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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.

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.

The INTEGRATE system is a gene-editing approach that offers advantages over the widely used CRISPR-Cas9 system. It does not introduce double strand breaks in the target DNA but rather integrates the desired DNA sequence directly into it. The first step in the integration process is PAM recognition, which is critical to understanding and optimizing the system. Experimental testing revealed varying integration efficiencies of different PAM mutants, and computational simulations were carried out to gain mechanistic insight into the conformational changes of Cas8 during PAM recognition. Our results showed that the interaction between Arg246 and Guanine at position (-1) of the target strand is critical for PAM recognition. We found that unfavorable interactions in the 5’-AC-3’ PAM mutant disrupted this interaction and may be responsible for its 0% integration efficiency. Additionally, we discovered that PAM sequences not only initiate the integration process but also regulate it through an allosteric mechanism that connects the N-terminal domain and the helical bundle of Cas8. This allosteric regulation was present in all PAMs tested, even those with lower integration efficiencies, such as 5’-TC-3’ and 5’-AC-3’. We identified the Cas8 residues that are involved in this regulation. Our findings provide valuable insights into PAM recognition mechanisms in the INTEGRATE system and can help improve the gene-editing technology.

Alzheimer’s disease (AD) is a neurodegenerative disorder that is characterized by the formation of extracellular amyloid- β (A β) plaques. The underlying cause of AD is unknown, however, post-translational modifications (PTMs) of A β have been found in AD patients and are thought to play a role in protein aggregation. One such PTM is pyroglutamylation, which can occur at two sites in A β, Glu3 and Glu11. This modification of A β involves the truncation and charge-neutralization of N-terminal glutamate, causing A β to become more hy- drophobic and prone to aggregation. The molecular mech- anism by which the introduction of pyroglutamate (pE) pro- motes aggregation has not been determined. To gain a greater understanding of the role that charge neutralization and trun- cation of the N-terminus plays on A β conformational sam- pling, we used the Drude polarizable force field (FF) to per- form molecular dynamics simulations on A β pE3-42 and A β pE11-42 and comparing their properties to previous simulations of A β 1-42. The Drude polarizable FF allows for a more accurate representation of electrostatic interactions, therefore pro- viding novel insights into the role that charge plays in pro- tein dynamics. Here, we report the parametrization of pE in the Drude polarizable FF and the effect of pyroglutamyla- tion on A β. We found that A β pE3-42 and A β pE11-42 alter the permanent and induced dipoles of the peptide. Specifically, we found that A β pE3-42 and A β pE11-42 have modification- specific backbone and sidechain polarization response and perturbed solvation properties that shift the A β conforma- tional ensemble.

Computing protein structure from amino acid sequence information has been a long-standing grand challenge. CASP (Critical Assessment of Structure Prediction) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every two years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of Proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein-ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.

This study presents TooT-PLM-ionCT, a holistic framework that exploits the capabilities of six diverse Protein Language Models (PLMs) - ProtBERT, ProtBERT-BFD, ESM-1b, ESM-2 (650M parameters), and ESM-2 (15B parameters) - for precise classification of integral membrane proteins, specifically ion channels (ICs) and ion transporters (ITs). As these proteins play a pivotal role in the regulation of ion movement across cellular membranes, they are integral to numerous biological processes and overall cellular vitality. To circumvent the costly and time-consuming nature of wet lab experiments, we harness the predictive prowess of PLMs, drawing parallels with techniques in natural language processing. Our strategy engages six classifiers, embracing both conventional methodologies and a deep learning model, to segregate ICs and ITs from other membrane proteins, as well as differentiate ICs from ITs. Furthermore, we delve into critical factors influencing our tasks, including the implications of dataset balancing, the effect of frozen versus fine-tuned PLM representations, and the potential variance between half and full precision floating-point computations. Our empirical results showcase superior performance in distinguishing ITs from other membrane proteins and differentiating ICs from ITs, while the task of discriminating ICs from other membrane proteins exhibits results commensurate with the current state-of-the-art.

For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.