The Old Yellow Enzyme from Ferrovum sp. JA12 (FOYE) displays an unusual thermal stability for an enzyme isolated from a mesophilic organism. We determined the crystal structure of this enzyme and performed a bioinformatic characterization to get insights into its thermal stability. The enzyme displays a tetrameric quaternary structure, but unlike the other tetrameric homologs, it clusters in a separate phylogenetic group and possesses unique interactions that stabilize the oligomerization. The thermal stability of this enzyme is due mainly to an unusually high number of intramolecular hydrogen bonds. Finally, this study provides a general analysis of the forces driving the oligomerization in Old Yellow Enzymes.

The HADDOCK team participated in CAPRI rounds 47-55 as both server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

Massive sampling with AlphaFold2 improves protein-protein complex predictions. This has been shown during the last CASP15-CAPRI blind prediction round by the AFsample tool. However, more difficult targets including antibody-antigen binding remain challenging. CAPRI Round 55 consisted of three antibody-antigen targets and one heterotrimer. We used our AlphaFold2-based MassiveFold, running 6 prediction pools, each with their own set of parameters, to produce in total more than 6000 predictions per target. We show here that massive sampling categorically produces acceptable to high quality predictions, however it is clear that the AlphaFold confidence score cannot be used to identify the best models in the set. We also show that, contrary to what was done before for CASP15-CAPRI with AFsample, increasing the sampling without activating the dropout does provide the best models in most cases.

This paper presents a novel method to assess the pathogenicity of Pyrin protein mutations by using mutual information (MI) as a measure to quantify the correlation between residue motions or fluctuations and associated changes affecting the phenotype. The concept of MI profile shift is presented to quantify changes in MI upon mutation, revealing insights into residue-residue interactions at critical positions. We apply this method to the Pyrin protein variants, which are associated with an autosomal recessively inherited disease called familial Mediterranean fever (FMF) since the available tools do not help predict the pathogenicity of the most penetrant variants. We demonstrate the utility of MI profile shifts in assessing the effects of mutations on protein stability, function, and disease phenotype. The importance of MI shifts, for the pyrin example the negative shifts, as indicators of severe functional effects is emphasized, along with exploring potential compensatory mechanisms indicated by positive MI shifts, which are otherwise random and inconsequential. The paper also discusses challenges in relating MI profile changes to disease severity and advocates for comprehensive analysis considering genetic, environmental, and stochastic factors. Overall, this study provides insights into the molecular mechanisms underlying the pathogenesis of FMF and offers a framework for identifying potential therapeutic targets based on MI profile changes induced by mutations.

A document by Anna Davies. Click on the document to view its contents.

SR/RS dipeptide repeats vary in both length and position and are phosphorylated by SR protein kinases (SRPKs). PIM-1L, the long isoform of PIM-1 kinase, the splicing of which has been implicated in acute myeloid leukemia, contains a domain which consists largely of repeating SR/RS and SH/HS dipeptides (SR/SH-rich). In order to extend our knowledge on the specificity and cellular functions of SRPK1, here we investigate whether PIM-1L could act as substrate of SRPK1 by a combination of biochemical and computational approaches. Our biochemical data showed that the SR/SH-rich domain of PIM-1L was able to associate with SRPK1, yet it could not act as a substrate but, instead, inactivated the kinase. In line with our biochemical data, molecular modeling followed by a microsecond-scale all-atom molecular dynamics (MD) simulation suggests that the SR/SH-rich domain acts as a pseudo-docking peptide that binds to the same acidic docking-groove used in other SRPK1 interactions and induces inactive SRPK1 conformations. Comparative community network analysis of the MD trajectories, unraveled the dynamic architecture of apo SRPK1 and notable alterations of allosteric communications upon PIM-1L peptide binding. This analysis also allowed us to identify key SRPK1 residues, including unique ones, with a pivotal role in mediating allosteric signal propagation within the kinase core. Interestingly, most of the identified amino acids correspond to cancer-associated amino acid changes, validating our results. In total, this work provides insights not only on the details of SRPK1 inhibition by the PIM-1L SR/SH-domain, but also contributes to an in-depth understanding of SRPK1 regulation.

Acquired resistance to first-line treatments in various cancers both promotes cancer recurrence as well as limits effective treatment. This is true for epidermal growth factor receptor (EGFR) mutations, for which secondary EGFR mutations are one of the principal mechanisms conferring resistance to the covalent inhibitor osimertinib. Thus, it is very important to develop a deeper understanding of the secondary mutational resistance mechanisms associated with EGFR mutations arising in tumors treated with osimertinib to expedite the development of innovative therapeutic drugs to overcome acquired resistance. This work uses all-atom molecular dynamics (MD) simulations to investigate the conformational variation of two reported EGFR mutants (L858R/L718Q and L858R/L792H) that resist osimertinib. The wild-type EGFR kinase domain and the L858R mutant are used as the reference. Our MD simulation results revealed that both the L718Q and L792 secondary mutations induce additional hydrogen bonds between the residues in the active pocket and the residues with the water molecules. These additional hydrogen bonds reduce the exposure area of C797, the covalent binding target of osimertinib. The additional hydrogen bonds also influence the binding affinity of the EGFR kinase domain by altering the secondary structure and flexibility of the amino acid residues in the domain. Our work highlights how the two reported mutations may alter both residue-residue and residue-solvent hydrogen bonds, affecting protein binding properties, which could be helpful for future drug discovery.

The last few years have seen the rapid proliferation of machine learning- (ML) based binding protein design methods. Although these methods have shown large increases in experimental success rates compared to prior approaches, the majority of their predictions fail when experimentally tested. It is evident that computational methods still struggle to distinguish the features of real protein binding interfaces from false predictions. To identify features of interactions that should occur in protein binding interfaces, short molecular dynamics simulations of 20 antibody-protein complexes were conducted. Intermolecular salt bridges, hydrogen bonds, and hydrophobic interactions were evaluated for their persistences, energies, and stabilities during the simulations. It was determined that only hydrogen bonds where both residues are stabilized in the bound complex are expected to persist and contribute meaningfully to the binding between proteins. In contrast, stabilization was not a requirement for salt bridges and hydrophobic interactions to persist but interactions where both residues are stabilized in the bound complex persist significantly longer and have significantly stronger energies. Using a dataset of 220 real antibody- protein complexes and 8194 false complexes from docking, a random forest classifier was trained and tested using features of the expected persistent interactions and compared to one only using the complex-level features of interaction energy (IE), buried surface area (BSA), IE/BSA, and shape complementarity. Inclusion of the features of the expected persistent interactions reduced the false positive rate of the classifier by two to five fold across a range of true positive classification rates.

Despite the ubiquity of Membrane Occupation Recognition Nexus (MORN) motifs across diverse species in both eukaryotic and prokaryotic organisms, these protein domains remain poorly characterized. Their significance is underscored in the context of the Alsin protein, implicated in the debilitating condition known as Infantile-onset Ascending Hereditary Spastic Paralysis (IAHSP). Studies have proposed that mutations within the Alsin MORN domain disrupt proper protein assembly, precluding the formation of the requisite tetrameric configuration essential for the protein’s inherent biological activity. However, a comprehensive understanding of the relationship between the biological functions of Alsin and its three-dimensional molecular structure is hindered by the lack of available experimental structures. In this study, by comparing experimentally resolved MORN domains, we predicted a three-dimensional structure for the putative MORN of Alsin. Furthermore, inspired by experimental pieces of evidence from previous studies, we employed the developed models to predict and investigate two homo-dimeric assemblies, characterising their stability. This study’s insights into the three-dimensional structure of the Alsin MORN domain and the stability dynamics of its homo-dimeric assemblies suggest an antiparallel linear configuration stabilized by a non-covalent interaction network. The elucidation of these intricate molecular relationships holds promise for advancing our understanding of pathogenic mechanisms and facilitating the development of treatments for this severe neurodegenerative disorder.

Bacterial laccases exhibit relatively high optimal reaction temperatures and possess a broad substrate spectrum, thereby expanding the range of potential applications for laccase enzymes. This study aims to investigate the molecular evolution of bacterial laccases using computational simulation tools such as AlphaFold2, Metal3D, AutoDockVina, and Rosetta. We isolated a bacterium with laccase activities from fecal samples from a Hermann´s tortoise ( Testudo hermanni), identified it as Klebsiella michiganensis using 16S rRNA sequencing and nanopore genome sequencing, and then identified a sequence encoding a laccase with a predicted molecular weight of approximately 27.5 kDa. Expression of the corresponding, chemically synthesized DNA fragment resulted in the isolation of an active laccase. The enzyme showed considerable thermostability, retaining 21% of its activity after boiling for 30 min. Using state-of-the-art information technology and machine learning techniques, we conducted simulations on this sequence, predicted its copper-ion binding sites, and validated these predictions through site-directed mutagenesis and expression. Subsequently, we performed computer-aided evolution studies on this sequence and found that 90% of the results from simulations exhibited improved performance. In summary, this study not only revealed a novel laccase but also demonstrated an efficient approach for advancing research on the molecular evolution of bacterial laccases using cutting-edge machine learning, next-generation sequencing, traditional bioinformatics approaches, and laboratory techniques, providing an effective strategy for discovering and design new bacterial laccases.

Protein cis-regulatory elements (CREs) are regions that modulate the activity of a protein through intramolecular interactions. Kinases, pivotal enzymes in numerous biological processes, often undergo regulatory control via inhibitory interactions in cis. This study delves into the mechanisms of cis regulation in kinases mediated by CREs, employing a combined structural and sequence analysis. To accomplish this, we curated an extensive dataset of kinases featuring annotated CREs, organized into homolog families through multiple sequence alignments. Key molecular attributes, including disorder and secondary structure content, active and ATP-binding sites, post-translational modifications, and disease-associated mutations, were systematically mapped onto all sequences. Additionally, we explored the potential for conformational changes between active and inactive states. Finally, we explored the presence of these kinases within membraneless organelles and elucidated their functional roles therein. CREs display a continuum of structures, ranging from short disordered stretches to fully folded domains. The adaptability demonstrated by CREs in achieving the common goal of kinase inhibition spans from direct autoinhibitory interaction with the active site within the kinase domain, to CREs binding to an alternative site, inducing allosteric regulation revealing distinct types of inhibitory mechanisms, which we exemplify by archetypical representative systems. While this study provides a systematic approach to comprehend kinase CREs, further experimental investigations are imperative to unravel the complexity within distinct kinase families. The insights gleaned from this research lay the foundation for future studies aiming to decipher the molecular basis of kinase dysregulation, and explore potential therapeutic interventions.

Amyloidosis are a group of diseases in which soluble proteins aggregate and deposit in fibrillar conformation extracellularly in tissues. The effectiveness of therapeutic strategies depends on the specific protein involved, being crucial to accurately determine its nature. Moreover, following the diagnosis, the search for the mutation within relatives allows the clinical advice. Here we report the precise diagnosis and explored the possible reasons of the structural pathogenicity. Whole exome sequencing and GATK calling pipeline were leveraged to characterize the protein variant present in a patient with kidney failure. Bioinformatics strategies were applied to suggest potential explanations of the variants aggregation. Our pipeline allowed the identification of a single point variant of fibrinogen Aα chain, which opened the possibility of curative transplantation. I n silico structural analysis, suggested that the pathogenicity of the variant may be attributed to a heightened susceptibility to yield a peptide prone to deposit as an oligomer with a β-sheet structure. Exploiting the comprehensive coverage of whole genome sequencing we managed to fill a vacant stage in the diagnosis of hereditary amyloidosis and to stimulate the advancement in biomedicine.

Structures at serine-proline sites in proteins were analyzed using a combination of peptide synthesis with structural methods and bioinformatics analysis of the PDB. Dipeptides were synthesized with the proline derivative (2 S,4 S)-(4-iodophenyl)hydroxyproline [hyp(4-I-Ph)]. The crystal structure of Boc-Ser-hyp(4-I-Ph)-OMe had two molecules in the unit cell. One molecule exhibited cis-proline and a type VIa2 β-turn (BcisD). The cis-proline conformation was stabilized by a C–H/O interaction between Pro C–H α and the Ser side-chain oxygen. NMR data were consistent with stabilization of cis-proline by a C–H/O interaction in solution. The other crystallographically observed molecule had trans-Pro and both residues in the PPII conformation. Two conformations were observed in the crystal structure of Ac-Ser-hyp(4-I-Ph)-OMe, with Ser adopting PPII in one and the β conformation in the other, each with Pro in the δ conformation and trans-Pro. Structures at Ser-Pro sequences were further examined via bioinformatics analysis of the PDB and via DFT calculations. Ser–Pro versus Ala-Pro sequences were compared to identify bases for Ser stabilization of local structures. C–H/O interactions between the Ser side-chain O γ and Pro C–H α were observed in 45% of structures with Ser- cis-Pro in the PDB, with nearly all Ser- cis-Pro structures adopting a type VI β-turn. 53% of Ser- trans-Pro sequences exhibited main-chain C=O i•••H–N i +3 or C=O i•••H–N i +4 hydrogen bonds, with Ser as the i residue and Pro as the i+1 residue. These structures were overwhelmingly either type I β-turns or N-terminal capping motifs on α-helices or a 3 10-helices. These results indicate that Ser-Pro sequences are particularly potent in favoring these structures. In each, Ser is in either the PPII or β conformation, with the Ser O γ capable of engaging in a hydrogen bond with the amide N–H of the i+2 (type I β-turn or 3 10-helix; Ser χ 1 t) or i+3 (α-helix; Ser χ 1 g+) residue. Non-proline cis amide bonds can also be stabilized by C–H/O interactions.

The HIV-1 protease is critical for the process of viral maturation, making it an attractive target for antiretroviral therapy. As such it is one of the most well characterised proteins in the Protein Data Bank. There is some evidence to suggest that the HIV-1 protease is capable of accommodating small molecule fragments at several locations on its surface outside of the active site; namely, the Exo site, flap-top pocket, face site, and Eye site. However, some pockets on the surface of proteins remain unformed in the apo structure and these “cryptic sites” have also been known to accommodate small molecule ligands. To date, no cryptic sites have been identified in the structure of the HIV-1 protease. Here, we characterise a novel cryptic cantilever pocket on the surface of the HIV-1 protease through mixed-solvent molecular dynamics simulations using several probes: acetone, benzene, imidazole, isopropanol, and phenol. Structure-based analysis of the cryptic cantilever pocket suggest that the pocket may be amenable to ligand binding. Interestingly, we note that several homologous retroviral proteases exhibit evolutionarily conserved dynamics in the cantilever region and possess a conserved pocket in the cantilever region. Immobilisation of the cantilever region of the HIV-1 protease via disulphide cross-linking resulted in curling-in of the flap tips and the propensity for the protease to adopt a semi-open flap conformation. Together these results suggest that the mobility of the cantilever region plays a key role in the global dynamics of retroviral proteases. Moreover, the cryptic cantilever pocket of the HIV protease may represent an interesting target for future fragment-based ligand screening campaigns.

RfaH is a two-domain metamorphic protein involved in transcription regulation and translation initiation. To carry out dual functions, RfaH relies on two coupled structural changes: domain dissociation and fold switching. In the free state, the C-terminal domain (CTD) of RfaH adopts an all- α fold and is tightly associated with the N-terminal domain (NTD). Upon binding to RNA polymerase (RNAP), the domains dissociate and the CTD completely transforms into an all- β fold, while the NTD remains largely, but not entirely, unchanged. We test the idea that a change in the conformation of an extended β-hairpin ( β3- β4) located on the NTD, helps trigger domain dissociation. To this end, we use homology modelling to construct a structure, H 1 , which is similar to free RfaH but with a remodeled β3- β4 hairpin. We then use an all-atom physics-based model enhanced with a dual-basin structure-based potential to simulate domain separation driven by thermal unfolding of the CTD with NTD in a fixed, folded conformation. We apply our model to both free RfaH and H 1 . For H 1 we find, in line with our hypothesis, that the CTD exhibits a lower stability and the domains dissociate at a lower temperature ( T), as compared to free RfaH. We do not, however, observe complete refolding to the all- β state in these simulations, suggesting that a change in β3- β4 orientation aid in, but is not sufficient for, domain dissociation. In addition, we study the reverse fold switch in which RfaH returns from a domain-open all- β state to its domain-closed all- α state. We observe a T-dependent transition rate; fold switching is slow at low T, where the CTD tend to be kinetically trapped in its all- β state, and at high- T, where the all- α state becomes unstable. Consequently, our simulations suggest an optimal T at which fold switching is most rapid. At this T, the stabilities of both folds are reduced. Overall, our study suggests that both inter-domain interactions and conformational changes within NTD may be important for proper functioning of RfaH.

Protein dynamics orchestrate allosteric regulation, but elucidating the sequence of events and causal relationships within these intricate processes remains challenging. We introduce the Dynamically Perturbed Gaussian Network Model (DP-GNM), a novel approach that uncovers the directionality of information flow within proteins. DP-GNM leverages time-dependent correlations to achieve two goals: identifying driver and driven residues and revealing communities of residues exhibiting synchronized dynamics. Applied to wild type and mutated structures of Cyclophilin A, DP-GNM unveils a hierarchical network of information flow, where key residues initiate conformational changes that propagate through the protein in a directed manner. This directional causality illuminates the intricate relationship between protein dynamics and allosteric regulation, providing valuable insights into protein function and potential avenues for drug design. Furthermore, DP-GNM’s potential to elucidate dynamics under periodic perturbations like the circadian rhythm suggests its broad applicability in understanding complex biological processes governed by environmental cycles.

Histidine kinases (HKs) are a central part of bacterial environmental-sensing two component systems, providing their hosts with the ability to respond to a wide range of physical and chemical signals, being a paradigmatic example of protein diversity and molecular evolution. HK are multidomain proteins consisting of at least a sensor domain, dimerization and phosphorylation domain (DHp) and a catalytic domain. They work as homodimers, and the existence of two different autophosphorylation mechanisms (cis and trans), has been proposed as relevant for pathway specificity. Although several HKs have been intensively studied, a precise sequence-to-structure explanation of why and how either cis or trans phosphorylation occurs, is still unavailable, nor is there any evolutionary analysis on the subject. In this work we show that AlphaFold can accurately determine whether a HK dimerizes in a cis or trans structure. We then use it to explore the molecular determinants of the phosphorylation mechanism. We conclude that it is the difference in lengths of the helices surrounding the DHp loop that determines the mechanism. We also show that very small changes in these helices can cause a mechanism switch, providing these systems with a way to diverge rapidly and adapt to new stimuli.

Genetically encoded fluorescent biosensors (GEFBs) proved to be reliable tracers for many metabolites and cellular processes. In the simplest case, a fluorescent protein (FP) is genetically fused to a sensing protein which undergoes a conformational change upon ligand binding. This drives a rearrangement in the chromophore environment and changes the spectral properties of the FP. Structural determinants of successful biosensors are revealed only in hindsight when the crystal structures of both ligand-bound and ligand-free forms are available. This makes the development of new biosensors for desired analytes a long trial-and-error process. In the current study, we conducted µs-long all atom molecular dynamics (MD) simulations of a maltose biosensor in both the apo (dark) and holo (bright) forms. We performed detailed hydrogen bond occupancy analyses to shed light on the mechanism of ligand induced conformational change in the sensor protein and its allosteric effect on the chromophore environment. We find that two strong indicators for distinguishing bright and dark states of biosensors are due to substantial changes in hydrogen bond dynamics in the system and solvent accessibility of the chromophore.