Computational Structure Prediction Research Articles

Histones and histone variant families in prokaryotes

Histones are important chromatin-organizing proteins in eukaryotes and archaea. They form superhelical structures around which DNA is wrapped. Recent studies have shown that some archaea and bacteria contain alternative histones that exhibit different DNA binding properties, in addition to highly divergent sequences. However, the vast majority of these histones are identified in metagenomes and thus are difficult to study in vivo. The recent revolutionary breakthroughs in computational protein structure prediction by AlphaFold2 and RoseTTAfold allow for unprecedented insights into the potential function and structure of previously uncharacterized proteins. Here, we categorize the prokaryotic histone space into 17 distinct groups based on AlphaFold2 predictions. We identify a superfamily of histones, termed α3 histones, which are common in archaea and present in several bacteria. Importantly, we establish the existence of a large family of histones throughout archaea and in some bacteriophages that, instead of wrapping DNA, bridge DNA, thereby diverging from conventional nucleosomal histones.

Computational structural prediction and chemical inhibition of the human mitochondrial pyruvate carrier protein heterodimer complex.

The mitochondrial pyruvate carrier (MPC) plays a role in numerous diseases including neurodegeneration, metabolically dependent cancers, and the development of insulin resistance. Several previous studies in genetic mouse models or with existing inhibitors suggest that inhibition of the MPC could be used as a viable therapeutic strategy in these diseases. However, the MPC structure is unknown, making it difficult to screen for and develop therapeutically viable inhibitors. Currently known MPC inhibitors would make for poor drugs due to their poor pharmacokinetic properties, or in the case of the thiazolidinediones (TZDs), off-target specificity for peroxisome-proliferator activated receptor gamma (PPARgamma) leads to unwanted side effects. In this study, we develop several structural models for the MPC heterodimer complex and investigate the chemical interactions required for the binding of these known inhibitors to MPC and PPARgamma. Based on these models, the MPC most likely takes on outward-facing (OF) and inward-facing (IF) conformations during pyruvate transport, and inhibitors likely plug the carrier to inhibit pyruvate transport. Although some chemical interactions are similar between MPC and PPARgamma binding, there is likely enough difference to reduce PPARgamma specificity for future development of novel, more specific MPC inhibitors.

Majority of the highly variable NLRs in maize share genomic location and contain additional target-binding domains.

Nucleotide-binding, Leucine Rich Repeat proteins (NLRs) are a major class of immune receptors in plants. NLRs include both conserved and rapidly evolving members, however their evolutionary trajectory in crops remains understudied. Availability of crop pan-genomes enables analysis of the recent events in the evolution of this highly complex gene family within domesticated species. Here, we investigated the NLR complement of 26 nested association mapping (NAM) founder lines of maize. We found that maize has just four main subfamilies containing rapidly evolving highly variable NLR (hvNLR) receptors. Curiously, three of these phylogenetically distinct hvNLR lineages are located in adjacent clusters on chromosome 10. Members of the same hvNLR clade show variable expression and methylation across lines and tissues, consistent with their rapid evolution. By combining sequence diversity analysis and AlphaFold2 computational structure prediction we predicted ligand binding sites in the hvNLRs. We also observed novel insertion domains in the LRR regions of two hvNLR subfamilies that likely contribute to target recogniton. To make this analysis accessible, we created NLRCladeFinder, a Google Colaboratory notebook, that accepts any newly identified NLR sequence, places it in the evolutionary context of the maize pan-NLRome, and provides an updated clade alignment, phylogenetic tree, and sequence diversity information for the gene of interest.

FAM122A ensures cell cycle interphase progression and checkpoint control by inhibiting B55α/PP2A through helical motifs

The Ser/Thr protein phosphatase 2 A (PP2A) regulates the dephosphorylation of many phosphoproteins. Substrate recognition are mediated by B regulatory subunits. Here, we report the identification of a substrate conserved motif [RK]-V-x-x-[VI]-R in FAM122A, an inhibitor of B55α/PP2A. This motif is necessary for FAM122A binding to B55α, and computational structure prediction suggests the motif, which is helical, blocks substrate docking to the same site. In this model, FAM122A also spatially constrains substrate access by occluding the catalytic subunit. Consistently, FAM122A functions as a competitive inhibitor as it prevents substrate binding and dephosphorylation of CDK substrates by B55α/PP2A in cell lysates. FAM122A deficiency in human cell lines reduces the proliferation rate, cell cycle progression, and hinders G1/S and intra-S phase cell cycle checkpoints. FAM122A-KO in HEK293 cells attenuates CHK1 and CHK2 activation in response to replication stress. Overall, these data strongly suggest that FAM122A is a short helical motif (SHeM)-dependent, substrate-competitive inhibitor of B55α/PP2A that suppresses multiple functions of B55α in the DNA damage response and in timely progression through the cell cycle interphase.

AggreProt: a web server for predicting and engineering aggregation prone regions in proteins.

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.

Data hazards in synthetic biology.

Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.

AlphaFold2 as a replacement for solution NMR structure determination of small proteins: Not so fast!

The determination of a protein’s structure is often a first step towards the development of a mechanistic understanding of its function. Considerable advances in computational protein structure prediction have been made in recent years, with AlphaFold2 (AF2) emerging as the primary tool used by researchers for this purpose. While AF2 generally predicts accurate structures of folded proteins, we present here a case where AF2 incorrectly predicts the structure of a small, folded and compact protein with high confidence. This protein, pro-interleukin-18 (pro-IL-18), is the precursor of the cytokine IL-18. Interestingly, the structure of pro-IL-18 predicted by AF2 matches that of the mature cytokine, and not the corresponding experimentally determined structure of the pro-form of the protein. Thus, while computational structure prediction holds immense promise for addressing problems in protein biophysics, there is still a need for experimental structure determination, even in the context of small well-folded, globular proteins.

SincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure.

Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement. In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

Molecular basis of human poly(A) polymerase recruitment by mPSF.

3' end processing of most eukaryotic precursor-mRNAs (pre-mRNAs) is a crucial cotranscriptional process that generally involves the cleavage and polyadenylation of the precursor transcripts. Within the human 3' end processing machinery, the four-subunit mammalian polyadenylation specificity factor (mPSF) recognizes the polyadenylation signal (PAS) in the pre-mRNA and recruits the poly(A) polymerase α (PAPOA) to it. To shed light on the molecular mechanisms of PAPOA recruitment to mPSF, we used a combination of cryogenic-electron microscopy (cryo-EM) single-particle analysis, computational structure prediction, and in vitro biochemistry to reveal an intricate interaction network. A short linear motif in the mPSF subunit FIP1 interacts with the structured core of human PAPOA, with a binding mode that is evolutionarily conserved from yeast to human. In higher eukaryotes, however, PAPOA contains a conserved C-terminal motif that can interact intramolecularly with the same residues of the PAPOA structured core used to bind FIP1. Interestingly, using biochemical assay and cryo-EM structural analysis, we found that the PAPOA C-terminal motif can also directly interact with mPSF at the subunit CPSF160. These results show that PAPOA recruitment to mPSF is mediated by two distinct intermolecular connections and further suggest the presence of mutually exclusive interactions in the regulation of 3' end processing.

Uncovering structural themes across cilia microtubule inner proteins with implications for human cilia function

Centrosomes and cilia are microtubule-based superstructures vital for cell division, signaling, and motility. The once thought hollow lumen of their microtubule core structures was recently found to hold a rich meshwork of microtubule inner proteins (MIPs). To address the outstanding question of how distinct MIPs evolved to recognize microtubule inner surfaces, we applied computational sequence analyses, structure predictions, and experimental validation to uncover evolutionarily conserved microtubule- and MIP-binding modules named NWE, SNYG, and ELLEn, and PYG and GFG-repeat by their signature motifs. These modules intermix with MT-binding DM10-modules and Mn-repeats in 24 Chlamydomonas and 33 human proteins. The modules molecular characteristics provided keys to identify elusive cross-species homologs, hitherto unknown human MIP candidates, and functional properties for seven protein subfamilies, including the microtubule seam-binding NWE and ELLEn families. Our work defines structural innovations that underpin centriole and axoneme assembly and demonstrates that MIPs co-evolved with centrosomes and cilia.

Structure and function of engineered stromal cell-derived factor-1α.

To design biologically active, collagen-based scaffolds for bone tissue engineering, we have synthesized chimeric proteins consisting of stromal cell-derived factor-1α (SDF) and the von Willebrand factor A3 collagen-binding domain (CBD). The chimeric proteins were used to evaluate the effect of domain linkage and its order on the structure and function of the SDF and CBD. The structure of the chimeric proteins was analyzed by circular dichroism spectroscopy, while functional analysis was performed by a cell migration assay for the SDF domain and a collagen-binding assay for the CBD domain. Furthermore, computational structural prediction was conducted for the chimeric proteins to examine the consistency with the results of structural and functional analyses. Our structural and functional analyses as well as structural prediction revealed that linking two domains can affect their functions. However, their order had minor effects on the three-dimensional structure of CBD and SDF in the chimeric proteins.

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes.

The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes. We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments. Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Computational Prediction of Structure, Function, and Interaction of Myzus persicae (Green Peach Aphid) Salivary Effector Proteins.

Similar to plant pathogens, phloem-feeding insects such as aphids deliver effector proteins inside their hosts that act to promote host susceptibility and enable feeding and infestation. Despite exciting progress toward identifying and characterizing effector proteins from these insects, their functions remain largely unknown. The recent groundbreaking development in protein structure prediction algorithms, combined with the availability of proteomics and transcriptomic datasets for agriculturally important pests, provides new opportunities to explore the structural and functional diversity of effector repertoires. In this study, we sought to gain insight into the infection strategy used by the Myzus persicae (green peach aphid) by predicting and analyzing the structures of a set of 71 effector candidate proteins. We used two protein structure prediction methods, AlphaFold and OmegaFold, that produced mutually consistent results. We observed a wide continuous spectrum of structures among the effector candidates, from disordered proteins to globular enzymes. We made use of the structural information and state-of-the-art computational methods to predict M. persicae effector protein properties, including function and interaction with host plant proteins. Overall, our investigation provides novel insights into prediction of structure, function, and interaction of M. persicae effector proteins and will guide the necessary experimental characterization to address new hypotheses. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Identification of potential inhibitor against Leishmania donovani mitochondrial DNA primase through in-silico and in vitro drug repurposing approaches

Leishmania donovani is the causal organism of leishmaniasis with critical health implications affecting about 12 million people around the globe. Due to less efficacy, adverse side effects, and resistance, the available therapeutic molecules fail to control leishmaniasis. The mitochondrial primase of Leishmania donovani (LdmtPRI1) is a vital cog in the DNA replication mechanism, as the enzyme initiates the replication of the mitochondrial genome of Leishmania donovani. Hence, we target this protein as a probable drug target against leishmaniasis. The de-novo approach enabled computational prediction of the three-dimensional structure of LdmtPRI1, and its active sites were identified. Ligands from commercially available drug compounds were selected and docked against LdmtPRI1. The compounds were chosen for pharmacokinetic study and molecular dynamics simulation based on their binding energies and protein interactions. The LdmtPRI1 gene was cloned, overexpressed, and purified, and a primase activity assay was performed. The selected compounds were verified experimentally by the parasite and primase inhibition assay. Capecitabine was observed to be effective against the promastigote form of Leishmania donovani, as well as inhibiting primase activity. This study's findings suggest capecitabine might be a potential anti-leishmanial drug candidate after adequate further studies.

Discovery of a trefoil knot in the RydC RNA: Challenging previous notions of RNA topology

Knots are very common in polymers, including DNA and protein molecules. Yet, no genuine knot has been identified in natural RNA molecules to date. Upon re-examining experimentally determined RNA 3D structures, we discovered a trefoil knot 31, the most basic non-trivial knot, in the RydC RNA. This knotted RNA is a member of a small family of short bacterial RNAs, whose secondary structure is characterized by an H-type pseudoknot. Molecular dynamics simulations suggest a folding pathway of the RydC RNA that starts with a native twisted loop. Based on sequence analyses and computational RNA 3D structure predictions, we postulate that this trefoil knot is a conserved feature of all RydC-related RNAs. The first discovery of a knot in a natural RNA molecule introduces a novel perspective on RNA 3D structure formation and on fundamental research on the relationship between function and spatial structure of biopolymers.

Predictive crystallography at scale: mapping, validating, and learning from 1,000 crystal energy landscapes

Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures...

How accurately can one predict drug binding modes using AlphaFold models?

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.

How accurately can one predict drug binding modes using AlphaFold models?

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.

Computational Prediction of Coiled-Coil Protein Gelation Dynamics and Structure.

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences. Provided this library, we explore the impact of in silico energies on structure and gelation kinetics, where we also reveal a range of blue autofluorescence that enables hydrogel disassembly and recovery. As a result of this library, we identify the new coiled-coil hydrogel sequence, Q5, capable of gelation within 24 h at 4 °C, a more than 2-fold increase over that of our previous iteration Q2. The fast gelation time of Q5 enables the assessment of structural transition in real time using small-angle X-ray scattering (SAXS) that is correlated to coarse-grained and atomistic molecular dynamics simulations revealing the supramolecular assembling behavior of coiled-coils toward nanofiber assembly and gelation. This work represents the first system of hydrogels with predictable self-assembly, autofluorescent capability, and a molecular model of coiled-coil fiber formation.

A comparative study of protein structure prediction tools for challenging targets: Snake venom toxins

Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold. However, their performance has primarily been evaluated on well-characterised proteins and their ability to predict sturtctures of proteins lacking experimental structures, such as many snake venom toxins, has been less scrutinised. In this study, we evaluated three modelling tools on their prediction of over 1000 snake venom toxin structures for which no experimental structures exist. Our findings show that AlphaFold2 (AF2) performed the best across all assessed parameters. We also observed that ColabFold (CF) only scored slightly worse than AF2, while being computationally less intensive. All tools struggled with regions of intrinsic disorder, such as loops and propeptide regions, and performed well in predicting the structure of functional domains. Overall, our study highlights the importance of exercising caution when working with proteins with no experimental structures available, particularly those that are large and contain flexible regions. Nonetheless, leveraging computational structure prediction tools can provide valuable insights into the modelling of protein interactions with different targets and reveal potential binding sites, active sites, and conformational changes, as well as into the design of potential molecular binders for reagent, diagnostic, or therapeutic purposes.