gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present gRNAde, a geometric RNA design pipeline operating on sets of 3D RNA backbone structures to design sequences that explicitly account for RNA 3D structure and dynamics. gRNAde is a graph neural network that uses an SE (3) equivariant encoder-decoder framework for generating RNA sequences conditioned on backbone structures where the identities of the bases are unknown. We demonstrate the utility of gRNAde for fixed-backbone re-design of existing RNA structures of interest from the PDB, including riboswitches, aptamers, and ribozymes. gRNAde is more accurate in terms of native sequence recovery while being significantly faster compared to existing physics-based tools for 3D RNA inverse design, such as Rosetta.

Selective deuteration of an RNA:RNA complex for structural analysis using small-angle scattering.

The structures of RNA:RNA complexes regulate many biological processes. Despite their importance, protein-free RNA:RNA complexes represent a tiny fraction of experimentally-determined structures. Here, we describe a joint small-angle X-ray and neutron scattering (SAXS/SANS) approach to structurally interrogate conformational changes in a model RNA:RNA complex. Using SAXS, we measured the solution structures of the individual RNAs in their free state and of the overall RNA:RNA complex. With SANS, we demonstrate, as a proof-of-principle, that isotope labeling and contrast matching (CM) can be combined to probe the bound state structure of an RNA within a selectively deuterated RNA:RNA complex. Furthermore, we show that experimental scattering data can validate and improve predicted AlphaFold 3 RNA:RNA complex structures to reflect its solution structure. Our work demonstrates that in silico modeling, SAXS, and CM-SANS can be used in concert to directly analyze conformational changes within RNAs when in complex, enhancing our understanding of RNA structure in functional assemblies.

Functional and structural characterization of a thermostable flavin reductase from Geobacillus mahadii Geo-05.

Flavin reductases play a vital role in catalyzing the reduction of flavin through NADH or NADPH oxidation. The gene encoding flavin reductase from the thermophilic bacterium Geobacillus mahadii Geo-05 (GMHpaC) was cloned, overexpressed in Escherichia coli BL21 (DE3) pLysS, and purified to homogeneity. The purified recombinant GMHpaC (Class II) contains chromogenic cofactors, evidenced by maximal absorbance peaks at 370 nm and 460 nm. GMHpaC stands out as the most thermostable and pH-tolerant flavin reductase reported to date, retaining up to 95 % catalytic activity after incubation at 70 °C for 30 min and maintaining over 80 % activity within a pH range of 2-12 for 30 min. Furthermore, GMHpaC's catalytic activity increases by 52 % with FMN as a co-factor compared to FAD and riboflavin. GMHpaC, coupled with 4-hydroxyphenylacetate-3-monooxygenase (GMHpaB) from G. mahadii Geo-05, enhances the hydroxylation of 4-hydroxyphenylacetate (HPA) by 85 %. The modeled structure of GMHpaC reveals relatively conserved flavin and NADH binding sites. Modeling and docking studies shed light on structural features and amino acid substitutions that determine GMHpaC's co-factor specificity. The remarkable thermostability, high catalytic activity, and general stability exhibited by GMHpaC position it as a promising enzyme candidate for various industrial applications.

Arsenate reductase of Rufibacter tibetensis is a metallophosphoesterase evolved to catalyze redox reactions.

An arsenate reductase (Car1) from the Bacteroidetes species Rufibacter tibetensis 1351T was isolated from the Tibetan Plateau. The strain exhibits resistance to arsenite [As(III)] and arsenate [As(V)] and reduces As(V) to As(III). Here we shed light on the mechanism of enzymatic reduction by Car1. AlphaFold2 structure prediction, active site energy minimization, and steady-state kinetics of wild-type and mutant enzymes give insight into the catalytic mechanism. Car1 is structurally related to calcineurin-like metallophosphoesterases (MPPs). It functions as a binuclear metal hydrolase with limited phosphatase activity, particularly relying on the divalent metal Ni2+. As an As(V) reductase, it displays metal promiscuity and is coupled to the thioredoxin redox cycle, requiring the participation of two cysteine residues, Cys74 and Cys76. These findings suggest that Car1 evolved from a common ancestor of extant phosphatases by incorporating a redox function into an existing MPP catalytic site. Its proposed mechanism of arsenate reduction involves Cys74 initiating a nucleophilic attack on arsenate, leading to the formation of a covalent intermediate. Next, a nucleophilic attack of Cys76 leads to the release of As(III) and the formation of a surface-exposed Cys74-Cys76 disulfide, ready for reduction by thioredoxin.

Improving AlphaFold Predicted Contacts for Alpha-Helical Transmembrane Proteins Using Structural Features.

Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2's predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments.

Natural history of eukaryotic DNA viruses with double jelly-roll major capsid proteins.

The phylum Preplasmiviricota (kingdom Bamfordvirae, realm Varidnaviria) is a broad assemblage of diverse viruses with comparatively short double-stranded DNA genomes (<50 kbp) that produce icosahedral capsids built from double jelly-roll major capsid proteins. Preplasmiviricots infect hosts from all cellular domains, testifying to their ancient origin and, in particular, are associated with six of the seven supergroups of eukaryotes. Preplasmiviricots comprise four major groups of viruses, namely, polintons, polinton-like viruses (PLVs), virophages, and adenovirids. We employed protein structure modeling and analysis to show that protein-primed DNA polymerases (pPolBs) of polintons, virophages, and cytoplasmic linear plasmids encompass an N-terminal domain homologous to the terminal proteins (TPs) of prokaryotic PRD1-like tectivirids and eukaryotic adenovirids that are involved in protein-primed replication initiation, followed by a viral ovarian tumor-like cysteine deubiquitinylase (vOTU) domain. The vOTU domain is likely responsible for the cleavage of the TP from the large pPolB polypeptide and is inactivated in adenovirids, in which TP is a separate protein. Many PLVs and transpovirons encode a distinct derivative of polinton-like pPolB that retains the TP, vOTU and pPolB polymerization palm domains but lacks the exonuclease domain and instead contains a supefamily 1 helicase domain. Analysis of the presence/absence and inactivation of the vOTU domains, and replacement of pPolB with other DNA polymerases in eukaryotic preplasmiviricots enabled us to outline a complete scenario for their origin and evolution.

Structural modeling and gene expression analysis of phosvitinless vitellogenin (vgc) in the Indian freshwater murrel, Channa punctatus (Bloch, 1793).

Vitellogenin (Vg) is a female-specific egg-yolk precursor protein, synthesized in the liver of fish in response to estrogens. In the present study, complete gene of phosvitinless vitellogenin (vgc) was sequenced, its 3D structure was predicted and validated by web-based softwares. The complete nucleotide sequence of vgc was 4126 bp which encodes for 1272 amino acids and showed the presence of three conserved domains viz. LPD_N, DUF1943 and DUF1944. The retrieved amino acid sequence of VgC protein was subjected to in silico analysis for understanding the structural and functional properties of protein. mRNA levels of multiple vg genes have also been quantified during annual reproductive cycle employing qPCR. A correlation has been observed between seasonal changes in gonadosomatic index with estradiol levels and hepatic expression of three types of vg genes (vga, vgb, vgc) during ovarian cycle of murrel. During preparatory phase, when photoperiod and temperature are low; low titre of E2 in blood induces expression of vgc gene. A rapid increase in the levels of E2 favours induction of vgb and vga genes in liver of murrel during early pre-spawning phase when photoperiod is long and temperature is high in nature. These results suggest that among three vitellogenin proteins, VgC is synthesized earlier than VgA and VgB during oogenesis.

Modeling and Analysis of HIV-1 Pol Polyprotein as a Case Study for Predicting Large Polyprotein Structures.

Acquired immunodeficiency syndrome (AIDS) is caused by human immunodeficiency virus (HIV). HIV protease, reverse transcriptase, and integrase are targets of current drugs to treat the disease. However, anti-viral drug-resistant strains have emerged quickly due to the high mutation rate of the virus, leading to the demand for the development of new drugs. One attractive target is Gag-Pol polyprotein, which plays a key role in the life cycle of HIV. Recently, we found that a combination of M50I and V151I mutations in HIV-1 integrase can suppress virus release and inhibit the initiation of Gag-Pol autoprocessing and maturation without interfering with the dimerization of Gag-Pol. Additional mutations in integrase or RNase H domain in reverse transcriptase can compensate for the defect. However, the molecular mechanism is unknown. There is no tertiary structure of the full-length HIV-1 Pol protein available for further study. Therefore, we developed a workflow to predict the tertiary structure of HIV-1 NL4.3 Pol polyprotein. The modeled structure has comparable quality compared with the recently published partial HIV-1 Pol structure (PDB ID: 7SJX). Our HIV-1 NL4.3 Pol dimer model is the first full-length Pol tertiary structure. It can provide a structural platform for studying the autoprocessing mechanism of HIV-1 Pol and for developing new potent drugs. Moreover, the workflow can be used to predict other large protein structures that cannot be resolved via conventional experimental methods.

Biochemical and in silico structural characterization of a cold-active arginase from the psychrophilic yeast, Glaciozyma antarctica PI12.

Glaciozyma antarctica PI12 is a psychrophilic yeast isolated from Antarctica. In this work, we describe the heterologous production, biochemical properties and in silico structure analysis of an arginase from this yeast (GaArg). GaArg is a metalloenzyme that catalyses the hydrolysis of L-arginine to L-ornithine and urea. The cDNA of GaArg was reversed transcribed, cloned, expressed and purified as a recombinant protein in Escherichia coli. The purified protein was active against L-arginine as its substrate in a reaction at 20 °C, pH 9. At 10-35 °C and pH 7-9, the catalytic activity of the protein was still present around 50%. Mn2+, Ni2+, Co2+ and K+ were able to enhance the enzyme activity more than two-fold, while GaArg is most sensitive to SDS, EDTA and DTT. The predicted structure model of GaArg showed a very similar overall fold with other known arginases. GaArg possesses predominantly smaller and uncharged amino acids, fewer salt bridges, hydrogen bonds and hydrophobic interactions compared to the other counterparts. GaArg is the first reported arginase that is cold-active, facilitated by unique structural characteristics for its adaptation of catalytic functions at low-temperature environments. The structure and function of cold-active GaArg provide insights into the potentiality of new applications in various biotechnology and pharmaceutical industries.

De Novo Atomic Protein Structure Modeling for Cryo-EM Density Maps Using 3D Transformer and Hidden Markov Model.

Accurately building three-dimensional (3D) atomic structures from 3D cryo-electron microscopy (cryo-EM) density maps is a crucial step in the cryo-EM-based determination of the structures of protein complexes. Despite improvements in the resolution of 3D cryo-EM density maps, the de novo conversion of density maps into 3D atomic structures for protein complexes that do not have accurate homologous or predicted structures to be used as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated ab initio cryo-EM structure modeling method that utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps first, and then employs a novel Hidden Markov Model (HMM) to connect predicted atoms to build backbone structures of proteins. Tested on a standard test dataset of 128 cryo-EM density maps with varying resolutions (2.1 - 5.6 °A) and different numbers of residues (730 - 8,416), Cryo2Struct built substantially more accurate and complete protein structural models than the widely used ab initio method - Phenix in terms of multiple evaluation metrics. Moreover, on a new test dataset of 500 recently released density maps with varying resolutions (1.9 - 4.0 °A) and different numbers of residues (234 - 8,828), it built more accurate models than on the standard dataset. And its performance is rather robust against the change of the resolution of density maps and the size of protein structures.

Developing a Multiband Electronic Band Structure Model and Predictive Maps for Bismuth-Rich Mg3(Sb1-xBix)2 Thermoelectric Materials.

In recent years, bismuth-rich Mg3(Sb1-xBix)2 (x = 0.5-0.8) compositions have generated significant interest due to their excellent thermoelectric (TE) performance near room temperature, making them potential applicants for recovery of low-grade waste heat. The superior performance in these materials is due to its complex electronic band structure (EBS) with presence of multiple near degenerate bands close to the conduction band edge. The position and curvature of these bands strongly depend on the alloy composition, doping amount as well as temperature. Thus, identifying optimal material compositions to get the best TE performance depends on an understanding of the temperature dynamics of EBS and forms the objective of this work. Mg3Sb0.6Bi1.4 (x = 0.7) is chosen for this study due to its reported high near room temperature performance, and compositions with varying doping concentrations (Te used as dopant) have been synthesized. EBS parameters like effective mass and deformation potential of bands, interband separation and band gap values have been estimated using a recently developed refinement approach. Refinement results indicate that the interband separation between conduction bands to be a function of both temperature and doping concentration. Further, thermal conductivity (κ) was estimated for all of the compositions. Utilizing the EBS and κ information, predictive 3D maps indicating the variation in zT (TE figure of merit) with doping concentration and temperature have been generated. The 3D maps reveal an interesting surface topography with a broad peak zT region. This observation explains why these materials have high TE performance and are less sensitive to doping inhomogeneities. Our results provide detailed EBS information and fundamental insights on the TE properties of Mg3Sb0.6Bi1.4. Further, the proposed technique can be utilized to probe other Mg3(Sb1-xBix)2 compositions and TE materials.

Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry.

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.

Clinical relevance of genetic variants in the von Willebrand factor according to in-silico methods.

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

Improving AlphaFold predicted contacts in alpha-helical transmembrane proteins structures using structural features.

Background: Residue contacts maps offer a 2-d reduced representation of 3-d protein structures and constitute a structural constraint and scaffold in structural modeling. In addition, contact maps are also an effective tool in identifying interhelical binding sites and drawing insights about protein function. While most works predict contact maps using features derived from sequences, we believe information from known structures can be leveraged for a prediction improvement in unknown structures where decent approximate structures such as ones predicted by AlphaFold2 are available. Results: Alphafold2's predicted structures are found to be quite accurate at inter-helical residue contact prediction task, achieving 83% average precision. We adopt an unconventional approach, using features extracted from atomic structures in the neighborhood of a residue pair and use them to predicting residue contact. We trained on features derived from experimentally determined structures and predicted on features derived from AlphaFold2's predicted structures. Our results demonstrate a remarkable improvement over AlphaFold2 achieving over 91.9% average precision for held-out and over 89.5% average precision in cross validation experiments. Conclusion: Training on features generated from experimentally determined structures, we were able to leverage knowledge from known structures to significantly improve the contacts predicted using AlphaFold2 structures. We demonstrated that using coordinates directly (instead of the proposed features) does not lead to an improvement in contact prediction performance.

lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures.

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

Integrated Protocol of Protein Structure Modeling for Cryo-EM with Deep Learning and Structure Prediction.

Structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy (cryo-EM). Although the resolution of determined cryo-EM maps has generally improved, there are still many cases where tracing protein main-chains is difficult, even in maps determined at a near atomic resolution. Here, we have developed a protein structure modeling method, called DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, since Alphafold2 demonstrates high accuracy in protein structure prediction, we have integrated complementary strengths of de novo density tracing using deep learning with Alphafold2's structure modeling to achieve even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign chain identity to the structure models of homo-multimers.

Application of conformational space annealing to the protein structure modeling using cryo-EM maps.

Conformational space annealing (CSA), a global optimization method, has been applied to various protein structure modeling tasks. In this paper, we applied CSA to the cryo-EM structure modeling task by combining the python subroutine of CSA (PyCSA) and the fast relax (FastRelax) protocol of PyRosetta. Refinement of initial structures generated from two methods, rigid fitting of predicted structures to the Cryo-EM map and de novo protein modeling by tracing the Cryo-EM map, was performed by CSA. In the refinement of the rigid-fitted structures, the final models showed that CSA can generate reliable atomic structures of proteins, even when large movements of protein domains were required. In the de novo modeling case, although the overall structural qualities of the final models were rather dependent on the initial models, the final models generated by CSA showed improved MolProbity scores and cross-correlation coefficients to the maps. These results suggest that CSA can accomplish flexible fitting and refinement together by sampling diverse conformations effectively and thus can be utilized for cryo-EM structure modeling.

Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server.

Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https://lzerd.kiharalab.org . This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.

Genome- and metabolome-guided discovery of marine BamA inhibitors revealed a dedicated darobactin halogenase.

Darobactins represent a class of ribosomally synthesized and post-translationally modified peptide (RiPP) antibiotics featuring a rare bicyclic structure. They target the Bam-complex of Gram-negative bacteria and exhibit in vivo activity against drug-resistant pathogens. First isolated from Photorhabdus species, the corresponding biosynthetic gene clusters (BGCs) are widespread among γ-proteobacteria, including the genera Vibrio, Yersinia, and Pseudoalteromonas (P.). While the organization of the BGC core is highly conserved, a small subset of Pseudoalteromonas carries an extended BGC with additional genes. Here, we report the identification of brominated and dehydrated darobactin derivatives from P. luteoviolacea strains. The marine derivatives are active against multidrug-resistant (MDR) Gram-negative bacteria and showed solubility and plasma protein binding ability different from darobactin A, rendering it more active than darobactin A. The halogenation reaction is catalyzed by DarH, a new class of flavin-dependent halogenases with a novel fold.

Environment-by-PGS Interaction in the Classical Twin Design: An Application to Childhood Anxiety and Negative Affect.

One type of genotype-environment interaction occurs when genetic effects on a phenotype are moderated by an environment; or when environmental effects on a phenotype are moderated by genes. Here we outline these types of genotype-environment interaction models, and propose a test of genotype-environment interaction based on the classical twin design, which includes observed genetic variables (polygenic scores: PGSs) that account for part of the genetic variance of the phenotype. We introduce environment-by-PGS interaction and the results of a simulation study to address statistical power and parameter recovery. Next, we apply the model to empirical data on anxiety and negative affect in children. The power to detect environment-by-PGS interaction depends on the heritability of the phenotype, and the strength of the PGS. The simulation results indicate that under realistic conditions of sample size, heritability and strength of the interaction, the environment-by-PGS model is a viable approach to detect genotype-environment interaction. In 7-year-old children, we defined two PGS based on the largest genetic association studies for 2 traits that are genetically correlated to childhood anxiety and negative affect, namely major depression (MDD) and intelligence (IQ). We find that common environmental influences on negative affect are amplified for children with a lower IQ-PGS.