Structure Prediction Research Articles

Assessing the Utility of ColabFold and AlphaMissense in Determining Missense Variant Pathogenicity for Congenital Myasthenic Syndromes

Background/Objectives: Congenital myasthenic syndromes (CMSs) are caused by variants in >30 genes with increasing numbers of variants of unknown significance (VUS) discovered by next-generation sequencing. Establishing VUS pathogenicity requires in vitro studies that slow diagnosis and treatment initiation. The recently developed protein structure prediction software AlphaFold2/ColabFold has revolutionized structural biology; such predictions have also been leveraged in AlphaMissense, which predicts ClinVar variant pathogenicity with 90% accuracy. Few reports, however, have tested these tools on rigorously characterized clinical data. We therefore assessed ColabFold and AlphaMissense as diagnostic aids for CMSs, using variants of the CHRN genes that encode the nicotinic acetylcholine receptor (nAChR). Methods: Utilizing a dataset of 61 clinically validated CHRN variants, (1) we evaluated the possibility of a ColabFold metric (either predicted structural disruption, prediction confidence, or prediction quality) that distinguishes variant pathogenicity; (2) we assessed AlphaMissense’s ability to differentiate variant pathogenicity; and (3) we compared AlphaMissense to the existing pathogenicity prediction programs AlamutVP and EVE. Results: Analyzing the variant effects on ColabFold CHRN structure prediction, prediction confidence, and prediction quality did not yield any reliable pathogenicity indicative metric. However, AlphaMissense predicted variant pathogenicity with 63.93% accuracy in our dataset—a much greater proportion than AlamutVP (27.87%) and EVE (28.33%). Conclusions: Emerging in silico tools can revolutionize genetic disease diagnosis—however, improvement, refinement, and clinical validation are imperative prior to practical acquisition.

A coronaviral pore-replicase complex links RNA synthesis and export from double-membrane vesicles.

Coronavirus-infected cells contain double-membrane vesicles (DMVs) that are key for viral RNA replication and transcription, perforated by hexameric pores connecting the vesicular lumen to the cytoplasm. How pores form and traverse two membranes, and how DMVs organize RNA synthesis, is unknown. Using structure prediction and functional assays, we show that the nonstructural viral membrane protein nsp4 is the key pore organizer, spanning the double membrane and forming most of the pore lining. Nsp4 interacts with nsp3 on the cytoplasmic side and with the viral replicase inside the DMV. Newly synthesized mRNAs exit the DMV into the cytoplasm, passing through a narrow ring of conserved nsp4 residues. Steric constraints imposed by the ring predict that modified nucleobases block mRNA transit, resulting in broad-spectrum anticoronaviral activity.

Biochemical characterization and structure prediction of the Cerrado soil CRB2(1) metagenomic dioxygenase

Dioxygenases are enzymes involved in the conversion of polyconic aromatic hydroxycarbons (PAHs), attracting significant biotechnological interest for the conversion of recalcitrant organic compounds. Furthermore, few studies show that dioxygenases can take on the function of resistance genes in clones. This enzymatic versatility opens up new opportunities for elucidating the mechanisms of microbial resistance, as well as its biotechnological application. In this work, a Cerrado soil dioxygenase named CRB2(1) was biochemically characterized. The enzyme was shown to have optimal activity at pH 7; a temperature of 30 °C; and using iron ions as a cofactor for substrate cleavage. The kinetic catalytic parameters of CRB2(1) were Vmax = 0.02281 µM/min and KM = 97.6. Its predicted three-dimensional structure obtained using the Modeller software v9.22 based on the crystal structure of gentisate 1,2-dioxygenase from Silicibacter pomeroyi (GDOsp) (PDB ID 3BU7, resolution 2.80 Å, residues 17–374) revealed substrate binding to the cupin domain, where the active site is located. The analyzed substrates interact directly with the iron ion, coordinated by three histidine residues. Changing the iron ion charge modifies the binding between the active site and the substrates. Currently, there is a demand for enzymes that have biotechnological activities of interest. Metagenomics allows analyzing the biotechnological potential of several organisms at the same time, based on sequence and functional activity analyses.

Development of a specific biosensor for sesquiterpene based on SELEX and directed evolution platforms

Biosensors are essential in synthetic biology, particularly for detecting compounds without distinct visual markers. In this study, a riboswitch biosensor specifically responsive to a sesquiterpene — amorpha-4,11-diene was developed for the first time. Through SELEX-SMB, a high-affinity aptamer library comprising 81,520 sequences was generated. Subsequent screening via the TBG-directed evolution platform identified riboswitch5, which downregulates downstream gene expression in response to amorpha-4,11-diene within a concentration range of 10–100 mg/L, achieving a log₂Foldchange of −0.51 at 100 mg/L. Structural predictions, combined with molecular docking and molecular dynamics simulations, revealed that ligand binding induced conformational changes that stabilize the riboswitch and enhance its repressive effect. This biosensor represents a powerful tool for the detection and regulation of small molecules, with broad applications in strain engineering, pathway regulation, and metabolic optimization in synthetic biology.

Sequence variation in the active site of MCR proteins is evolutionarily accommodated through inter-domain interactions.

Sequence variation among homologous proteins can shed light on their function and ancestry. In this study, we analyse variation at catalytic residues among MCR (mobile colistin resistance) proteins, which confer resistance to the last resort antibiotic, colistin, in gram-negative bacteria. We show that not all naturally occurring variants at a lipid A-binding residue, Ser284, are tolerated in MCR-1. In particular, the substitution of Ser284 with Asp, found naturally in MCR-5, resulted in diminished colistin resistance. Using phylogenetic analyses and structure predictions we trace back variation at this site among MCRs to their ancestors, i.e. EptA phosphoethanolamine transferases that are encoded by diverse bacterial genomes. Mutational studies and Alphafold-based structural modelling revealed that the functional importance of position 284 varies between two phylogenetically distant MCRs, i.e. MCR-1 and MCR-5. Despite a high degree of similarity among their catalytic domains, inter-domain interactions were not conserved between MCR-1 and MCR-5 due to their different ancestries, providing a mechanistic basis behind the different phenotypes of similar mutations at position 284. Our study thus uncovers subtle differences in the organisation of domains among MCR proteins that can lead to substantial differences in their catalytic properties and mutational tolerance.

Analysis of the structure and interactions of the SARS-CoV-2 ORF7b accessory protein

SARS-CoV-2 carries a sizeable number of proteins that are accessory to replication but may be essential for virus-host interactions and modulation of the host immune response. Here, we investigated the structure and interactions of the largely unknown ORF7b, a small membranous accessory membrane protein of SARS-CoV-2. We show that structural predictions indicate a transmembrane (TM) leucine zipper for ORF7b, and experimentally confirm the predominantly α-helical secondary structure within a phospholipid membrane mimetic by solid-state NMR. We also show that ORF7b forms heterogeneous higher-order multimers. We determined ORF7b interactions with cellular TM leucine zipper proteins using both biochemical and NMR approaches, providing evidence for ORF7b interaction with the TM domains of E-cadherin, as well as phospholamban. Our results place ORF7b as a hypothetical interferer in cellular processes that utilize leucine zipper motifs in transmembrane multimerization domains.

Molecular evolution and genetic diversity of defective chorion 1 in Anastrepha fraterculus and Anastrepha obliqua (Diptera, Tephritidae).

The family Tephritidae comprises numerous fruit fly species, some of which are economically significant, such as several in the genus Anastrepha. Most pest species in this genus belong to the fraterculus group, characterized by closely related species that are difficult to differentiate due to recent divergence and gene flow. Identifying genetic markers for their study is paramount for understanding the group's evolution and eventual phytosanitary control. Because there is variation in eggshell morphology among species in the genus, the study of the rapidly evolving defective chorion 1 (dec-1) gene, which is crucial for chorion formation and reproduction, could provide relevant information for Anastrepha differentiation. We compared transcriptome sequences of dec-1 from two of the most important pest species in the genus, Anastrepha fraterculus and Anastrepha obliqua to dec-1 sequences from Anastrepha ludens, which was used for structure prediction. Furthermore, we amplified a conserved exon across populations of these species. These data revealed three alternative transcripts in A. fraterculus and A. obliqua, consistent with patterns found in other Tephritidae; we obtained orthologous sequences for these other tephritids from NCBI to investigate patterns of selection affecting this gene at different hierarchical levels using different methods. These analyses show a general pattern of purifying selection across the whole gene and throughout its history at different hierarchical levels, from populations to more distantly related species. That notwithstanding, we still found evidence of positive and episodic diversifying selection at different levels. Different parts of the gene have shown distinct evolutionary rates, which were associated with the diverse proproteins produced by posttranslational changes of DEC-1, with proproteins that are incorporated in the chorion earlier in egg formation being in general more conserved than others that are incorporated later. This correlation appears more evident in certain lineages, including the branch that separates Anastrepha, as well as other internal branches that differentiate species within the genus. Our data showed that this gene shows remarkable variation across its different exons, which has proven to be informative at different evolutionary levels. These changes hold promise not only for studying differentiation in Anastrepha but also for the eventual management of selected pest species.

Critical Assessment of Protein Engineering (CAPE): A Student Challenge on the Cloud.

The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by limited access to high-quality data sets and experimental feedback. The Critical Assessment of Protein Engineering (CAPE) challenge addresses these issues through a student-focused competition, utilizing cloud computing and biofoundries to lower barriers to entry. CAPE serves as an open platform for community learning, where mutant data sets and design algorithms from past contestants help improve overall performance in subsequent rounds. Through two competition rounds, student participants collectively designed >1500 new mutant sequences, with the best-performing variants exhibiting catalytic activity up to 5-fold higher than the wild-type parent. We envision CAPE as a collaborative platform to engage young researchers and promote computational protein engineering.

Deep generative design of RNA aptamers using structural predictions.

RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fluoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fluorescent activity, show that these aptamers can be optimized for activity in silico, and find that they exhibit a mechanism of fluorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efficient design of new RNA sequences.

StaPep: An Open-Source Toolkit for Structure Prediction, Feature Extraction, and Rational Design of Hydrocarbon-Stapled Peptides.

All-hydrocarbon stapled peptides, with their covalent side-chain constraints, provide enhanced proteolytic stability and membrane permeability, making them superior to linear peptides. However, tools for extracting structural and physicochemical descriptors to predict the properties of hydrocarbon-stapled peptides are lacking. To address this, we present StaPep, a Python-based toolkit for generating 3D structures and calculating 21 features for hydrocarbon-stapled peptides. StaPep supports peptides containing two non-standard amino acids (norleucine and 2-aminoisobutyric acid) and six non-natural anchoring residues (S3, S5, S8, R3, R5, and R8), with customization options for other non-standard amino acids. We showcase StaPep's utility through three case studies. The first generates 3D structures of these peptides with a mean RMSD of 1.62 ± 0.86, offering essential structural insights for drug design and biological activity prediction. The second develops machine learning models based on calculated molecular features to differentiate between membrane-permeable and non-permeable stapled peptides, achieving an AUC of 0.93. The third constructs regression models to predict the antimicrobial activity of stapled peptides against Escherichia coli, with a Pearson correlation of 0.84. StaPep's pipeline spans data retrieval, structure generation, feature calculation, and machine learning modeling for hydrocarbon-stapled peptides. The source codes and data set are freely available on Github: https://github.com/dahuilangda/stapep_package.

Benchmark Investigation of SARS-CoV-2 Mutants’ Immune Escape with 2B04 Murine Antibody: A Step Towards Unraveling a Larger Picture

Even though COVID-19 is no longer the primary focus of the global scientific community, its high mutation rate (nearly 30 substitutions per year) poses a threat of a potential comeback. Effective vaccines have been developed and administered to the population, ending the pandemic. Nonetheless, reinfection by newly emerging subvariants, particularly the latest JN.1 strain, remains common. The rapid mutation of this virus demands a fast response from the scientific community in case of an emergency. While the immune escape of earlier variants was extensively investigated, one still needs a comprehensive understanding of how specific mutations, especially in the newest subvariants, influence the antigenic escape of the pathogen. Here, we tested comprehensive in silico approaches to identify methods for fast and accurate prediction of antibody neutralization by various mutants. As a benchmark, we modeled the complexes of the murine antibody 2B04, which neutralizes infection by preventing the SARS-CoV-2 spike glycoprotein’s association with angiotensin-converting enzyme (ACE2). Complexes with the wild-type, B.1.1.7 Alpha, and B.1.427/429 Epsilon SARS-CoV-2 variants were used as positive controls, while complexes with the B.1.351 Beta, P.1 Gamma, B.1.617.2 Delta, B.1.617.1 Kappa, BA.1 Omicron, and the newest JN.1 Omicron variants were used as decoys. Three essentially different algorithms were employed: forced placement based on a template, followed by two steps of extended molecular dynamics simulations; protein–protein docking utilizing PIPER (an FFT-based method extended for use with pairwise interaction potentials); and the AlphaFold 3.0 model for complex structure prediction. Homology modeling was used to assess the 3D structure of the newly emerged JN.1 Omicron subvariant, whose crystallographic structure is not yet available in the Protein Database. After a careful comparison of these three approaches, we were able to identify the pros and cons of each method. Protein–protein docking yielded two false-positive results, while manual placement reinforced by molecular dynamics produced one false positive and one false negative. In contrast, AlphaFold resulted in only one doubtful result and a higher overall accuracy-to-time ratio. The reasons for inaccuracies and potential pitfalls of various approaches are carefully explained. In addition to a comparative analysis of methods, some mechanisms of immune escape are elucidated herein. This provides a critical foundation for improving the predictive accuracy of vaccine efficacy against new viral subvariants, introducing accurate methodologies, and pinpointing potential challenges.

Identification of a novel mycovirus belonging to the “flexivirus”-related family with icosahedral virion

Abstract The order Tymovirales currently comprises five viral families with positive-sense RNA ((+)RNA) genomes that infect plants, fungi, and insects. Virion morphologies within the order Tymovirales differ between families, with icosahedral virions in the Tymoviridae and filamentous virions in the other “flexi”viridae families. Despite their different morphologies, these viruses are placed in the same order based on phylogenetic analyses of replicase-associated polyproteins. However, one of the families in the Tymovirales, Deltaflexiviridae, is considered to be capsidless because there have been no published reports of virion isolation. Here we report that a new “flexivirus”-related (+)RNA virus, prospectively named Fusarium oxysporum icosahedral virus 1 (FoIV1), is icosahedral, and that most deltaflexiviruses may have icosahedral virions. Phylogenetic analyses based on replicase-associated polyproteins indicated that FoIV1 forms a distinct group in the Tymovirales with some viruses originally assigned to the Deltaflexiviridae. Electron microscopy, protein analysis, and protein structure predictions indicate that FoIV1 ORF4 encodes a single jelly-roll (SJR)-like coat protein (CP) that constitutes the icosahedral virions. Results of clustering analyses based on amino acid sequences and predicted CP structures suggested that most of the deltaflexiviruses have icosahedral virions composed of SJR-like CPs as in FoIV1, rather than having filamentous virions or capsidless. These results challenge the conventional understanding of viruses in the order Tymovirales, with important implications for revising its taxonomic framework and providing insights into the evolutionary relationships within this diverse and broad host range group of (+)RNA viruses.

Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.

Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-consuming and labor-intensive. Several computational methods have been developed to predict antibody-antigen interaction while suffering from the lack of characterizing the underlying structure of the antibody. Beneficial from the recent breakthroughs in deep learning for antibody structure prediction, we propose a novel neural network architecture to predict antibody-antigen interaction. We first introduce AbAgIPA: an antibody structure prediction network to obtain the antibody backbone structure, where the structural features of antibodies and antigens are encoded into representation vectors according to the amino acid physicochemical features and Invariant Point Attention (IPA) computation methods. Finally, the antibody-antigen interaction is predicted by global max pooling, feature concatenation, and a fully connected layer. We evaluated our method on antigen diversity and antigen-specific antibody-antigen interaction datasets. Additionally, our model exhibits a commendable level of interpretability, essential for understanding underlying interaction mechanisms. Quantitative experimental results demonstrate that the new neural network architecture significantly outperforms the best sequence-based methods as well as the methods based on residue contact maps and graph convolution networks (GCNs). The source code is freely available on GitHub at https://github.com/gmthu66/AbAgIPA .

AI-integrated network for RNA complex structure and dynamic prediction.

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

Structure-aware annotation of leucine-rich repeat domains.

Protein domain annotation is typically done by predictive models such as HMMs trained on sequence motifs. However, sequence-based annotation methods are prone to error, particularly in calling domain boundaries and motifs within them. These methods are limited by a lack of structural information accessible to the model. With the advent of deep learning-based protein structure prediction, existing sequenced-based domain annotation methods can be improved by taking into account the geometry of protein structures. We develop dimensionality reduction methods to annotate repeat units of the Leucine Rich Repeat solenoid domain. The methods are able to correct mistakes made by existing machine learning-based annotation tools and enable the automated detection of hairpin loops and structural anomalies in the solenoid. The methods are applied to 127 predicted structures of LRR-containing intracellular innate immune proteins in the model plant Arabidopsis thaliana and validated against a benchmark dataset of 172 manually-annotated LRR domains.

Structure Prediction and Mechanical Properties of Tantalum Carbide (TaC) on ab initio Level

AbstractTantalum carbide (TaC) is an extremely hard, brittle, refractory ceramic material with excellent physical properties, which makes it a desirable material in e. g. aerospace industries. In order to explore the range of feasible modifications of TaC, we have executed a crystal structure prediction study of the TaC chemical system using a multi‐methodological approach, via enthalpy landscape explorations of pristine TaC at different pressures, supplemented by data mining searches in the ICSD database. Local structure relaxations have been accomplished by using Density Functional Theory (DFT). The global minimum is found to correspond to the equilibrium rock salt (NaCl) type modification. Additionally, eight new phases of tantalum carbide are predicted to be feasible: the WC‐type, the NiAs‐type, the 5‐5‐type, the ZnS‐type, the RingTaC‐type, the CsCl‐type, the OrthoTaC‐type, and the TetraTaC‐type. Furthermore, the elastic and mechanical properties of the predicted TaC modifications were explored on the DFT level of computation. The promising values of some of the mechanical properties of the proposed tantalum carbide modifications suggest that various scientific, industrial, and technological applications of TaC should be possible.

Prediction of the lowest energy structure of Sn(BH4)2 and its electronic properties

Prediction of the lowest energy structure of Sn(BH4)2 and its electronic properties

Crystal structure prediction of lithium-beryllium alloys under pressure with distinctive electronic and dynamic behaviors

Crystal structure prediction of lithium-beryllium alloys under pressure with distinctive electronic and dynamic behaviors

Recalibration of MARTINI-3 Parameters for Improved Interactions between Peripheral Proteins and Lipid Bilayers.

The MARTINI force field is one of the most used coarse-grained models for biomolecular simulations. Many limitations of the model including the protein-protein overaggregation have been improved in its latest version, MARTINI-3. In this study, we investigate the efficacy of the MARTINI-3 parameters for capturing the interactions of peripheral proteins with model plasma membranes. Particularly, we consider two classes of proteins, namely, annexin and epsin, which are known to generate negative and positive membrane curvatures, respectively. We find that current MARTINI-3 parameters are not able to correctly describe the protein-membrane interface and the protein-induced membrane curvatures for any of these proteins. The problem arises due to the lack of proper hydrophobic interactions between the protein residues and lipid tails. Making systematic adjustments, we show that a combination of reduction in the protein-water interactions and enhancement of protein-lipid hydrophobic interactions is essential for accurate prediction of the interfacial structure including the protein-induced membrane curvature. Next, we apply our model to a couple of other peripheral proteins, namely, Snf7, a core component of the ESCRT-III complex, and the PH domain of evectin-2. We find that our model captures the protein-membrane interfacial structure much more accurately than the MARTINI-3 model for all of the peripheral proteins considered in this study. However, the strategy described in this study may not be suitable for oligomeric transmembrane proteins where protein-protein hydrophobic interactions should be increased instead of protein-lipid hydrophobic interactions.

Interplay between Two Paralogous Human Silencing Hub (HuSH) Complexes in Regulating LINE-1 Element Silencing

The Human Silencing Hub (HuSH) complex silences retrotransposable elements in vertebrates. Here, we identify a second HuSH complex, designated HuSH2, which is centered around TASOR2, a paralog of the core TASOR protein in HuSH. Our findings reveal that HuSH and HuSH2 localize to distinct and non-overlapping genomic loci. Specifically, HuSH localizes to and represses LINE-1 retrotransposons, whereas HuSH2 targets and represses KRAB-ZNFs and interferon signaling and response genes. We use in silico protein structure predictions to simulate MPP8 interactions with TASOR paralogs, guiding amino acid substitutions that disrupted binding to HuSH complexes. These MPP8 transgenes and other constructs reveal the importance of HuSH complex quantities in regulating LINE-1 activity. Furthermore, our results suggest that dynamic changes in TASOR and TASOR2 expression enable cells to finely tune HuSH-mediated silencing. This study offers insights into the interplay of HuSH complexes, highlighting their vital role in retrotransposon regulation.