Sirtuin 3 overexpression preserves maximal sarco(endo)plasmic reticulum calcium ATPase activity in the skeletal muscle of mice subjected to high fat - high sucrose feeding.

Sarco(endo)plasmic reticulum calcium (Ca2+) ATPase (SERCA) transports Ca2+ in muscle. Impaired SERCA activity may contribute to diabetic myopathy. Sirtuin (SIRT) 3 regulates muscle metabolism and function; however, it is unknown if SIRT3 regulates muscle SERCA activity or acetylation. We determined if SIRT3 overexpression enhances SERCA activity in mouse gastrocnemius muscle and if SIRT3 overexpression preserves gastrocnemius SERCA activity in a model of type 2 diabetes, induced by high fat - high sucrose (HFHS) feeding. We also determined if the acetylation status of SERCA proteins in mouse gastrocnemius is altered by SIRT3 overexpression or HFHS feeding. Wild-type (WT) and SIRT3 transgenic (SIRT3TG) mice, overexpressing SIRT3 in skeletal muscle, were fed a standard or HFHS diet for 4 months. SIRT3TG and WT mice developed obesity and glucose intolerance after 4 months of HFHS feeding. SERCA Vmax was higher in gastrocnemius of SIRT3TG mice compared with WT mice. HFHS-fed mice had lower SERCA1a protein levels and lower SERCA Vmax in their gastrocnemius than control-fed mice. The decrease in SERCA Vmax in gastrocnemius muscle due to HFHS feeding was attenuated by SIRT3 overexpression in HFHS-fed SIRT3TG mice. SERCA1a and SERCA2a acetylation in mouse gastrocnemius was not altered by genotype or diet. These findings suggest SIRT3 overexpression improves SERCA function in mouse skeletal muscle.

Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues.

Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner.

Intrinsic Disorder in Human RNA-Binding Proteins.

Although RNA-binding proteins (RBPs) are known to be enriched in intrinsic disorder, no previous analysis focused on RBPs interacting with specific RNA types. We fill this gap with a comprehensive analysis of the putative disorder in RBPs binding to six common RNA types: messenger RNA (mRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), non-coding RNA (ncRNA), ribosomal RNA (rRNA), and internal ribosome RNA (irRNA). We also analyze the amount of putative intrinsic disorder in the RNA-binding domains (RBDs) and non-RNA-binding-domain regions (non-RBD regions). Consistent with previous studies, we show that in comparison with human proteome, RBPs are significantly enriched in disorder. However, closer examination finds significant enrichment in predicted disorder for the mRNA-, rRNA- and snRNA-binding proteins, while the proteins that interact with ncRNA and irRNA are not enriched in disorder, and the tRNA-binding proteins are significantly depleted in disorder. We show a consistent pattern of significant disorder enrichment in the non-RBD regions coupled with low levels of disorder in RBDs, which suggests that disorder is relatively rarely utilized in the RNA-binding regions. Our analysis of the non-RBD regions suggests that disorder harbors posttranslational modification sites and is involved in the putative interactions with DNA. Importantly, we utilize experimental data from DisProt and independent data from Pfam to validate the above observations that rely on the disorder predictions. This study provides new insights into the distribution of disorder across proteins that bind different RNA types and the functional role of disorder in the regions where it is enriched.

Muscle-specific sirtuin 3 overexpression does not attenuate the pathological effects of high-fat/high-sucrose feeding but does enhance cardiac SERCA2a activity.

Obesity, type 2 diabetes, and heart disease are linked to an unhealthy diet. Sarco(endo)plasmic reticulum calcium (Ca2+ ) ATPase 2a (SERCA2a) controls cardiac function by transporting Ca2+ in cardiomyocytes. SERCA2a is altered by diet and acetylation, independently; however, it is unknown if diet alters cardiac SERCA2a acetylation. Sirtuin (SIRT) 3 is an enzyme that might preserve health under conditions of macronutrient excess by modulating metabolism via regulating deacetylation of target proteins. Our objectives were to determine if muscle-specific SIRT3 overexpression attenuates the pathological effects of high fat-high sucrose (HFHS) feeding and if HFHS feeding alters cardiac SERCA2a acetylation. We also determined if SIRT3 alters cardiac SERCA2a acetylation and regulates cardiac SERCA2a activity. C57BL/6J wild-type (WT) mice and MCK-mSIRT3-M1-Flag transgenic (SIRT3TG ) mice, overexpressing SIRT3 in cardiac and skeletal muscle, were fed a standard-diet or a HFHS-diet for 4 months. SIRT3TG and WT mice developed obesity, glucose intolerance, cardiac dysfunction, and pathological cardiac remodeling after 4 months of HFHS feeding, indicating muscle-specific SIRT3 overexpression does not attenuate the pathological effects of HFHS-feeding. Overall cardiac lysine acetylation was increased by 63% in HFHS-fed mice (p = 0.022), though HFHS feeding did not alter cardiac SERCA2a acetylation. Cardiac SERCA2a acetylation was not altered by SIRT3 overexpression, whereas SERCA2a Vmax was 21% higher in SIRT3TG (p = 0.039) than WT mice. This suggests that SIRT3 overexpression enhanced cardiac SERCA2a activity without direct SERCA2a deacetylation. Muscle-specific SIRT3 overexpression may not prevent the complications associated with an unhealthy diet in mice, but it appears to enhance SERCA2a activity in the mouse heart.

Understanding COVID-19 via comparative analysis of dark proteomes of SARS-CoV-2, human SARS and bat SARS-like coronaviruses.

The recently emerged coronavirus designated as SARS-CoV-2 (also known as 2019 novel coronavirus (2019-nCoV) or Wuhan coronavirus) is a causative agent of coronavirus disease 2019 (COVID-19), which is rapidly spreading throughout the world now. More than 1.21 million cases of SARS-CoV-2 infection and more than 67,000 COVID-19-associated mortalities have been reported worldwide till the writing of this article, and these numbers are increasing every passing hour. The World Health Organization (WHO) has declared the SARS-CoV-2 spread as a global public health emergency and admitted COVID-19 as a pandemic now. Multiple sequence alignment data correlated with the already published reports on SARS-CoV-2 evolution indicated that this virus is closely related to the bat severe acute respiratory syndrome-like coronavirus (bat SARS-like CoV) and the well-studied human SARS coronavirus (SARS-CoV). The disordered regions in viral proteins are associated with the viral infectivity and pathogenicity. Therefore, in this study, we have exploited a set of complementary computational approaches to examine the dark proteomes of SARS-CoV-2, bat SARS-like, and human SARS CoVs by analysing the prevalence of intrinsic disorder in their proteins. According to our findings, SARS-CoV-2 proteome contains very significant levels of structural order. In fact, except for nucleocapsid, Nsp8, and ORF6, the vast majority of SARS-CoV-2 proteins are mostly ordered proteins containing less intrinsically disordered protein regions (IDPRs). However, IDPRs found in SARS-CoV-2 proteins are functionally important. For example, cleavage sites in its replicase 1ab polyprotein are found to be highly disordered, and almost all SARS-CoV-2 proteins contains molecular recognition features (MoRFs), which are intrinsic disorder-based protein-protein interaction sites that are commonly utilized by proteins for interaction with specific partners. The results of our extensive investigation of the dark side of SARS-CoV-2 proteome will have important implications in understanding the structural and non-structural biology of SARS or SARS-like coronaviruses.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Identification of Intrinsic Disorder in Complexes from the Protein Data Bank.

BACKGROUND: Intrinsically disordered proteins or regions (IDPs or IDRs) lack stable structures in solution, yet often fold upon binding with partners. IDPs or IDRs are highly abundant in all proteomes and represent a significant modification of sequence → structure → function paradigm. The Protein Data Bank (PDB) includes complexes containing disordered segments bound to globular proteins, but the molecular mechanisms of such binding interactions remain largely unknown. RESULTS: In this study, we present the results of various disorder predictions on a nonredundant set of PDB complexes. In contrast to their structural appearances, many PDB proteins were predicted to be disordered when separated from their binding partners. These predicted-to-be-disordered proteins were observed to form structures depending upon various factors, including heterogroup binding, protein/DNA/RNA binding, disulfide bonds, and ion binding. CONCLUSIONS: This study collects many examples of disorder-to-order transition in IDP complex formation, thus revealing the unusual structure-function relationships of IDPs and providing an additional support for the newly proposed paradigm of the sequence → IDP/IDR ensemble → function.

Computational Prediction of Intrinsic Disorder in Protein Sequences with the disCoP Meta-predictor.

Intrinsically disordered proteins are either entirely disordered or contain disordered regions in their native state. These proteins and regions function without the prerequisite of a stable structure and were found to be abundant across all kingdoms of life. Experimental annotation of disorder lags behind the rapidly growing number of sequenced proteins, motivating the development of computational methods that predict disorder in protein sequences. DisCoP is a user-friendly webserver that provides accurate sequence-based prediction of protein disorder. It relies on meta-architecture in which the outputs generated by multiple disorder predictors are combined together to improve predictive performance. The architecture of disCoP is presented, and its accuracy relative to several other disorder predictors is briefly discussed. We describe usage of the web interface and explain how to access and read results generated by this computational tool. We also provide an example of prediction results and interpretation. The disCoP's webserver is publicly available at http://biomine.cs.vcu.edu/servers/disCoP/ .

Prediction of Intrinsic Disorder with Quality Assessment Using QUARTER.

Intrinsically disordered regions (IDRs) are estimated to be highly abundant in nature. While only several thousand proteins are annotated with experimentally derived IDRs, computational methods can be used to predict IDRs for the millions of currently uncharacterized protein chains. Several dozen disorder predictors were developed over the last few decades. While some of these methods provide accurate predictions, unavoidably they also make some mistakes. Consequently, one of the challenges facing users of these methods is how to decide which predictions can be trusted and which are likely incorrect. This practical problem can be solved using quality assessment (QA) scores that predict correctness of the underlying (disorder) predictions at a residue level. We motivate and describe a first-of-its-kind toolbox of QA methods, QUARTER (QUality Assessment for pRotein inTrinsic disordEr pRedictions), which provides the scores for a diverse set of ten disorder predictors. QUARTER is available to the end users as a free and convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTER/ . We briefly describe the predictive architecture of QUARTER and provide detailed instructions on how to use the webserver. We also explain how to interpret results produced by QUARTER with the help of a case study.

Myokines as biomarkers of frailty and cardiovascular disease risk in females.

Frailty is a risk factor for cardiovascular disease (CVD). Biomarkers have the potential to detect the early stages of frailty, such as pre-frailty. Myokines may act as biomarkers of frailty-related disease progression, as a decline in muscle health is a hallmark of the frailty phenotype. This study is a secondary analysis of 104 females 55 years of age or older with no previous history of CVD. Differences in systemic myokine concentrations based on frailty status and CVD risk profile were examined using a case-control design. Propensity matching identified two sets of 26 pairs with pre-frailty as the exposure variable in low or elevated CVD risk groups for a total 104 female participants. Frailty was assessed using the Fried Criteria (FC) and CVD risk was assessed using the Framingham Risk Score (FRS). Factorial ANOVA compared the main effects of frailty, CVD risk, and their interaction on the concentrations of 15 myokines. Differences were found when comparing elevated CVD risk status with low for the concentrations of EPO (384.76 ± 1046.07 vs. 206.63 ± 284.61 pg/mL, p = .001), FABP3 (2772.61 ± 3297.86 vs. 1693.31 ± 1019.34 pg/mL, p = .017), FGF21 (193.17 ± 521.09 vs. 70.18 ± 139.51 pg/mL, p = .010), IL-6 (1.73 ± 4.97 vs. 0.52 ± 0.89 pg/mL, p = .023), and IL-15 (2.62 ± 10.56 vs. 0.92 ± 1.25 pg/mL, p = .022). Pre-frail females had lower concentrations of fractalkine compared to robust (27.04 ± 20.60 vs. 103.62 ± 315.45 pg/mL, p = .004). Interaction effects between frailty status and CVD risk for FGF21 and OSM were identified. In elevated CVD risk, pre-frail females, concentrations of FGF21 and OSM were lower than that of elevated CVD risk, robust females (69.10 ± 62.86 vs. 317.24 ± 719.69, p = .011; 1.73 ± 2.32 vs. 24.43 ± 69.21, p = .018, respectively). These data identified specific biomarkers of CVD risk and biomarkers of frailty that are exacerbated with CVD risk.

Disordered RNA-Binding Region Prediction with DisoRDPbind.

RNA chaperone activity is one of the many functions of intrinsically disordered regions (IDRs). IDRs function without the prerequisite of a stable structure. Instead, their functions arise from structural ensembles. A common theme in IDR function is molecular recognition; IDRs mediate interactions with other proteins, RNA, and DNA. Many computational methods are available to predict IDRs from protein sequence, but relatively few are available for predicting IDR functions. Available methods primarily focus on protein-protein interactions. DisoRDPbind was developed to predict several protein functions including interactions with RNA. This method is available as a user-friendly web interface, located at http://biomine.cs.vcu.edu/servers/DisoRDPbind/ . The development and architecture of DisoRDPbind is briefly presented, and its accuracy relative to other RNA-binding residue predictors is discussed. We explain usage of the web interface in detail and provide an example of prediction results and interpretation. While DisoRDPbind does not identify RNA chaperones directly, we provide a case study of an RNA chaperone, HCV core protein, as an example of the method's utility in the study of RNA chaperones.

Accuracy of protein-level disorder predictions.

Experimental annotations of intrinsic disorder are available for 0.1% of 147 000 000 of currently sequenced proteins. Over 60 sequence-based disorder predictors were developed to help bridge this gap. Current benchmarks of these methods assess predictive performance on datasets of proteins; however, predictions are often interpreted for individual proteins. We demonstrate that the protein-level predictive performance varies substantially from the dataset-level benchmarks. Thus, we perform first-of-its-kind protein-level assessment for 13 popular disorder predictors using 6200 disorder-annotated proteins. We show that the protein-level distributions are substantially skewed toward high predictive quality while having long tails of poor predictions. Consequently, between 57% and 75% proteins secure higher predictive performance than the currently used dataset-level assessment suggests, but as many as 30% of proteins that are located in the long tails suffer low predictive performance. These proteins typically have relatively high amounts of disorder, in contrast to the mostly structured proteins that are predicted accurately by all 13 methods. Interestingly, each predictor provides the most accurate results for some number of proteins, while the best-performing at the dataset-level method is in fact the best for only about 30% of proteins. Moreover, the majority of proteins are predicted more accurately than the dataset-level performance of the most accurate tool by at least four disorder predictors. While these results suggests that disorder predictors outperform their current benchmark performance for the majority of proteins and that they complement each other, novel tools that accurately identify the hard-to-predict proteins and that make accurate predictions for these proteins are needed.

Codon selection reduces GC content bias in nucleic acids encoding for intrinsically disordered proteins.

Protein-coding nucleic acids exhibit composition and codon biases between sequences coding for intrinsically disordered regions (IDRs) and those coding for structured regions. IDRs are regions of proteins that are folding self-insufficient and which function without the prerequisite of folded structure. Several authors have investigated composition bias or codon selection in regions encoding for IDRs, primarily in Eukaryota, and concluded that elevated GC content is the result of the biased amino acid composition of IDRs. We substantively extend previous work by examining GC content in regions encoding IDRs, from 44 species in Eukaryota, Archaea, and Bacteria, spanning a wide range of GC content. We confirm that regions coding for IDRs show a significantly elevated GC content, even across all domains of life. Although this is largely attributable to the amino acid composition bias of IDRs, we show that this bias is independent of the overall GC content and, most importantly, we are the first to observe that GC content bias in IDRs is significantly different than expected from IDR amino acid composition alone. We empirically find compensatory codon selection that reduces the observed GC content bias in IDRs. This selection is dependent on the overall GC content of the organism. The codon selection bias manifests as use of infrequent, AT-rich codons in encoding IDRs. Further, we find these relationships to be independent of the intrinsic disorder prediction method used, and independent of estimated translation efficiency. These observations are consistent with the previous work, and we speculate on whether the observed biases are causal or symptomatic of other driving forces.

Many-to-one binding by intrinsically disordered protein regions.

Disordered binding regions (DBRs), which are embedded within intrinsically disordered proteins or regions (IDPs or IDRs), enable IDPs or IDRs to mediate multiple protein-protein interactions. DBR-protein complexes were collected from the Protein Data Bank for which two or more DBRs having different amino acid sequences bind to the same (100% sequence identical) globular protein partner, a type of interaction herein called many-to-one binding. Two distinct binding profiles were identified: independent and overlapping. For the overlapping binding profiles, the distinct DBRs interact by means of almost identical binding sites (herein called "similar"), or the binding sites contain both common and divergent interaction residues (herein called "intersecting"). Further analysis of the sequence and structural differences among these three groups indicate how IDP flexibility allows different segments to adjust to similar, intersecting, and independent binding pockets.

Disordered Function Conjunction: On the in-silico function annotation of intrinsically disordered regions.

Intrinsically disorder regions (IDRs) lack a stable structure, yet perform biological functions. The functions of IDRs include mediating interactions with other molecules, including proteins, DNA, or RNA and entropic functions, including domain linkers. Computational predictors provide residue-level indications of function for disordered proteins, which contrasts with the need to functionally annotate the thousands of experimentally and computationally discovered IDRs. In this work, we investigate the feasibility of using residue-level prediction methods for region-level function predictions. For an initial examination of the multiple function region-level prediction problem, we constructed a dataset of (likely) single function IDRs in proteins that are dissimilar to the training datasets of the residue-level function predictors. We find that available residue-level prediction methods are only modestly useful in predicting multiple region-level functions. Classification is enhanced by simultaneous use of multiple residue-level function predictions and is further improved by inclusion of amino acids content extracted from the protein sequence. We conclude that multifunction prediction for IDRs is feasible and benefits from the results produced by current residue-level function predictors, however, it has to accommodate inaccuracy in functional annotations.

DISOselect: Disorder predictor selection at the protein level.

The intense interest in the intrinsically disordered proteins in the life science community, together with the remarkable advancements in predictive technologies, have given rise to the development of a large number of computational predictors of intrinsic disorder from protein sequence. While the growing number of predictors is a positive trend, we have observed a considerable difference in predictive quality among predictors for individual proteins. Furthermore, variable predictor performance is often inconsistent between predictors for different proteins, and the predictor that shows the best predictive performance depends on the unique properties of each protein sequence. We propose a computational approach, DISOselect, to estimate the predictive performance of 12 selected predictors for individual proteins based on their unique sequence-derived properties. This estimation informs the users about the expected predictive quality for a selected disorder predictor and can be used to recommend methods that are likely to provide the best quality predictions. Our solution does not depend on the results of any disorder predictor; the estimations are made based solely on the protein sequence. Our solution significantly improves predictive performance, as judged with a test set of 1,000 proteins, when compared to other alternatives. We have empirically shown that by using the recommended methods the overall predictive performance for a given set of proteins can be improved by a statistically significant margin. DISOselect is freely available for non-commercial users through the webserver at http://biomine.cs.vcu.edu/servers/DISOselect/.

Mechanisms for the transition from physiological to pathological cardiac hypertrophy.

The heart is capable of responding to stressful situations by increasing muscle mass, which is broadly defined as cardiac hypertrophy. This phenomenon minimizes ventricular wall stress for the heart undergoing a greater than normal workload. At initial stages, cardiac hypertrophy is associated with normal or enhanced cardiac function and is considered to be adaptive or physiological; however, at later stages, if the stimulus is not removed, it is associated with contractile dysfunction and is termed as pathological cardiac hypertrophy. It is during physiological cardiac hypertrophy where the function of subcellular organelles, including the sarcolemma, sarcoplasmic reticulum, mitochondria, and myofibrils, may be upregulated, while pathological cardiac hypertrophy is associated with downregulation of these subcellular activities. The transition of physiological cardiac hypertrophy to pathological cardiac hypertrophy may be due to the reduction in blood supply to hypertrophied myocardium as a consequence of reduced capillary density. Oxidative stress, inflammatory processes, Ca2+-handling abnormalities, and apoptosis in cardiomyocytes are suggested to play a critical role in the depression of contractile function during the development of pathological hypertrophy.

Prehabilitation: The Right Medicine for Older Frail Adults Anticipating Transcatheter Aortic Valve Replacement, Coronary Artery Bypass Graft, and Other Cardiovascular Care.

The wait before elective cardiac intervention or surgery presents an opportunity to prevent further physiologic decline preoperatively in older patients. Implementation of prehabilitation programs decreases length of hospital stay postoperatively, decreases time spent in the intensive care unit, decreases postoperative complications, and improves self-reported quality of life postsurgery. Prehabilitation programs should adopt multimodal approaches including nutrition, exercise, and worry reduction to improve patient resilience in the preoperative period. High-quality research in larger cohorts is needed, and interventions focusing on underrepresented frailer populations and women. Creative ways to improve accessibility, adherence, and benefits received from prehabilitation should be explored.

Intrinsically disordered domains: Sequence ➔ disorder ➔ function relationships.

Disordered domains are long regions of intrinsic disorder that ideally have conserved sequences, conserved disorder, and conserved functions. These domains were first noticed in protein-protein interactions that are distinct from the interactions between two structured domains and the interactions between structured domains and linear motifs or molecular recognition features (MoRFs). So far, disordered domains have not been systematically characterized. Here, we present a bioinformatics investigation of the sequence-disorder-function relationships for a set of probable disordered domains (PDDs) identified from the Pfam database. All the Pfam seed proteins from those domains with at least one PDD sequence were collected. Most often, if a set contains one PDD sequence, then all members of the set are PDDs or nearly so. However, many seed sets have sequence collections that exhibit diverse proportions of predicted disorder and structure, thus giving the completely unexpected result that conserved sequences can vary substantially in predicted disorder and structure. In addition to the induction of structure by binding to protein partners, disordered domains are also induced to form structure by disulfide bond formation, by ion binding, and by complex formation with RNA or DNA. The two new findings, (a) that conserved sequences can vary substantially in their predicted disorder content and (b) that homologues from a single domain can evolve from structure to disorder (or vice versa), enrich our understanding of the sequence ➔ disorder ensemble ➔ function paradigm.

Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.

Many new methods for the sequence-based prediction of the secondary and supersecondary structures have been developed over the last several years. These and older sequence-based predictors are widely applied for the characterization and prediction of protein structure and function. These efforts have produced countless accurate predictors, many of which rely on state-of-the-art machine learning models and evolutionary information generated from multiple sequence alignments. We describe and motivate both types of predictions. We introduce concepts related to the annotation and computational prediction of the three-state and eight-state secondary structure as well as several types of supersecondary structures, such as ß hairpins, coiled coils, and α-turn-α motifs. We review 34 predictors focusing on recent tools and provide detailed information for a selected set of 14 secondary structure and 3 supersecondary structure predictors. We conclude with several practical notes for the end users of these predictive methods.