Rosetta in CASP4: progress in ab initio protein structure prediction

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

Artificial Intelligence Learns Protein Prediction

From AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: AlphaFold2 leaped forward in protein structure prediction, and protein language models (pLMs) replaced expertise and evolutionary information from multiple sequence alignments with information learned from reoccurring patterns in databases of billions of proteins without experimental annotations other than the amino acid sequences. None of those tools could have been developed 10 years ago; all will increase the wealth of experimental data and speed up the cycle from idea to proof. AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design.

Microphase Separation Produces Interfacial Environment within Diblock Biomolecular Condensates

The phase separation of intrinsically disordered proteins is emerging as an important mechanism for cellular organization. However, efforts to connect protein sequences to the physical properties of condensates, i.e., the molecular grammar, are hampered by a lack of effective approaches for probing high-resolution structural details. Using a combination of multiscale simulations and fluorescence lifetime imaging microscopy experiments, we systematically explored a series of systems consisting of diblock elastin-like polypeptides (ELP). The simulations succeeded in reproducing the variation of condensate stability upon amino acid substitution and revealed different microenvironments within a single condensate, which we verified with environmentally sensitive fluorophores. The interspersion of hydrophilic and hydrophobic residues and a lack of secondary structure formation result in an interfacial environment, which explains both the strong correlation between ELP condensate stability and interfacial hydrophobicity scales, as well as the prevalence of protein-water hydrogen bonds. Our study uncovers new mechanisms for condensate stability and organization that may be broadly applicable.

Orphan receptor GPR88 as a potential therapeutic target for CNS disorders - an in silico approach

The G-protein-coupled receptors are a part of the largest and most physiologically relevant family of membrane proteins. One-third of the medications, now on the market, target the GPCR receptor family, which is one of the most important therapeutic targets for many disorders. In the reported work, we have focussed on orphan GPR88 receptor which is a part of the GPCR protein family and a potential target for central nervous system disorders. GPR88 is known to show the highest expression in the striatum, which is a key region in motor control and cognitive functions. Recent studies have reported that GPR88 is activated by two agonists, 2-PCCA and RTI-13951-33. In this study, we have predicted the three-dimensional protein structure for the orphan GPR88 using the homology modeling approach. We then used shape-based screening techniques based on known agonists and structure-based virtual screening methods employing docking to uncover novel GPR88 ligands. The screened GPR88-ligand complexes were further subjected to molecular dynamics simulation studies. The selected ligands could fasten the development of novel treatments for the vast list of movement and central nervous system disorders.Communicated by Ramaswamy H. Sarma.

Design of human ACE2 mimic miniprotein binders that interact with RBD of SARS-CoV-2 variants of concerns

The world of medicine demands from the research community solutions to the emerging problem of SARS-CoV-2 variants and other such potential global pandemics. With advantages of specificity over small molecule drugs and designability over antibodies, miniprotein therapeutics offers a unique solution to the threats of rapidly emerging SARS-CoV-2 variants. Unfortunately, most of the promising miniprotein binders are de novo designed and it is not viable to generate molecules for each new variant. Therefore in this study, we demonstrate a method for design of miniprotein mimics from the interaction interphase of human angiotensin converting enzyme 2 (ACE2). ACE2 is the natural interacting partner for the SARS-CoV-2 spike receptor binding domain (RBD) and acts as a recognition molecule for viral entry into the host cells. Starting with ACE2 N-terminal triple helix interaction interphase, we generated more than 70 miniprotein sequences. Employing Rosetta folding and docking scores we selected 10 promising miniprotein candidates amongst which 3 were found to be soluble in lab studies. Further, using molecular mechanics (MM) calculations on molecular dynamics (MD) trajectories we test interaction of miniproteins with RBD from various variants of concern (VOC). Presently, we report two key findings; miniproteins in this study are generated using less than 10 lab testing experiments, yet when tested through in-vitro experiments, they show submicro to nanomolar affinities towards SARS-CoV-2 RBD. Also in simulation studies, when compared with previously developed therapeutics, our miniproteins display remarkable ability to mimic ACE2 interphase; making them an ideal solution to the ever evolving problem of VOCs.Communicated by Ramaswamy H. Sarma.

AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABBREVIATIONS

AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like.

Delineating Structural Propensities of the 4E-BP2 Protein via Integrative Modeling and Clustering

The intrinsically disordered 4E-BP2 protein regulates mRNA cap-dependent translation through interaction with the predominantly folded eukaryotic initiation factor 4E (eIF4E). Phosphorylation of 4E-BP2 dramatically reduces the level of eIF4E binding, in part by stabilizing a binding-incompatible folded domain. Here, we used a Rosetta-based sampling algorithm optimized for IDRs to generate initial ensembles for two phospho forms of 4E-BP2, non- and 5-fold phosphorylated (NP and 5P, respectively), with the 5P folded domain flanked by N- and C-terminal IDRs (N-IDR and C-IDR, respectively). We then applied an integrative Bayesian approach to obtain NP and 5P conformational ensembles that agree with experimental data from nuclear magnetic resonance, small-angle X-ray scattering, and single-molecule Förster resonance energy transfer (smFRET). For the NP state, inter-residue distance scaling and 2D maps revealed the role of charge segregation and pi interactions in driving contacts between distal regions of the chain (∼70 residues apart). The 5P ensemble shows prominent contacts of the N-IDR region with the two phosphosites in the folded domain, pT37 and pT46, and, to a lesser extent, delocalized interactions with the C-IDR region. Agglomerative hierarchical clustering led to partitioning of each of the two ensembles into four clusters with different global dimensions and contact maps. This helped delineate an NP cluster that, based on our smFRET data, is compatible with the eIF4E-bound state. 5P clusters were differentiated by interactions of C-IDR with the folded domain and of the N-IDR with the two phosphosites in the folded domain. Our study provides both a better visualization of fundamental structural poses of 4E-BP2 and a set of falsifiable insights on intrachain interactions that bias folding and binding of this protein.

The intrinsically disordered cytoplasmic tail of a dendrite branching receptor uses two distinct mechanisms to regulate the actin cytoskeleton

Dendrite morphogenesis is essential for neural circuit formation, yet the molecular mechanisms underlying complex dendrite branching remain elusive. Previous studies on the highly branched Caenorhabditis elegans PVD sensory neuron identified a membrane co-receptor complex that links extracellular signals to intracellular actin remodeling machinery, promoting high-order dendrite branching. In this complex, the claudin-like transmembrane protein HPO-30 recruits the WAVE regulatory complex (WRC) to dendrite branching sites, stimulating the Arp2/3 complex to polymerize actin. We report here our biochemical and structural analysis of this interaction, revealing that the intracellular domain (ICD) of HPO-30 is intrinsically disordered and employs two distinct mechanisms to regulate the actin cytoskeleton. First, HPO-30 ICD binding to the WRC requires dimerization and involves the entire ICD sequence, rather than a short linear peptide motif. This interaction enhances WRC activation by the GTPase Rac1. Second, HPO-30 ICD directly binds to the sides and barbed end of actin filaments. Binding to the barbed end requires ICD dimerization and inhibits both actin polymerization and depolymerization, resembling the actin capping protein CapZ. These dual functions provide an intriguing model of how membrane proteins can integrate distinct mechanisms to fine-tune local actin dynamics.

Structural basis of protein condensation on microtubules underlying branching microtubule nucleation

Targeting protein for Xklp2 (TPX2) is a key factor that stimulates branching microtubule nucleation during cell division. Upon binding to microtubules (MTs), TPX2 forms condensates via liquid-liquid phase separation, which facilitates recruitment of microtubule nucleation factors and tubulin. We report the structure of the TPX2 C-terminal minimal active domain (TPX2α5-α7) on the microtubule lattice determined by magic-angle-spinning NMR. We demonstrate that TPX2α5-α7 forms a co-condensate with soluble tubulin on microtubules and binds to MTs between two adjacent protofilaments and at the intersection of four tubulin heterodimers. These interactions stabilize the microtubules and promote the recruitment of tubulin. Our results reveal that TPX2α5-α7 is disordered in solution and adopts a folded structure on MTs, indicating that TPX2α5-α7 undergoes structural changes from unfolded to folded states upon binding to microtubules. The aromatic residues form dense interactions in the core, which stabilize folding of TPX2α5-α7 on microtubules. This work informs on how the phase-separated TPX2α5-α7 behaves on microtubules and represents an atomic-level structural characterization of a protein that is involved in a condensate on cytoskeletal filaments.

Progress at protein structure prediction, as seen in CASP15

In Dec 2020, the results of AlphaFold version 2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of single protein domains. The code of AlphaFold v2 was released in the summer of 2021, and since then, it has been shown that it can be used to accurately predict the structure of most ordered proteins and many protein-protein interactions. It has also sparked an explosion of development in the field, improving AI-based methods to predict protein complexes, disordered regions, and protein design. Here I will review some of the inventions sparked by the release of AlphaFold.

In Silico Evaluation of Nonsynonymous SNPs in Human ADAM33: The Most Common Form of Genetic Association to Asthma Susceptibility

ADAM33 is a zinc-dependent metalloprotease of the ADAM family, which plays a vital biological role as an activator of Th2 cytokines and growth factors. Moreover, this protein is crucial for the normal development of the lung in the fetus two months after gestation leading to determining lung functions all over life. In this regard, mutations in ADAM33 have been linked with asthma risk factors. Consequently, identifying ADAM33 pathogenic nonsynonymous single-nucleotide polymorphisms (nsSNPs) can be very important in asthma treatment. In the present study, 1055 nsSNPs of human ADAM33 were analyzed using biocomputational software, 31 of which were found to be detrimental mutations. Precise structural and stability analysis revealed D219V, C669G, and C606S as the most destabilizing SNPs. Furthermore, MD simulations disclosed higher overall fluctuation and alteration in intramolecular interactions compared with the wild-type structure. Overall, the results suggest D219V, C669G, and C606S detrimental mutations as a starting point for further case-control studies on the ADAM33 protein as well as an essential source for future targeted mechanisms.

Femtomolar detection of SARS-CoV-2 via peptide beacons integrated on a miniaturized TIRF microscope

The novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) continues to pose a substantial global health threat. Along with vaccines and targeted therapeutics, there is a critical need for rapid diagnostic solutions. In this work, we use computational protein modeling tools to suggest molecular beacon architectures that function as conformational switches for high-sensitivity detection of the SARS-CoV-2 spike protein receptor binding domain (S-RBD). Integrating these beacons on a miniaturized total internal reflection fluorescence (mini-TIRF) microscope, we detect the S-RBD and pseudotyped SARS-CoV-2 with limits of detection in the femtomolar range. We envision that our designed mini-TIRF platform will serve as a robust platform for point-of-care diagnostics for SARS-CoV-2 and future emergent viral threats.

Systems Biology of Virus-Host Protein Interactions: From Hypothesis Generation to Mechanisms of Replication and Pathogenesis

As obligate intracellular parasites, all viruses must co-opt cellular machinery to facilitate their own replication. Viruses often co-opt these cellular pathways and processes through physical interactions between viral and host proteins. In addition to facilitating fundamental aspects of virus replication cycles, these virus-host protein interactions can also disrupt physiological functions of host proteins, causing disease that can be advantageous to the virus or simply a coincidence. Consequently, unraveling virus-host protein interactions can serve as a window into molecular mechanisms of virus replication and pathogenesis. Identifying virus-host protein interactions using unbiased systems biology approaches provides an avenue for hypothesis generation. This review highlights common systems biology approaches for identification of virus-host protein interactions and the mechanistic insights revealed by these methods. We also review conceptual innovations using comparative and integrative systems biology that can leverage global virus-host protein interaction data sets to more rapidly move from hypothesis generation to mechanism. Expected final online publication date for the Annual Review of Virology, Volume 9 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

Computationally grafting an IgE epitope onto a scaffold: Implications for a pan anti-allergy vaccine design

Allergy is becoming an intensifying disease among the world population, particularly in the developed world. Once allergy develops, sufferers are permanently trapped in a hyper-immune response that makes them sensitive to innocuous substances. The immune pathway concerned with developing allergy is the Th2 immune pathway where the IgE antibody binds to its Fc ∊ RI receptor on Mast and Basophil cells. This paper discusses a protocol that could disrupt the binding between the antibody and its receptor for a potential permanent treatment. Ten proteins were computationally designed to display a human IgE motif very close in proximity to the IgE antibody's Fc ∊ RI receptor's binding site in an effort for these proteins to be used as a vaccine against our own IgE antibody. The motif of interest was the FG loop motif and it was excised and grafted onto a Staphylococcus aureus protein (PDB ID 1YN3), then the motif + scaffold structure had its sequence re-designed around the motif to find an amino acid sequence that would fold to the designed structure correctly. These ten computationally designed proteins showed successful folding when simulated using Rosetta's AbinitioRelax folding simulation and the IgE epitope was clearly displayed in its native three-dimensional structure in all of them. These designed proteins have the potential to be used as a pan anti-allergy vaccine. This work employedin silicobased methods for designing the proteins and did not include any experimental verifications.

Extension of a de novo TIM barrel with a rationally designed secondary structure element

The ability to construct novel enzymes is a major aim in de novo protein design. A popular enzyme fold for design attempts is the TIM barrel. This fold is a common topology for enzymes and can harbor many diverse reactions. The recent de novo design of a four-fold symmetric TIM barrel provides a well understood minimal scaffold for potential enzyme designs. Here we explore opportunities to extend and diversify this scaffold by adding a short de novo helix on top of the barrel. Due to the size of the protein, we developed a design pipeline based on computational ab initio folding that solves a less complex sub-problem focused around the helix and its vicinity and adapt it to the entire protein. We provide biochemical characterization and a high-resolution X-ray structure for one variant and compare it to our design model. The successful extension of this robust TIM-barrel scaffold opens opportunities to diversify it towards more pocket like arrangements and as such can be considered a building block for future design of binding or catalytic sites.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Genetic Algorithm Embedded with a Search Space Dimension Reduction Scheme for Efficient Peptide Structure Predictions

The computational determination of peptide conformations is a challenging task of finding minima in a high dimensional space. By combining the sampling efficiency of the genetic algorithm (GA) and the dimensionality reduction resulted from the backbone dihedral angle correlations, named as the path matrix (PM) method, a new searching algorithm, parallel microgenetic algorithm (PMGA), is proposed. Meanwhile, PMGA employs the density functional theory based energy as the fitness function and performs local geometry optimizations to enhance the reliability of its GA encoding strategy. Tests on peptides with up to eight amino-acid residues show PMGA is quite efficient for providing high-quality conformational coverages. The computational cost of the PMGA search increases slowly with the number of amino-acid residues in a peptide, with no sign of deterioration on the searching results for the increased length of the peptide. The PMGA method should therefore be useful for determining the conformations of oligopeptide, studying the protein-ligand interactions, and designing the peptide-based drugs.

Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data

Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.

Transcriptome profiling of cells exposed to particular and intense electromagnetic radiation emitted by the "SG-III" prototype laser facility

The experiment of inertial confinement fusion by the "ShengGuang (SG)-III" prototype laser facility is a transient and extreme reaction process within several nanoseconds, which could form a very complicated and intense electromagnetic field around the target chamber of the facility and may lead to harmful effect on people around. In particular, the biological effects arising from such specific environment field could hardly be ignored and have never been investigated yet, and thus, we reported on the investigation of the biological effects of radiation on HaCat cells and PC12 cells to preliminarily assess the biological safety of the target range of the "SG-III" prototype laser facility. The viability revealed that the damage of cells was dose-dependent. Then we compared the transcriptomes of exposed and unexposed PC12 cells by RNA-Seq analysis based on Illumina Novaseq 6000 platform and found that most significantly differentially expressed genes with corresponding Gene Ontology terms and pathways were strongly involved in proliferation, transformation, necrosis, inflammation response, apoptosis and DNA damage. Furthermore, we find increase in the levels of several proteins responsible for cell-cycle regulation and tumor suppression, suggesting that pathways or mechanisms regarding DNA damage repair was are quickly activated. It was found that "SG-III" prototype radiation could induce DNA damage and promote apoptotic necrosis.

Computational Approach for Rational Design of Fusion Uricase with PAS Sequences

Tumor lysis syndrome is a life-threatening condition for humans due to the lack of urate oxidase. In this study, several variants of PASylated uricase from the Aspergillus flavus species were analyzed computationally to find the appropriate fusions to solve short half-life and stability concern. The Ab initio method was performed using Rosetta software to structurally characterize the PAS sequences. The 3D structures of fusions were predicted for fused C- or N-terminally PAS sequences in different length to the uricase. The refinement and energy minimization steps revealed that physicochemical and conformational properties of fusions improved while the structures possessed prolonged PAS sequences. Molecular docking results showed that the highest binding affinity to uric acid belonged to uricase-PAS1-100 by the formation of six hydrogen and four non-hydrogen bonds. Altogether, the results indicated that the PASylation process would be promising upon the production of urate oxidase with improved solubility and stability.

Structural Determinants of the Dopamine Transporter Regulation Mediated by G Proteins

Dopamine clearance in the brain is controlled by the dopamine transporter (DAT), a protein residing in the plasma membrane, which drives reuptake of extracellular dopamine into presynaptic neurons. Studies have revealed that the βγ subunits of heterotrimeric G proteins modulate DAT function through a physical association with the C-terminal region of the transporter. Regulation of neurotransmitter transporters by Gβγ subunits is unprecedented in the literature; therefore, it is interesting to investigate the structural details of this particular protein-protein interaction. Here, we refined the crystal structure of the Drosophila melanogaster DAT (dDAT), modeling de novo the N- and C-terminal domains; subsequently, we used the full-length dDAT structure to generate a comparative model of human DAT (hDAT). Both proteins were assembled with Gβ1γ2 subunits employing protein-protein docking, and subsequent molecular dynamics simulations were run to identify the specific interactions governing the formation of the hDAT:Gβγ and dDAT:Gβγ complexes. A [L/F]R[Q/E]R sequence motif containing the residues R588 in hDAT and R587 in dDAT was found as key to bind the Gβγ subunits through electrostatic interactions with a cluster of negatively charged residues located at the top face of the Gβ subunit. Alterations of DAT function have been associated with multiple devastating neuropathological conditions; therefore, this work represents a step toward better understanding DAT regulation by signaling proteins, allowing us to predict therapeutic target regions.

Using NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD Protein Structure Refinement

Cryo-EM has become one of the prime methods for protein structure elucidation, frequently yielding density maps with near-atomic or medium resolution. If protein structures cannot be deduced unambiguously from the density maps, computational structure refinement tools are needed to generate protein structural models. We have previously developed an iterative Rosetta-MDFF protocol that used cryo-EM densities to refine protein structures. Here we show that, in addition to cryo-EM densities, incorporation of other experimental restraints into the Rosetta-MDFF protocol further improved refined structures. We used NMR chemical shift (CS) data integrated with cryo-EM densities in our hybrid protocol in both the Rosetta step and the molecular dynamics (MD) simulations step. In 15 out of 18 cases for all MD rounds, the refinement results obtained when density maps and NMR chemical shift data were used in combination outperformed those of density map-only refinement. Notably, the improvement in refinement was highest when medium and low-resolution density maps were used. With our hybrid method, the RMSDs of final models obtained were always better than the RMSDs obtained by our previous protocol with just density refinement for both medium (6.9 Å) and low (9 Å) resolution maps. For all the six test proteins with medium resolution density maps (6.9 Å), the final refined structure RMSDs were lower for the hybrid method than for the cryo-EM only refinement. The final refined RMSDs were less than 1.5 Å when our hybrid protocol was used with 4 Å density maps. For four out of the six proteins the final RMSDs were even less than 1 Å. This study demonstrates that by using a combination of cryo-EM and NMR restraints, it is possible to refine structures to atomic resolution, outperforming single restraint refinement. This hybrid protocol will be a valuable tool when only low-resolution cryo-EM density data and NMR chemical shift data are available to refine structures.

Better together: Elements of successful scientific software development in a distributed collaborative community

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations

Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.

Propensities of Amino Acid Pairings in Secondary Structure of Globular Proteins

A class of secondary structure prediction algorithms use the information from the statistics of the residue pairs found in secondary structural elements. Because the protein folding process is dominated by backbone hydrogen bonding, an approach based on backbone hydrogen-bonded residue pairings would improve the predicting capabilities of these class algorithms. The reliability of the prediction algorithms depends on the quality of the statistics, therefore, of the data set. In this study, it was aimed to determine the propensities of the backbone hydrogen-bonded residue pairings for secondary structural elements of α-helix and β-sheet in globular proteins using a new and comprehensive data set created from the peptides deposited in Worldwide Protein Data Bank. A master data set including 4882 globular peptide chains with resolution better than 2.5 Å, sequence identity smaller than 25% and length of no shorter than 100 residues were created. Separate data sub sets also were created for helix and sheet structures from master set and each sub set includes 4594 and 4483 chains, respectively. Backbone hydrogen-bonded residue pairings in helices and sheets were detected and the propensities of them were represented as odds ratios (observed/[random or expected]) in matrices. Propensities assigned by this study to the residue pairings in secondary structural elements (as helix, overall strands, parallel strands and antiparallel strands) differ from the previous studies by 19 to 34%. These dissimilarities are important and they would cause further improvements in secondary structure prediction algorithms.

EF-hand like Region in the N-terminus of Anoctamin 1 Modulates Channel Activity by Ca2+ and Voltage

Anoctamin1 (ANO1) also known as TMEM16A is a transmembrane protein that functions as a Ca²⁺ activated chloride channel. Recently, the structure determination of a fungal Nectria haematococca TMEM16 (nhTMEM16) scramblase by X-ray crystallography and a mouse ANO1 by cryo-electron microscopy has provided the insight in molecular architecture underlying phospholipid scrambling and Ca²⁺ binding. Because the Ca²⁺ binding motif is embedded inside channel protein according to defined structure, it is still unclear how intracellular Ca²⁺ moves to its deep binding pocket effectively. Here we show that EF-hand like region containing multiple acidic amino acids at the N-terminus of ANO1 is a putative site regulating the activity of ANO1 by Ca²⁺ and voltage. The EF-hand like region of ANO1 is highly homologous to the canonical EF hand loop in calmodulin that contains acidic residues in key Ca²⁺-coordinating positions in the canonical EF hand. Indeed, deletion and Ala-substituted mutation of this region resulted in a significant reduction in the response to Ca²⁺ and changes in its key biophysical properties evoked by voltage pulses. Furthermore, only ANO1 and ANO2, and not the other TMEM16 isoforms, contain the EF-hand like region and are activated by Ca²⁺. Moreover, the molecular modeling analysis supports that EF-hand like region could play a key role during Ca²⁺ transfer. Therefore, these findings suggest that EF-hand like region in ANO1 coordinates with Ca²⁺ and modulate the activation by Ca²⁺ and voltage.

Structural complementarity of distance constraints obtained from chemical cross-linking and amino acid coevolution

The analysis of amino acid coevolution has emerged as a practical method for protein structural modeling by providing structural contact information from alignments of amino acid sequences. In parallel, chemical cross-linking/mass spectrometry (XLMS) has gained attention as a universally applicable method for obtaining low-resolution distance constraints to model the quaternary arrangements of proteins, and more recently even protein tertiary structures. Here, we show that the structural information obtained by XLMS and coevolutionary analysis are effectively complementary: the distance constraints obtained by each method are almost exclusively associated with non-coincident pairs of residues, and modeling results obtained by the combination of both sets are improved relative to considering the same total number of constraints of a single type. The structural rationale behind the complementarity of the distance constraints is discussed and illustrated for a representative set of proteins with different sizes and folds.

Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network

In protein tertiary structure prediction, model quality assessment programs (MQAPs) are often used to select the final structural models from a pool of candidate models generated by multiple templates and prediction methods. The 3-dimensional convolutional neural network (3DCNN) is an expansion of the 2DCNN and has been applied in several fields, including object recognition. The 3DCNN is also used for MQA tasks, but the performance is low due to several technical limitations related to protein tertiary structures, such as orientation alignment. We proposed a novel single-model MQA method based on local structure quality evaluation using a deep neural network containing 3DCNN layers. The proposed method first assesses the quality of local structures for each residue and then evaluates the quality of whole structures by integrating estimated local qualities. We analyzed the model using the CASP11, CASP12, and 3D-Robot datasets and compared the performance of the model with that of the previous 3DCNN method based on whole protein structures. The proposed method showed a significant improvement compared to the previous 3DCNN method for multiple evaluation measures. We also compared the proposed method to other state-of-the-art methods. Our method showed better performance than the previous 3DCNN-based method and comparable accuracy as the current best single-model methods; particularly, in CASP11 stage2, our method showed a Pearson coefficient of 0.486, which was better than those of the best single-model methods (0.366–0.405). A standalone version of the proposed method and data files are available at https://github.com/ishidalab-titech/3DCNN_MQA.

Inferring protein 3D structure from deep mutation scans

We describe an experimental method of three-dimensional (3D) structure determination that exploits the increasing ease of high-throughput mutational scans. Inspired by the success of using natural, evolutionary sequence covariation to compute protein and RNA folds, we explored whether 'laboratory', synthetic sequence variation might also yield 3D structures. We analyzed five large-scale mutational scans and discovered that the pairs of residues with the largest positive epistasis in the experiments are sufficient to determine the 3D fold. We show that the strongest epistatic pairings from genetic screens of three proteins, a ribozyme and a protein interaction reveal 3D contacts within and between macromolecules. Using these experimental epistatic pairs, we compute ab initio folds for a GB1 domain (within 1.8 Å of the crystal structure) and a WW domain (2.1 Å). We propose strategies that reduce the number of mutants needed for contact prediction, suggesting that genomics-based techniques can efficiently predict 3D structure.

Utility of Covalent Labeling Mass Spectrometry Data in Protein Structure Prediction with Rosetta

Covalent labeling mass spectrometry experiments are growing in popularity and provide important information regarding protein structure. Information obtained from these experiments correlates with residue solvent exposure within the protein in solution. However, it is impossible to determine protein structure from covalent labeling data alone. Incorporation of sparse covalent labeling data into the protein structure prediction software Rosetta has been shown to improve protein tertiary structure prediction. Here, covalent labeling techniques were analyzed computationally to provide insight into what labeling data is needed to optimize tertiary protein structure prediction in Rosetta. We have successfully implemented a new scoring functionality that provides improved predictions. We developed two new covalent labeling based score terms that use a "cone"-based neighbor count to quantify the relative solvent exposure of each amino acid. To test our method, we used a set of 20 proteins with structures deposited in the Protein Data Bank. Decoy model sets were generated for each of these 20 proteins, and the normalized covalent labeling score versus RMSD distributions were evaluated. On the basis of these distributions, we have determined an optimal subset of residues to use when performing covalent labeling experiments in order to maximize the structure prediction capabilities of the covalent labeling data. We also investigated how much false negative and false positive data can be tolerated without meaningfully impacting protein structure prediction. Using these new covalent labeling score terms, protein models were rescored and the resulting models improved by 3.9 Å RMSD on average. New models were also generated using Rosetta's AbinitioRelax program under the guidance of covalent labeling information, and improvement in model quality was observed.

Statistical force-field for structural modeling using chemical cross-linking/mass spectrometry distance constraints

Motivation

Chemical cross-linking/mass spectrometry (XLMS) is an experimental method to obtain distance constraints between amino acid residues which can be applied to structural modeling of tertiary and quaternary biomolecular structures. These constraints provide, in principle, only upper limits to the distance between amino acid residues along the surface of the biomolecule. In practice, attempts to use of XLMS constraints for tertiary protein structure determination have not been widely successful. This indicates the need of specifically designed strategies for the representation of these constraints within modeling algorithms.

Results

A force-field designed to represent XLMS-derived constraints is proposed. The potential energy functions are obtained by computing, in the database of known protein structures, the probability of satisfaction of a topological cross-linking distance as a function of the Euclidean distance between amino acid residues. First, the strategy suggests that XL constraints should be set to shorter distances than usually assumed. Second, the complete statistical force-field improves the models obtained and can be easily incorporated into current modeling methods and software. The force-field was implemented and is distributed to be used within the Rosetta ab initio relax protocol.

Availability and implementation

Force-field parameters and usage instructions are freely available online (http://m3g.iqm.unicamp.br/topolink/xlff).

Supplementary information

Supplementary data are available at Bioinformatics online.

60S dynamic state of bacterial ribosome is fixed by yeast mitochondrial initiation factor 3

The processes of association and dissociation of ribosomal subunits are of great importance for the protein biosynthesis. The mechanistic details of these processes, however, are not well known. In bacteria, upon translation termination, the ribosome dissociates into subunits which is necessary for its further involvement into new initiation step. The dissociated state of the ribosome is maintained by initiation factor 3 (IF3) which binds to free small subunits and prevents their premature association with large subunits. In this work, we have exchanged IF3 in Escherichia coli cells by its ortholog from Saccharomyces cerevisiae mitochondria (Aim23p) and showed that yeast protein cannot functionally substitute the bacterial one and is even slightly toxic for bacterial cells. Our in vitro experiments have demonstrated that Aim23p does not split E. coli ribosomes into subunits. Instead, it fixes a state of ribosomes characterized by sedimentation coefficient about 60S which is not a stable structure but rather reflects a shift of dynamic equilibrium between associated and dissociated states of the ribosome. Mitochondria-specific terminal extensions of Aim23p are necessary for “60S state” formation, and molecular modeling results point out that these extensions might stabilize the position of the protein on the bacterial ribosome.

Chemical shift-based methods in NMR structure determination

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

Rotamer Libraries for the High-Resolution Design of β-Amino Acid Foldamers

β-Amino acids offer attractive opportunities to develop biologically active peptidomimetics, either employed alone or in conjunction with natural α-amino acids. Owing to their potential for unique conformational preferences that deviate considerably from α-peptide geometries, β-amino acids greatly expand the possible chemistries and physical properties available to polyamide foldamers. Complete in silico support for designing new molecules incorporating non-natural amino acids typically requires representing their side-chain conformations as sets of discrete rotamers for model refinement and sequence optimization. Such rotamer libraries are key components of several state-of-the-art design frameworks. Here we report the development, incorporation in to the Rosetta macromolecular modeling suite, and validation of rotamer libraries for β3-amino acids.

Heterologous expression of abaecin peptide from Apis mellifera in Pichia pastoris

Background

Antimicrobial peptides (AMPs) are the first line of host immune defense against pathogens. Among AMPs from the honeybee Apis mellifera, abaecin is a major broad-spectrum antibacterial proline-enriched cationic peptide.

Results

For heterologous expression of abaecin in Pichia pastoris, we designed an ORF with HisTag, and the codon usage was optimized. The gene was chemically synthetized and cloned in the pUC57 vector. The new ORF was sub-cloned in the pPIC9 expression vector and transformed into P. pastoris. After selection of positive clones, the expression was induced by methanol. The supernatant was analyzed at different times to determine the optimal time for the recombinant peptide expression. As a proof-of-concept, Escherichia coli was co-incubated with the recombinant peptide to verify its antimicrobial potential.

Discussion

Briefly, the recombinant Abaecin (rAbaecin) has efficiently decreased E. coli growth (P < 0.05) through an in vitro assay, and may be considered as a novel therapeutic agent that may complement other conventional antibiotic therapies.

GP0.4 from bacteriophage T7: in silico characterisation of its structure and interaction with E. coli FtsZ

Background

Proteins produced by bacteriophages can have potent antimicrobial activity. The study of phage-host interactions can therefore inform small molecule drug discovery by revealing and characterising new drug targets. Here we characterise in silico the predicted interaction of gene protein 0.4 (GP0.4) from the Escherichia coli (E. coli) phage T7 with E. coli filamenting temperature-sensitive mutant Z division protein (FtsZ). FtsZ is a tubulin homolog which plays a key role in bacterial cell division and that has been proposed as a drug target.

Results

Using ab initio, fragment assembly structure modelling, we predicted the structure of GP0.4 with two programs. A structure similarity-based network was used to identify a U-shaped helix-turn-helix candidate fold as being favoured. ClusPro was used to dock this structure prediction to a homology model of E. coli FtsZ resulting in a favourable predicted interaction mode. Alternative docking methods supported the proposed mode which offered an immediate explanation for the anti-filamenting activity of GP0.4. Importantly, further strong support derived from a previously characterised insertion mutation, known to abolish GP0.4 activity, that is positioned in close proximity to the proposed GP0.4/FtsZ interface.

Conclusions

The mode of interaction predicted by bioinformatics techniques strongly suggests a mechanism through which GP0.4 inhibits FtsZ and further establishes the latter’s druggable intrafilament interface as a potential drug target.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-016-2149-5) contains supplementary material, which is available to authorized users.

Porcine circovirus type 2 protective epitope densely carried by chimeric papaya ringspot virus-like particles expressed in Escherichia coli as a cost-effective vaccine manufacture alternative

Porcine circovirus type 2 (PCV2) still represents a major problem to the swine industry worldwide, causing high mortality rates in infected animals. Virus-like particles (VLPs) have gained attention for vaccine development, serving both as scaffolds for epitope expression and immune response enhancers. The commercial subunit vaccines against PCV2 consist of VLPs formed by the self-assembly of PCV2 capsid protein (CP) expressed in the baculovirus vector system. In this work, a PCV2 protective epitope was inserted into three different regions of papaya ringspot virus (PRSV) CP, namely, the N- and C-termini and a predicted antigenic region located near the N-terminus. Wild-type and chimeric CPs were modeled in silico, expressed in Escherichia coli, purified, and visualized by transmission electron microscopy. This is the first report that shows the formation of chimeric VLPs using PRSV as epitope-presentation scaffold. Moreover, it was found that PCV2 epitope localization strongly influences VLP length. Also, the estimated yields of the chimeric VLPs at a small-scale level ranged between 65 and 80 mg/L of culture medium. Finally, the three chimeric VLPs induced high levels of immunoglobulin G against the PCV2 epitope in immunized BALB/c mice, suggesting that these chimeric VLPs can be used for swine immunoprophylaxis against PCV2.

General overview on structure prediction of twilight-zone proteins

Protein structure prediction from amino acid sequence has been one of the most challenging aspects in computational structural biology despite significant progress in recent years showed by critical assessment of protein structure prediction (CASP) experiments. When experimentally determined structures are unavailable, the predictive structures may serve as starting points to study a protein. If the target protein consists of homologous region, high-resolution (typically <1.5 Å) model can be built via comparative modelling. However, when confronted with low sequence similarity of the target protein (also known as twilight-zone protein, sequence identity with available templates is less than 30%), the protein structure prediction has to be initiated from scratch. Traditionally, twilight-zone proteins can be predicted via threading or ab initio method. Based on the current trend, combination of different methods brings an improved success in the prediction of twilight-zone proteins. In this mini review, the methods, progresses and challenges for the prediction of twilight-zone proteins were discussed.

An Integrated Framework Advancing Membrane Protein Modeling and Design

Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

Design of symmetric TIM barrel proteins from first principles

Background

Computational protein design is a rapidly maturing field within structural biology, with the goal of designing proteins with custom structures and functions. Such proteins could find widespread medical and industrial applications. Here, we have adapted algorithms from the Rosetta software suite to design much larger proteins, based on ideal geometric and topological criteria. Furthermore, we have developed techniques to incorporate symmetry into designed structures. For our first design attempt, we targeted the (α/β)₈ TIM barrel scaffold. We gained novel insights into TIM barrel folding mechanisms from studying natural TIM barrel structures, and from analyzing previous TIM barrel design attempts.

Methods

Computational protein design and analysis was performed using the Rosetta software suite and custom scripts. Genes encoding all designed proteins were synthesized and cloned on the pET20-b vector. Standard circular dichroism and gel chromatographic experiments were performed to determine protein biophysical characteristics. 1D NMR and 2D HSQC experiments were performed to determine protein structural characteristics.

Results

Extensive protein design simulations coupled with ab initio modeling yielded several all-atom models of ideal, 4-fold symmetric TIM barrels. Four such models were experimentally characterized. The best designed structure (Symmetrin-1) contained a polar, histidine-rich pore, forming an extensive hydrogen bonding network. Symmetrin-1 was easily expressed and readily soluble. It showed circular dichroism spectra characteristic of well-folded alpha/beta proteins. Temperature melting experiments revealed cooperative and reversible unfolding, with a T_m of 44 °C and a Gibbs free energy of unfolding (ΔG°) of 8.0 kJ/mol. Urea denaturing experiments confirmed these observations, revealing a C_m of 1.6 M and a ΔG° of 8.3 kJ/mol. Symmetrin-1 adopted a monomeric conformation, with an apparent molecular weight of 32.12 kDa, and displayed well resolved 1D-NMR spectra. However, the HSQC spectrum revealed somewhat molten characteristics.

Conclusions

Despite the detection of molten characteristics, the creation of a soluble, cooperatively folding protein represents an advancement over previous attempts at TIM barrel design. Strategies to further improve Symmetrin-1 are elaborated. Our techniques may be used to create other large, internally symmetric proteins.

Electronic supplementary material

The online version of this article (doi:10.1186/s12858-015-0047-4) contains supplementary material, which is available to authorized users.

Decoy Database Improvement for Protein Folding

Predicting protein structures and simulating protein folding are two of the most important problems in computational biology today. Simulation methods rely on a scoring function to distinguish the native structure (the most energetically stable) from non-native structures. Decoy databases are collections of non-native structures used to test and verify these functions. We present a method to evaluate and improve the quality of decoy databases by adding novel structures and removing redundant structures. We test our approach on 20 different decoy databases of varying size and type and show significant improvement across a variety of metrics. We also test our improved databases on two popular modern scoring functions and show that for most cases they contain a greater or equal number of native-like structures than the original databases, thereby producing a more rigorous database for testing scoring functions.

Novel Cathelicidins from Pigeon Highlights Evolutionary Convergence in Avain Cathelicidins and Functions in Modulation of Innate Immunity

Cathelicidins are short cationic host defense peptides and play a central role in host innate immune system. Here we identified two novel cathelicidins, Cl-CATH2 and 3, from Columba livia. Evolutionary analysis of avian cathelicidins via phylogenetic tree and Ka/Ks calculations supported the positive selection that prompted evolution of CATH2 to CATH1 and 3, which originate from common ancestor and could belong to one superfamily. Cl-CATH2 and 3 both adopt amphipathic α-helical comformations identified by circular dichroism and the 3D structures built by Rosetta. Cl-CATH2 of CATH2 family with the most expression abundance in bird, exhibited relatively weak antimicrobial activity, but acted instead on the innate immune response without showing undesirable toxicities. In macrophages primed by LPS, Cl-CATH2 significantly down-regulated the gene and protein expressions of inducible nitric oxide synthase and pro-inflammatory cytokines while enhancing the anti-inflammatory cytokine, acting through MAPK and NF-κB signaling pathways. Molecular docking shows for the first time that cathelicidin binds to the opening region of LPS-binding pocket on myeloid differentiation factor 2 (MD-2) of toll-like receptor (TLR)4-MD-2 complex, which in turn inhibits the TLR4 pathway. Our results, therefore, provide new insight into the mechanism underlying the blockade of TLR4 signaling by cathelicidins.