Estimating conformational heterogeneity of tryptophan synthase with a template-based Alphafold2 approach

A naturally occurring standalone TrpB enzyme provides insights into allosteric communication within tryptophan synthase

Allosteric regulation of catalytic activity is a widespread property of multi-enzyme complexes. The tryptophan synthase is a prototypical allosteric enzyme where the constituting α (TrpA) and β (TrpB) subunits mutually activate each other in a manner that is incompletely understood. Experimental and computational studies have shown that LBCA-TrpB from the last bacterial common ancestor contains six residues (Res6) distal from the active site that allow for high stand-alone catalytic activity in the absence of a TrpA subunit. In the present study, a database search revealed that Res6 is also present in the extant plTrpB from Pelodictyon luteolum. The plTrpB enzyme showed a high stand-alone activity and only a moderate activation by plTrpA. The replacement of LBCA-Res6 in plTrpB with the consensus residues from a multiple sequence alignment yielded plTrpB-con, which showed a dramatically decreased stand-alone activity but was strongly stimulated by plTrpA. These findings suggest that the effect of these six key allosteric residues is largely independent of the protein context within a specific TrpB enzyme. Analysis of the conformational landscapes of plTrpB and plTrpB-con revealed that plTrpB in isolation displays efficient closure of both the active site and the communication (COMM) domain. In contrast, these catalytically competent states are destabilized in plTrpB-con but can be recovered by the addition of plTrpA. A correlation-based shortest path map (SPM) analysis reveals that the catalytically and allosterically relevant domains-specifically, the COMM domain in TrpB and loops 2 and 6 in TrpA-are tightly interconnected exclusively in plTrpA:plTrpB-con.

Advances in the biological production of sugar alcohols from biomass-derived xylose

Sugar alcohols are a common class of low-calorie sweeteners. The advancement of technologies utilizing renewable resources has heightened interest in synthesizing sugar alcohols from biomass-derived xylose for cost down of process and sustainability. This review focuses on the potential of biomass-derived xylose and its effective conversion into sugar alcohols, underscoring the significance of this process in sustainable industrial applications. The two main approaches for producing sugar alcohols which include enzyme catalysis and microbial fermentation are thoroughly discussed. The microbial fermentation pathway relies on genetically engineered strains, which are modified to efficiently convert xylose into target sugar alcohols. Enzyme catalysis, on the other hand, directly converts xylose to sugar alcohols through specific reactions. In addition, strategies to improve product selectivity and reduce by-products are discussed in the paper, which are crucial for improving the economic viability and environmental sustainability of sugar alcohol production. Overall, utilizing xylose from biomass to produce sugar alcohols manifests environmental and economic benefits, indicating its substantial potential in the shift towards a low-carbon economy. Future studies may further explore cutting edge technologies to maximize the utilization of biomass-derived xylose and the sustainable production of sugar alcohols.

AFsample2 predicts multiple conformations and ensembles with AlphaFold2

Understanding protein dynamics and conformational states is crucial for insights into biological processes and disease mechanisms, which can aid drug development. Recently, several methods have been devised to broaden the conformational predictions made by AlphaFold2 (AF2). We introduce AFsample2, a method using random MSA column masking to reduce co-evolutionary signals, enhancing structural diversity in AF2-generated models. AFsample2 effectively predicts alternative states for various proteins, producing high-quality end states and diverse conformational ensembles. In the OC23 dataset, alternate state models improved (ΔTM>0.05) in 9 out of 23 cases without affecting preferred state generation. Similar results were seen in 16 membrane protein transporters, with 11 out of 16 targets showing improvement. TM-score improvements to experimental end states were substantial, sometimes exceeding 50%, improving from 0.58 to 0.98. Additionally, AFsample2 increased the diversity of intermediate conformations by 70% compared to standard AF2, producing highly confident models potentially representing intermediate states. For four targets, predicted intermediate states were structurally similar to known structural homologs in the PDB, suggesting that they are true intermediate states. These findings indicate that AFsample2 can used to provide structural insights into proteins with multiple states, as well as potential paths between the states.

Advances in biosynthesis of 7-Dehydrocholesterol through de novo cell factory strategies

7-Dehydrocholesterol (7-DHC) is an important sterol for maintaining human health and is present in the skin. After sun exposure, 7-DHC in the skin is converted to vitamin D3 to strengthen the immune system. In recent years, synthetic biology has gained importance due to the effective and efficient production of various important compounds using microorganisms. Despite the understanding of the mechanisms and pathways of 7-DHC biosynthesis, achieving higher production yields remains a significant challenge. This review aims to provide a comprehensive overview of the current state of 7-DHC biosynthesis. Various synthetic strategies including optimization of rate-limiting enzymes, metabolic fluxes, redox balance, and subcellular localization are discussed. Moreover, the role of omics technology in designing important proteins and gene editing techniques for strain modification to efficiently synthesize 7-DHC will also be discussed.

Proteomic Evidence for Amyloidogenic Cross-Seeding in Fibrinaloid Microclots

In classical amyloidoses, amyloid fibres form through the nucleation and accretion of protein monomers, with protofibrils and fibrils exhibiting a cross-β motif of parallel or antiparallel β-sheets oriented perpendicular to the fibre direction. These protofibrils and fibrils can intertwine to form mature amyloid fibres. Similar phenomena can occur in blood from individuals with circulating inflammatory molecules (and also some originating from viruses and bacteria). Such pathological clotting can result in an anomalous amyloid form termed fibrinaloid microclots. Previous proteomic analyses of these microclots have shown the presence of non-fibrin(ogen) proteins, suggesting a more complex mechanism than simple entrapment. We thus provide evidence against such a simple entrapment model, noting that clot pores are too large and centrifugation would have removed weakly bound proteins. Instead, we explore whether co-aggregation into amyloid fibres may involve axial (multiple proteins within the same fibril), lateral (single-protein fibrils contributing to a fibre), or both types of integration. Our analysis of proteomic data from fibrinaloid microclots in different diseases shows no significant quantitative overlap with the normal plasma proteome and no correlation between plasma protein abundance and their presence in fibrinaloid microclots. Notably, abundant plasma proteins like α-2-macroglobulin, fibronectin, and transthyretin are absent from microclots, while less abundant proteins such as adiponectin, periostin, and von Willebrand factor are well represented. Using bioinformatic tools, including AmyloGram and AnuPP, we found that proteins entrapped in fibrinaloid microclots exhibit high amyloidogenic tendencies, suggesting their integration as cross-β elements into amyloid structures. This integration likely contributes to the microclots' resistance to proteolysis. Our findings underscore the role of cross-seeding in fibrinaloid microclot formation and highlight the need for further investigation into their structural properties and implications in thrombotic and amyloid diseases. These insights provide a foundation for developing novel diagnostic and therapeutic strategies targeting amyloidogenic cross-seeding in blood clotting disorders.

Harnessing conformational dynamics in enzyme catalysis to achieve nature-like catalytic efficiencies: the shortest path map tool for computational enzyme redesign

Enzymes exhibit diverse conformations, as represented in the free energy landscape (FEL). Such conformational diversity provides enzymes with the ability to evolve towards novel functions. The challenge lies in identifying mutations that enhance specific conformational changes, especially if located in distal sites from the active site cavity. The shortest path map (SPM) method, which we developed to address this challenge, constructs a graph based on the distances and correlated motions of residues observed in nanosecond timescale molecular dynamics (MD) simulations. We recently introduced a template based AlphaFold2 (tAF2) approach coupled with 10 nanosecond MD simulations to quickly estimate the conformational landscape of enzymes and assess how the FEL is shifted after mutation. In this study, we evaluate the potential of SPM when coupled with tAF2-MD in estimating conformational heterogeneity and identifying key conformationally-relevant positions. The selected model system is the beta subunit of tryptophan synthase (TrpB). We compare how the SPM pathways differ when integrating tAF2 with different MD simulation lengths from as short as 10 ns until 50 ns and considering two distinct Amber forcefield and water models (ff14SB/TIP3P versus ff19SB/OPC). The new methodology can more effectively capture the distal mutations found in laboratory evolution, thus showcasing the efficacy of tAF2-MD-SPM in rapidly estimating enzyme dynamics and identifying the key conformationally relevant hotspots for computational enzyme engineering.

Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase

Despite the success of AlphaFold methods in predicting single protein structures, these methods showed intrinsic limitations in the characterization of multiple functional conformations of allosteric proteins. The recent NMR-based structural determination of the unbound ABL kinase in the active state and discovery of the inactive low-populated functional conformations that are unique for ABL kinase present an ideal challenge for the AlphaFold2 approaches. In the current study, we employ several adaptations of the AlphaFold2 methodology to predict protein conformational ensembles and allosteric states of the ABL kinase including randomized alanine sequence scanning combined with the multiple sequence alignment subsampling proposed in this study. We show that the proposed new AlphaFold2 adaptation combined with local frustration profiling of conformational states enables accurate prediction of the protein kinase structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AlphaFold2 predictions and detection of hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AlphaFold2. The results of this study uncovered previously unappreciated fundamental connections between local frustration profiles of the functional allosteric states and the ability of AlphaFold2 methods to predict protein structural ensembles of the active and inactive states. This study showed that integration of the randomized sequence scanning adaptation of AlphaFold2 with a robust landscape-based analysis allows for interpretable atomistic predictions and characterization of protein conformational ensembles, providing a physical basis for the successes and limitations of current AlphaFold2 methods in detecting functional allosteric states that play a significant role in protein kinase regulation.

Deep learning for protein structure prediction and design-progress and applications

Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.

From nature to industry: Harnessing enzymes for biocatalysis

Biocatalysis harnesses enzymes to make valuable products. This green technology is used in countless applications from bench scale to industrial production and allows practitioners to access complex organic molecules, often with fewer synthetic steps and reduced waste. The last decade has seen an explosion in the development of experimental and computational tools to tailor enzymatic properties, equipping enzyme engineers with the ability to create biocatalysts that perform reactions not present in nature. By using (chemo)-enzymatic synthesis routes or orchestrating intricate enzyme cascades, scientists can synthesize elaborate targets ranging from DNA and complex pharmaceuticals to starch made in vitro from CO2-derived methanol. In addition, new chemistries have emerged through the combination of biocatalysis with transition metal catalysis, photocatalysis, and electrocatalysis. This review highlights recent key developments, identifies current limitations, and provides a future prospect for this rapidly developing technology.

Are fibrinaloid microclots a cause of autoimmunity in Long Covid and other post-infection diseases?

It is now well established that the blood-clotting protein fibrinogen can polymerise into an anomalous form of fibrin that is amyloid in character; the resultant clots and microclots entrap many other molecules, stain with fluorogenic amyloid stains, are rather resistant to fibrinolysis, can block up microcapillaries, are implicated in a variety of diseases including Long COVID, and have been referred to as fibrinaloids. A necessary corollary of this anomalous polymerisation is the generation of novel epitopes in proteins that would normally be seen as 'self', and otherwise immunologically silent. The precise conformation of the resulting fibrinaloid clots (that, as with prions and classical amyloid proteins, can adopt multiple, stable conformations) must depend on the existing small molecules and metal ions that the fibrinogen may (and is some cases is known to) have bound before polymerisation. Any such novel epitopes, however, are likely to lead to the generation of autoantibodies. A convergent phenomenology, including distinct conformations and seeding of the anomalous form for initiation and propagation, is emerging to link knowledge in prions, prionoids, amyloids and now fibrinaloids. We here summarise the evidence for the above reasoning, which has substantial implications for our understanding of the genesis of autoimmunity (and the possible prevention thereof) based on the primary process of fibrinaloid formation.

AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design

The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately predicting the folded three-dimensional (3D) structure of proteins and enzymes has revolutionized the structural biology and protein design fields. The 3D structure indeed reveals key information on the arrangement of the catalytic machinery of enzymes and which structural elements gate the active site pocket. However, comprehending enzymatic activity requires a detailed knowledge of the chemical steps involved along the catalytic cycle and the exploration of the multiple thermally accessible conformations that enzymes adopt when in solution. In this Perspective, some of the recent studies showing the potential of AF2 in elucidating the conformational landscape of enzymes are provided. Selected examples of the key developments of AF2-based and DL methods for protein design are discussed, as well as a few enzyme design cases. These studies show the potential of AF2 and DL for allowing the routine computational design of efficient enzymes.

Allosteric regulation of β-reaction stage I in tryptophan synthase upon the α-ligand binding

Tryptophan synthase (TRPS) is a bifunctional enzyme consisting of α- and β-subunits that catalyzes the last two steps of L-tryptophan (L-Trp) biosynthesis. The first stage of the reaction at the β-subunit is called β-reaction stage I, which converts the β-ligand from an internal aldimine [E(Ain)] to an α-aminoacrylate [E(A-A)] intermediate. The activity is known to increase 3-10-fold upon the binding of 3-indole-D-glycerol-3'-phosphate (IGP) at the α-subunit. The effect of α-ligand binding on β-reaction stage I at the distal β-active site is not well understood despite the abundant structural information available for TRPS. Here, we investigate the β-reaction stage I by carrying out minimum-energy pathway searches based on a hybrid quantum mechanics/molecular mechanics (QM/MM) model. The free-energy differences along the pathway are also examined using QM/MM umbrella sampling simulations with QM calculations at the B3LYP-D3/aug-cc-pVDZ level of theory. Our simulations suggest that the sidechain orientation of βD305 near the β-ligand likely plays an essential role in the allosteric regulation: a hydrogen bond is formed between βD305 and the β-ligand in the absence of the α-ligand, prohibiting a smooth rotation of the hydroxyl group in the quinonoid intermediate, whereas the dihedral angle rotates smoothly after the hydrogen bond is switched from βD305-β-ligand to βD305-βR141. This switch could occur upon the IGP-binding at the α-subunit, as evidenced by the existing TRPS crystal structures.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.