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Zika virus (ZIKV) is an emergent flavivirus that represents a global public health concern due to its association with severe neurological disorders. NS2B is a multifunctional viral membrane protein primarily used to regulate viral protease activity and is crucial for virus replication, making it an appealing target for antiviral drugs. This study presents the structural elucidation of full-length ZIKV NS2B in sodium dodecyl sulfate (SDS) micelles using solution nuclear magnetic resonance experimental data and RosettaMP. The protein structure has four transmembrane α-helices, two amphipathic α-helices, and a ß-hairpin in the hydrophilic region. NS2B presented secondary and tertiary stability in different concentrations of SDS. Furthermore, we studied the dynamics of NS2B in SDS micelles through relaxation parameters and paramagnetic relaxation enhancement experiments. The findings were consistent with the structural calculations. Our work will be essential in understanding the role of NS2B in viral replication and screening for inhibitors against ZIKV.

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Thirty years since the first report on the PilY1 protein in bacteria, only the C-terminal domain has been crystallized; there is no study in which the N-terminal domain, let alone the complete protein, has been crystallized. In our laboratory, we are interested in characterizing the Type IV Pili (T4P) of Acidithiobacillus thiooxidans. We performed an in silico characterization of PilY1 and other pilins of the T4P of this acidophilic bacterium. In silico characterization is crucial for understanding how proteins adapt and function under extreme conditions. By analyzing the primary and secondary structures of proteins through computational methods, researchers can gain valuable insights into protein stability, key structural features, and unique amino acid compositions that contribute to resilience in harsh environments. Here, it is presented a description of the particularities of At. thiooxidans PilY1 through predictor software and homology data. Our results suggest that PilY1 from At. thiooxidans may have the same role as has been described for other PilY1 associated with T4P in neutrophilic bacteria; also, its C-terminal interacts (interface interaction) with the minor pilins PilX, PilW and PilV. The N-terminal region comprises domains such as the vWA and the MIDAS, involved in signaling, ligand-binding, and protein-protein interaction. In fact, the vWA domain has intrinsically disordered regions that enable it to maintain its structure over a wide pH range, not only at extreme acidity to which At. thiooxidans is adapted. The results obtained helped us design the correct methodology for its heterologous expression. This allowed us partially experimentally characterize it by obtaining the N-terminal domain recombinantly and evaluating its acid stability through fluorescence spectroscopy. The data suggest that it remains stable across pH changes. This work thus provides guidance for the characterization of extracellular proteins from extremophilic organisms.

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The formation and analysis of amyloid fibers by two ß-glucosidases, BglA and BglB, belonging to the GH1 enzyme family, are reported. Both proteins have the (ß/α)8 TIM-barrel fold, which is characteristic of this family and is also the most common protein structure. BglA is an octamer, whereas BglB is a monomer. Amyloid fibrillation using pH and temperature as perturbing agents was investigated using fluorescence spectroscopy as a preliminary approach and corroborated using wide-field optical microscopy, confocal microscopy, and field-emission scanning electron microscopy. These analyses showed that both enzymes fibrillate at a wide range of acidic and alkaline conditions and at several temperature conditions, particularly at acidic pH (3-4) and at temperatures between 45 and 65 °C. Circular dichroism spectroscopy corroborated the transition from an α-helix to a ß-sheet secondary structure of both proteins in conditions where fibrillation was observed. Overall, our results suggest that fibrillation is a rather common phenomenon caused by protein misfolding, driven by a transition from an α-helix to a ß-sheet secondary structure, that many proteins can undergo if subjected to conditions that disturb their native conformation.

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Septins are filamentous nucleotide-binding proteins which can associate with membranes in a curvature-dependent manner leading to structural remodelling and barrier formation. Ciona intestinalis, a model for exploring the development and evolution of the chordate lineage, has only four septin-coding genes within its genome. These represent orthologues of the four classical mammalian subgroups, making it a minimalist non-redundant model for studying the modular assembly of septins into linear oligomers and thereby filamentous polymers. Here, we show that C. intestinalis septins present a similar biochemistry to their human orthologues and also provide the cryo-EM structures of an octamer, a hexamer and a tetrameric sub-complex. The octamer, which has the canonical arrangement (2-6-7-9-9-7-6-2) clearly shows an exposed NC-interface at its termini enabling copolymerization with hexamers into mixed filaments. Indeed, only combinations of septins which had CiSEPT2 occupying the terminal position were able to assemble into filaments via NC-interface association. The CiSEPT7-CiSEPT9 tetramer is the smallest septin particle to be solved by Cryo-EM to date and its good resolution (2.7 Å) provides a well-defined view of the central NC-interface. On the other hand, the CiSEPT7-CiSEPT9 G-interface shows signs of fragility permitting toggling between hexamers and octamers, similar to that seen in human septins but not in yeast. The new structures provide insights concerning the molecular mechanism for cross-talk between adjacent interfaces. This indicates that C. intestinalis may represent a valuable tool for future studies, fulfilling the requirements of a complete but simpler system to understand the mechanisms behind the assembly and dynamics of septin filaments.

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Transthyretin (TTR) is an homotetrameric protein involved in the transport of thyroxine. More than 150 different mutations have been described in the TTR gene, several of them associated with familial amyloid cardiomyopathy. Recently, our group described a new variant of TTR in Brazil, namely A39D-TTR, which causes a severe cardiac condition. Position 39 is in the AB loop, a region of the protein that is located within the thyroxine-binding channels and is involved in tetramer formation. In the present study, we solved the structure and characterize the thermodynamic stability of this new variant of TTR using urea and high hydrostatic pressure. Interestingly, during the process of purification, A39D-TTR turned out to be a dimer and not a tetramer, a variation that might be explained by the close contact of the four aspartic acids at position 39, where they face each other inside the thyroxine channel. In the presence of subdenaturing concentrations of urea, bis-ANS binding and dynamic light scattering revealed A39D-TTR in the form of a molten-globule dimer. Co-expression of A39D and WT isoforms in the same bacterial cell did not produce heterodimers or heterotetramers, suggesting that somehow a negative charge at the AB loop precludes tetramer formation. A39D-TTR proved to be highly amyloidogenic, even at mildly acidic pH values where WT-TTR does not aggregate. Interestingly, despite being a dimer, aggregation of A39D-TTR was inhibited by diclofenac, which binds to the thyroxine channel in the tetramer, suggesting the existence of other pockets in A39D-TTR able to accommodate this molecule.

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The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.

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Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.

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Background: Molecular diagnosis of cystic fibrosis (CF) is challenging in Mexico due to the population's high genetic heterogeneity. To date, 46 pathogenic variants (PVs) have been reported, yielding a detection rate of 77%. We updated the spectrum and frequency of PVs responsible for this disease in mexican patients. Methods: We extracted genomic DNA from peripheral blood lymphocytes obtained from 297 CF patients and their parents. First, we analyzed the five most frequent PVs in the Mexican population using PCR-mediated site-directed mutagenesis. In patients with at least one identified allele, CFTR sequencing was performed using next-generation sequencing tools and multiplex ligation-dependent probe amplification. For variants not previously classified as pathogenic, we used a combination of in silico prediction, CFTR modeling, and clinical characteristics to determine a genotype-phenotype correlation. Results: We identified 95 PVs, increasing the detection rate to 87.04%. The most frequent variants were p.(PheF508del) (42.7%), followed by p.(Gly542*) (5.6%), p.(Ser945Leu) (2.9%), p.(Trp1204*) and p.(Ser549Asn) (2.5%), and CFTRdel25-26 and p.(Asn386Ilefs*3) (2.3%). The remaining variants had frequencies of <2.0%, and some were exclusive to one family. We identified 10 novel PVs localized in different exons (frequency range: 0.1-0.8%), all of which produced structural changes, deletions, or duplications in different domains of the protein, resulting in dysfunctional ion flow. The use of different in silico software and American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) criteria allowed us to assume that all of these PVs were pathogenic, causing a severe phenotype. Conclusions: In a highly heterogeneous population, combinations of different tools are needed to identify the variants responsible for CF and enable the establishment of appropriate strategies for CF diagnosis, prevention, and treatment.

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Tandem repeats (TRs) in protein sequences are consecutive, highly similar sequence motifs. Some types of TRs fold into structural units that pack together in ensembles, forming either an (open) elongated domain or a (closed) propeller, where the last unit of the ensemble packs against the first one. Here, we examine TR proteins (TRPs) to see how their sequence, structure, and evolutionary properties favor them for a function as mediators of protein interactions. Our observations suggest that TRPs bind other proteins using large, structured surfaces like globular domains; in particular, open-structured TR ensembles are favored by flexible termini and the possibility to tightly coil against their targets. While, intuitively, open ensembles of TRs seem prone to evolve due to their potential to accommodate insertions and deletions of units, these evolutionary events are unexpectedly rare, suggesting that they are advantageous for the emergence of the ancestral sequence but are early fixed. We hypothesize that their flexibility makes it easier for further proteins to adapt to interact with them, which would explain their large number of protein interactions. We provide insight into the properties of open TR ensembles, which make them scaffolds for alternative protein complexes to organize genes, RNA and proteins.

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We present a web server that predicts the far-UV circular dichroism (CD) spectra of proteins by utilizing their three-dimensional (3D) structures from the Protein Data Bank (PDB). The main algorithm is based on the classical theory of optical activity together with a set of atomic complex polarizabilities, which are obtained from the analysis of a series of synchrotron radiation CD spectra and their related 3D structures from the PDB. The results of our knowledge-based CD method (KCD) are in good agreement with measured spectra that could include the effect of D-amino acids. Our method also delivers some of the most accurate predictions, in comparison with the calculated spectra from well-established models. Specifically, using a metric of closeness based on normalized absolute deviations between experimental and calculated spectra, the mean values for a series of 57 test proteins give the following figures for such models: 0.26 KCD, 0.27 PDBMD2CD, 0.30 SESCA, and 0.47 DichroCalc. From another point of view, it is worth mentioning the remarkable capabilities of the recent approaches based on artificial intelligence, which can precisely predict the native structure of proteins. The structure of proteins, however, is flexible and can be modified by a diversity of environmental factors such as interactions with other molecules, mechanical stresses, variations of temperature, pH, or ionic strength. Experimental CD spectra together with reliable predictions can be utilized to assess eventual secondary structural changes. A similar kind of evaluation can be done for the case of an incomplete protein structure that has been reconstructed by using different approaches. The KCD method can be freely accessed from: https://kcd.cinvestav.mx/.

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Clinical interpretation of genetic variants in the context of the patient's phenotype is a time-consuming and costly process. In-silico analysis using in-silico prediction tools, and molecular modeling have been developed to predict the influence of genetic variants on the quality and/or quantity of the resulting translated protein, and in this way, to alert clinicians of disease likelihood in the absence of previous evidence. Our objectives were to evaluate the success rate of the in-silico analysis in predicting the disease-causing variants as pathogenic and the single-nucleotide variants as neutral, and to establish the reliability of in-silico analysis for determining pathogenicity or neutrality of von Willebrand factor gene-associated genetic variants. Using in-silico analysis, we studied pathogenicity in 31 disease-causing variants, and neutrality in 61 single-nucleotide variants from patients previously diagnosed as type 2 von Willebrand disease. Disease-causing variants and non-synonymous single-nucleotide variants were explored by in-silico tools that analyze the amino acidic sequence. Intronic and synonymous single-nucleotide variants were analyzed by in-silico methods that evaluate the nucleotidic sequence. We found a consistent agreement between predictions achieved by in-silico prediction tools and molecular modeling, both for defining the pathogenicity of disease-causing variants and the neutrality of single-nucleotide variants. Based on our results, the in-silico analysis would help to define the pathogenicity or neutrality in novel genetic variants observed in patients with clinical and laboratory phenotypes suggestive of von Willebrand disease.

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BACKGROUND: In a previous work, an IL-2Rßγ biased mutant derived from human IL-2 and called IL-2noα, was designed and developed. Greater antitumor effects and lower toxicity were observed compared to native IL-2. Nevertheless, mutein has some disadvantages, such as a very short half-life of about 9-12 min, propensity for aggregation, and solubility problems. OBJECTIVE: In this study, PEGylation was employed to improve the pharmacokinetic and antitumoral properties of the novel protein. METHODS: Pegylated IL-2noα was characterized by polyacrylamide gel electrophoresis, size exclusion chromatography, in vitro cell proliferation and in vivo cell expansion bioassays, and pharmacokinetic and antitumor studies. RESULTS: IL-2noα-conjugates with polyethylene glycol (PEG) of 1.2 kDa, 20 kDa, and 40 kDa were obtained by classical acylation. No significant changes in the secondary and tertiary structures of the modified protein were detected. A decrease in biological activity in vitro and a significant improvement in half-life were observed, especially for IL-2noα-PEG20K. PEGylation of IL-2noα with PEG20K did not affect the capacity of the mutant to induce preferential expansion of T effector cells over Treg cells. This pegylated IL-2noα exhibited a higher antimetastatic effect compared to unmodified IL-2noα in the B16F0 experimental metastases model, even when administered at lower doses and less frequently. CONCLUSION: PEG20K was selected as the best modification strategy, to improve the blood circulation time of the IL-2noα with a superior antimetastatic effect achieved with lower doses.

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Due to their ability to produce isomaltulose, sucrose isomerases are enzymes that have caught the attention of researchers and entrepreneurs since the 1950s. However, their low activity and stability at temperatures above 40 °C have been a bottleneck for their industrial application. Specifically, the instability of these enzymes has been a challenge when it comes to their use for the synthesis and manufacturing of chemicals on a practical scale. This is because industrial processes often require biocatalysts that can withstand harsh reaction conditions, like high temperatures. Since the 1980s, there have been significant advancements in the thermal stabilization engineering of enzymes. Based on the literature from the past few decades and the latest achievements in protein engineering, this article systematically describes the strategies used to enhance the thermal stability of sucrose isomerases. Additionally, from a theoretical perspective, we discuss other potential mechanisms that could be used for this purpose.

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The capacity of Pseudomonas aeruginosa to assimilate nutrients is essential for niche colonization and contributes to its pathogenicity. Isocitrate lyase (ICL), the first enzyme of the glyoxylate cycle, redirects isocitrate from the tricarboxylic acid cycle to render glyoxylate and succinate. P. aeruginosa ICL (PaICL) is regarded as a virulence factor due to its role in carbon assimilation during infection. The AceA/ICL protein family shares the catalytic domain I, triosephosphate isomerase barrel (TIM-barrel). The carboxyl terminus of domain I is essential for Escherichia coli ICL (EcICL) of subfamily 1. PaICL, which belongs to subfamily 3, has domain II inserted at the periphery of domain I, which is believed to participate in enzyme oligomerization. In addition, PaICL has the α13-loop-α14 (extended motif), which protrudes from the enzyme core, being of unknown function. This study investigates the role of domain II, the extended motif, and the carboxyl-terminus (C-ICL) and amino-terminus (N-ICL) regions in the function of the PaICL enzyme, also as their involvement in the virulence of P. aeruginosa PAO1. Deletion of domain II and the extended motif results in enzyme inactivation and structural instability of the enzyme. The His6-tag fusion at the C-ICL protein produced a less efficient enzyme than fusion at the N-ICL, but without affecting the acetate assimilation or virulence. The PaICL homotetrameric structure of the enzyme was more stable in the N-His6-ICL than in the C-His6-ICL, suggesting that the C-terminus is critical for the ICL quaternary conformation. The ICL-mutant A39 complemented with the recombinant proteins N-His6-ICL or C-His6-ICL were more virulent than the WT PAO1 strain. The findings indicate that the domain II and the extended motif are essential for the ICL structure/function, and the C-terminus is involved in its quaternary structure conformation, confirming that in P. aeruginosa, the ICL is essential for acetate assimilation and virulence.

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Serine protease autotransporters of Enterobacteriaceae (SPATE) constitute a superfamily of virulence factors, resembling the trypsin-like superfamily of serine proteases. SPATEs accomplish multiple functions associated to disease development of their hosts, which could be the consequence of SPATE cleavage of host cell components. SPATEs have been divided into class-1 and class-2 based on structural differences and biological effects, including similar substrate specificity, cytotoxic effects on cultured cells, and enterotoxin activity on intestinal tissues for class-1 SPATEs, whereas most class-2 SPATEs exhibit a lectin-like activity with a predilection to degrade a variety of mucins, including leukocyte surface O-glycoproteins and soluble host proteins, resulting in mucosal colonization and immune modulation. In this review, the structure of class-1 and class-2 are analyzed, making emphasis on their putative functional subdomains as well as a description of their function is provided, including prototypical mechanism of action.

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Introduction: Blood coagulation is an essential process to cease bleeding in humans and other species. This mechanism is characterized by a molecular cascade of more than a dozen components activated after an injury to a blood vessel. In this process, the coagulation factor VIII (FVIII) is a master regulator, enhancing the activity of other components by thousands of times. In this sense, it is unsurprising that even single amino acid substitutions result in hemophilia A (HA)-a disease marked by uncontrolled bleeding and that leaves patients at permanent risk of hemorrhagic complications. Methods: Despite recent advances in the diagnosis and treatment of HA, the precise role of each residue of the FVIII protein remains unclear. In this study, we developed a graph-based machine learning framework that explores in detail the network formed by the residues of the FVIII protein, where each residue is a node, and two nodes are connected if they are in close proximity on the FVIII 3D structure. Results: Using this system, we identified the properties that lead to severe and mild forms of the disease. Finally, in an effort to advance the development of novel recombinant therapeutic FVIII proteins, we adapted our framework to predict the activity and expression of more than 300 in vitro alanine mutations, once more observing a close agreement between the in silico and the in vitro results. Discussion: Together, the results derived from this study demonstrate how graph-based classifiers can leverage the diagnostic and treatment of a rare disease.

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The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, the structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold, and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling CDR3 still represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies.

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Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.

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Increasing the repertoire of available complementary tools to advance the knowledge of protein structures is fundamental for structural biology. The Neighbors Influence of Amino Acids and Secondary Structures (NIAS) is a server that analyzes a protein's conformational preferences of amino acids. NIAS is based on the Angle Probability List, representing the normalized frequency of empirical conformational preferences, such as torsion angles, of different amino acid pairs and their corresponding secondary structure information, as available in the Protein Data Bank. In this work, we announce the updated NIAS server with the data comprising all structures deposited until Sep 2022, 7 years after the initial release. Unlike the original publication, which accounted for only studies conducted with X-ray crystallography, we added data from solid nuclear magnetic resonance (NMR), solution NMR, CullPDB, Electron Microscopy, and Electron Crystallography using multiple filtering parameters. We also provide examples of how NIAS can be applied as a complementary analysis tool for different structural biology works and what are its limitations.

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Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.