Protein Science

De novo protein design often produces thermostable proteins that denature above 100 {degrees}C, which complicates the analysis of their stability. Thermostable proteins can be unfolded by combined chemical and thermal denaturation followed by global analysis of multiple melting curves. Here, we have developed CheMelt, a new online tool for global analysis of unfolding data via an intuitive graphical user interface. We use nanoscale differential scanning fluorimetry followed by CheMelt data analysis to dissect the combined thermal and chemical denaturation of thirty-five de novo designed protein binders. Fifteen present sufficient fluorescence changes to extract thermodynamic parameters of unfolding. These de novo designed proteins have systematically lower {Delta}Cp and m-values than comparable natural proteins, which implies that they expose fewer hydrophobic residues upon unfolding. We show that a high thermostability of a designed protein does not necessarily imply a high equilibrium stability; and demonstrate the potential of CheMelt in dissecting thermodynamic properties for protein design and engineering.

The industrial application of enzymes is often hampered by poor stability and low expression yields. While computational tools can predict stabilizing mutations, many are bound by restrictive licenses that hinder their broader adoption. To address this, we developed StabilizeIT, a powerful, open-access webserver for enhancing protein stability and expression. StabilizeIT integrates a pipeline of curated open-source tools such as ProteinMPNN, AlphaFold2 and SaProt with our state-of-the-art model, SolvIT, which accurately predicts heterologous expression titers in E. coli. This unique combination allows for the simultaneous optimization of melting temperature (Tm) and solubility. The pipeline exhibits remarkable speed, generating dozens of high-quality candidates with predicted high titers and increased stability in under an hour, streamlining the path to experimental validation. To demonstrate its efficacy, StabilizeIT was used to engineer multiple enzymes in our novel biosynthetic pathway for Hyaluronic Acid. The resulting variants showed greatly enhanced thermal stability and expression, proving the pipelines real-world utility. StabilizeIT is now available to the community, offering an accessible and validated solution to accelerate the development of robust proteins for diverse applications. The webserver is freely available at https://stabilizeit.enzymit.com

Surface factors that contribute to the virulence of Staphylococcus aureus have become therapeutic targets in the treatment of illness associated with this bacterium. Staphylococcal protein A (SpA) is a well-known contributor to S. aureus toxicity and virulence, although relatively little is known about protein A and how its biological function has evolved. SpA is displayed on the surface of the bacterium and contains 5 nearly identical helical ({approx} 60 aa) domains that bind antibodies with high affinity (Kd {approx} 10 nM). The folding free energies of only domains E and B have been determined. In this study we used intrinsic fluorescence detected denaturation to measure the folding thermodynamics of each domain in isolation and in the native multidomain context using a construct that includes the N-terminal half of the mature protein (SpA-N). We also constructed a series of proteins with 1 to 5 repeats of B domain, linked exactly as the five domains of WT SpA are linked. We used nearest neighbor thermodynamic models to explicitly demonstrate that the domains in B domain repeat proteins fold independently. We also showed that the domains in SpA-N fold independently by comparing the folding free energies of domains in isolation and in their multidomain context. Previous dynamic NMR experiments detected highly flexible linkers between domains in 5B, suggesting that the domains of SpA are structurally independent, which is likely responsible for the lack of thermodynamic coupling. Our results also showed a steep increase in domain stability from the N-to C-terminus in SpA-N, from 0.97 {+/-} 0.05 to 5.57 {+/-} 0.28 kcal/mol. We hypothesize that this stability gradient is related to efficient secretion of protein A.

As more genome libraries become accessible across multiple cephalopod species, long standing assumptions about the molecular basis of their dynamic optical systems are being revisited. For example, cephalopod-specific reflectin proteins now appear far more variable as newly annotated sequences diverge from the canonical features that have long defined this protein family. In this perspective, we discuss these nuances and introduce a theoretical framework that expands the reflectin domain classification while preserving specificity across 141 known and previously uncharacterized reflectin sequences from ten cephalopod species. By combining this broadened domain architecture with quantitative, bioinformatics-driven metrics, we establish a generalizable framework that reliably identifies reflectins across their full sequence diversity, allowing for deeper structural comparisons and future functional discovery.

AlphaFold2 protein structure predictions are widely available for structural biology uses. These predictions, especially for eukaryotic proteins, frequently contain extensive regions predicted below the pLDDT 70 level, the rule-of-thumb cutoff for high confidence. This work identifies major modes of behavior within low-pLDDT regions through a survey of human proteome predictions provided by the AlphaFold Protein Structure Database. The near-predictive mode resembles folded protein and can be a nearly accurate prediction. Barbed wire is extremely unproteinlike, being recognized by wide looping coils, an absence of packing contacts, and numerous signature validation outliers, and it likely represents a nonpredicted region. Pseudostructure presents an intermediate behavior with a misleading appearance of isolated and badly formed secondary structure-like elements. These prediction modes are compared with annotations of disorder from MobiDB, showing general correlation between barbed wire/pseudostructure and many measures of disorder, an association between pseudostructure and signal peptides, and an association between near-predictive and regions of conditional folding. To enable users to identify these regions within a prediction, a new Phenix tool is developed encompassing the results of this work, including prediction annotation, visual markup, and residue selection based on these prediction modes. This tool will help users develop expertise in interpreting difficult AlphaFold predictions and identify the near-predictive regions that can aid in molecular replacement when a prediction does not contain enough high-pLDDT regions.

How large, flexible enzymes assemble into defined oligomeric architectures remains a central question in biology. NADPH-dependent assimilatory sulfite reductase (SiR) forms a heterododecamer built on an octameric flavoprotein (SiRFP) core, yet the molecular basis for this assembly has been unresolved because of its disordered N-terminus. Here, we use ion mobility mass spectrometry, small-angle neutron scattering, and mutagenesis to define the mechanism of SiRFP oligomerization. We show that SiRFP forms a discrete, stable octamer in solution. We also report that its N-terminal 52-residue segment is necessary and sufficient to mediate assembly, also mediating oligomerization when fused to a heterologous protein. Structure-guided mutagenesis identifies four residues (Gln22, Tyr39, Phe40, and Gln47) whose substitution disrupts the octamer, producing concentration-dependent lower-order species while retaining catalytic activity. These findings define the determinants of SiRFP assembly with broader implications for engineering homomeric protein complexes. ImportanceThis work seeks to understand the basis for oligomerization of a large oxidoreductase that is important for metabolizing sulfur, an essential chemical for all of biology. A 52-residue long leader peptide is necessary and sufficient for assembly into a particularly stable octamer that is resistant to chemical denaturation under diverse conditions.

Protein aggregation is central to amyloid-related disorders and remains a major developability challenge for protein therapeutics. Over the past two decades, significant advances have been made to predict aggregation-prone regions (APRs) and estimate aggregation propensity in proteins and peptides. In contrast, the prediction of aggregation kinetics has received relatively less attention due to the limited availability and heterogeneity of experimental data. Consequently, aggregation propensities from APR prediction algorithms were widely accepted as a means to predict relative changes in the aggregation kinetics of proteins and mutants. Previous studies have demonstrated, using large-scale datasets, that aggregation propensity shows a weak or inconsistent correlation with aggregation kinetics. In the present study, we have integrated complementary state-of-the-art mechanistic and kinetic prediction tools for protein aggregation into a unified, user-friendly web framework entitled "Amylo-Pipe". Amylo-Pipe also implements practical features that are especially useful for protein engineering, such as gatekeeper-residue mutational scanning to support the design of aggregation-resistant variants. By consolidating multiple prediction tasks in a single interface, Amylo-Pipe enables a more comprehensive assessment of aggregation behavior than APR-only workflows. The web server is freely accessible at: https://web.iitm.ac.in/bioinfo2/amylopipe/.

Differential Scanning Fluorimetry (DSF) is a method that enables rapid determination of a proteins apparent melting temperature (Tma). Owing to its high throughput, DSF has found widespread application in fields ranging from structural biology to chemical screening. Yet DSF has developed two opposing reputations: one as an indispensable laboratory tool to probe protein stability, another as a frustrating platform that often fails. Here, we aim to reconcile these disparate reputations and help users perform more successful DSF experiments with three resources: an updated, interactive theoretical framework, practical tips, and online data analysis. We anticipate that these resources, made available online at DSFworld (https://gestwickilab.shinyapps.io/dsfworld/), will broaden the utility of DSF.

Proteins with intrinsically disordered regions (IDRs) migrate at a higher apparent molecular weight in sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) complicating their analysis and identification. Here, we investigate the sequence determinants of the hypomobility of IDRs using a series of synthetic low complexity domains. We find that negative charge increases the apparent molecular weight, but neutral polar tracts also have abnormally slow migration. Positive charge and hydrophobic residues decrease the apparent molecular weight, although lysine residues show a biphasic effect with decreased migration at high fractional contents. Combinations of residues show that different sequence contributions to the apparent molecular weight are not additive. The results can be rationalized by the protein-decorated micelle model by considering both SDS binding and the compaction of protein SDS-complexes.

The design of proteins with tailored functions is of immense interest to biotechnology, medicine, and the chemical industry. While protein design is rapidly evolving with the use of AI techniques, the design of complex enzymes remains a challenge. Here, we present the use of two large language models (LLMs), ZymCTRL and ProtGPT2, for the generation of de novo enzymes that catalyze the triosephosphate isomerase (TIM) reaction. Natural TIM enzymes are obligatory oligomers that catalyze a multi-step isomerization reaction near the diffusion limit. This makes TIM an ideal target to assess the generative ability of protein language models. Newly generated sequences were filtered to obtain a set of twelve candidates from each approach for experimental validation. Multiple constructs from both language models exhibit the intended function in vivo through their ability to complement a TIM-deficient E. coli strain. In-depth characterization of the best-behaving artificial enzyme reveals behavior and catalytic efficiency close to its natural counterparts. These findings support the use of conditional and fine-tuned unconditional LLMs for the generation of complex enzymes.

Recent advances in predicting and modeling conformational ensembles of intrinsically disordered proteins (IDPs) have provided much needed insights into sequence-ensemble relationships. It is thought that conservation of physicochemical properties, but not the exact identity or order of the amino acids, maintains IDP ensemble properties that are crucial for function. However, detailed experimental studies are still required to fully understand the relationships between sequence and function in IDPs. The human CITED proteins, which are fully disordered transcriptional regulators, share conserved C-terminal transactivation domains (CTADs) that interact with the TAZ1 domain of the transcriptional coactivators CBP/p300. The conserved CTADs harbor amino acid substitutions in regions that are known to be important for interactions of CITED2 with TAZ1, but the effects of these substitutions on TAZ1 binding for the other CITED proteins are unknown. Here, we use solution NMR spectroscopy, circular dichroism, and surface plasmon resonance to characterize the conformational ensembles, dynamics, and interactions of the CITED CTADs. The CTADs are disordered in isolation, although the CITED2 CTAD uniquely displays residual helical structure that is sensitive to ionic strength and protein concentration. In contrast, the CITED1 and CITED4 CTADs remain largely disordered and exhibit more uniform dynamics. Quantitative binding measurements reveal differences in thermodynamics and kinetics for the CTADs interactions with TAZ1, with CITED2 binding most tightly and CITED4 exhibiting significantly weaker affinity. Our results highlight the sensitivity of IDP conformational ensembles to minor sequence changes and the impacts that changes in IDP structures and dynamics can have on biological functions.

The biases revealed in protein sequence alignments have been shown to provide information related to protein structure, stability, and function. For example, sequence biases at individual positions can be used to design consensus proteins that are often more stable than naturally occurring counterparts. Likewise, correlations between pairs of residue can be used to predict protein structures. Recent work using Potts models show that together, single-site biases and pair correlations lead to improved predictions of protein fitness, activity, and stability. Here we use a Potts model to design groups of protein sequences with different amounts of single-site biases and pair correlations, and determine the thermodynamic stabilities of a representative set of sequences from each group. Surprisingly, sequences excluding pair correlations maximize stability, whereas sequences that maximize pair correlations are less stable, suggesting that pair correlations contribute to another aspect of protein fitness. Consistent with this interpretation, we find that for adenylate kinase, enzyme activity is greatly increased by maximizing pair correlations. The finding that elimination of covariant residue pairs increases protein stability suggests a route to enhance stability of designed proteins; indeed, this strategy produces hyperstable homeodomain and adenylate kinase proteins that retain significant activity. Significance statementRecent methods for protein structure analysis and design have used sequence covariance to help predict protein structure, stability, and function. Here, by designing homeodomain and adenylate kinase sequences with different amounts of single-site bias and pairwise covariance, we find that stability is solely determined by single-site bias but not pairwise covariance. However, pairwise covariance makes an important contribution to catalysis in adenylate kinase. Our findings suggest a new way to generate highly stable proteins: by separating single-site biases from pairwise covariance, the single-site coefficients can be used to design proteins with stabilities even higher than those obtained by consensus design.

YcjN is a putative substrate-binding protein expressed from a cluster of genes involved in carbohydrate import and metabolism in Escherichia coli. Here, we determine the crystal structure of YcjN to a resolution of 1.95 [A], revealing that its three-dimensional structure is similar to substrate binding proteins in subcluster D-I, which includes the well-characterized maltose binding protein (MBP). Furthermore, we found that recombinant overexpression of YcjN results in the formation of a lipidated form of YcjN that is posttranslationally diacylated at cysteine 21. Comparisons of size-exclusion chromatography profiles and dynamic light scattering measurements of lipidated and non-lipidated YcjN proteins suggest that lipidated YcjN aggregates in solution via its lipid moiety. Additionally, bioinformatic analysis indicates that YcjN-like proteins may exist in both Bacteria and Archaea, potentially in both lipidated and non-lipidated forms. Together, our results provide a better understanding of the aggregation properties of recombinantly expressed bacterial lipoproteins in solution and establish a foundation for future studies that aim to elucidate the role of these proteins in bacterial physiology.

Over the past decade, protein design has evolved from a specialized discipline into a broadly accessible approach for engineering and interrogating biological systems. Despite these advances, protein design continues to be a technically challenging task, often requiring knowledge of programming to be able to use and combine the different software packages. To address this challenge, we have developed Prosculpt, an easy-to-use protein design pipeline. Prosculpt integrates RFdiffusion for backbone generation, ProteinMPNN for sequence design and multiple structure-prediction platforms (AF2, AF3, Colabfold, Boltz2). Candidate designs are evaluated using customizable Rosetta-based scoring protocols. Each project is specified through a single configuration file, enabling users with minimal computational expertise to perform sophisticated protein design tasks without writing code, while also allowing advanced users to access the full capabilities of the underlying programs. Prosculpt supports a wide range of applications, including design of symmetric homo-oligomers, design of binders, motif scaffolding, partial diffusion and fixed-backbone sequence redesign. By combining these capabilities within a single, user-friendly platform, Prosculpt provides a practical entry point to modern protein design for both novice and expert users.

Automated domain annotation plays a number of important roles in structural informatics and typically involves searching query sequences against Hidden Markov Model (HMM) profiles. This process can be ambiguous or inaccurate when proteins contain domains with non-contiguous residue ranges, and especially when insertional domains are hosted within them. Here we present DomainMapper, an algorithm that accurately assigns a unique domain structure annotation to any query sequence, including those with complex topologies. We validate our domain assignments using the AlphaFold database and confirm that non-contiguity is pervasive (6.5% of all domains in yeast and 2.5% in human). Using this resource, we find that certain folds have strong propensities to be non-contiguous or insertional across the Tree of Life, likely underlying evolutionary preferences for domain topology. DomainMapper is freely available and can be run as a single command line function. HIGHLIGHTSDomainMapper generates a unique domain structure annotation, including non-contiguous and insertional domains Automated annotations of non-contiguous domains are validated against the AlphaFold database DomainMapper can be easily installed and used by non-experts Certain folds have strong preferences to be non-contiguous or insertional GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/484986v1_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@1900be8org.highwire.dtl.DTLVardef@1fdae2borg.highwire.dtl.DTLVardef@1b5bd5corg.highwire.dtl.DTLVardef@a31d56_HPS_FORMAT_FIGEXP M_FIG C_FIG

The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters. To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes. Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme. Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.

Amyloid fibrils formed from leukocyte chemotactic factor 2 (LECT2), a secreted human cytokine, are associated with kidney failure in the disease amyloid LECT2 (ALECT2) amyloidosis. This rare disease was recognized in 2008 and has a variable prevalence worldwide. The mechanisms which lead to ALECT2 fibril deposition are not known and there are no treatments other than kidney transplant. The LECT2 gene harbors a single nucleotide polymorphism that leads to either a valine or isoleucine residue at position 40 of the mature protein. Most of the individuals diagnosed with ALECT2 amyloidosis are homozygous for valine at this position, which led us to hypothesize that the valine-containing variant of LECT2 protein is less stable and more prone to aggregation than the isoleucine-containing variant. Here, we investigate the structure, stability and aggregation of both variants of recombinant LECT2. Both variants have similar structures in solution; unfold in similar concentrations of urea; and aggregate at similar rates under native-like conditions, forming structures that bind to thioflavin T. Chelation of the structural zinc ion destabilizes both variants to a similar extent, and increases the rate at which they aggregate. We do not observe a consistent difference in stability or aggregation between the variants of LECT2, so we suggest that the presence of the valine residue at position 40 does not determine whether an individual is at increased risk of ALECT2 amyloidosis.

Ancestral sequence resurrection (ASR) is the inference of extinct biological sequences from extant sequences, the most popular of which are based on probabilistic models of evolution. ASR is becoming a popular method for studying the evolution of enzyme characteristics. The properties of ancestral enzymes are biochemically and biophysically characterized to gain some knowledge regarding the origin of some enzyme property. Current methodology relies on resurrection of the single most probable (SMP) sequence and is systematically biased. Previous theoretical work suggests this will result in a thermostability bias in resurrected SMP sequences, and even the activity, calling into question inferences derived from ancestral protein properties. We experimentally test the potential stability bias hypothesis by resurrecting 40 malate and lactate dehydrogenases. Despite the methodological bias in resurrecting an SMP protein, the measured biophysical and biochemical properties of the SMP protein are not biased in comparison to other, less probable, resurrections. In addition, the SMP protein property seems to be representative of the ancestral probability distribution. Therefore, the conclusions and inferences drawn from the SMP protein are likely not a source of bias. SignificanceAncestral sequence resurrection (ASR) is a powerful tool for: determining how new protein functions evolve; inferring the properties of an environment in which species existed; and protein engineering applications. We demonstrate, using lactate and malate dehydrogenases (L/MDHs), that resurrecting the single most probable sequence (SMP) from a maximum likelihood phylogeny does not result in biased activity and stability relative to sequences sampled from the posterior probability distribution. Previous studies using experimentally measured phenotypes of SMP sequences to make inferences about the environmental conditions and the path of evolution are likely not biased in their conclusions. Serendipitously, we discover ASR is also a valid tool for protein engineering because sampled reconstructions are both highly active and stable.

Outer membrane proteins are all beta barrels and these barrels have a variety of well-documented loop conformations. Here we test the effect of three different loop types on outer membrane protein A (OmpA) folding. We designed twelve 5-residue loops and experimentally tested the effect of replacing the long loops of outer membrane protein OmpA with the designed loops. Our studies succeeded in creating the smallest known outer membrane barrel. We find that significant changes in OmpA loops do not have a strong overall effect on OmpA folding. However, when decomposing folding into a fast rate and a slow rate we find that changes in loops strongly affect the slow rate of OmpA folding. Extracellular loop types with higher levels of hydrogen bonds had more instances of increasing the slow folding rate and extracellular loop types with low levels of hydrogen bonds had more instances of decreasing the slow folding rate. Having the slow rate affected by loop composition is consistent with the slow rate being associated with the insertion step of outer membrane protein folding.

Coiled-coil regions were among the first protein motifs described structurally and theoretically. The beauty and simplicity of the motif gives hope to detecting coiled-coil regions with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil prediction tools with respect to the most comprehensive reference data set available, the entire Protein Data Base (PDB), down to each amino acid and its secondary structure. Apart from the thirtyfold difference in number of predicted coiled-coils the tools strongly vary in their predictions, across structures and within structures. The evaluation of the false discovery rate and Matthews correlation coefficient, a widely used performance metric for imbalanced data sets, suggests that the tested tools have only limited applicability for large data sets. Coiled-coil predictions strongly impact the functional characterization of proteins, are used for functional genome annotation, and should therefore be supported and validated by additional information.