Single Protein Sequence Research Articles

A structural rationale for reversible vs irreversible amyloid fibril formation from a single protein.

Reversible and irreversible amyloids are two diverging cases of protein (mis)folding associated with the cross-β motif in the protein folding and aggregation energy landscape. Yet, the molecular origins responsible for the formation of reversible vs irreversible amyloids have remained unknown. Here we provide evidence at the atomic level of distinct folding motifs for irreversible and reversible amyloids derived from a single protein sequence: human lysozyme. We compare the 2.8 Å structure of irreversible amyloid fibrils determined by cryo-electron microscopy helical reconstructions with molecular insights gained by solid-state NMR spectroscopy on reversible amyloids. We observe a canonical cross-β-sheet structure in irreversible amyloids, whereas in reversible amyloids, there is a less-ordered coexistence of β-sheet and helical secondary structures that originate from a partially unfolded lysozyme, thus carrying a "memory" of the original folded protein precursor. We also report the structure of hen egg-white lysozyme irreversible amyloids at 3.2 Å resolution, revealing another canonical amyloid fold, and reaffirming that irreversible amyloids undergo a complete conversion of the native protein into the cross-β structure. By combining atomic force microscopy, cryo-electron microscopy and solid-state NMR, we show that a full unfolding of the native protein precursor is a requirement for establishing irreversible amyloid fibrils.

Intrinsic Disorder and Other Malleable Arsenals of Evolved Protein Multifunctionality.

Microscopic evolution at the functional biomolecular level is an ongoing process. Leveraging functional and high-throughput assays, along with computational data mining, has led to a remarkable expansion of our understanding of multifunctional protein (and gene) families over the past few decades. Various molecular and intermolecular mechanisms are now known that collectively meet the cumulative multifunctional demands in higher organisms along an evolutionary path. This multitasking ability is attributed to a certain degree of intrinsic or adapted flexibility at the structure-function level. Evolutionary diversification of structure-function relationships in proteins highlights the functional importance of intrinsically disordered proteins/regions (IDPs/IDRs) which are highly dynamic biological soft matter. Multifunctionality is favorably supported by the fluid-like shapes of IDPs/IDRs, enabling them to undergo disorder-to-order transitions upon binding to different molecular partners. Other new malleable members of the protein superfamily, such as those involved in fold-switching, also undergo structural transitions. This new insight diverges from all traditional notions of functional singularity in enzyme classes and emphasizes a far more complex, multi-layered diversification of protein functionality. However, a thorough review in this line, focusing on flexibility and function-driven structural transitions related to evolved multifunctionality in proteins, is currently missing. This review attempts to address this gap while broadening the scope of multifunctionality beyond single protein sequences. It argues that protein intrinsic disorder is likely the most striking mechanism for expressing multifunctionality in proteins. A phenomenological analogy has also been drawn to illustrate the increasingly complex nature of modern digital life, driven by the need for multitasking, particularly involving media.

Estimating error rates for single molecule protein sequencing experiments.

The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets.

The conformational landscape of fold-switcher KaiB is tuned to the circadian rhythm timescale.

How can a single protein domain encode a conformational landscape with multiple stably-folded states, and how do those states interconvert? Here, we use real-time and relaxation-dispersion NMR to characterize the conformational landscape of the circadian rhythm protein KaiB from Rhodobacter sphaeroides. Unique among known natural metamorphic proteins, this variant of KaiB spontaneously interconverts between two monomeric states: the "Ground" and "Fold-switched" (FS) state. KaiB in its FS state interacts with multiple binding partners, including the central KaiC protein, to regulate circadian rhythms. We find that KaiB itself takes hours to interconvert between the Ground and KaiC binding-competent FS state, underscoring the ability of a single protein sequence to encode the slow process needed for its biological function. We reveal that the rate-limiting step between the Ground and FS state is the cis-trans isomerization of three prolines in the C-terminal fold-switching region by demonstrating acceleration of interconversion by the prolyl cis/trans isomerase CypA. The interconversion proceeds through a "partially disordered" (PD) state, where the N-terminal half remains stably folded while the C-terminal half becomes disordered. We discovered two additional properties of KaiB's landscape: firstly, the Ground state experiences cold denaturation: at 4°C, the PD state becomes the majorly populated state. Secondly, the Ground state exchanges with a fourth state, the "Enigma" state, on the millisecond timescale. We combine AlphaFold2-based predictions and NMR chemical shift predictions to suggest that this "Enigma" state represents a beta-strand register shift that eases buried charged residues in the Ground state. These results provide mechanistic insight in how evolution can design a single sequence that achieves specific timing needed for its function.

Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier.

We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot's hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates.

Insight into the phylogeny and metabolic divergence of Monascus species (M. pilosus, M. ruber, and M. purpureus) at the genome level.

Species of the genus Monascus are economically important and widely used in the production of food colorants and monacolin K. However, they have also been known to produce the mycotoxin citrinin. Currently, taxonomic knowledge of this species at the genome level is insufficient. This study presents genomic similarity analyses through the analysis of the average nucleic acid identity of the genomic sequence and the whole genome alignment. Subsequently, the study constructed a pangenome of Monascus by reannotating all the genomes and identifying a total of 9,539 orthologous gene families. Two phylogenetic trees were constructed based on 4,589 single copy orthologous protein sequences and all the 5,565 orthologous proteins, respectively. In addition, carbohydrate active enzymes, secretome, allergic proteins, as well as secondary metabolite gene clusters were compared among the included 15 Monascus strains. The results clearly revealed a high homology between M. pilosus and M. ruber, and their distant relationship with M. purpureus. Accordingly, all the included 15 Monascus strains should be classified into two distinctly evolutionary clades, namely the M. purpureus clade and the M. pilosus-M. ruber clade. Moreover, gene ontology enrichment showed that the M. pilosus-M. ruber clade had more orthologous genes involved with environmental adaptation than the M. purpureus clade. Compared to Aspergillus oryzae, all the Monascus species had a substantial gene loss of carbohydrate active enzymes. Potential allergenic and fungal virulence factor proteins were also found in the secretome of Monascus. Furthermore, this study identified the pigment synthesis gene clusters present in all included genomes, but with multiple nonessential genes inserted in the gene cluster of M. pilosus and M. ruber compared to M. purpureus. The citrinin gene cluster was found to be intact and highly conserved only among M. purpureus genomes. The monacolin K gene cluster was found only in the genomes of M. pilosus and M. ruber, but the sequence was more conserved in M. ruber. This study provides a paradigm for phylogenetic analysis of the genus Monascus, and it is believed that this report will lead to a better understanding of these food microorganisms in terms of classification, metabolic differentiation, and safety.

Directly mapping the O-sulfation sites within CCR2 via an engineered nanopore.

Tyrosine O-sulfation is one of the significant post-translational modifications that participates in and mediates various life activities in cells. However, until now, the detailed functions of tyrosine O-sulfation have hardly been understood. Although mass spectrometry, ELISA, and radioactive isotope-label have been explored to study tyrosine O-sulfation, the in-depth characterization of this post-translational modification is still rare due to the lack of general, label-free methods for accurately mapping and quantifying the tyrosine O-sulfation sites. Specifically, for mass spectrometry, sulfotyrosine is easily broken down at high temperatures and low pH, usually leading to the loss of sulfated modification in both positive and negative ion MS/MS signals. Moreover, since sulfotyrosine and phosphotyrosine have a similar mass change around 80 Da (sulfotyrosine: 79.9568 Da; phosphotyrosine: 79.9663 Da), it is quite difficult to directly discriminate them unless using ultra-high accuracy mass measurements. Therefore, mass spectrometry still faces huge limitations in rapid sulfotyrosine identification and quantification. Recently, nanopore has raised its potential for single protein sequencing and achieved the detection of various post-translational modifications, such as phosphorylation, glycosylation, methylation, etc. Here, mutant aerolysin nanopores are used for the detection of sulfotyrosine and the mapping of its site without labeling. It should be noted that the experiments are performed under mild conditions without impact on the stability of sulfotyrosine. The results show that phosphorylated, sulfated and nonmodified peptides can be clearly distinguished by differences in the relative current blockades. Furthermore, the mapping of the sites of sulfated tyrosine on CCR2 was achieved by further improving the sensitivity of aerolysin nanopore. These findings offer a path to the single-molecule, label-free identification of sulfotyrosine.

Fc-Mediated Antibody Effector Function, Inflammation Resolution and Oligoclonality on TCR Rearrangements Predict Sustained MRD Negativity in Newly Diagnosed Multiple Myeloma Treated with Immunotherapy Regimens

Background: Targeted immunotherapy combinations including anti-CD38 monoclonal antibody daratumumab have significantly increased the depth of response and clinical outcome in newly diagnosed multiple myeloma (NDMM). Despite this improvement, 30-40% of patients still progress early and fail to achieve sustained minimal residual disease (MRD) negativity through largely unknown resistance mechanisms. Here we integrate whole genome sequencing (WGS) and single cell RNA (scRNA), surface protein (CITE), and TCR sequencing from patients with NDMM to characterize genomic and immune microenvironment features related to the failure to achieve sustained remission. Methods: 5'single-cell RNA-sequencing with an additional capture of the TCR and surface protein markers (CITEseq) was performed at both induction (T1) and at the end of induction (T2) with DKRd (Daratumumab-Carfilzomib, Lenalidomide and dexamethasone). A total of 148,280 cells were analyzed for RNA and CITE from 17 patients at T1 and 18 at T2. Twenty-seven cell types were identified and changes in their gene expression were examined. Data were integrated with 40,000 healthy bone marrow cells from the Human Cell Atlas. For TCR-seq, a total of 22,882 ⍺β clonotypes were analyzed. Fc-mediated antibody effector function is associated with sustained MRD negativity The microenvironment changed drastically between diagnosis and end of induction. The achievement of sustained MRD negativity were associated with an expansion of activated CD56-bright NK cells at T1 (chi-squared=4, p=0.044). Furthermore, significant changes in the monocyte profile were seen between timepoints and response groups. We identified a subset of resident-like macrophages expressing markers such as IL1B and CXCL2 suggesting that there are some immune hot tumors that are associated with suboptimal responses. Although there weren't absolute differences in T-cell subsets, PCA analysis showed that the combination of CD8 effector 1, CD16+monocytes and CD56+NK profiles could be used to identify a subset of good responders achieving sustained MRD negativity. Inflammation resolution is associated with sustained MRD negativity when complete response is achieved, significant changes in the microenvironment were observed and expansion of CD16+ monocytes associated with sustained MRD negativity. These data were validated prospectively using flow cytometry (n=31). These data suggest that the presence of cells mediating Fc-dependent immune effector mechanisms at diagnosis could predict good response and the resolution of tumor induced inflammation after treatment is associated with anti-inflammatory CD16+ monocytes. Oligoclonality in T-cell is associated with treatment and response TCR analysis was available for 22,882 cells and showed that there were significantly fewer clonotypes between T1 and T2 (Kruskal-Wallis chi-squared = 4.0, df = 1, p-value = 0.044) reflecting both the impact of treatment, possible antigen selection, and/or the disappearance of the chronic antigen in the form of the malignant plasma cells. Patients that failed to achieve MRD negativity at T2 had significantly more clonotypes suggesting an ineffective T-cell response to residual tumor. Diversity analysis is currently ongoing. Conclusion: In this multilayered analysis we show changes that the composition of the microenvironment is related to the level of response in the context of DKRd treatment. Fc-mediated antibody effector function and inflammation resolution are associated with sustained MRD negativity phenotypes response suggesting a significant contribution of the microenvironment to response, opening the way for therapeutic manipulation to enhance response. TCR analysis reveals for the first time that oligoclonal profiles seen on treatment may influence the fitness of the immune response.

Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond.

AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise disorder. Here, we combined AF2 predictions with pyHCA, a tool we previously developed to identify foldable segments and estimate their order/disorder ratio, from a single protein sequence. We focused our analysis on the AF2 predictions available for 21 reference proteomes (AFDB v1), in particular on their long foldable segments (>30 amino acids) that exhibit characteristics of soluble domains, as estimated by pyHCA. Among these segments, we provided a global analysis of those with very low pLDDT values along their entire length and compared their characteristics to those of segments with very high pLDDT values. We highlighted cases containing conditional order, as well as cases that could form well-folded structures but escape the AF2 prediction due to a shallow multiple sequence alignment and/or undocumented structure or fold. AF2 and pyHCA can therefore be advantageously combined to unravel cryptic structural features in whole proteomes and to refine predictions for different flavors of disorder.

Protein sequence profile prediction using ProtAlbert transformer

Profiles are used to model protein families and domains. They are built by multiple sequence alignments obtained by mapping a query sequence against a database to generate a profile based on the substitution scoring matrix. The profile applications are very dependent on the alignment algorithm and scoring system for amino acid substitution. However, sometimes there are no similar sequences in the database with the query sequence based on the scoring schema. In these cases, it is not possible to make a profile. This paper proposes a method named PA_SPP, based on pre-trained ProtAlbert transformer to predict the profile for a single protein sequence without alignment. The performance of transformers on natural languages is impressive. Protein sequences can be viewed as a language; we can benefit from these models. We analyze the attention heads in different layers of ProtAlbert to show that the transformer can capture five essential protein characteristics of a single sequence. This assessment shows that ProtAlbert considers some protein properties when suggesting amino acids for each position in the sequence. In other words, transformers can be considered an appropriate alternative for alignment and scoring schema to predict a profile. We evaluate PA_SPP on the Casp13 dataset, including 55 proteins. Meanwhile, one thermophilic and two mesophilic proteins are used as case studies. The results display high similarity between the predicted profiles and HSSP profiles.

AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein.

Using the molecular modeling program Rosetta, we designed a de novo protein, called SEWN0.1, which binds the heterotrimeric G protein Gαq. The design is helical, well-folded, and primarily monomeric in solution at a concentration of 10μM. However, when we solved the crystal structure of SEWN0.1 at 1.9Å, we observed a dimer in a conformation incompatible with binding Gαq . Unintentionally, we had designed a protein that adopts alternate conformations depending on its oligomeric state. Recently, there has been tremendous progress in the field of protein structure prediction as new methods in artificial intelligence have been used to predict structures with high accuracy. We were curious if the structure prediction method AlphaFold could predict the structure of SEWN0.1 and if the prediction depended on oligomeric state. When AlphaFold was used to predict the structure of monomeric SEWN0.1, it produced a model that resembles the Rosetta design model and is compatible with binding Gαq , but when used to predict the structure of a dimer, it predicted a conformation that closely resembles the SEWN0.1 crystal structure. AlphaFold's ability to predict multiple conformations for a single protein sequence should be useful for engineering protein switches.

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

Using new techniques to detect and sequence individual proteins

Developing technologies for single molecule protein sequencing to improve results in clinical and biological research

Using sound to understand protein sequence data: new sonification algorithms for protein sequences and multiple sequence alignments

BackgroundThe use of sound to represent sequence data—sonification—has great potential as an alternative and complement to visual representation, exploiting features of human psychoacoustic intuitions to convey nuance more effectively. We have created five parameter-mapping sonification algorithms that aim to improve knowledge discovery from protein sequences and small protein multiple sequence alignments. For two of these algorithms, we investigated their effectiveness at conveying information. To do this we focussed on subjective assessments of user experience. This entailed a focus group session and survey research by questionnaire of individuals engaged in bioinformatics research.ResultsFor single protein sequences, the success of our sonifications for conveying features was supported by both the survey and focus group findings. For protein multiple sequence alignments, there was limited evidence that the sonifications successfully conveyed information. Additional work is required to identify effective algorithms to render multiple sequence alignment sonification useful to researchers. Feedback from both our survey and focus groups suggests future directions for sonification of multiple alignments: animated visualisation indicating the column in the multiple alignment as the sonification progresses, user control of sequence navigation, and customisation of the sound parameters.ConclusionsSonification approaches undertaken in this work have shown some success in conveying information from protein sequence data. Feedback points out future directions to build on the sonification approaches outlined in this paper. The effectiveness assessment process implemented in this work proved useful, giving detailed feedback and key approaches for improvement based on end-user input. The uptake of similar user experience focussed effectiveness assessments could also help with other areas of bioinformatics, for example in visualisation.

α-Synuclein Strains: Does Amyloid Conformation Explain the Heterogeneity of Synucleinopathies?

Synucleinopathies are a heterogeneous group of neurodegenerative diseases with amyloid deposits that contain the α-synuclein (SNCA/α-Syn) protein as a common hallmark. It is astonishing that aggregates of a single protein are able to give rise to a whole range of different disease manifestations. The prion strain hypothesis offers a possible explanation for this conundrum. According to this hypothesis, a single protein sequence is able to misfold into distinct amyloid structures that can cause different pathologies. In fact, a growing body of evidence suggests that conformationally distinct α-Syn assemblies might be the causative agents behind different synucleinopathies. In this review, we provide an overview of research on the strain hypothesis as it applies to synucleinopathies and discuss the potential implications for diagnostic and therapeutic purposes.

Fundamentals to function: Quantitative and scalable approaches for measuring protein stability

Folding a linear chain of amino acids into a three-dimensional protein is a complex physical process that ultimately confers an impressive range of diverse functions. Although recent advances have driven significant progress in predicting three-dimensional protein structures from sequence, proteins are not static molecules. Rather, they exist as complex conformational ensembles defined by energy landscapes spanning the space of sequence and conditions. Quantitatively mapping the physical parameters that dictate these landscapes and protein stability is therefore critical to develop models that are capable of predicting how mutations alter function of proteins in disease and informing the design of proteins with desired functions. Here, we review the approaches that are used to quantify protein stability at a variety of scales, from returning multiple thermodynamic and kinetic measurements for a single protein sequence to yielding indirect insights into folding across a vast sequence space. The physical parameters derived from these approaches will provide a foundation for models that extend beyond the structural prediction to capture the complexity of conformational ensembles and, ultimately, their function.

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis.

This protocol identifies the immunity proteins of the bactericidal enzymes: colicin E3 and bacteriocin, produced by a pathogenic Escherichia coli strain using antibiotic induction, and identified by MALDI-TOF-TOF tandem mass spectrometry and top-down proteomic analysis with software developed in-house. The immunity protein of colicin E3 (Im3) and the immunity protein of bacteriocin (Im-Bac) were identified from prominent b- and/or y-type fragment ions generated by the polypeptide backbone cleavage (PBC) on the C-terminal side of aspartic acid, glutamic acid, and asparagine residues by the aspartic acid effect fragmentation mechanism. The software rapidly scans in silico protein sequences derived from the whole genome sequencing of the bacterial strain. The software also iteratively removes amino acid residues of a protein sequence in the event that the mature protein sequence is truncated. A single protein sequence possessed mass and fragment ions consistent with those detected for each immunity protein. The candidate sequence was then manually inspected to confirm that all detected fragment ions could be assigned. The N-terminal methionine of Im3 was post-translationally removed, whereas Im-Bac had the complete sequence. In addition, we found that only two or three non-complementary fragment ions formed by PBC are necessary to identify the correct protein sequence. Finally, a promoter (SOS box) was identified upstream of the antibacterial and immunity genes in a plasmid genome of the bacterial strain.

Single cell profiling identifies IgMMDA-LDL-producing human B cells and a novel role for CD24

Abstract Objectives: IgMs that inactivate oxidation specific epitopes (IgMOSE) on phospholipids such as low density lipoprotein (LDL) were shown to confer athero-protection. We developed a 24 antibodies mass cytometry panel (CyTOF) to identify the human B cell subtype producing IgM to malondialdehyde modified LDL (IgMMDA-LDL), a predominant IgMOSE in humans. We also utilized humanized mice model to characterize a role of CD24 mediated IgM production. Methods: Peripheral blood mononuclear cells from healthy donors and subjects with coronary artery disease (CAD) undergoing quantitative coronary angiography were used to perform high dimensional analysis using CyTOF, RNAseq and Abseq as well as adoptive transfer into humanized mice model. Results: CD20+CD27+IgM+ cells (B27+IgM+) spontaneously produced IgM and produced IgMMDA-LDL in response to MDA stimulation when injected into humanized mice; an effect significantly augmented in B27+IgM+ cells with high expression of CD24 (B27+IgM+CD24hi). CD24 expression also enhanced splenic and bone marrow trafficking of B27+IgM+ cells. Blocking CD24 with a mAb reduced CCR6 expression, increased CCL20-induced CCR6 internalization and impaired migration to the spleen leading to lower IgM production. Lastly, single cell protein and transcriptome sequencing of PBMCs from 60 CAD subjects revealed enhanced CCR6 and IgM signaling in B27+IgM+CD24hi cells in subjects with low compared to high CAD severity. Conclusions: Identification of IgMMDA-LDL-producing cells in humans has the potential to allow for the development of therapeutics aimed at cellular targeting that may allow for enhancing production of IgM to the many OSE produced during inflammatory states such as atherosclerosis.

Fully Automated Ancestral Sequence Reconstruction using FireProtASR.

Protein evolution and protein engineering techniques are of great interest in basic science and industrial applications such as pharmacology, medicine, or biotechnology. Ancestral sequence reconstruction (ASR) is a powerful technique for probing evolutionary relationships and engineering robust proteins with good thermostability and broad substrate specificity. The following protocol describes the setting up and execution of an automated FireProtASR workflow using a dedicated web site. The service allows for inference of ancestral proteins automatically, from a single protein sequence. Once a protein sequence is submitted, the server will build a dataset of homology sequences, perform a multiple sequence alignment (MSA), build a phylogenetic tree, and reconstruct ancestral nodes. The protocol is also highly flexible and allows for multiple forms of input, advanced settings, and the ability to start jobs from: (i) a single sequence, (ii) a set of homologous sequences, (iii) an MSA, and (iv) a phylogenetic tree. This approach automates all necessary steps and offers a way for novices with limited exposure to ASR techniques to improve the properties of a protein of interest. The technique can even be used to introduce catalytic promiscuity into an enzyme. A web server for accessing the fully automated workflow is freely accessible at https://loschmidt.chemi.muni.cz/fireprotasr/. © 2021 Wiley Periodicals LLC. Basic Protocol: ASR using the Web Server FireProtASR.

Nanopore Confinement for Single‐Molecule Measurement of Proteins

Nanopore Confinement for Single‐Molecule Measurement of Proteins