Performance of computational tools in evaluating the functional impact of laboratory-induced amino acid mutations

Rheostatic contributions to protein stability can obscure a position's functional role

Rheostat positions, which can be substituted with various amino acids to tune protein function across a range of outcomes, are a developing area for advancing personalized medicine and bioengineering. Current methods cannot accurately predict which proteins contain rheostat positions or their substitution outcomes. To compare the prevalence of rheostat positions in homologs, we previously investigated their occurrence in two pyruvate kinase (PYK) isozymes. Human liver PYK contained numerous rheostat positions that tuned the apparent affinity for the substrate phosphoenolpyruvate (Kapp-PEP) across a wide range. In contrast, no functional rheostat positions were identified in Zymomonas mobilis PYK (ZmPYK). Further, the set of ZmPYK substitutions included an unusually large number that lacked measurable activity. We hypothesized that the inactive substitution variants had reduced protein stability, precluding detection of Kapp-PEP tuning. Using modified buffers, robust enzymatic activity was obtained for 19 previously-inactive ZmPYK substitution variants at three positions. Surprisingly, both previously-inactive and previously-active substitution variants all had Kapp-PEP values close to wild-type. Thus, none of the three positions were functional rheostat positions, and, unlike human liver PYK, ZmPYK's Kapp-PEP remained poorly tunable by single substitutions. To directly assess effects on stability, we performed thermal denaturation experiments for all ZmPYK substitution variants. Many diminished stability, two enhanced stability, and the three positions showed different thermal sensitivity to substitution, with one position acting as a "stability rheostat." The differences between the two PYK homologs raises interesting questions about the underlying mechanism(s) that permit functional tuning by single substitutions in some proteins but not in others.

Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function

Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.

Rate-Perturbing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling

Hydrolases are a critical component for modern chemical, pharmaceutical, and environmental sciences. Identifying mutations that enhance catalytic efficiency presents a roadblock to design and to discover new hydrolases for broad academic and industrial uses. Here, we report the statistical profiling for rate-perturbing mutant hydrolases with a single amino acid substitution. We constructed an integrated structure-kinetics database for hydrolases, IntEnzyDB, which contains 3907 k_cats, 4175 K_Ms, and 2715 Protein Data Bank IDs. IntEnzyDB adopts a relational architecture with a flattened data structure, enabling facile and efficient access to clean and tabulated data for machine learning uses. We conducted statistical analyses on how single amino acids mutations influence the turnover number (i.e., k_cat) and efficiency (i.e., k_cat/K_M), with a particular emphasis on profiling the features for rate-enhancing mutations. The results show that mutation to bulky nonpolar residues with a hydrocarbon chain involves a higher likelihood for rate acceleration than to other types of residues. Linear regression models reveal geometric descriptors of substrate and mutation residues that mediate rate-perturbing outcomes for hydrolases with bulky nonpolar mutations. On the basis of the analyses of the structure-kinetics relationship, we observe that the propensity for rate enhancement is independent of protein sizes. In addition, we observe that distal mutations (i.e., >10 Å from the active site) in hydrolases are significantly more prone to induce efficiency neutrality and avoid efficiency deletion but involve similar propensity for rate enhancement. The studies reveal the statistical features for identifying rate-enhancing mutations in hydrolases, which will potentially guide hydrolase discovery in biocatalysis.

Rheostat functional outcomes occur when substitutions are introduced at nonconserved positions that diverge with speciation

When amino acids vary during evolution, the outcome can be functionally neutral or biologically-important. We previously found that substituting a subset of nonconserved positions, "rheostat" positions, can have surprising effects on protein function. Since changes at rheostat positions can facilitate functional evolution or cause disease, more examples are needed to understand their unique biophysical characteristics. Here, we explored whether "phylogenetic" patterns of change in multiple sequence alignments (such as positions with subfamily specific conservation) predict the locations of functional rheostat positions. To that end, we experimentally tested eight phylogenetic positions in human liver pyruvate kinase (hLPYK), using 10-15 substitutions per position and biochemical assays that yielded five functional parameters. Five positions were strongly rheostatic and three were non-neutral. To test the corollary that positions with low phylogenetic scores were not rheostat positions, we combined these phylogenetic positions with previously-identified hLPYK rheostat, "toggle" (most substitution abolished function), and "neutral" (all substitutions were like wild-type) positions. Despite representing 428 variants, this set of 33 positions was poorly statistically powered. Thus, we turned to the in vivo phenotypic dataset for E. coli lactose repressor protein (LacI), which comprised 12-13 substitutions at 329 positions and could be used to identify rheostat, toggle, and neutral positions. Combined hLPYK and LacI results show that positions with strong phylogenetic patterns of change are more likely to exhibit rheostat substitution outcomes than neutral or toggle outcomes. Furthermore, phylogenetic patterns were more successful at identifying rheostat positions than were co-evolutionary or eigenvector centrality measures of evolutionary change.

Rational design of hyper-glycosylated human follicle-stimulating hormone analogs (a bioinformatics approach)

N-glycosylation is a complex mechanism in which the carbohydrate molecules bind to the Asn amino acid in the N-glycan consensus sequence (AsnXxxThr/Ser sequon, where Xxx is any residue, excluding Pro). Introduction of additional N-linked glycosylation site into proposed location in the protein causes to its hyper-glycosylation and can enhance the protein characteristics to provide promising prospects in treatment. Glycoengineering is a favorably used strategy to design and generate hyper-glycosylated variants. In this research, human follicle-stimulating hormone (HuFSH) was considered to identify appropriate positions for adding novel N-glycan sites. A rational computational strategy was applied to predict functional/structural variations induced through changes in polypeptide chain. We analyzed the amino acid chain of FSH to find out the proper locations to introduce asparagine and/or threonine for creating novel N-glycan positions. This analysis resulted in the recognition of 40 possible N-glycosylation positions, and then the eight adequate ones were chosen for additional investigation. The model validation techniques were used to examine 3-dimensional structures of the chosen mutant proteins. Finally, 2 mutants with a further glycan site were recommended as eligible FSH hyper-glycosylated analogs, which may be regarded for subsequent experimental studies. Our in silico approach may decrease tedious and time-wasting laboratory researches of the mutants.Communicated by Ramaswamy H. Sharma.

Protein folding stability and binding interactions through the lens of evolution: a dynamical perspective

While the function of a protein depends heavily on its ability to fold into a correct 3D structure, billions of years of evolution have tailored proteins from highly stable objects to flexible molecules as they adapted to environmental changes. Nature maintains the fine balance of protein folding and stability while still evolving towards new function through generations of fine-tuning necessary interactions with other proteins and small molecules. Here we focus on recent computational and experimental studies that shed light onto how evolution molds protein folding and the functional landscape from a conformational dynamics' perspective. Particularly, we explore the importance of dynamic allostery throughout protein evolution and discuss how the protein anisotropic network can give rise to allosteric and epistatic interactions.

A clinically relevant polymorphism in the Na+/taurocholate cotransporting polypeptide (NTCP) occurs at a rheostat position

Conventionally, most amino acid substitutions at “important” protein positions are expected to abolish function. However, in several soluble-globular proteins, we identified a class of nonconserved positions for which various substitutions produced progressive functional changes; we consider these evolutionary “rheostats”. Here, we report a strong rheostat position in the integral membrane protein, Na⁺/taurocholate (TCA) cotransporting polypeptide, at the site of a pharmacologically relevant polymorphism (S267F). Functional studies were performed for all 20 substitutions (S267X) with three substrates (TCA, estrone-3-sulfate, and rosuvastatin). The S267X set showed strong rheostatic effects on overall transport, and individual substitutions showed varied effects on transport kinetics (K_m and V_max) and substrate specificity. To assess protein stability, we measured surface expression and used the Rosetta software (https://www.rosettacommons.org) suite to model structure and stability changes of S267X. Although buried near the substrate-binding site, S267X substitutions were easily accommodated in the Na⁺/TCA cotransporting polypeptide structure model. Across the modest range of changes, calculated stabilities correlated with surface-expression differences, but neither parameter correlated with altered transport. Thus, substitutions at rheostat position 267 had wide-ranging effects on the phenotype of this integral membrane protein. We further propose that polymorphic positions in other proteins might be locations of rheostat positions.

Identification of biochemically neutral positions in liver pyruvate kinase

Understanding how each residue position contributes to protein function has been a long-standing goal in protein science. Substitution studies have historically focused on conserved protein positions. However, substitutions of nonconserved positions can also modify function. Indeed, we recently identified nonconserved positions that have large substitution effects in human liver pyruvate kinase (hLPYK), including altered allosteric coupling. To facilitate a comparison of which characteristics determine when a nonconserved position does vs does not contribute to function, the goal of the current work was to identify neutral positions in hLPYK. However, existing hLPYK data showed that three features commonly associated with neutral positions-high sequence entropy, high surface exposure, and alanine scanning-lacked the sensitivity needed to guide experimental studies. We used multiple evolutionary patterns identified in a sequence alignment of the PYK family to identify which positions were least patterned, reasoning that these were most likely to be neutral. Nine positions were tested with a total of 117 amino acid substitutions. Although exploring all potential functions is not feasible for any protein, five parameters associated with substrate/effector affinities and allosteric coupling were measured for hLPYK variants. For each position, the aggregate functional outcomes of all variants were used to quantify a "neutrality" score. Three positions showed perfect neutral scores for all five parameters. Furthermore, the nine positions showed larger neutral scores than 17 positions located near allosteric binding sites. Thus, our strategy successfully enriched the dataset for positions with neutral and modest substitutions.

Rheostat positions: A new classification of protein positions relevant to pharmacogenomics

To achieve the full potential of pharmacogenomics, one must accurately predict the functional outcomes that arise from amino acid substitutions in proteins. Classically, researchers have focused on understanding the consequences of individual substitutions. However, literature surveys have shown that most substitutions were created at evolutionarily conserved positions. Awareness of this bias leads to a shift in perspective, from considering the outcomes of individual substitutions to understanding the roles of individual protein positions. Conserved positions tend to act as “toggle” switches, with most substitutions abolishing function. However, nonconserved positions have been found equally capable of affecting protein function. Indeed, many nonconserved positions act like functional dimmer switches (“rheostat” positions): this is revealed when multiple substitutions are made at a single position. Each substitution has a different functional outcome; the set of substitutions spans a range of outcomes. Finally, some nonconserved positions appear neutral, capable of accommodating all amino acid types without modifying function. This paper reviews the currently-known properties of rheostat positions, with examples shown for pyruvate kinase, organic anion transporting polypeptide 1B1, the beta-lactamase inhibitory protein, and angiotensin-converting enzyme 2. Outcomes observed for rheostat positions have implications for the rational design of drug analogs and allosteric drugs. Furthermore, this new framework—comprising three types of protein positions—provides a new approach to interpreting disease and population-based databases of amino acid changes. In conclusion, although a full understanding of substitution outcomes at rheostat positions poses a challenge, utilization of this new frame of reference will further advance the application of pharmacogenomics.

Clinical application of next-generation sequencing to the practice of neurology

Next-generation sequencing technologies allow for rapid and inexpensive large-scale genomic analysis, creating unprecedented opportunities to integrate genomic data into the clinical diagnosis and management of neurological disorders. However, the scale and complexity of these data make them difficult to interpret and require the use of sophisticated bioinformatics applied to extensive datasets, including whole exome and genome sequences. Detailed analysis of genetic data has shown that accurate phenotype information is essential for correct interpretation of genetic variants and might necessitate re-evaluation of the patient in some cases. A multidisciplinary approach that incorporates bioinformatics, clinical evaluation, and human genetics can help to address these challenges. However, despite numerous studies that show the efficacy of next-generation sequencing in establishing molecular diagnoses, pathogenic mutations are generally identified in fewer than half of all patients with genetic neurological disorders, exposing considerable gaps in the understanding of the human genome and providing opportunities to focus research on improving the usefulness of genomics in clinical practice. Looking forward, the emergence of precision health in neurological care will increasingly apply genomic data analysis to pharmacogenetics, preventive medicine, and patient-targeted therapies.

A Novel Missense Mutation in Human Receptor Roundabout-1 (ROBO1) Gene Associated with Pituitary Stalk Interruption Syndrome

Pituitary stalk interruption syndrome (PSIS) is characterized by the association of an absent or thin pituitary stalk, an absent or hypoplastic anterior pituitary lobe and an ectopic posterior pituitary (EPP) lobe. The causes of this anatomical defect include both genetic and environmental factors. Molecular genetic defects have been indentified in a small number of patients with PSIS. A 4-year-old boy presented with hypoglycemia and hyponatremia associated with growth hormone, thyroid stimulating hormone, and adrenocorticotropic hormone deficiencies. The patient had right sided strabismus. magnetic resonance imaging images showed pituitary hypoplasia, EPP and absent pituitary stalk. A novel Receptor Roundabout-1 (ROBO1) missense mutation (c.1690C>T, p.Pro564Ser) that may contribute to the disorder was found in this patient and his mother, who also exhibited pituitary abnormalities.

Functional tunability from a distance: Rheostat positions influence allosteric coupling between two distant binding sites

For protein mutagenesis, a common expectation is that important positions will behave like on/off “toggle” switches (i.e., a few substitutions act like wildtype, most abolish function). However, there exists another class of important positions that manifests a wide range of functional outcomes upon substitution: “rheostat” positions. Previously, we evaluated rheostat positions located near the allosteric binding sites for inhibitor alanine (Ala) and activator fructose-1,6-bisphosphate (Fru-1,6-BP) in human liver pyruvate kinase. When substituted with multiple amino acids, many positions demonstrated moderate rheostatic effects on allosteric coupling between effector binding and phosphoenolpyruvate (PEP) binding in the active site. Nonetheless, the combined outcomes of all positions sampled the full range of possible allosteric coupling (full tunability). However, that study only evaluated allosteric tunability of “local” positions, i.e., positions were located near the binding sites of the allosteric ligand being assessed. Here, we evaluated tunability of allosteric coupling when mutated sites were distant from the allosterically-coupled binding sites. Positions near the Ala binding site had rheostatic outcomes on allosteric coupling between Fru-1,6-BP and PEP binding. In contrast, positions in the Fru-1,6-BP site exhibited modest effects on coupling between Ala and PEP binding. Analyzed in aggregate, both PEP/Ala and PEP/Fru-1,6-BP coupling were again fully tunable by amino acid substitutions at this limited set of distant positions. Furthermore, some positions exhibited rheostatic control over multiple parameters and others exhibited rheostatic effects on one parameter and toggle control over a second. These findings highlight challenges in efforts to both predict/interpret mutational outcomes and engineer functions into proteins.

What went wrong with variant effect predictor performance for the PCM1 challenge

The recent years have seen a drastic increase in the amount of available genomic sequences. Alongside this explosion, hundreds of computational tools were developed to assess the impact of observed genetic variation. Critical Assessment of Genome Interpretation (CAGI) provides a platform to evaluate the performance of these tools in experimentally relevant contexts. In the CAGI-5 challenge assessing the 38 missense variants affecting the human Pericentriolar material 1 protein (PCM1), our SNAP-based submission was the top performer, although it did worse than expected from other evaluations. Here, we compare the CAGI-5 submissions, and 24 additional commonly used variant effect predictors, to analyze the reasons for this observation. We identified per residue conservation, structural, and functional PCM1 characteristics, which may be responsible. As expected, predictors had a hard time distinguishing effect variants in nonconserved positions. They were also better able to call effect variants in a structurally rich region than in a less-structured one; in the latter, they more often correctly identified benign than effect variants. Curiously, most of the protein was predicted to be functionally robust to mutation-a feature that likely makes it a harder problem for generalized variant effect predictors.

SCN5A (NaV1.5) Variant Functional Perturbation and Clinical Presentation: Variants of a Certain Significance

BACKGROUND

Accurately predicting the impact of rare nonsynonymous variants on disease risk is an important goal in precision medicine. Variants in the cardiac sodium channel SCN5A (protein Na_V1.5; voltage-dependent cardiac Na+ channel) are associated with multiple arrhythmia disorders, including Brugada syndrome and long QT syndrome. Rare SCN5A variants also occur in ≈1% of unaffected individuals. We hypothesized that in vitro electrophysiological functional parameters explain a statistically significant portion of the variability in disease penetrance.

METHODS

From a comprehensive literature review, we quantified the number of carriers presenting with and without disease for 1712 reported SCN5A variants. For 356 variants, data were also available for 5 Na_V1.5 electrophysiological parameters: peak current, late/persistent current, steady-state V1/2 of activation and inactivation, and recovery from inactivation.

RESULTS

We found that peak and late current significantly associate with Brugada syndrome (P<0.001; ρ=-0.44; Spearman rank test) and long QT syndrome disease penetrance (P<0.001; ρ=0.37). Steady-state V1/2 activation and recovery from inactivation associate significantly with Brugada syndrome and long QT syndrome penetrance, respectively. Continuous estimates of disease penetrance align with the current American College of Medical Genetics classification paradigm.

CONCLUSIONS

Na_V1.5 in vitro electrophysiological parameters are correlated with Brugada syndrome and long QT syndrome disease risk. Our data emphasize the value of in vitro electrophysiological characterization and incorporating counts of affected and unaffected carriers to aid variant classification. This quantitative analysis of the electrophysiological literature should aid the interpretation of Na_V1.5 variant electrophysiological abnormalities and help improve Na_V1.5 variant classification.

Rational design of hyper-glycosylated human luteinizing hormone analogs (a bioinformatics approach)

Glycoengineering is a recently used approach to extend serum half-life of valuable protein therapeutics. One aspect of glycoengineering is to introduce new N-glycosylation site (Asn-X-Thr/Ser, where X ≠ Pro) into desirable positions in the peptide backbone, resulting in the generation of hyper-glycosylated protein. In this study, human luteinizing hormone (LH) was considered for identification of the suitable positions for the addition of new N-linked glycosylation sites. A rational in silico approach was applied for prediction of structural and functional alterations caused by changes in amino acid sequence. As the first step, we explored the amino acid sequence of LH to find out desirable positions for introducing Asn or/and Thr to create new N-glycosylation sites. This exploration led to the identification of 38 potential N-glycan sites, and then the four acceptable ones were selected for further analysis. Three-dimensional (3D) structures of the selected analogs were generated and examined by the model evaluation methods. Finally, two analogs with one additional glycosylation site were suggested as the qualified analogs for hyper-glycosylation of the LH, which can be considered for further experimental investigations. Our computational strategy can reduce laborious and time-consuming experimental analyses of the analogs.

Coevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs

The conformational dynamics of proteins is rarely used in methodologies used to predict the impact of genetic mutations due to the paucity of three-dimensional protein structures as compared to the vast number of available sequences. Until now a three-dimensional (3D) structure has been required to predict the conformational dynamics of a protein. We introduce an approach that estimates the conformational dynamics of a protein, without relying on structural information. This de novo approach utilizes coevolving residues identified from a multiple sequence alignment (MSA) using Potts models. These coevolving residues are used as contacts in a Gaussian network model (GNM) to obtain protein dynamics. B-factors calculated using sequence-based GNM (Seq-GNM) are in agreement with crystallographic B-factors as well as theoretical B-factors from the original GNM that utilizes the 3D structure. Moreover, we demonstrate the ability of the calculated B-factors from the Seq-GNM approach to discriminate genomic variants according to their phenotypes for a wide range of proteins. These results suggest that protein dynamics can be approximated based on sequence information alone, making it possible to assess the phenotypes of nSNVs in cases where a 3D structure is unknown. We hope this work will promote the use of dynamics information in genetic disease prediction at scale by circumventing the need for 3D structures.

Proteins are dynamic machines that undergo atomic fluctuations, side chain rotations, and collective domain movements that are required for biological function. There is, therefore, a need for quantitative metrics that capture the dynamic fluctuations per position to understand the critical role of protein dynamics in shaping biological functions. A limiting factor in incorporating structural dynamics information in the classification of non-synonymous single nucleotide variants (nSNVs) is the limited number of known 3D structures compared to the vast number of available sequences. We have developed a new sequence-based GNM method, termed Seq-GNM, which uses co-evolving amino acid positions based on the multiple sequence alignment of a given query sequence to estimate the thermal motions of C-alpha atoms. In this paper, we have demonstrated that the predicted thermal motions using Seq-GNM are in reasonable agreement with experimental B-factors as well as B-factors computed using 3D crystal structures. We also provide evidence that B-factors predicted by Seq-GNM are capable of distinguishing between disease-associated and neutral nSNVs.

RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

Human mutations often cause amino acid changes (variants) that can alter protein function or stability. Some variants fall at protein positions that experimentally exhibit "rheostatic" mutation outcomes (different amino acid substitutions lead to a range of functional outcomes). In ongoing studies of rheostat positions, we encountered the need to aggregate experimental results from multiple variants, to describe the overall roles of individual positions. Here, we present "RheoScale" which generates quantitative scores to discriminate rheostat positions from those with "toggle" (most substitutions abolish function) or "neutral" (most substitutions have wild-type function) outcomes. RheoScale scores facilitate correlations of experimental data (such as binding affinity or stability) with structural and bioinformatic analyses. The RheoScale calculator is encoded into a Microsoft Excel workbook and an R script. Example analyses are shown for three model protein systems, including one assessed via deep mutational scanning. The RheoScale calculator quickly and efficiently provided quantitative descriptions that were in good agreement with prior qualitative observations. As an example application, scores were compared to the example proteins' structures; strong rheostat positions tended to occur in dynamic locations. In the future, RheoScale scores can be easily integrated into computational studies to facilitate improved algorithms for predicting outcomes of human variants.

Genetic and genomic testing for neurologic disease in clinical practice

The influence of genetics on neurologic disease is broad and it is becoming more common that clinicians are presented with a patient whose disease is likely of genetic origin. In the search for mutations causing Mendelian disorders, advances in genetic testing methodology have propelled modern neurologic practice beyond single-gene testing into the realm of genomic medicine, where routine evaluations encompass hundreds or thousands of genes, or even the entire exome, representing all protein-coding genes in the genome. The role of various single-gene, multigene, and genomic testing methods, including chromosomal microarray and next-generation sequencing, in the evaluation of neurologic disease is discussed here to provide a framework for their use in a modern neurologic practice. Understanding the inherent issues that arise during the interpretation of sequence variants as pathogenic or benign and the potential discovery of incidental medically relevant findings are important considerations for neurologists utilizing these tests clinically. Strategies for the evaluation of clinically heterogeneous disorders are presented to guide neurologists in the transition from single-gene to genomic considerations and toward the prospect of the widespread routine use of exome sequencing in the continuing goal to achieve more rapid and more precise diagnoses that will improve management and outcome in patients challenged by neurologic disease.

Genome-wide sequencing technologies: A primer for paediatricians

Genetic testing has been a routine part of paediatic medicine for decades. Over time, the number of genetic tests available for children presenting with features thought to be explained by an underlying genetic aetiology has expanded considerably. Genome-wide sequencing approaches (e.g., whole-exome sequencing, whole-genome sequencing) are now emerging as the most comprehensive approaches to genetic diagnosis that we have seen to date; multiple serial tests that were once required for a child under diagnostic investigation can now be accomplished in a single assay. Moreover, the performance of this single assay appears to be superior to the sum of its parts. Despite this promise, technical, ethical and access-related complexities require considerable attention prior to the implementation of these tools in mainstream paediatrics. To ready paediatricians for the eventual transition to genome-based diagnostics, herein we review both the elements and delivery considerations of this emerging technology.

Using Evolution to Guide Protein Engineering: The Devil IS in the Details

For decades, protein engineers have endeavored to reengineer existing proteins for novel applications. Overall, protein folds and gross functions can be readily transferred from one protein to another by transplanting large blocks of sequence (i.e., domain recombination). However, predictably fine-tuning function (e.g., by adjusting ligand affinity, specificity, catalysis, and/or allosteric regulation) remains a challenge. One approach has been to use the sequences of protein families to identify amino acid positions that change during the evolution of functional variation. The rationale is that these nonconserved positions could be mutated to predictably fine-tune function. Evolutionary approaches to protein design have had some success, but the engineered proteins seldom replicate the functional performances of natural proteins. This Biophysical Perspective reviews several complexities that have been revealed by evolutionary and experimental studies of protein function. These include 1) challenges in defining computational and biological thresholds that define important amino acids; 2) the co-occurrence of many different patterns of amino acid changes in evolutionary data; 3) difficulties in mapping the patterns of amino acid changes to discrete functional parameters; 4) the nonconventional mutational outcomes that occur for a particular group of functionally important, nonconserved positions; 5) epistasis (nonadditivity) among multiple mutations; and 6) the fact that a large fraction of a protein's amino acids contribute to its overall function. To overcome these challenges, new goals are identified for future studies.

Computational predictors fail to identify amino acid substitution effects at rheostat positions

Many computational approaches exist for predicting the effects of amino acid substitutions. Here, we considered whether the protein sequence position class - rheostat or toggle - affects these predictions. The classes are defined as follows: experimentally evaluated effects of amino acid substitutions at toggle positions are binary, while rheostat positions show progressive changes. For substitutions in the LacI protein, all evaluated methods failed two key expectations: toggle neutrals were incorrectly predicted as more non-neutral than rheostat non-neutrals, while toggle and rheostat neutrals were incorrectly predicted to be different. However, toggle non-neutrals were distinct from rheostat neutrals. Since many toggle positions are conserved, and most rheostats are not, predictors appear to annotate position conservation better than mutational effect. This finding can explain the well-known observation that predictors assign disproportionate weight to conservation, as well as the field's inability to improve predictor performance. Thus, building reliable predictors requires distinguishing between rheostat and toggle positions.

The Loss and Gain of Functional Amino Acid Residues Is a Common Mechanism Causing Human Inherited Disease

Elucidating the precise molecular events altered by disease-causing genetic variants represents a major challenge in translational bioinformatics. To this end, many studies have investigated the structural and functional impact of amino acid substitutions. Most of these studies were however limited in scope to either individual molecular functions or were concerned with functional effects (e.g. deleterious vs. neutral) without specifically considering possible molecular alterations. The recent growth of structural, molecular and genetic data presents an opportunity for more comprehensive studies to consider the structural environment of a residue of interest, to hypothesize specific molecular effects of sequence variants and to statistically associate these effects with genetic disease. In this study, we analyzed data sets of disease-causing and putatively neutral human variants mapped to protein 3D structures as part of a systematic study of the loss and gain of various types of functional attribute potentially underlying pathogenic molecular alterations. We first propose a formal model to assess probabilistically function-impacting variants. We then develop an array of structure-based functional residue predictors, evaluate their performance, and use them to quantify the impact of disease-causing amino acid substitutions on catalytic activity, metal binding, macromolecular binding, ligand binding, allosteric regulation and post-translational modifications. We show that our methodology generates actionable biological hypotheses for up to 41% of disease-causing genetic variants mapped to protein structures suggesting that it can be reliably used to guide experimental validation. Our results suggest that a significant fraction of disease-causing human variants mapping to protein structures are function-altering both in the presence and absence of stability disruption.

Identifying the molecular changes caused by mutations is a major challenge in understanding and treating human genetic disease. To address this problem, we have developed a wide range of profiling tools designed to predict specific types of functional site from protein 3D structures. We then apply these tools to data sets of inherited disease-associated and putatively neutral amino acid substitutions and estimate the relative contribution of the loss and gain of functional residues in disease. Our results suggest that alterations of molecular function are involved in a significant number of cases of human genetic disease and are over-represented as compared to putatively neutral variants. Additionally, we use experimental data to show that it is possible to computationally identify the loss of specific functional events in disease pathogenesis. Finally, our methodology can be used to reliably identify the potential molecular consequences of disease-causing genetic variants and hence prioritize experimental validation.

Clinical exome sequencing in neurologic disease

PURPOSE OF REVIEW

The landscape of genetic diagnostic testing has changed dramatically with the introduction of next-generation clinical exome sequencing (CES), which provides an unbiased analysis of all protein-coding sequences in the roughly 21,000 genes in the human genome. Use of this testing, however, is currently limited in clinical neurologic practice by the lack of a framework for appropriate use and payer coverage.

RECENT FINDINGS

CES can be cost-effective due to its high diagnostic yield in comparison to other genetic tests in current use and should be utilized as a routine diagnostic test in patients with heterogeneous neurologic phenotypes facing a broad genetic differential diagnosis. CES can eliminate the need for escalating sequences of conventional neurodiagnostic tests.

SUMMARY

This review discusses the role of clinical exome sequencing in neurologic disease, including its benefits to patients, limitations, appropriate use, and billing. We also provide a reference template policy for payer use when considering testing requests.

Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations

Systematic mappings of the effects of protein mutations are becoming increasingly popular. Unexpectedly, these experiments often find that proteins are tolerant to most amino acid substitutions, including substitutions in positions that are highly conserved in nature. To obtain a more realistic distribution of the effects of protein mutations, we applied a laboratory drift comprising 17 rounds of random mutagenesis and selection of M.HaeIII, a DNA methyltransferase. During this drift, multiple mutations gradually accumulated. Deep sequencing of the drifted gene ensembles allowed determination of the relative effects of all possible single nucleotide mutations. Despite being averaged across many different genetic backgrounds, about 67% of all nonsynonymous, missense mutations were evidently deleterious, and an additional 16% were likely to be deleterious. In the early generations, the frequency of most deleterious mutations remained high. However, by the 17th generation, their frequency was consistently reduced, and those remaining were accepted alongside compensatory mutations. The tolerance to mutations measured in this laboratory drift correlated with sequence exchanges seen in M.HaeIII’s natural orthologs. The biophysical constraints dictating purging in nature and in this laboratory drift also seemed to overlap. Our experiment therefore provides an improved method for measuring the effects of protein mutations that more closely replicates the natural evolutionary forces, and thereby a more realistic view of the mutational space of proteins.

Understanding and predicting the effects of single nucleotide polymorphisms (SNPs) is of fundamental importance in many fields. Systematic experimental mappings of the effects of such mutations within a given gene/protein comprise an essential experimental tool for determining protein function and for refining models of protein evolution, as well as an important resource for improving prediction algorithms. Here, we present the results of a laboratory system that mimics the manner by which protein sequences diverge in nature: a prolonged process of gradually accumulating random mutations that retain the protein’s structure and function. The change in frequencies of mutations over generations, as obtained by deep sequencing, enabled us to assess the relative effects of all possible SNPs at the background of an accumulating number of mutations. Compared to previous reports, we found that > 80% of all possible amino acid exchanges have potential deleterious effects, with 67% being clearly deleterious. Tolerance vs. purging of mutations in our prolonged drift also showed better correlation with natural diversity. Overall, our experimental setup provides a better understanding of how protein sequences diverge in nature, plus a new basis for improving the prediction accuracy of the effects of protein mutations, and specifically of SNPs.

Clinical exome sequencing in neurogenetic and neuropsychiatric disorders

Exome sequencing has recently been elevated to the standard of care for genetic diagnostic testing, particularly for genetically diverse and clinically heterogeneous disorders. This review provides a clinically oriented discussion of the next-generation sequencing technology that makes exome sequencing possible and how such technology is applied to the diagnosis of Mendelian disease, including clinically significant de novo variation, interpretation of variants of uncertain clinical significance, the future potential for genetic assessments of disease risk, and the substantial benefits in diagnostic efficiency. Important caveats are also discussed, including the implications of incidental or secondary findings detected during exome sequencing and the relationship of exome sequencing to other methods of clinical genomic testing, such as chromosomal microarray and genome sequencing. Overall, the widespread adoption and use of exome sequencing in routine clinical practice is expected to improve diagnosis rates and reduce test costs, while leading to improvements in patient outcomes and a renewed emphasis on disease management.

Single-Nucleotide Polymorphisms on the RYD5 Gene in Nasal Polyposis

Nasal polyposis (NP) is a chronic inflammatory disease. Several genes play major roles in the pathophysiology of the disease. We analyzed RYD5 gene polymorphisms to determine the effect of these variants or their genetic combinations on NP. We genotyped the RYD5 gene in 434 participants (196 patients with NP and 238 controls). Data were analyzed with SPSS, SNPStats, and multifactor dimensionality reduction (MDR) software. We genotyped 10 single-nucleotide polymorphisms (SNPs) in the RYD5 gene. RYD5 (+152G>T) (p.Gly51Va) has not been reported previously. The PolyPhen and PROVEAN predicted the missense mutation as deleterious, but sorting intolerant from tolerant (SIFT) did not. In the genotype analysis, we found that four SNPs (RYD5 [-264A>G], [-103G>A], [+57-14C>T], and [+66A>G]) were significantly associated with NP. The individuals with combined genotypes of six risk alleles (RYD5-264G, -103A, +13C, +57-14T, +66G, and +279T) had significantly higher risks for NP compared with the ones with one or four risk alleles. Haplotype analysis revealed that the two haplotypes were associated with risk of NP. As indicated by MDR analysis, RYD5 (-264A>G and -103G>A) and RYD5 (-264A>G, -177C>A, and -103G>A) were the best predictive combinations and they had the highest synergistic interaction on NP. In addition, RYD5 (+13C>T) was significantly associated with increased risk of both NP with asthma and NP with allergy and asthma. Some SNPs and their combinations in the RYD5 gene are associated with increased probability for developing NP. We emphasize the importance of genetic factors on NP and NP-related clinical phenotypes.

The SickKids Genome Clinic: developing and evaluating a pediatric model for individualized genomic medicine

Our increasing knowledge of how genomic variants affect human health and the falling costs of whole-genome sequencing are driving the development of individualized genomic medicine. This new clinical paradigm uses knowledge of an individual's genomic variants to anticipate, diagnose and manage disease. While individualized genetic medicine offers the promise of transformative change in health care, it forces us to reconsider existing ethical, scientific and clinical paradigms. The potential benefits of pre-symptomatic identification of at-risk individuals, improved diagnostics, individualized therapy, accurate prognosis and avoidance of adverse drug reactions coexist with the potential risks of uninterpretable results, psychological harm, outmoded counseling models and increased health care costs. Here we review the challenges, opportunities and limits of integrating genomic analysis into pediatric clinical practice and describe a model for implementing individualized genomic medicine. Our multidisciplinary team of bioinformaticians, health economists, health services and policy researchers, ethicists, geneticists, genetic counselors and clinicians has designed a 'Genome Clinic' research project that addresses multiple challenges in pediatric genomic medicine--ranging from development of bioinformatics tools for the clinical assessment of genomic variants and the discovery of disease genes to health policy inquiries, assessment of clinical care models, patient preference and the ethics of consent.

Deep mutational scanning: a new style of protein science

Mutagenesis provides insight into proteins, but only recently have assays that couple genotype to phenotype been used to assess the activities of as many as 1 million mutant versions of a protein in a single experiment. This approach-'deep mutational scanning'-yields large-scale data sets that can reveal intrinsic protein properties, protein behavior within cells and the consequences of human genetic variation. Deep mutational scanning is transforming the study of proteins, but many challenges must be tackled for it to fulfill its promise.

Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspective

Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual's susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application of in silico prediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient's drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians and in silico resources in tailoring treatments to the patients' specific genotype.

Rheostats and toggle switches for modulating protein function

The millions of protein sequences generated by genomics are expected to transform protein engineering and personalized medicine. To achieve these goals, tools for predicting outcomes of amino acid changes must be improved. Currently, advances are hampered by insufficient experimental data about nonconserved amino acid positions. Since the property “nonconserved” is identified using a sequence alignment, we designed experiments to recapitulate that context: Mutagenesis and functional characterization was carried out in 15 LacI/GalR homologs (rows) at 12 nonconserved positions (columns). Multiple substitutions were made at each position, to reveal how various amino acids of a nonconserved column were tolerated in each protein row. Results showed that amino acid preferences of nonconserved positions were highly context-dependent, had few correlations with physico-chemical similarities, and were not predictable from their occurrence in natural LacI/GalR sequences. Further, unlike the “toggle switch” behaviors of conserved positions, substitutions at nonconserved positions could be rank-ordered to show a “rheostatic”, progressive effect on function that spanned several orders of magnitude. Comparisons to various sequence analyses suggested that conserved and strongly co-evolving positions act as functional toggles, whereas other important, nonconserved positions serve as rheostats for modifying protein function. Both the presence of rheostat positions and the sequence analysis strategy appear to be generalizable to other protein families and should be considered when engineering protein modifications or predicting the impact of protein polymorphisms.

Eating disorder predisposition is associated with ESRRA and HDAC4 mutations

Anorexia nervosa and bulimia nervosa are common and severe eating disorders (EDs) of unknown etiology. Although genetic factors have been implicated in the psychopathology of EDs, a clear biological pathway has not been delineated. DNA from two large families affected by EDs was collected, and mutations segregating with illness were identified by whole-genome sequencing following linkage mapping or by whole-exome sequencing. In the first family, analysis of twenty members across three generations identified a rare missense mutation in the estrogen-related receptor α (ESRRA) gene that segregated with illness. In the second family, analysis of eight members across four generations identified a missense mutation in the histone deacetylase 4 (HDAC4) gene that segregated with illness. ESRRA and HDAC4 were determined to interact both in vitro in HeLa cells and in vivo in mouse cortex. Transcriptional analysis revealed that HDAC4 potently represses the expression of known ESRRA-induced target genes. Biochemical analysis of candidate mutations revealed that the identified ESRRA mutation decreased its transcriptional activity, while the HDAC4 mutation increased transcriptional repression of ESRRA. Our findings suggest that mutations that result in decreased ESRRA activity increase the risk of developing EDs.

Rare sequence variants in ANO3 and GNAL in a primary torsion dystonia series and controls

BACKGROUND

Rare autosomal-dominant mutations in ANO3 and GNAL have been recently shown to represent novel genetic factors underlying primary torsion dystonia (PTD) with predominantly craniocervical involvement.

METHODS

We used high-resolution melting to screen all exons of ANO3 and GNAL for rare sequence variants in a population of 342 German individuals with mainly sporadic PTD and 376 general population controls.

RESULTS

We identified 2 novel missense variants in ANO3 (p.Ile833Val and p.Gly973Arg) and 1 novel missense variant in GNAL (p.Val146Met) in three different nonfamilial cases. Variant carriers presented with adult-onset dystonia involving the neck and/or face. In controls, 3 rare ANO3 missense variants (p.Tyr235Cys, p.Asn256Ser, and p.Pro893Leu) but no rare nonsynonymous GNAL variants were present.

CONCLUSIONS

GNAL variants seem to be a rare cause of PTD in our mainly sporadic German sample. Low frequency missense variants in ANO3 occur in both cases and controls, warranting further assessment of this gene in PTD pathogenesis.

A gene-specific method for predicting hemophilia-causing point mutations

A fundamental goal of medical genetics is the accurate prediction of genotype-phenotype correlations. As an approach to develop more accurate in silico tools for prediction of disease-causing mutations of structural proteins, we present a gene- and disease-specific prediction tool based on a large systematic analysis of missense mutations from hemophilia A (HA) patients. Our HA-specific prediction tool, HApredictor, showed disease prediction accuracy comparable to other publicly available prediction software. In contrast to those methods, its performance is not limited to non-synonymous mutations. Given the role of synonymous mutations in disease and drug codon optimization, we propose that utilizing a gene- and disease-specific method can be highly useful to make functional predictions possible even for synonymous mutations. Incorporating computational metrics at both nucleotide and amino acid levels along with multiple protein sequence/structure alignment significantly improved the predictive performance of our tool. HApredictor is freely available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/HA_Predict/index.htm.

Exome sequencing of senescence-accelerated mice (SAM) reveals deleterious mutations in degenerative disease-causing genes

BACKGROUND

Senescence-accelerated mice (SAM) are a series of mouse strains originally derived from unexpected crosses between AKR/J and unknown mice, from which phenotypically distinct senescence-prone (SAMP) and -resistant (SAMR) inbred strains were subsequently established. Although SAMP strains have been widely used for aging research focusing on their short life spans and various age-related phenotypes, such as immune dysfunction, osteoporosis, and brain atrophy, the responsible gene mutations have not yet been fully elucidated.

RESULTS

To identify mutations specific to SAMP strains, we performed whole exome sequencing of 6 SAMP and 3 SAMR strains. This analysis revealed 32,019 to 38,925 single-nucleotide variants in the coding region of each SAM strain. We detected Ogg1 p.R304W and Mbd4 p.D129N deleterious mutations in all 6 of the SAMP strains but not in the SAMR or AKR/J strains. Moreover, we extracted 31 SAMP-specific novel deleterious mutations. In all SAMP strains except SAMP8, we detected a p.R473W missense mutation in the Ldb3 gene, which has been associated with myofibrillar myopathy. In 3 SAMP strains (SAMP3, SAMP10, and SAMP11), we identified a p.R167C missense mutation in the Prx gene, in which mutations causing hereditary motor and sensory neuropathy (Dejerine-Sottas syndrome) have been identified. In SAMP6 we detected a p.S540fs frame-shift mutation in the Il4ra gene, a mutation potentially causative of ulcerative colitis and osteoporosis.

CONCLUSIONS

Our data indicate that different combinations of mutations in disease-causing genes may be responsible for the various phenotypes of SAMP strains.

Systematic investigation of predicted effect of nonsynonymous SNPs in human prion protein gene: a molecular modeling and molecular dynamics study

Nonsynonymous mutations in the human prion protein (HuPrP) gene contribute to the conversion of HuPrP(C) to HuPrP(Sc) and amyloid formation which in turn leads to prion diseases such as familial Creutzfeldt-Jakob disease and Gerstmann-Straussler-Scheinker disease. In order to better understand and predict the role of HuPrP mutations, we developed the following procedure: first, we consulted the Human Genome Variation database and dbSNP databases, and we reviewed literature for the retrieval of aggregation-related nsSNPs of the HuPrP gene. Next, we used three different methods - Polymorphism Phenotyping (PolyPhen), PANTHER, and Auto-Mute - to predict the effect of nsSNPs on the phenotype. We compared the predictions against experimentally reported effects of these nsSNPs to evaluate the accuracy of the three methods: PolyPhen predicted 17 out of 22 nsSNPs as "probably damaging" or "possibly damaging"; PANTHER predicted 8 out of 22 nsSNPs as "Deleterious"; and Auto-Mute predicted 9 out of 20 nsSNPs as "Disease". Finally, structural analyses of the native protein against mutated models were investigated using molecular modeling and molecular dynamics (MD) simulation methods. In addition to comparing predictor methods, our results show the applicability of our procedure for the prediction of damaging nsSNPs. Our study also elucidates the obvious relationship between predicted values of aggregation-related nsSNPs in HuPrP gene and molecular modeling and MD simulations results. In conclusion, this procedure would enable researchers to select outstanding candidates for extensive MD simulations in order to decipher more details of HuPrP aggregation. An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:34.