spectrum disorder, as well as 773 genes under unusually weak selection, which tend to be associated with metabolism. This combination of classical population genetic theory with modern machine-learning and large-scale genomic data is a powerful paradigm for the study of both human evolution and disease.

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

Three additional patients with EED-associated overgrowth: potential mutation hotspots identified?

Variants have been identified in the embryonic ectoderm development (EED) gene in seven patients with syndromic overgrowth similar to that observed in Weaver syndrome. Here, we present three additional patients with missense variants in the EED gene. All the missense variants reported to date (including the three presented here) have localized to one of seven WD40 domains of the EED protein, which are necessary for interaction with enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2). In addition, among the seven patients reported in the literature and the three new patients presented here, all of the reported pathogenic variants except one occurred at one of four amino acid residues in the EED protein. The recurrence of pathogenic variation at these loci suggests that these residues are functionally important (mutation hotspots). In silico modeling and calculations of the free energy changes resulting from these variants suggested that they not only destabilize the EED protein structure but also adversely affect interactions between EED, EZH2, and/or H3K27me3. These cases help demonstrate the mechanism(s) by which apparently deleterious variants in the EED gene might cause overgrowth and lend further support that amino acid residues in the WD40 domain region may be mutation hotspots.

Empirical ways to identify novel Bedaquiline resistance mutations in AtpE

Clinical resistance against Bedaquiline, the first new anti-tuberculosis compound with a novel mechanism of action in over 40 years, has already been detected in Mycobacterium tuberculosis. As a new drug, however, there is currently insufficient clinical data to facilitate reliable and timely identification of genomic determinants of resistance. Here we investigate the structural basis for M. tuberculosis associated bedaquiline resistance in the drug target, AtpE. Together with the 9 previously identified resistance-associated variants in AtpE, 54 non-resistance-associated mutations were identified through comparisons of bedaquiline susceptibility across 23 different mycobacterial species. Computational analysis of the structural and functional consequences of these variants revealed that resistance associated variants were mainly localized at the drug binding site, disrupting key interactions with bedaquiline leading to reduced binding affinity. This was used to train a supervised predictive algorithm, which accurately identified likely resistance mutations (93.3% accuracy). Application of this model to circulating variants present in the Asia-Pacific region suggests that current circulating variants are likely to be susceptible to bedaquiline. We have made this model freely available through a user-friendly web interface called SUSPECT-BDQ, StrUctural Susceptibility PrEdiCTion for bedaquiline (http://biosig.unimelb.edu.au/suspect_bdq/). This tool could be useful for the rapid characterization of novel clinical variants, to help guide the effective use of bedaquiline, and to minimize the spread of clinical resistance.

Whole-Exome Sequencing Identified a De Novo Mutation of Junction Plakoglobin (p.R577C) in a Chinese Patient with Arrhythmogenic Right Ventricular Cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare and potentially life-threatening disorder of the heart. The clinical spectrum of ARVC includes myocyte loss and fibro-fatty tissue replacement. With the progress of ARVC, the patient can present serious ventricular arrhythmias, heart failure, and even sudden cardiac death. Previous studies have demonstrated that desmosomes and intermediate junctions play a crucial role in the generation and development of ARVC. In this study, we enrolled a Chinese patient with suspicious ARVC. The patient suffered from right ventricular enlargement and less thickening of right ventricular wall. ECG record showed an epsilon wave. However, there was no obvious symptom in his parents. After whole-exome sequencing and data filtering, we identified a de novo mutation (c.1729C>T/p.R577C) of junction plakoglobin (JUP) in this patient. Bioinformatics programs predicted that this mutation was deleterious. Western blot revealed that, compared to cells transfected with WT plasmids, the expressions of desmoglein 2 (DSG2) and Connexin 43 were decreased overtly in cells transfected with the mutant plasmid. Previous studies have proven that the reduction of DSG2 and Connexin 43 may disturb the stability of desmosomes. In this research, we reported a novel de novo mutation (c.1729C>T/p.R577C) of JUP in a Chinese patient with suspicious ARVC. Functional research further confirmed the pathogenicity of this novel mutation. Our study expanded the spectrum of JUP mutations and may contribute to the genetic diagnosis and counseling of patients with ARVC.

Structural and molecular basis of angiotensin-converting enzyme by computational modeling: Insights into the mechanisms of different inhibitors

Angiotensin-I converting enzyme (ACE) is a two-domain dipeptidylcarboxypeptidase involved in regulating blood pressure via the kallikrein-kininand renin-angiotensin-aldosterone complex. Therefore, ACE is a key drug target for the treatment of cardiovascular system diseases. At present many works are focus on searching for new inhibitory peptides of ACE to control the blood pressure. In order to exploit the interactions between ACE and its inhibitors, molecular dynamics simulations were used. The results showed that (a) the secondary structures of the three inhibitor-protein complexes did not change significantly; (b) root-mean-square deviation (RMSD), radius of gyration (Rg), and solvent-accessible surface area (SASA) values of Leu-Ile-Val-Thr (LIVT)-ACE complexes were significantly higher than that of other systems; (c) the backbone movement of LIVT was vigorous in Asp300-Val350, compared with that in Tyr-Leu-Val-Pro-His (YLVPH) and Tyr-Leu-Val-Arg(YLVR), as shown by the center-of-mass distance; and (d) the backbone movement of Asp300-Val350 may contribute to the interaction between ACE and its inhibitors. Our theoretical results will be helpful to further the design of specific inhibitors of ACE.

Computational design for thermostabilization of GPCRs

GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery.

Shedding Light on the Interaction of Human Anti-Apoptotic Bcl-2 Protein with Ligands through Biophysical and in Silico Studies

Bcl-2 protein is involved in cell apoptosis and is considered an interesting target for anti-cancer therapy. The present study aims to understand the stability and conformational changes of Bcl-2 upon interaction with the inhibitor venetoclax, and to explore other drug-target regions. We combined biophysical and in silico approaches to understand the mechanism of ligand binding to Bcl-2. Thermal shift assay (TSA) and urea electrophoresis showed a significant increase in protein stability upon venetoclax incubation, which is corroborated by molecular docking and molecular dynamics simulations. An 18 °C shift in Bcl-2 melting temperature was observed in the TSA, corresponding to a binding affinity multiple times higher than that of any other reported Bcl-2 inhibitor. This protein-ligand interaction does not implicate alternations in protein conformation, as suggested by SAXS. Additionally, bioinformatics approaches were used to identify deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of Bcl-2 and their impact on venetoclax binding, suggesting that venetoclax interaction is generally favored against these deleterious nsSNPs. Apart from the BH3 binding groove of Bcl-2, the flexible loop domain (FLD) also plays an important role in regulating the apoptotic process. High-throughput virtual screening (HTVS) identified 5 putative FLD inhibitors from the Zinc database, showing nanomolar affinity toward the FLD of Bcl-2.

Whole exome sequencing identified a novel mutation (p.Ala1884Pro) of β-spectrin in a Chinese family with hereditary spherocytosis

BACKGROUND

Hereditary spherocytosis (HS) is an inherited disorder of erythrocyte. The typical feature of HS is the presence of spherical-shaped erythrocytes on the peripheral blood smear. According to previous studies, more than five candidate genes, such as ANK1, SPTB, SPTA1, SLC4A1 and EPB42 have been identified in HS patients.

METHODS

In the present study, a Chinese HS family was investigated. The proband suffered from pathologic jaundice and splenomegaly. A blood test and peripheral blood smear experiment further confirmed the diagnosis of HS. We selected the proband to perform the whole exome sequencing.

RESULTS

After data filtering and co-segregation analysis, we identified 12 mutations in affected members that were absent in healthy members. In consideration of the inheritance pattern, Online Mendelian Inheritance in Man clinical phenotypes, Toppgene function and American College of Medical Genetics classification, we considered the novel mutation (c.5650G > C/p.Ala1884Pro) of β-spectrin (SPTB) to be the genetic lesion in this family. The novel mutation, resulting in a substitution of alanine by proline, may lead to transformation of the SPTB protein structure, which affects the binding between SPTB and ankyrin.

CONCLUSIONS

The present study confirmed the hereditary red blood cell membrane disorders at a molecular level and expanded the spectrum of SPTB mutations. This may contribute to the clinical management and genetic counseling with respect to HS.

Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations-whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico⁻chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

Mycobacterial genomics and structural bioinformatics: opportunities and challenges in drug discovery

Of the more than 190 distinct species of Mycobacterium genus, many are economically and clinically important pathogens of humans or animals. Among those mycobacteria that infect humans, three species namely Mycobacterium tuberculosis (causative agent of tuberculosis), Mycobacterium leprae (causative agent of leprosy) and Mycobacterium abscessus (causative agent of chronic pulmonary infections) pose concern to global public health. Although antibiotics have been successfully developed to combat each of these, the emergence of drug-resistant strains is an increasing challenge for treatment and drug discovery. Here we describe the impact of the rapid expansion of genome sequencing and genome/pathway annotations that have greatly improved the progress of structure-guided drug discovery. We focus on the applications of comparative genomics, metabolomics, evolutionary bioinformatics and structural proteomics to identify potential drug targets. The opportunities and challenges for the design of drugs for M. tuberculosis, M. leprae and M. abscessus to combat resistance are discussed.

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.

pStab: prediction of stable mutants, unfolding curves, stability maps and protein electrostatic frustration

Summary

We present a web-server for rapid prediction of changes in protein stabilities over a range of temperatures and experimental conditions upon single- or multiple-point substitutions of charged residues. Potential mutants are identified by a charge-shuffling procedure while the stability changes (i.e. an unfolding curve) are predicted employing an ensemble-based statistical-mechanical model. We expect this server to be a simple yet detailed tool for engineering stabilities, identifying electrostatically frustrated residues, generating local stability maps and in constructing fitness landscapes.

Availability and implementation

The web-server is freely available at http://pbl.biotech.iitm.ac.in/pStab and supports recent versions of all major browsers.

Contact

athi@iitm.ac.in.

Supplementary information

Supplementary data are available at Bioinformatics online.

Understanding molecular consequences of putative drug resistant mutations in Mycobacterium tuberculosis

Genomic studies of Mycobacterium tuberculosis bacteria have revealed loci associated with resistance to anti-tuberculosis drugs. However, the molecular consequences of polymorphism within these candidate loci remain poorly understood. To address this, we have used computational tools to quantify the effects of point mutations conferring resistance to three major anti-tuberculosis drugs, isoniazid (n = 189), rifampicin (n = 201) and D-cycloserine (n = 48), within their primary targets, katG, rpoB, and alr. Notably, mild biophysical effects brought about by high incidence mutations were considered more tolerable, while different structural effects brought about by haplotype combinations reflected differences in their functional importance. Additionally, highly destabilising mutations such as alr Y388, highlighted a functional importance of the wildtype residue. Our qualitative analysis enabled us to relate resistance mutations onto a theoretical landscape linking enthalpic changes with phenotype. Such insights will aid the development of new resistance-resistant drugs and, via an integration into predictive tools, in pathogen surveillance.

Combined Blockade of Activating ERBB2 Mutations and ER Results in Synthetic Lethality of ER+/HER2 Mutant Breast Cancer

PURPOSE

We examined the role of ERBB2-activating mutations in endocrine therapy resistance in estrogen receptor positive (ER+) breast cancer.

EXPERIMENTAL DESIGN

ERBB2 mutation frequency was determined from large genomic databases. Isogenic knock-in ERBB2 mutations in ER+ MCF7 cells and xenografts were used to investigate estrogen-independent growth. Structural analysis was used to determine the molecular interaction of HER ^L755S with HER3. Small molecules and siRNAs were used to inhibit PI3Kα, TORC1, and HER3.

RESULTS

Genomic data revealed a higher rate of ERBB2 mutations in metastatic versus primary ER+ tumors. MCF7 cells with isogenically incorporated ERBB2 kinase domain mutations exhibited resistance to estrogen deprivation and to fulvestrant both in vitro and in vivo, despite maintaining inhibition of ERα transcriptional activity. Addition of the irreversible HER2 tyrosine kinase inhibitor neratinib restored sensitivity to fulvestrant. HER2-mutant MCF7 cells expressed higher levels of p-HER3, p-AKT, and p-S6 than cells with wild-type HER2. Structural analysis of the HER2 ^L755S variant implicated a more flexible active state, potentially allowing for enhanced dimerization with HER3. Treatment with a PI3Kα inhibitor, a TORC1 inhibitor or HER3 siRNA, but not a MEK inhibitor, restored sensitivity to fulvestrant and to estrogen deprivation. Inhibition of mutant HER2 or TORC1, when combined with fulvestrant, equipotently inhibited growth of MCF7/ERBB2 ^V777L xenografts, suggesting a role for TORC1 in antiestrogen resistance induced by ERBB2 mutations.

CONCLUSIONS

ERBB2 mutations hyperactivate the HER3/PI3K/AKT/mTOR axis, leading to antiestrogen resistance in ER+ breast cancer. Dual blockade of the HER2 and ER pathways is required for the treatment of ER+/HER2 mutant breast cancers.

Large-scale analysis of B-cell epitopes of envelope: Implications for Zika vaccine and immunotherapeutic development

Background: Cases of the re-emergence of Zika virus in 2015 were associated with severe neurologic complications, including Gillien-Barre syndrome in adults and congenital Zika syndrome in newborns. The major structural determinant of immunity to the Zika virus is the E protein. Although B-cell epitopes of Zika E protein were recently identified, data regarding epitope variations among Zika strains in pre-epidemic and epidemic periods are lacking.

Methods: Here, we conducted systematic bioinformatics analyses of Zika strains isolated between 1968 and 2017. Multiple sequence alignment of E protein as well as B-cell epitopes annotations were performed. In addition, homology-based approach was utilized to construct three-dimensional structures of monomeric E glycoproteins to annotate epitope variations. Lastly, prediction of of N-glycosylation patterns and prediction of protein stability upon mutations were also investigated.

Results: Our analyses indicates that epitopes recognized by human mAbs ZIKV-117, ZIKV-15, and ZIKV-19 were highly conserved, suggesting as attractive targets for the development of vaccines and immunotherapeutics directed against diverse Zika strains. In addition, the epitope recognized by ZIKV-E-2A10G6 mAb derived from immunized mice was mostly conserved across Zika strains.

Conclusions: Our data provide new insights regarding antigenic similarities between Zika strains circulating worldwide. These data are essential for understanding the impact of evolution on antigenic cross-reactivity between Zika lineages and strains. Further in-vitro analyses are needed to determine how mutationsat predefined epitopes could impact the development of vaccines that can effectively neutralize Zika viruses.

RankProt: A multi criteria-ranking platform to attain protein thermostabilizing mutations and its in vitro applications - Attribute based prediction method on the principles of Analytical Hierarchical Process

Attaining recombinant thermostable proteins is still a challenge for protein engineering. The complexity is the length of time and enormous efforts required to achieve the desired results. Present work proposes a novel and economic strategy of attaining protein thermostability by predicting site-specific mutations at the shortest possible time. The success of the approach can be attributed to Analytical Hierarchical Process and the outcome was a rationalized thermostable mutation(s) prediction tool- RankProt. Briefly the method involved ranking of 17 biophysical protein features as class predictors, derived from 127 pairs of thermostable and mesostable proteins. Among the 17 predictors, ionic interactions and main-chain to main-chain hydrogen bonds were the highest ranked features with eigen value of 0.091. The success of the tool was judged by multi-fold in silico validation tests and it achieved the prediction accuracy of 91% with AUC 0.927. Further, in vitro validation was carried out by predicting thermostabilizing mutations for mesostable Bacillus subtilis lipase and performing the predicted mutations by multi-site directed mutagenesis. The rationalized method was successful to render the lipase thermostable with optimum temperature stability and T_m increase by 20°C and 7°C respectively. Conclusively it can be said that it was the minimum number of mutations in comparison to the number of mutations incorporated to render Bacillus subtilis lipase thermostable, by directed evolution techniques. The present work shows that protein stabilizing mutations can be rationally designed by balancing the biophysical pleiotropy of proteins, in accordance to the selection pressure.

Impact of structural prior knowledge in SNV prediction: Towards causal variant finding in rare disease

Can structural information of proteins generate essential features for predicting the deleterious effect of a single nucleotide variant (SNV) independent of the known existence of the SNV in diseases? In this work, we answer the question by examining the performance of features generated from prior knowledge with the goal towards determining the pathogenic effect of rare variants in rare disease. We take the approach of prioritizing SNV loci focusing on protein structure-based features. The proposed structure-based features are generated from geometric, physical, chemical, and functional properties of the variant loci and structural neighbors of the loci utilizing multiple homologous structures. The performance of the structure-based features alone, trained on 80% of HumVar-HumDiv combination (HumVD-train) and tested on 20% of HumVar-HumDiv (HumVD-test), ClinVar and ClinVar rare variant rare disease (ClinVarRVRD) datasets, showed high levels of discernibility in determining the SNV’s pathogenic or benign effects on patients. Combined structure- and sequence-based features generated from prior knowledge on a random forest model further improved the F scores to 0.84 (HumVD-test), 0.75 (ClinVar), and 0.75 (ClinVarRVRD). Including features based on the difference between wild-type in addition to the features based on loci information increased the F score slightly more to 0.90 (HumVD-test), 0.78 (ClinVar), and 0.76 (ClinVarRVRD). The empirical examination and high F scores of the results based on loci information alone suggest that location of SNV plays a primary role in determining functional impact of mutation and that structure-based features can help enhance the prediction performance.

A review study: Computational techniques for expecting the impact of non-synonymous single nucleotide variants in human diseases

Non-Synonymous Single-Nucleotide Variants (nsSNVs) and mutations can create a diversity effect on proteins as changing genotype and phenotype, which interrupts its stability. The alterations in the protein stability may cause diseases like cancer. Discovering of nsSNVs and mutations can be a useful tool for diagnosing the disease at a beginning stage. Many studies introduced the various predicting singular and consensus tools that based on different Machine Learning Techniques (MLTs) using diverse datasets. Therefore, we introduce the current comprehensive review of the most popular and recent unique tools that predict pathogenic variations and Meta-tool that merge some of them for enhancing their predictive power. Also, we scanned the several types computational techniques in the state-of-the-art and methods for predicting the effect both of coding and noncoding variants. We then displayed, the protein stability predictors. We offer the details of the most common benchmark database for variations including the main predictive features used by the different methods. Finally, we address the most common fundamental criteria for performance assessment of predictive tools. This review is targeted at bioinformaticians attentive in the characterization of regulatory variants, geneticists, molecular biologists attentive in understanding more about the nature and effective role of such variants from a functional point of views, and clinicians who may hope to learn about variants in human associated with a specific disease and find out what to do next to uncover how they impact on the underlying mechanisms.

Accurate prediction of functional, structural, and stability changes in PITX2 mutations using in silico bioinformatics algorithms

Mutations in PITX2 have been implicated in several genetic disorders, particularly Axenfeld-Rieger syndrome. In order to determine the most reliable bioinformatics tools to assess the likely pathogenicity of PITX2 variants, the results of bioinformatics predictions were compared to the impact of variants on PITX2 structure and function. The MutPred, Provean, and PMUT bioinformatic tools were found to have the highest performance in predicting the pathogenicity effects of all 18 characterized missense variants in PITX2, all with sensitivity and specificity >93%. Applying these three programs to assess the likely pathogenicity of 13 previously uncharacterized PITX2 missense variants predicted 12/13 variants as deleterious, except A30V which was predicted as benign variant for all programs. Molecular modeling of the PITX2 homoedomain predicts that of the 31 known PITX2 variants, L54Q, F58L, V83F, V83L, W86C, W86S, and R91P alter PITX2's structure. In contrast, the remaining 24 variants are not predicted to change PITX2's structure. The results of molecular modeling, performed on all the PITX2 missense mutations located in the homeodomain, were compared with the findings of eight protein stability programs. CUPSAT was found to be the most reliable in predicting the effect of missense mutations on PITX2 stability. Our results showed that for PITX2, and likely other members of this homeodomain transcription factor family, MutPred, Provean, PMUT, molecular modeling, and CUPSAT can reliably be used to predict PITX2 missense variants pathogenicity.

Prediction and interpretation of deleterious coding variants in terms of protein structural stability

The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/.

Key apoptotic genes APAF1 and CASP9 implicated in recurrent folate-resistant neural tube defects

Neural tube defects (NTDs) remain one of the most serious birth defects, and although genes in several pathways have been implicated as risk factors for neural tube defects via knockout mouse models, very few molecular causes in humans have been identified. Whole exome sequencing identified deleterious variants in key apoptotic genes in two families with recurrent neural tube defects. Functional studies in fibroblasts indicate that these variants are loss-of-function, as apoptosis is significantly reduced. This is the first report of variants in apoptotic genes contributing to neural tube defect risk in humans.

Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability

Predicting how a point mutation alters a protein’s stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time as well as financial resources needed to assess the effect of even a single amino acid substitution. Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates. In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. We use these as features for our Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) methods. We validate the predictions of our in silico mutations against experimental ΔΔG stability data, and attain Pearson Correlation values upwards of 0.71 for single mutations, and 0.81 for double mutations. We perform ablation studies to assess which features contribute most to a model’s success, and also introduce a voting scheme to synthesize a single prediction from the individual predictions of the three models.

Exploring the association of rs10490924 polymorphism with age-related macular degeneration: An in silico approach

The polymorphism rs10490924 (A69S) in the age-related maculopathy susceptibility 2 (ARMS2) gene is highly associated with age-related macular degeneration, which is the leading cause of blindness among the elderly population. ARMS2 gene encodes a putative small (11 kDa) protein, which the function and localization of the ARMS2 protein remain under debate. For a better understanding of functional impacts of A69S mutation, we performed a detailed analysis of an ARMS2 sequence with a broad set of bioinformatics tools. In silico analysis was followed to predict the tertiary structure, putative binding site regions, and binding site residues. Also, the effects of this mutation on protein stability, aggregation propensity, and homodimerization were analyzed. Next, a molecular dynamic simulation was carried out to understand the dynamic behavior of wild-type, A69S, and phosphorylated A69S structures. The results showed alterations in the putative post-translational modification sites on the ARMS2 protein, due to the mutation. Furthermore, the stability of protein and putative homodimer conformations were affected by the mutation. Molecular dynamic simulation results revealed that A69S mutation enhances the rigidity of the ARMS2 structure and residue serine at position 69 is buried and may not be phosphorylated; however, phosphorylated serine enhances the flexibility of the ARMS2 structure. In conclusion, our study provides new insights into the deleterious effects of A69S mutation on the ARMS2 structure.

Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.

A novel antithrombin domain dictates the journey's end of a proteinase

Antithrombin (AT) is an anticoagulant serpin that irreversibly inactivates the clotting proteinases factor Xa and thrombin by forming covalent complexes with them. Mutations in its critical domains, such as those that impair the conformational rearrangement required for proteinase inactivation, increase the risk of venous thrombosis. Águila et al. characterize for the first time the destabilizing effects of mutations in the region of AT that makes contact with the proteinase in the final acyl-enzyme complex. Their work adds new insight into the unique structural intricacies of the inhibitory mechanism.

Mutation Sensitivity Maps: Identifying Residue Substitutions That Impact Protein Structure Via a Rigidity Analysis In Silico Mutation Approach

Understanding how an amino acid substitution affects a protein's structure can aid in the design of pharmaceutical drugs that aim at countering diseases caused by protein mutants. Unfortunately, performing even a few amino acid substitutions in vitro is both time and cost prohibitive, whereas an exhaustive analysis that involves systematically mutating all amino acids in the physical protein is infeasible. Computational methods have been developed to predict the effects of mutations, but even many of them are computationally intensive or are else dependent on homology or experimental data that may not be available for the protein being studied. In this work, we motivate and present a computation pipeline whose only input is a Protein Data Bank file containing the 3D coordinates of the atoms of a biomolecule. Our high-throughput approach uses our ProMuteHT algorithm to exhaustively generate in silico amino acid substitutions at each residue, and it also includes an energy minimization option. This is in contrast to our previous work, where we analyzed the effects of in silico mutations to Alanine, Serine, and Glycine only. We exploit the speed of a fast rigidity analysis approach to analyze our protein variants, and develop a Mutation Sensitivity (MuSe) Map, to permit identifying residues that are most sensitive to mutations. We present a case study to show the degree to which a MuSe Map and whisker plots are able to locate amino acids whose mutations most affect a protein's structure as inferred from a rigidity analysis approach.

Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure

Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a proper analysis of the structure-function relationship of proteins: (i) the lack of a formal definition of what critical residues are and (ii) the lack of a systematic evaluation of methods and protein structure features. To address this problem, here we introduce an index to quantify the protein-function criticality of a residue based on experimental data and a strategy aimed to optimize both, descriptors of protein structure (physicochemical and centrality descriptors) and machine learning algorithms, to minimize the error in the classification of critical residues. We observed that both physicochemical and centrality descriptors of residues effectively relate protein structure and protein function, and that physicochemical descriptors better describe critical residues. We also show that critical residues are better classified when residue criticality is considered as a binary attribute (i.e., residues are considered critical or not critical). Using this binary annotation for critical residues 8 models rendered accurate and non-overlapping classification of critical residues, confirming the multi-factorial character of the structure-function relationship of proteins.

Novel compound heterozygous mutations in the GPR98 (USH2C) gene identified by whole exome sequencing in a Moroccan deaf family

In the present work, we identified two novel compound heterozygote mutations in the GPR98 (G protein-coupled receptor 98) gene causing Usher syndrome. Whole-exome sequencing was performed to study the genetic causes of Usher syndrome in a Moroccan family with three affected siblings. We identify two novel compound heterozygote mutations (c.1054C > A, c.16544delT) in the GPR98 gene in the three affected siblings carrying post-linguale bilateral moderate hearing loss with normal vestibular functions and before installing visual disturbances. This is the first time that mutations in the GPR98 gene are described in the Moroccan deaf patients.