Rotamer libraries in the 21st century

Deciphering the pathogenic role of rare RAF1 heterozygous missense mutation in the late-presenting DDH

Background

Developmental Dysplasia of the Hip (DDH) is a skeletal disorder where late-presenting forms often escape early diagnosis, leading to limb and pain in adults. The genetic basis of DDH is not fully understood despite known genetic predispositions.

Methods

We employed Whole Genome Sequencing (WGS) to explore the genetic factors in late-presenting DDH in two unrelated families, supported by phenotypic analyses and in vitro validation.

Results

In both cases, a novel de novo heterozygous missense mutation in RAF1 (c.193A>G [p.Lys65Glu]) was identified. This mutation impacted RAF1 protein structure and function, altering downstream signaling in the Ras/ERK pathway, as demonstrated by bioinformatics, molecular dynamics simulations, and in vitro validations.

Conclusion

This study contributes to our understanding of the genetic factors involved in DDH by identifying a novel mutation in RAF1. The identification of the RAF1 mutation suggests a possible involvement of the Ras/ERK pathway in the pathogenesis of late-presenting DDH, indicating its potential role in skeletal development.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Effects of the Y432S Cancer-Associated Variant on the Reaction Mechanism of Human DNA Polymerase κ

Human DNA polymerases are vital for genetic information management. Their function involves catalyzing the synthesis of DNA strands with unparalleled accuracy, which ensures the fidelity and stability of the human genomic blueprint. Several disease-associated mutations and their functional impact on DNA polymerases have been reported. One particular polymerase, human DNA polymerase kappa (Pol κ), has been reported to be susceptible to several cancer-associated mutations. The Y432S mutation in Pol κ, associated with various cancers, is of interest due to its impact on polymerization activity and markedly reduced thermal stability. Here, we have used computational simulations to investigate the functional consequences of the Y432S using classical molecular dynamics (MD) and coupled quantum mechanics/molecular mechanics (QM/MM) methods. Our findings suggest that Y432S induces structural alterations in domains responsible for nucleotide addition and ternary complex stabilization while retaining structural features consistent with possible catalysis in the active site. Calculations of the minimum energy path associated with the reaction mechanism of the wild type (WT) and Y432S Pol κ indicate that, while both enzymes are catalytically competent (in terms of energetics and the active site's geometries), the cancer mutation results in an endoergic reaction and an increase in the catalytic barrier. Interactions with a third magnesium ion and environmental effects on nonbonded interactions, particularly involving key residues, contribute to the kinetic and thermodynamic distinctions between the WT and mutant during the catalytic reaction. The energetics and electronic findings suggest that active site residues favor the catalytic reaction with dCTP3- over dCTP4-.

Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here we introduce a modular strategy to generate conformations based on "backmapping" from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

Invariant point message passing for protein side chain packing

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using χ-angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ~1,400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.

Design of efficient artificial enzymes using crystallographically-enhanced conformational sampling

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing catalytic efficiency of de novo enzymes to a level comparable to their natural counterparts without relying on directed evolution. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models. Our work shows how computational design can generate efficient artificial enzymes by exploiting the true conformational ensemble to more effectively stabilize the transition state.

De novo PHF5A variants are associated with craniofacial abnormalities, developmental delay, and hypospadias

PURPOSE

The SF3B splicing complex is composed of SF3B1-6 and PHF5A. We report a developmental disorder caused by de novo variants in PHF5A.

METHODS

Clinical, genomic, and functional studies using subject-derived fibroblasts and a heterologous cellular system were performed.

RESULTS

We studied 9 subjects with congenital malformations, including preauricular tags and hypospadias, growth abnormalities, and developmental delay who had de novo heterozygous PHF5A variants, including 4 loss-of-function (LOF), 3 missense, 1 splice, and 1 start-loss variant. In subject-derived fibroblasts with PHF5A LOF variants, wild-type and variant PHF5A mRNAs had a 1:1 ratio, and PHF5A mRNA levels were normal. Transcriptome sequencing revealed alternative promoter use and downregulated genes involved in cell-cycle regulation. Subject and control fibroblasts had similar amounts of PHF5A with the predicted wild-type molecular weight and of SF3B1-3 and SF3B6. SF3B complex formation was unaffected in 2 subject cell lines.

CONCLUSION

Our data suggest the existence of feedback mechanisms in fibroblasts with PHF5A LOF variants to maintain normal levels of SF3B components. These compensatory mechanisms in subject fibroblasts with PHF5A or SF3B4 LOF variants suggest disturbed autoregulation of mutated splicing factor genes in specific cell types, that is, neural crest cells, during embryonic development rather than haploinsufficiency as pathomechanism.

Computational remodeling of an enzyme conformational landscape for altered substrate selectivity

Structural plasticity of enzymes dictates their function. Yet, our ability to rationally remodel enzyme conformational landscapes to tailor catalytic properties remains limited. Here, we report a computational procedure for tuning conformational landscapes that is based on multistate design of hinge-mediated domain motions. Using this method, we redesign the conformational landscape of a natural aminotransferase to preferentially stabilize a less populated but reactive conformation and thereby increase catalytic efficiency with a non-native substrate, resulting in altered substrate selectivity. Steady-state kinetics of designed variants reveals activity increases with the non-native substrate of approximately 100-fold and selectivity switches of up to 1900-fold. Structural analyses by room-temperature X-ray crystallography and multitemperature nuclear magnetic resonance spectroscopy confirm that conformational equilibria favor the target conformation. Our computational approach opens the door to targeted alterations of conformational states and equilibria, which should facilitate the design of biocatalysts with customized activity and selectivity.

The design of functional proteins using tensorized energy calculations

In protein design, the energy associated with a huge number of sequence-conformer perturbations has to be routinely estimated. Hence, enhancing the throughput and accuracy of these energy calculations can profoundly improve design success rates and enable tackling more complex design problems. In this work, we explore the possibility of tensorizing the energy calculations and apply them in a protein design framework. We use this framework to design enhanced proteins with anti-cancer and radio-tracing functions. Particularly, we designed multispecific binders against ligands of the epidermal growth factor receptor (EGFR), where the tested design could inhibit EGFR activity in vitro and in vivo. We also used this method to design high-affinity Cu2+ binders that were stable in serum and could be readily loaded with copper-64 radionuclide. The resulting molecules show superior functional properties for their respective applications and demonstrate the generalizable potential of the described protein design approach.

Solvent Accessibility Promotes Rotamer Errors during Protein Modeling with Major Side-Chain Prediction Programs

Side-chain rotamer prediction is one of the most critical late stages in protein 3D structure building. Highly advanced and specialized algorithms (e.g., FASPR, RASP, SCWRL4, and SCWRL4v) optimize this process by use of rotamer libraries, combinatorial searches, and scoring functions. We seek to identify the sources of key rotamer errors as a basis for correcting and improving the accuracy of protein modeling going forward. In order to evaluate the aforementioned programs, we process 2496 high-quality single-chained all-atom filtered 30% homology protein 3D structures and use discretized rotamer analysis to compare original with calculated structures. Among 513,024 filtered residue records, increased amino acid residue-dependent rotamer errors─associated in particular with polar and charged amino acid residues (ARG, LYS, and GLN)─clearly correlate with increased amino acid residue solvent accessibility and an increased residue tendency toward the adoption of non-canonical off rotamers which modeling programs struggle to predict accurately. Understanding the impact of solvent accessibility now appears key to improved side-chain prediction accuracies.

Building protein structure-specific rotamer libraries

MOTIVATION

Identifying the probable positions of the protein side-chains is one of the protein modelling steps that can improve the prediction of protein-ligand and protein-protein interactions. Most of the strategies predicting the side-chain conformations use predetermined dihedral angle lists, also called rotamer libraries, that are usually generated from a subset of high-quality protein structures. Although these methods are fast to apply, they tend to average out geometries instead of taking into account the surrounding atoms and molecules and ignore structures not included in the selected subset. Such simplifications can result in inaccuracies when predicting possible side-chain atom positions.

RESULTS

We propose an approach that takes into account both of these circumstances by scanning through sterically accessible side-chain conformations and generating dihedral angle libraries specific to the target proteins. The method avoids the drawbacks of lacking conformations due to unusual or rare protein structures and successfully suggests potential rotamers with average RMSD closer to the experimentally determined side-chain atom positions than other widely used rotamer libraries.

AVAILABILITY AND IMPLEMENTATION

The technique is implemented in open-source software package rotag and available at GitHub: https://www.github.com/agrybauskas/rotag, under GNU Lesser General Public License.

Getting 'ϕψχal' with proteins: minimum message length inference of joint distributions of backbone and sidechain dihedral angles

The tendency of an amino acid to adopt certain configurations in folded proteins is treated here as a statistical estimation problem. We model the joint distribution of the observed mainchain and sidechain dihedral angles (〈ϕ,ψ,χ1,χ2,…〉) of any amino acid by a mixture of a product of von Mises probability distributions. This mixture model maps any vector of dihedral angles to a point on a multi-dimensional torus. The continuous space it uses to specify the dihedral angles provides an alternative to the commonly used rotamer libraries. These rotamer libraries discretize the space of dihedral angles into coarse angular bins, and cluster combinations of sidechain dihedral angles (〈χ1,χ2,…〉) as a function of backbone 〈ϕ,ψ〉 conformations. A 'good' model is one that is both concise and explains (compresses) observed data. Competing models can be compared directly and in particular our model is shown to outperform the Dunbrack rotamer library in terms of model complexity (by three orders of magnitude) and its fidelity (on average 20% more compression) when losslessly explaining the observed dihedral angle data across experimental resolutions of structures. Our method is unsupervised (with parameters estimated automatically) and uses information theory to determine the optimal complexity of the statistical model, thus avoiding under/over-fitting, a common pitfall in model selection problems. Our models are computationally inexpensive to sample from and are geared to support a number of downstream studies, ranging from experimental structure refinement, de novo protein design, and protein structure prediction. We call our collection of mixture models as PhiSiCal (ϕψχal).

AVAILABILITY AND IMPLEMENTATION

PhiSiCal mixture models and programs to sample from them are available for download at http://lcb.infotech.monash.edu.au/phisical.

An end-to-end deep learning method for protein side-chain packing and inverse folding

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.

Toxic Determination of Cry11 Mutated Proteins Obtained Using Rational Design and Its Computational Analysis

Cry11 proteins are toxic to Aedes aegypti, the vector of dengue, chikungunya, and Zika viruses. Cry11Aa and Cry11Bb are protoxins, which when activated present their active-toxin form in two fragments between 30 and 35 kDa respectively. Previous studies conducted with Cry11Aa and Cry11Bb genes using DNA shuffling generated variant 8, which presented a deletion in the first 73 amino acids and one at position 572 and 9 substitutions including L553F and L556W. In this study, variant 8 mutants were constructed using site-directed mutagenesis, resulting in conversion of phenylalanine (F) and tryptophan (W) to leucine (L) at positions 553 and 556, respectively, producing the mutants 8F553L, 8W556L, and 8F553L/8W556L. Additionally, two mutants, A92D and C157R, derived from Cry11Bb were also generated. The proteins were expressed in the non-crystal strain BMB171 of Bacillus thuringiensis and subjected to median-lethal concentration (LC₅₀) tests on first-instar larvae of A. aegypti. LC₅₀ analysis showed that the 8F553L, 8W556L, 8F553L/8W556L, and C157R variants lost their toxic activity (>500 ng·mL^-1), whereas the A92D protein presented a loss of toxicity of 11.4 times that of Cry11Bb. Cytotoxicity assays performed using variant 8, 8W556L and the controls Cry11Aa, Cry11Bb, and Cry-negative BMB171 on the colorectal cancer cell line SW480 reported 30-50% of cellular viability except for BMB171. Molecular dynamic simulations performed to identify whether the mutations at positions 553 and 556 were related to the stability and rigidity of the functional tertiary structure (domain III) of the Cry11Aa protein and variant 8 showed the importance of these mutations in specific regions for the toxic activity of Cry11 against A. aegypti. This generates pertinent knowledge for the design of Cry11 proteins and their biotechnological applications in vector-borne disease control and cancer cell lines.

Ligand-based 3D pharmacophore modeling, virtual screening, and molecular dynamic simulation of potential smoothened inhibitors

CONTEXT

The Hedgehog (Hh) signaling pathway is a crucial regulator of various cellular processes. Dysregulated activation of the Smoothened (SMO) oncoprotein, a key component of the Hh pathway, has been implicated in several types of cancer. Although SMO inhibitors are important anti-cancer therapeutics, drug-resistant SMO mutants have emerged, limiting their efficacy. This study aimed to discover stable SMO inhibitors for both wild-type and mutant SMOs, using a 12-feature pharmacophore model validated for virtual screening. One lead compound, LCT10312, was identified with high affinity to SMO and showed a significant conformational change in the SMO structure upon binding. Molecular dynamic simulation revealed stable interaction of LCT10312 with SMO and large atom motions, indicating SMO structural fluctuation. The lead compound showed high predicted binding scores to several clinically relevant SMO mutants.

METHODS

A ligand-based pharmacophore model was developed from 25 structurally clustered SMO inhibitors using LigandScout v3.12 software and virtually screened for hit identification from a library of 511,878 chemicals. Molecular docking was employed to identify potential leads based on SMO affinities. Molecular dynamic simulation (MDS) with GROMACS v5.1.4 was performed to analyze the structural changes of SMO oncoprotein upon binding lead compound(s) and cyclopamine as the control for 100 ns. The binding affinity of lead compound(s) was predicted on clinical and laboratory SMO mutants.

Viral capsid, antibody, and receptor interactions: experimental analysis of the antibody escape evolution of canine parvovirus

Canine parvovirus (CPV) is a small non-enveloped single-stranded DNA virus that causes serious diseases in dogs worldwide. The original strain of the virus (CPV-2) emerged in dogs during the late-1970s due to a host range switch of a virus similar to the feline panleukopenia virus (FPV) that infected another host. The virus that emerged in dogs had altered capsid receptor- and antibody-binding sites, with some changes affecting both functions. Further receptor and antibody binding changes arose when the virus became better adapted to dogs or to other hosts. Here, we use in vitro selection and deep sequencing to reveal how two antibodies with known interactions select for escape mutations in CPV. The antibodies bind two distinct epitopes, and one largely overlaps the host receptor binding site. We also engineered antibody variants with altered binding structures. Viruses were passaged with the wild type or mutated antibodies, and their genomes deep sequenced during the selective process. A small number of mutations were detected only within the capsid protein gene during the first few passages of selection, and most sites remained polymorphic or were slow to go to fixation. Mutations arose both within and outside the antibody binding footprints on the capsids, and all avoided the TfR-binding footprint. Many selected mutations matched those that have arisen in the natural evolution of the virus. The patterns observed reveal the mechanisms by which these variants have been selected in nature and provide a better understanding of the interactions between antibody and receptor selections.

IMPORTANCE

Antibodies protect animals against infection by many different viruses and other pathogens, and we are gaining new information about the epitopes that induce antibody responses against viruses and the structures of the bound antibodies. However, less is known about the processes of antibody selection and antigenic escape and the constraints that apply in this system. Here, we use an in vitro model system and deep genome sequencing to reveal the mutations that arise in the virus genome during selection by each of two monoclonal antibodies or their engineered variants. High-resolution structures of each of the Fab: capsid complexes revealed their binding interactions. The engineered forms of the wild-type antibodies or mutant forms allowed us to examine how changes in antibody structure influence the mutational selection patterns seen in the virus. The results shed light on the processes of antibody binding, neutralization escape, and receptor binding, and likely have parallels for many other viruses.

Computational Evaluation of Peptide-Protein Binding Affinities: Application of Potential of Mean Force Calculations to SH2 Domains

Many biological functions are mediated by protein-protein interactions (PPIs), often involving specific structural modules, such as SH2 domains. Inhibition of PPIs is a pharmaceutical strategy of growing importance. However, a major challenge in the design of PPI inhibitors is the large interface involved in these interactions, which, in many cases, makes inhibition by small organic molecules ineffective. Peptides, which cover a wide range of dimensions and can be opportunely designed to mimic protein sequences at PPI interfaces, represent a valuable alternative to small molecules. Computational techniques able to predict the binding affinity of peptides for the target domain or protein represent a crucial stage in the workflow for the design of peptide-based drugs. This chapter describes a protocol to obtain the potential of mean force (PMF) for peptide-SH2 domain binding, starting from umbrella sampling (US) molecular dynamics (MD) simulations. The PMF profiles can be effectively used to predict the relative standard binding free energies of different peptide sequences.

Analyses of Mutation Displacements from Homology Models

Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.

Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus's butyrylcholinesterase inhibitors

Machine learning (ML), an emerging field in drug design, has the potential to predict in silico toxicity, shape-based analysis of inhibitors, scoring function (SF) etc. In the present study, a homology model, docking protocol, and a dedicated SF have been developed to identify the inhibitors of horse butyrylcholinesterase (BChE) enzyme. Horse BChE enzyme has homology with human BChE and is a substitute for the screening of in vitro inhibitors. The developed homology model was validated and the active site residues were identified from Cavityplus to generate grid box for docking. The validation of docking involved comparison of interactions of ligands co-crystallised with human BChE and the docked poses of the corresponding ligands with horse BChE. A high degree of similarity in the interaction profiles of generated poses validated the docking protocol. Scoring of ligands was further validated by docking with known BChE inhibitors. The binding energies obtained from SF was correlated with IC₅₀ values of inhibitors through classification and regression-based methods, which indicated poor predictivity of native SF. Therefore, protein-ligand binding energy, interaction profile, and ligand descriptors were used to develop and validate the classification and regression-based models. The validated extra tree binary classifier, random forest and extra tree regression-based models were compiled as a protein-ligand SF and were made available to the users through web application and python library. ML models exhibited improved area under the curve for ROC and good correlation between the predicted and observed IC₅₀ values, than the Autodock SF. Communicated by Ramaswamy H. Sarma.

Quantum computing algorithms: getting closer to critical problems in computational biology

The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.

Dominant ARF3 variants disrupt Golgi integrity and cause a neurodevelopmental disorder recapitulated in zebrafish

Vesicle biogenesis, trafficking and signaling via Endoplasmic reticulum-Golgi network support essential developmental processes and their disruption lead to neurodevelopmental disorders and neurodegeneration. We report that de novo missense variants in ARF3, encoding a small GTPase regulating Golgi dynamics, cause a developmental disease in humans impairing nervous system and skeletal formation. Microcephaly-associated ARF3 variants affect residues within the guanine nucleotide binding pocket and variably perturb protein stability and GTP/GDP binding. Functional analysis demonstrates variably disruptive consequences of ARF3 variants on Golgi morphology, vesicles assembly and trafficking. Disease modeling in zebrafish validates further the dominant behavior of the mutants and their differential impact on brain and body plan formation, recapitulating the variable disease expression. In-depth in vivo analyses traces back impaired neural precursors' proliferation and planar cell polarity-dependent cell movements as the earliest detectable effects. Our findings document a key role of ARF3 in Golgi function and demonstrate its pleiotropic impact on development.

Identification of Active Compounds against Melanoma Growth by Virtual Screening for Non-Classical Human DHFR Inhibitors

Antifolates such as methotrexate (MTX) have been largely known as anticancer agents because of their role in blocking nucleic acid synthesis and cell proliferation. Their mechanism of action lies in their ability to inhibit enzymes involved in the folic acid cycle, especially human dihydrofolate reductase (hDHFR). However, most of them have a classical structure that has proven ineffective against melanoma, and, therefore, inhibitors with a non-classical lipophilic structure are increasingly becoming an attractive alternative to circumvent this clinical resistance. In this study, we conducted a protocol combining virtual screening (VS) and cell-based assays to identify new potential non-classical hDHFR inhibitors. Among 173 hit compounds identified (average logP = 3.68; average MW = 378.34 Da), two-herein, called C1 and C2-exhibited activity against melanoma cell lines B16 and A375 by MTT and Trypan-Blue assays. C1 showed cell growth arrest (39% and 56%) and C2 showed potent cytotoxic activity (77% and 51%) in a dose-dependent manner. The effects of C2 on A375 cell viability were greater than MTX (98% vs 60%) at equivalent concentrations and times. Our results indicate that the integrated in silico/in vitro approach provided a benchmark to identify novel promising non-classical DHFR inhibitors showing activity against melanoma cells.

Exploiting Sequence-Dependent Rotamer Information in Global Optimization of Proteins

Rotamers, namely amino acid side chain conformations common to many different peptides, can be compiled into libraries. These rotamer libraries are used in protein modeling, where the limited conformational space occupied by amino acid side chains is exploited. Here, we construct a sequence-dependent rotamer library from simulations of all possible tripeptides, which provides rotameric states dependent on adjacent amino acids. We observe significant sensitivity of rotamer populations to sequence and find that the library is successful in locating side chain conformations present in crystal structures. The library is designed for applications with basin-hopping global optimization, where we use it to propose moves in conformational space. The addition of rotamer moves significantly increases the efficiency of protein structure prediction within this framework, and we determine parameters to optimize efficiency.

Battling BTK mutants with noncovalent inhibitors that overcome Cys481 and Thr474 mutations in Waldenström macroglobulinemia therapy: structural mechanistic insights on the role of fenebrutinib

Recently, the non-covalent Bruton tyrosine kinase (BTK) inhibitor fenebrutinib was presented as a therapeutic option with strong inhibitory efficacy against a single (C481S) and double (T474S/C481S) BTK variant in the treatment of Waldenström macroglobulinemia (WM). However, the molecular events surrounding its inhibition mechanism towards this variant remain unresolved. Herein, we employed in silico methods such as molecular dynamic simulation coupled with binding free energy estimations to explore the mechanistic activity of the fenebrutinib on (C481S) and (T474S/C481S) BTK variant, at a molecular level. Our investigations reveal that amino acid arginine contributed immensely to the total binding energy, this establishing the cruciality of amino acid residues, Arg132 and Arg156 in (C481S) and Arg99, Arg137, and Arg132 in (T474S/C481S) in the binding of fenebrutinib towards both BTK variants. The structural orientations of fenebrutinib within the respective hydrophobic pockets allowed favorable interactions with binding site residues, accounting for its superior binding affinity by 24.5% and relative high hydrogen bond formation towards (T474S/C481S) when compared with (C481S) BTK variants. Structurally, fenebrutinib impacted the stability, flexibility, and solvent accessible surface area of both BTK variants, characterized by various alterations observed in the bound and unbound structures, which proved enough to disrupt their biological function. Findings from this study, therefore, provide insights into the inhibitory mechanism of fenebrutinib at the atomistic level and reveal its high selectivity towards BTK variants. These insights could be key in designing and developing BTK mutants' inhibitors to treat Waldenström macroglobulinemia (WM).

Computational compensatory mutation discovery approach: Predicting a PARP1 variant rescue mutation

The prediction of protein mutations that affect function may be exploited for multiple uses. In the context of disease variants, the prediction of compensatory mutations that reestablish functional phenotypes could aid in the development of genetic therapies. In this work, we present an integrated approach that combines coevolutionary analysis and molecular dynamics (MD) simulations to discover functional compensatory mutations. This approach is employed to investigate possible rescue mutations of a poly(ADP-ribose) polymerase 1 (PARP1) variant, PARP1 V762A, associated with lung cancer and follicular lymphoma. MD simulations show PARP1 V762A exhibits noticeable changes in structural and dynamical behavior compared with wild-type (WT) PARP1. Our integrated approach predicts A755E as a possible compensatory mutation based on coevolutionary information, and molecular simulations indicate that the PARP1 A755E/V762A double mutant exhibits similar structural and dynamical behavior to WT PARP1. Our methodology can be broadly applied to a large number of systems where single-nucleotide polymorphisms have been identified as connected to disease and can shed light on the biophysical effects of such changes as well as provide a way to discover potential mutants that could restore WT-like functionality. This can, in turn, be further utilized in the design of molecular therapeutics that aim to mimic such compensatory effect.

Digging for the discovery of SARS-CoV-2 nsp12 inhibitors: a pharmacophore-based and molecular dynamics simulation study

Aim: COVID-19 is a global health threat. Therapeutics are urgently needed to cure patients severely infected with COVID-19. Objective: to investigate potential candidates of nsp12 inhibitors by searching for druggable cavity pockets within the viral protein and drug discovery. Methods: A virtual screening of ZINC natural products on SARS-CoV-2 nsp12's druggable cavity was performed. A lead compound with the highest affinity to nsp12 was simulated dynamically for 10 ns. Results: ZINC03977803 was nominated as the lead compound. The results showed stable interaction between ZINC03977803 and nsp12 during 10 ns. Discussion: ZINC03977803 showed stable interaction with the catalytic subunit of SARS-CoV-2, nsp12. It could inhibit the SARS-CoV-2 life cycle by direct interaction with nsp12 and inhibit RdRp complex formation.

A comprehensive protein design protocol to identify resistance mutations and signatures of adaptation in pathogens

Most pathogens mutate and evolve over time to escape immune and drug pressure. To achieve this, they alter specific hotspot residues in their intracellular proteins to render the targeted drug(s) ineffective and develop resistance. Such hotspot residues may be located as a cluster or uniformly as a signature of adaptation in a protein. Identifying the hotspots and signatures is extremely important to comprehensively understand the disease pathogenesis and rapidly develop next-generation therapeutics. As experimental methods are time-consuming and often cumbersome, there is a need to develop efficient computational protocols and adequately utilize them. To address this issue, we present a unique computational protein design protocol that identifies hotspot residues, resistance mutations and signatures of adaptation in a pathogen’s protein against a bound drug. Using the protocol, the binding affinity between the designed mutants and drug is computed quickly, which offers predictions for comparison with biophysical experiments. The applicability and accuracy of the protocol are shown using case studies of a few protein–drug complexes. As a validation, resistance mutations in severe acute respiratory syndrome coronavirus 2 main protease (Mpro) against narlaprevir (an inhibitor of hepatitis C NS3/4A serine protease) are identified. Notably, a detailed methodology and description of the working principles of the protocol are presented. In conclusion, our protocol will assist in providing a first-hand explanation of adaptation, hotspot-residue variations and surveillance of evolving resistance mutations in a pathogenic protein.

Phosphomimetic Mutation at Ser165 of α-Tubulin Promotes the Persistence of GTP Caps in Microtubules

Protein kinase C (PKC)-mediated phosphorylation of α-tubulin at Ser165 or expression of phosphomimetic (S165D)-α-tubulin stimulates microtubule (MT) polymerization (Cytoskeleton 2014, 71, 257-272). Ser165 lies near the interface between adjacent αβ-tubulin heterodimers and helix H8, which contains Glu254, the catalytic residue in α-tubulin that hydrolyzes the exchangeable GTP in β-tubulin (β:GTP) and triggers MT depolymerization. It was hypothesized that S165D, a phosphomimetic variant of α-tubulin, perturbs the alignment of α:Glu254 with respect to β:GTP, thereby impairing its hydrolysis. Molecular simulations were performed with cryoEM structures of MTs (PDB ID: 3J6E) in which phosphomimetic S165D or control S165N had been substituted. Unlike native and S165N structures, the distance between S165D and α:Glu254 increased by 0.6 Å, while the distance between α:Glu254 and β:GTP decreased by 0.4 Å. Rotation of β:GTP by 4 Å occurred in the S165D variant, whereas β:GTP in the S165N control was unchanged from the native structure. Additionally, the S165D variant exhibited an altered pattern of H-bonding to β:GTP, including the loss of three H-bonds. The significance of these findings to β:GTP hydrolysis was analyzed in MCF-10A human breast cells treated with an antibody that detects GTP-bound tubulin. Compared with controls, GTP-tubulin signals were at higher levels in cells that ectopically expressed S165D-α-tubulin (TUBA1C) or had been treated with PKC activator DAG-lactone to induce phosphorylation of Ser165 in native α-tubulin. These findings support a model whereby conformational changes induced by Ser165 phosphorylation alter the spatial relationship between β:GTP and α:Glu254, thereby slowing GTP hydrolysis and promoting GTP caps.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Understanding structural variability in proteins using protein structural networks

Proteins perform their function by accessing a suitable conformer from the ensemble of available conformations. The conformational diversity of a chosen protein structure can be obtained by experimental methods under different conditions. A key issue is the accurate comparison of different conformations. A gold standard used for such a comparison is the root mean square deviation (RMSD) between the two structures. While extensive refinements of RMSD evaluation at the backbone level are available, a comprehensive framework including the side chain interaction is not well understood. Here we employ protein structure network (PSN) formalism, with the non-covalent interactions of side chain, explicitly treated. The PSNs thus constructed are compared through graph spectral method, which provides a comparison at the local and at the global structural level. In this work, PSNs of multiple crystal conformers of single-chain, single-domain proteins, are subject to pair-wise analysis to examine the dissimilarity in their network topologies and in order to determine the conformational diversity of their native structures. This information is utilized to classify the structural domains of proteins into different categories. It is observed that proteins typically tend to retain structure and interactions at the backbone level. However, some of them also depict variability in either their overall structure or only in their inter-residue connectivity at the sidechain level, or both. Variability of sub-networks based on solvent accessibility and secondary structure is studied. The types of specific interactions are found to contribute differently to structure variability. An ensemble analysis by computing the mathematical variance of edge-weights across multiple conformers provided information on the contribution to overall variability from each edge of the PSN. Interactions that are highly variable are identified and their impact on structure variability has been discussed with the help of a case study. The classification based on the present side-chain network-based studies provides a framework to correlate the structure-function relationships in protein structures.

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Monomeric, single domain protein structures can exhibit non-rigid behaviour and be highly variable.

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The comparison of protein structural networks can better discriminate conformations with similar backbones.

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Specific interactions between solvent accessible and inaccessible residues are poorly preserved.

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Network edge-variation offers insights on which interacting residues are likely to influence their dynamics and function.

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These side-chain network-based studies provide a framework to correlate protein structure-function relationships.

Expanding the molecular spectrum of pathogenic SHOC2 variants underlying Mazzanti syndrome

We previously molecularly and clinically characterized Mazzanti syndrome, a RASopathy related to Noonan syndrome that is mostly caused by a single recurrent missense variant (c.4A > G, p.Ser2Gly) in SHOC2, which encodes a leucine-rich repeat (LRR)-containing protein facilitating signal flow through the RAS-mitogen-associated protein kinase (MAPK) pathway. We also documented that the pathogenic p.Ser2Gly substitution causes upregulation of MAPK signaling and constitutive targeting of SHOC2 to the plasma membrane due to the introduction of an N-myristoylation recognition motif. The almost invariant occurrence of the pathogenic c.4A > G missense change in SHOC2 is mirrored by a relatively homogeneous clinical phenotype of Mazzanti syndrome. Here we provide new data on the clinical spectrum and molecular diversity of this disorder, and functionally characterize new pathogenic variants. The clinical phenotype of six unrelated individuals carrying novel disease-causing SHOC2 variants is delineated, and public and newly collected clinical data are utilized to profile the disorder. In silico, in vitro and in vivo characterization of the newly identified variants provides evidence that the consequences of these missense changes on SHOC2 functional behavior differ from what had been observed for the canonical p.Ser2Gly change but converge towards an enhanced activation of the RAS-MAPK pathway. Our findings expand the molecular spectrum of pathogenic SHOC2 variants, provide a more accurate picture of the phenotypic expression associated with variants in this gene, and definitively establish a GoF behavior as the mechanism of disease.

Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol

In recent years, photoactive proteins such as rhodopsins have become a common target for cutting-edge research in the field of optogenetics. Alongside wet-lab research, computational methods are also developing rapidly to provide the necessary tools to analyze and rationalize experimental results and, most of all, drive the design of novel systems. The Automatic Rhodopsin Modeling (ARM) protocol is focused on providing exactly the necessary computational tools to study rhodopsins, those being either natural or resulting from mutations. The code has evolved along the years to finally provide results that are reproducible by any user, accurate and reliable so as to replicate experimental trends. Furthermore, the code is efficient in terms of necessary computing resources and time, and scalable in terms of both number of concurrent calculations as well as features. In this review, we will show how the code underlying ARM achieved each of these properties.

Delta Variant with P681R Critical Mutation Revealed by Ultra-Large Atomic-Scale Ab Initio Simulation: Implications for the Fundamentals of Biomolecular Interactions

The SARS-CoV-2 Delta variant is emerging as a globally dominant strain. Its rapid spread and high infection rate are attributed to a mutation in the spike protein of SARS-CoV-2 allowing for the virus to invade human cells much faster and with an increased efficiency. In particular, an especially dangerous mutation P681R close to the furin cleavage site has been identified as responsible for increasing the infection rate. Together with the earlier reported mutation D614G in the same domain, it offers an excellent instance to investigate the nature of mutations and how they affect the interatomic interactions in the spike protein. Here, using ultra large-scale ab initio computational modeling, we study the P681R and D614G mutations in the SD2-FP domain, including the effect of double mutation, and compare the results with the wild type. We have recently developed a method of calculating the amino-acid-amino-acid bond pairs (AABP) to quantitatively characterize the details of the interatomic interactions, enabling us to explain the nature of mutation at the atomic resolution. Our most significant finding is that the mutations reduce the AABP value, implying a reduced bonding cohesion between interacting residues and increasing the flexibility of these amino acids to cause the damage. The possibility of using this unique mutation quantifiers in a machine learning protocol could lead to the prediction of emerging mutations.

The stability and dynamics of computationally designed proteins

Protein stability, dynamics and function are intricately linked. Accordingly, protein designers leverage dynamics in their designs and gain insight to their successes and failures by analyzing their proteins' dynamics. Molecular dynamics (MD) simulations are a powerful computational tool for quantifying both local and global protein dynamics. This review highlights studies where MD simulations were applied to characterize the stability and dynamics of designed proteins and where dynamics were incorporated into computational protein design. First, we discuss the structural basis underlying the extreme stability and thermostability frequently observed in computationally designed proteins. Next, we discuss examples of designed proteins, where dynamics were not explicitly accounted for in the design process, whose coordinated motions or active site dynamics, as observed by MD simulation, enhanced or detracted from their function. Many protein functions depend on sizeable or subtle conformational changes, so we finally discuss the computational design of proteins to perform a specific function that requires consideration of motion by multi-state design.

Computational Design of Single-Peptide Nanocages with Nanoparticle Templating

Protein complexes perform a diversity of functions in natural biological systems. While computational protein design has enabled the development of symmetric protein complexes with spherical shapes and hollow interiors, the individual subunits often comprise large proteins. Peptides have also been applied to self-assembly, and it is of interest to explore such short sequences as building blocks of large, designed complexes. Coiled-coil peptides are promising subunits as they have a symmetric structure that can undergo further assembly. Here, an α-helical 29-residue peptide that forms a tetrameric coiled coil was computationally designed to assemble into a spherical cage that is approximately 9 nm in diameter and presents an interior cavity. The assembly comprises 48 copies of the designed peptide sequence. The design strategy allowed breaking the side chain conformational symmetry within the peptide dimer that formed the building block (asymmetric unit) of the cage. Dynamic light scattering (DLS) and transmission electron microscopy (TEM) techniques showed that one of the seven designed peptide candidates assembled into individual nanocages of the size and shape. The stability of assembled nanocages was found to be sensitive to the assembly pathway and final solution conditions (pH and ionic strength). The nanocages templated the growth of size-specific Au nanoparticles. The computational design serves to illustrate the possibility of designing target assemblies with pre-determined specific dimensions using short, modular coiled-coil forming peptide sequences.

The role of hydrogen bonds and electrostatic interactions in enhancing two-photon absorption in green and yellow fluorescent proteins

The spectral properties of fluorescent proteins (FPs) depend on the protein environment of the chromophore (CRO). A deeper understanding of the CRO - environment interactions in terms of FPs spectral characteristics will allow for a rational design of novel markers with desired properties. Here, we are taking a step towards achieving this important goal. With the time-dependent density functional theory (TDDFT), we calculate one- and two-photon absorption (OPA and TPA) spectra for 5 green FPs (GFPs) and 3 yellow FPs (YFPs) differing in amino acid sequence. The goal is to reveal a role of: (i) electrostatic interactions, (ii) hydrogen-bonds (h-bonds), and (iii) h-bonds together with distant electrostatic field in absorption spectra tuning. Our results point to design hypothesis towards FPs optimised for TPA-based applications. Both h-bonds and electrostatic interactions co-operate in enhancing TPA cross-section (σ TPA ) for the S 0 ->S 1   transition in GFPs. Furthermore, it seems that details of h-bonds network in the CRO's vicinity influences σ TPA   response to CRO - environment electrostatic interactions in YFPs. We postulate that engineering FPs with more hydrophilic CRO's environment can lead to greater σ TPA . We also find that removing h-bonds formed with the CRO's phenolate leads to TPA enhancement for transition to higher excited states than S 1 . Particularly Y145 and T203 residues are important in this regard.

Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing

Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield and thereby brightness of a dim monomeric RFP (mRojoA, quantum yield = 0.02) by optimizing chromophore packing with aliphatic residues, which we hypothesized would reduce torsional motions causing non-radiative decay. Experimental characterization of the top 10 designed sequences yielded mSandy1 (λ em = 609 nm, quantum yield = 0.26), a variant with equivalent brightness to mCherry, a widely used RFP. We next used directed evolution to further increase brightness, resulting in mSandy2 (λ em = 606 nm, quantum yield = 0.35), the brightest Discosoma sp. derived monomeric RFP with an emission maximum above 600 nm reported to date. Crystallographic analysis of mSandy2 showed that the chromophore p-hydroxybenzylidene moiety is sandwiched between the side chains of Leu63 and Ile197, a structural motif that has not previously been observed in RFPs, and confirms that aliphatic packing leads to chromophore rigidification. Our results demonstrate that computational protein design can be used to generate bright monomeric RFPs, which can serve as templates for the evolution of novel far-red fluorescent proteins.

Thermostabilization mechanisms in thermophilic versus mesophilic three-helix bundle proteins

The engineered three‐helix bundle, UVF, is thermostabilized entropically due to heightened, native‐state dynamics. However, it is unclear whether this thermostabilization strategy is observed in natural proteins from thermophiles. We performed all‐atom, explicit solvent molecular dynamics simulations of two three‐helix bundles from thermophilic H. butylicus (2lvsN and 2lvsC) and compared their dynamics to a mesophilic three‐helix bundle, the Engrailed homeodomain (EnHD). Like UVF, 2lvsC had heightened native dynamics, which it maintained without unfolding at 100°C. Shortening and rigidification of loops in 2lvsN and 2lvsC and increased surface hydrogen bonds in 2lvsN were observed, as is common in thermophilic proteins. A buried disulfide and salt bridge in 2lvsN and 2lvsC, respectively, provided some stabilization, and addition of a homologous disulfide bond in EnHD slowed unfolding. The transferability and commonality of stabilization strategies among members of the three‐helix bundle fold suggest that these strategies may be general and deployable in designing thermostable proteins.

Computational Molecular Characterization of the Interaction of Acetylcholine and the NMDA Receptor to Explain the Direct Glycine-Competitive Potentiation of NMDA-Mediated Neuronal Currents

The activation of N-methyl-d-aspartate receptor (NMDAR) is triggered by the closure of bilobed (D1 and D2) clamshell-like clefts upon binding glycine (Gly) and glutamate. There is evidence that cholinergic compounds modulate NMDAR-mediated currents via direct receptor-ligand interactions; however, molecular bases are unknown. Here, we first propose a mechanistic structure-based explanation for the observed ACh-induced submaximal potentiation of NMDA-elicited currents in striatal neurons by predicting competitive inhibition with Gly. Then, the model was validated, in principle, by confirming that the coapplication of Gly and ACh significantly reduces these neuronal currents. Finally, we delineate the interplay of ACh with the NMDAR by a combination of computational strategies. Crystallographic ACh-bound complexes were studied, revealing a similar ACh binding environment on the GluN1 subunit of the NMDAR. We illustrate how ACh can occupy X-ray monomeric open, dimeric "semiopen" cleft conformations obtained by molecular dynamics and a full-active cryo-EM NMDAR structure, explaining the suboptimal NMDAR electrophysiological activity under the "Venus Flytrap model". At an evolutionary biology level, the binding mode of ACh coincides with that of the homologous ornithine-bound periplasmic LAO binding protein complex. Our computed results indicate an analogous mechanism of action, inasmuch as ACh may stabilize the GluN1 subunit "semiclosed" conformations by inducing direct and indirect D1-to-D2 interdomain bonds. Additionally, an alternative binding site was detected, shared by the known NMDAR allosteric modulators. Experimental and computed results strongly suggest that ACh acts as a Gly-competitive, submaximal potentiating agent of the NMDAR, possibly constituting a novel chemotype for multitarget-directed drug development, e.g., to treat Alzheimer's, and it may lead to a new understanding of glutamatergic neurotransmission.

Structure-function relationships of the disease-linked A218T oxytocin receptor variant

Various single nucleotide polymorphisms (SNPs) in the oxytocin receptor (OXTR) gene have been associated with behavioral traits, autism spectrum disorder (ASD) and other diseases. The non-synonymous SNP rs4686302 results in the OXTR variant A218T and has been linked to core characteristics of ASD, trait empathy and preterm birth. However, the molecular and intracellular mechanisms underlying those associations are still elusive. Here, we uncovered the molecular and intracellular consequences of this mutation that may affect the psychological or behavioral outcome of oxytocin (OXT)-treatment regimens in clinical studies, and provide a mechanistic explanation for an altered receptor function. We created two monoclonal HEK293 cell lines, stably expressing either the wild-type or A218T OXTR. We detected an increased OXTR protein stability, accompanied by a shift in Ca²⁺ dynamics and reduced MAPK pathway activation in the A218T cells. Combined whole-genome and RNA sequencing analyses in OXT-treated cells revealed 7823 differentially regulated genes in A218T compared to wild-type cells, including 429 genes being associated with ASD. Furthermore, computational modeling provided a molecular basis for the observed change in OXTR stability suggesting that the OXTR mutation affects downstream events by altering receptor activation and signaling, in agreement with our in vitro results. In summary, our study provides the cellular mechanism that links the OXTR rs4686302 SNP with genetic dysregulations associated with aspects of ASD.

Dihedral Angle Measurements for Structure Determination by Biomolecular Solid-State NMR Spectroscopy

In structural studies of immobilized, aggregated and self-assembled biomolecules, solid-state NMR (ssNMR) spectroscopy can provide valuable high-resolution structural information. Among the structural restraints provided by magic angle spinning (MAS) ssNMR the canonical focus is on inter-atomic distance measurements. In the current review, we examine the utility of ssNMR measurements of angular constraints, as a complement to distance-based structure determination. The focus is on direct measurements of angular restraints via the judicious recoupling of multiple anisotropic ssNMR parameters, such as dipolar couplings and chemical shift anisotropies. Recent applications are highlighted, with a focus on studies of nanocrystalline polypeptides, aggregated peptides and proteins, receptor-substrate interactions, and small molecule interactions with amyloid protein fibrils. The review also examines considerations of when and where ssNMR torsion angle experiments are (most) effective, and discusses challenges and opportunities for future applications.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

An integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes

Protein engineering and design principles employing the 20 standard amino acids have been extensively used to achieve stable protein scaffolds and deliver their specific activities. Although this confers some advantages, it often restricts the sequence, chemical space, and ultimately the functional diversity of proteins. Moreover, although site-specific incorporation of non-natural amino acids (nnAAs) has been proven to be a valuable strategy in protein engineering and therapeutics development, its utility in the affinity-maturation of nanobodies is not fully explored. Besides, current experimental methods do not routinely employ nnAAs due to their enormous library size and infinite combinations. To address this, we have developed an integrated computational pipeline employing structure-based protein design methodologies, molecular dynamics simulations and free energy calculations, for the binding affinity prediction of an nnAA-incorporated nanobody toward its target and selection of potent binders. We show that by incorporating halogenated tyrosines, the affinity of 9G8 nanobody can be improved toward epidermal growth factor receptor (EGFR), a crucial cancer target. Surface plasmon resonance (SPR) assays showed that the binding of several 3-chloro-l-tyrosine (3MY)-incorporated nanobodies were improved up to 6-fold into a picomolar range, and the computationally estimated binding affinities shared a Pearson's r of 0.87 with SPR results. The improved affinity was found to be due to enhanced van der Waals interactions of key 3MY-proximate nanobody residues with EGFR, and an overall increase in the nanobody's structural stability. In conclusion, we show that our method can facilitate screening large libraries and predict potent site-specific nnAA-incorporated nanobody binders against crucial disease-targets.

SPRED2 loss-of-function causes a recessive Noonan syndrome-like phenotype

Upregulated signal flow through RAS and the mitogen-associated protein kinase (MAPK) cascade is the unifying mechanistic theme of the RASopathies, a family of disorders affecting development and growth. Pathogenic variants in more than 20 genes have been causally linked to RASopathies, the majority having a dominant role in promoting enhanced signaling. Here, we report that SPRED2 loss of function is causally linked to a recessive phenotype evocative of Noonan syndrome. Homozygosity for three different variants-c.187C>T (p.Arg63^∗), c.299T>C (p.Leu100Pro), and c.1142_1143delTT (p.Leu381Hisfs^∗95)-were identified in four subjects from three families. All variants severely affected protein stability, causing accelerated degradation, and variably perturbed SPRED2 functional behavior. When overexpressed in cells, all variants were unable to negatively modulate EGF-promoted RAF1, MEK, and ERK phosphorylation, and time-course experiments in primary fibroblasts (p.Leu100Pro and p.Leu381Hisfs^∗95) documented an increased and prolonged activation of the MAPK cascade in response to EGF stimulation. Morpholino-mediated knockdown of spred2a and spred2b in zebrafish induced defects in convergence and extension cell movements indicating upregulated RAS-MAPK signaling, which were rescued by expressing wild-type SPRED2 but not the SPRED2^{Leu381Hisfs∗95} protein. The clinical phenotype of the four affected individuals included developmental delay, intellectual disability, cardiac defects, short stature, skeletal anomalies, and a typical facial gestalt as major features, without the occurrence of the distinctive skin signs characterizing Legius syndrome. These features, in part, characterize the phenotype of Spred2^-/- mice. Our findings identify the second recessive form of Noonan syndrome and document pleiotropic consequences of SPRED2 loss of function in development.

Tau fibril with membrane lipids: Insight from computational modeling and simulations

The microtubule-binding protein tau has been the center of researches concerning Alzheimer's disease (AD) due to several clinical trials of β-amyloid therapies failing recently. The availability of the tau fibril structure from AD brain enables computational modeling studies to calculate binding affinities with different ligands. In this study, the tau paired helical filaments (PHF-Tau) (PDB ID: 5O3L) was used as receptor and interactions with the lipids: 3-alpha-cholesterol; 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine; and C18:1 sphingomyelin, were explored with molecular docking, molecular dynamics, and natural bond orbital analysis. Docking sites upon solvation of the protein with transferable interatomic potential-3 points reveal the amphipathic nature of PHF-Tau and molecular dynamics simulations show that the embedded phosphocholine at the tail side gives high potential energy values with some amino acids forming H-bond interactions.

Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient α-amylase inhibitors: Synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies

In an effort to explore a class of novel antidiabetic agents, we have made an effort to synergize the α-amylase inhibitory potential of 1,3-benzothiazole and 1,3,4-oxadiazole scaffolds by combining the two into a single structure via an ether linkage. The structure of synthesized benzothiazole clubbed oxadiazole derivatives are established by different spectral techniques. The synthesized hybrids are evaluated for their in vitro inhibitory potential against α-amylase. Compound 8f is found to be the most potent with a significant inhibition (87.5 ± 0.74% at 50 μg/mL, 82.27 ± 1.85% at 25 μg/mL and 79.94 ± 1.88% at 12.5 μg/mL) when compared to positive control acarbose (77.96 ± 2.06%, 71.17 ± 0.60%, 67.24 ± 1.16% at 50 μg/mL, 25 μg/mL and 12.5 μg/mL concentration). Molecular docking of the most potent enzyme inhibitor, 8f, shows promising interaction with the binding site of biological macromolecule Aspergillus oryzae α-amylase (PDB ID: 7TAA) and human pancreatic α-amylase (PDB ID: 3BAJ). To a step further, in-depth QSAR studies show a significant correlation between the experimental and the predicted inhibitory activities with the best R_validation²= 0.8701. The developed QSAR model can provide ample information about the structural features responsible for the increase and decrease of inhibitory activity. The mechanistic interpretation of the structure-activity relationship (SAR) is done with the help of combined computational calculations i.e. molecular docking and QSAR. Finally, molecular dynamic simulations are performed to get an insight into the binding mode of the most potent derivative with α-amylase from A. oryzae (PDB ID: 7TAA) and human pancreas (PDB ID: 3BAJ).

NEDD8 Deamidation Inhibits Cullin RING Ligase Dynamics

Cullin-RING ligases (CRLs) are a significant subset of Ubiquitin E3 ligases that regulate multiple cellular substrates involved in innate immunity, cytoskeleton modeling, and cell cycle. The glutamine deamidase Cycle inhibitory factor (Cif) from enteric bacteria inactivates CRLs to modulate these processes in the host cell. The covalent attachment of a Ubiquitin-like protein NEDD8 catalytically activates CRLs by driving conformational changes in the Cullin C-terminal domain (CTD). NEDDylation results in a shift from a compact to an open CTD conformation through non-covalent interactions between NEDD8 and the WHB subdomain of CTD, eliminating the latter’s inhibitory interactions with the RING E3 ligase-Rbx1/2. It is unknown whether the non-covalent interactions are sufficient to stabilize Cullin CTD’s catalytic conformation. We studied the dynamics of Cullin-CTD in the presence and absence of NEDD8 using atomistic molecular dynamics (MD) simulations. We uncovered that NEDD8 engages in non-covalent interactions with 4HB/αβ subdomains in Cullin-CTD to promote open conformations. Cif deamidates glutamine 40 in NEDD8 to inhibit the conformational change in CRLs by an unknown mechanism. We investigated the effect of glutamine deamidation on NEDD8 and its interaction with the WHB subdomain post-NEDDylation using MD simulations and NMR spectroscopy. Our results suggest that deamidation creates a new intramolecular salt bridge in NEDD8 to destabilize the NEDD8/WHB complex and reduce CRL activity.