The backrub motion: how protein backbone shrugs when a sidechain dances

Automated multiconformer model building for X-ray crystallography and cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination

Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.

High-resolution double vision of the allosteric phosphatase PTP1B

Protein tyrosine phosphatase 1B (PTP1B) plays important roles in cellular homeostasis and is a highly validated therapeutic target for multiple human ailments, including diabetes, obesity and breast cancer. However, much remains to be learned about how conformational changes may convey information through the structure of PTP1B to enable allosteric regulation by ligands or functional responses to mutations. High-resolution X-ray crystallography can offer unique windows into protein conformational ensembles, but comparison of even high-resolution structures is often complicated by differences between data sets, including non-isomorphism. Here, the highest resolution crystal structure of apo wild-type (WT) PTP1B to date is presented out of a total of ∼350 PTP1B structures in the PDB. This structure is in a crystal form that is rare for PTP1B, with two unique copies of the protein that exhibit distinct patterns of conformational heterogeneity, allowing a controlled comparison of local disorder across the two chains within the same asymmetric unit. The conformational differences between these chains are interrogated in the apo structure and between several recently reported high-resolution ligand-bound structures. Electron-density maps in a high-resolution structure of a recently reported activating double mutant are also examined, and unmodeled alternate conformations in the mutant structure are discovered that coincide with regions of enhanced conformational heterogeneity in the new WT structure. These results validate the notion that these mutations operate by enhancing local dynamics, and suggest a latent susceptibility to such changes in the WT enzyme. Together, these new data and analysis provide a detailed view of the conformational ensemble of PTP1B and highlight the utility of high-resolution crystallography for elucidating conformational heterogeneity with potential relevance for function.

The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions

Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedures and their validation break down and do not ensure reliably correct models. This is because in the broad density at lower resolution, critical features such as protein backbone carbonyl O atoms are not just less accurate but are not seen at all, and so peptide orientations are frequently wrongly fitted by 90-180°. This puts both backbone and side chains into the wrong local energy minimum, and they are then worsened rather than improved by further refinement into a valid but incorrect rotamer or Ramachandran region. On the positive side, new tools are being developed to locate this type of pernicious error in PDB depositions, such as CaBLAM, EMRinger, Pperp diagnosis of ribose puckers, and peptide flips in PDB-REDO, while interactive modeling in Coot or ISOLDE can help to fix many of them. Another positive trend is that artificial intelligence predictions such as those made by AlphaFold2 contribute additional evidence from large multiple sequence alignments, and in high-confidence parts they provide quite good starting models for loops, termini or whole domains with otherwise ambiguous density.

Understanding Cysteine Reactivity in Protein Environments with Electric Fields

The role cysteine residues play in proteins is mediated by their protonation state, whereby the thiolate form of the side chain is highly reactive while the thiol form is more inert. However, the pKa of cysteine residues is hard to predict as it can differ widely from its reference value in solution, an effect that is accentuated by local effects in the heterogeneous protein environment. Here, we present a new approach to the prediction of cysteine reactivity based on electric field calculations at the thiol/thiolate group. We validated our approach by predicting the protonation state of cysteine residues in different protein environments (in the active site, at the protein surface, and buried within the protein interior), including Cys-25 in papaya protease omega, which was proven problematic for the more traditional constant pH molecular dynamics (MD) technique. We predict pKa shifts consistent with experimental observations, and the decomposition of the electric fields into contributions from molecular fragments provides a direct handle to rationalize local pH and pKa effects in proteins without introducing parameters other than those of the force field used for MD simulations.

Refinement of multiconformer ensemble models from multi-temperature X-ray diffraction data

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363 K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

Elucidating Residue-Level Determinants Affecting Dimerization of Ebola Virus Matrix Protein Using High-Throughput Site Saturation Mutagenesis and Biophysical Approaches

The Ebola virus (EBOV) is a filamentous virus that acquires its lipid envelope from the plasma membrane of the host cell it infects. EBOV assembly and budding from the host cell plasma membrane are mediated by a peripheral protein, known as the matrix protein VP40. VP40 is a 326 amino acid protein with two domains that are loosely linked. The VP40 N-terminal domain (NTD) contains a hydrophobic α-helix, which mediates VP40 dimerization. The VP40 C-terminal domain has a cationic patch, which mediates interactions with anionic lipids and a hydrophobic region that mediates VP40 dimer-dimer interactions. The VP40 dimer is necessary for trafficking to the plasma membrane inner leaflet and interactions with anionic lipids to mediate the VP40 assembly and oligomerization. Despite significant structural information available on the VP40 dimer structure, little is known on how the VP40 dimer is stabilized and how residues outside the NTD hydrophobic portion of the α-helical dimer interface contribute to dimer stability. To better understand how VP40 dimer stability is maintained, we performed computational studies using per-residue energy decomposition and site saturation mutagenesis. These studies revealed a number of novel keystone residues for VP40 dimer stability just adjacent to the α-helical dimer interface as well as distant residues in the VP40 CTD that can stabilize the VP40 dimer form. Experimental studies with representative VP40 mutants in vitro and in cells were performed to test computational predictions that reveal residues that alter VP40 dimer stability. Taken together, these studies provide important biophysical insights into VP40 dimerization and may be useful in strategies to weaken or alter the VP40 dimer structure as a means of inhibiting the EBOV assembly.

Synergism between x-ray crystallography and NMR residual dipolar couplings in characterizing protein dynamics

The important role of structural dynamics in protein function is widely recognized. Thermal or B-factors and their anisotropy, seen in x-ray analysis of protein structures, report on the presence of atomic coordinate heterogeneity that can be attributed to motion. However, their quantitative evaluation in terms of protein dynamics by x-ray ensemble refinement remains challenging. NMR spectroscopy provides quantitative information on the amplitudes and time scales of motional processes. Unfortunately, with a few exceptions, the NMR data do not provide direct insights into the atomic details of dynamic trajectories. Residual dipolar couplings, measured by solution NMR, are very precise parameters reporting on the time-averaged bond-vector orientations and may offer the opportunity to derive correctly weighted dynamic ensembles of structures for cases where multiple high-resolution x-ray structures are available. Applications to the SARS-CoV-2 main protease, Mpro, and ubiquitin highlight this complementarity of NMR and crystallography for quantitative assessment of internal motions.

Validation of X-ray Crystal Structure Ensemble Representations of SARS-CoV-2 Main Protease by Solution NMR Residual Dipolar Couplings

Considerable debate has focused on whether sampling of molecular dynamics trajectories restrained by crystallographic data can be used to develop realistic ensemble models for proteins in their natural, solution state. For the SARS-CoV-2 main protease, Mpro, we evaluated agreement between solution residual dipolar couplings (RDCs) and various recently reported multi-conformer and dynamic-ensemble crystallographic models. Although Phenix-derived ensemble models showed only small improvements in crystallographic Rfree, substantially improved RDC agreement over fits to a conventionally refined 1.2-Å X-ray structure was observed, in particular for residues with above average disorder in the ensemble. For a set of six lower resolution (1.55-2.19 Å) Mpro X-ray ensembles, obtained at temperatures ranging from 100 to 310 K, no significant improvement over conventional two-conformer representations was found. At the residue level, large differences in motions were observed among these ensembles, suggesting high uncertainties in the X-ray derived dynamics. Indeed, combining the six ensembles from the temperature series with the two 1.2-Å X-ray ensembles into a single 381-member "super ensemble" averaged these uncertainties and substantially improved agreement with RDCs. However, all ensembles showed excursions that were too large for the most dynamic fraction of residues. Our results suggest that further improvements to X-ray ensemble refinement are feasible, and that RDCs provide a sensitive benchmark in such endeavors. Remarkably, a weighted ensemble of 350 PDB Mpro X-ray structures provided slightly better cross-validated agreement with RDCs than any individual ensemble refinement, implying that differences in lattice confinement also limit the fit of RDCs to X-ray coordinates.

Refinement of Multiconformer Ensemble Models from Multi-temperature X-ray Diffraction Data

Conformational ensembles underlie all protein functions. Thus, acquiring atomic-level ensemble models that accurately represent conformational heterogeneity is vital to deepen our understanding of how proteins work. Modeling ensemble information from X-ray diffraction data has been challenging, as traditional cryo-crystallography restricts conformational variability while minimizing radiation damage. Recent advances have enabled the collection of high quality diffraction data at ambient temperatures, revealing innate conformational heterogeneity and temperature-driven changes. Here, we used diffraction datasets for Proteinase K collected at temperatures ranging from 313 to 363K to provide a tutorial for the refinement of multiconformer ensemble models. Integrating automated sampling and refinement tools with manual adjustments, we obtained multiconformer models that describe alternative backbone and sidechain conformations, their relative occupancies, and interconnections between conformers. Our models revealed extensive and diverse conformational changes across temperature, including increased bound peptide ligand occupancies, different Ca2+ binding site configurations and altered rotameric distributions. These insights emphasize the value and need for multiconformer model refinement to extract ensemble information from diffraction data and to understand ensemble-function relationships.

Enhanced conformational exploration of protein loops using a global parameterization of the backbone geometry

Flexible loops are paramount to protein functions, with action modes ranging from localized dynamics contributing to the free energy of the system, to large amplitude conformational changes accounting for the repositioning whole secondary structure elements or protein domains. However, generating diverse and low energy loops remains a difficult problem. This work introduces a novel paradigm to sample loop conformations, in the spirit of the hit-and-run (HAR) Markov chain Monte Carlo technique. The algorithm uses a decomposition of the loop into tripeptides, and a novel characterization of necessary conditions for Tripeptide Loop Closure to admit solutions. Denoting m the number of tripeptides, the algorithm works in an angular space of dimension 12 m. In this space, the hyper-surfaces associated with the aforementioned necessary conditions are used to run a HAR-like sampling technique. On classical loop cases up to 15 amino acids, our parameter free method compares favorably to previous work, generating more diverse conformational ensembles. We also report experiments on a 30 amino acids long loop, a size not processed in any previous work.

A Retrospective on the Development of Methods for the Analysis of Protein Conformational Ensembles

Analysing protein conformational ensembles whether from molecular dynamics (MD) simulation or other sources for functionally relevant conformational changes can be very challenging. In the nineteen nineties dimensional reduction methods were developed primarily for analysing MD trajectories to determine dominant motions with the aim of understanding their relationship to function. Coarse-graining methods were also developed so the conformational change between two structures could be described in terms of the relative motion of a small number of quasi-rigid regions rather than in terms of a large number of atoms. When these methods are combined, they can characterize the large-scale motions inherent in a conformational ensemble providing insight into possible functional mechanism. The dimensional reduction methods first applied to protein conformational ensembles were referred to as Quasi-Harmonic Analysis, Principal Component Analysis and Essential Dynamics Analysis. A retrospective on the origin of these methods is presented, the relationships between them explained, and more recent developments reviewed.

Computer-aided design of a cyclic di-AMP synthesizing enzyme CdaA inhibitor

Cyclic di-AMP (c-di-AMP) is an essential secondary messenger regulating cell wall homeostasis and myriads of physiological processes in several Gram-positive and mycobacteria, including human pathogens. Hence, c-di-AMP synthesizing enzymes (DACs) have become a promising antibacterial drug target. To overcome a scarcity of small molecule inhibitors of c-di-AMP synthesizing enzyme CdaA, a computer-aided design of a new compound that should block the enzyme has been performed. This has led to the identification of a molecule comprising two thiazole rings and showing inhibitory potential based on ITC measurements. Thiazole scaffold is a good pharmacophore nucleus known due to its various pharmaceutical applications. It is contained in more than 18 FDA-approved drugs as well as in dozens of experimental drugs. Hence, the designed inhibitor can serve as a potent lead compound for further development of inhibitor against CdaA.

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.

Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin

CYP105AS1 is a cytochrome P450 from Amycolatopsis orientalis that catalyzes monooxygenation of compactin to 6-epi-pravastatin. For fermentative production of the cholesterol-lowering drug pravastatin, the stereoselectivity of the enzyme needs to be inverted, which has been partially achieved by error-prone PCR mutagenesis and screening. In the current study, we report further optimization of the stereoselectivity by a computationally aided approach. Using the CoupledMoves protocol of Rosetta, a virtual library of mutants was designed to bind compactin in a pro-pravastatin orientation. By examining the frequency of occurrence of beneficial substitutions and rational inspection of their interactions, a small set of eight mutants was predicted to show the desired selectivity and these variants were tested experimentally. The best CYP105AS1 variant gave >99% stereoselective hydroxylation of compactin to pravastatin, with complete elimination of the unwanted 6-epi-pravastatin diastereomer. The enzyme-substrate complexes were also examined by ultrashort molecular dynamics simulations of 50 × 100 ps and 5 × 22 ns, which revealed that the frequency of occurrence of near-attack conformations agreed with the experimentally observed stereoselectivity. These results show that a combination of computational methods and rational inspection could improve CYP105AS1 stereoselectivity beyond what was obtained by directed evolution. Moreover, the work lays out a general in silico framework for specificity engineering of enzymes of known structure.

The temperature-dependent conformational ensemble of SARS-CoV-2 main protease (Mpro)

The COVID-19 pandemic, instigated by the SARS-CoV-2 coronavirus, continues to plague the globe. The SARS-CoV-2 main protease, or Mpro, is a promising target for the development of novel antiviral therapeutics. Previous X-ray crystal structures of Mpro were obtained at cryogenic tem-per-ature or room tem-per-ature only. Here we report a series of high-resolution crystal structures of unliganded Mpro across multiple tem-per-atures from cryogenic to physiological, and another at high humidity. We inter-rogate these data sets with parsimonious multiconformer models, multi-copy ensemble models, and isomorphous difference density maps. Our analysis reveals a perturbation-dependent conformational landscape for Mpro, including a mobile zinc ion inter-leaved between the catalytic dyad, mercurial conformational heterogeneity at various sites including a key substrate-binding loop, and a far-reaching intra-molecular network bridging the active site and dimer inter-face. Our results may inspire new strategies for antiviral drug development to aid preparation for future coronavirus pandemics.

Principal Component Analysis and Related Methods for Investigating the Dynamics of Biological Macromolecules

Principal component analysis (PCA) is used to reduce the dimensionalities of high-dimensional datasets in a variety of research areas. For example, biological macromolecules, such as proteins, exhibit many degrees of freedom, allowing them to adopt intricate structures and exhibit complex functions by undergoing large conformational changes. Therefore, molecular simulations of and experiments on proteins generate a large number of structure variations in high-dimensional space. PCA and many PCA-related methods have been developed to extract key features from such structural data, and these approaches have been widely applied for over 30 years to elucidate macromolecular dynamics. This review mainly focuses on the methodological aspects of PCA and related methods and their applications for investigating protein dynamics.

Tuning the Catalytic Activity of Synthetic Enzyme KE15 with DNA

Efficiency improvement of synthetic enzymes through scaffold modifications suffers from limitations in terms of effectiveness, cost, and potential devastating consequences for protein structural stability. Here, we propose an alternative to scaffold modification, within electrostatic preorganization theory, where the enzyme's greater environment is designed to support the evolution of the reaction in the active site. We demonstrate the feasibility of such an approach by placing a (polar) DNA fragment in the surroundings of the Kemp eliminase enzyme KE15 (structure from Houk's group) and computing the resulting change in catalytic activity. We find that the introduction of a DNA fragment magnifies the contribution of protein residues to the stabilization of the transition state, estimated from electric field calculations with polarizable molecular dynamics. Our randomly generated test systems reveal a 2.0 kcal/mol reduction in activation energy, suggesting that even more significant catalytic improvements could be made by optimizing DNA size, sequence, and orientation with respect to the enzyme, validating our approach.

Algorithms for protein design

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

Structural dynamics support electrostatic interactions in the active site of Adenylate Kinase

Electrostatic preorganization as well as structural and dynamic heterogeneity are often used to rationalize the remarkable catalytic efficiency of enzymes. However, they are often presented as incompatible because the generation of permanent electrostatic effects implies that the protein structure remains rigid. Here, we use a metric, electric fields, that can treat electrostatic contributions and dynamics effects on equal footing, for a unique perspective on enzymatic catalysis. We find that the residues that contribute the most to electrostatic interactions with the substrate in the active site of Adenylate Kinase (our working example) are also the most flexible residues. Further, entropy-tuning mutations raise flexibility at the picosecond timescale where more conformations can be visited on short time periods, thereby softening the sharp heterogeneity normally visible at the microsecond timescale.

Computational Design of Miniprotein Binders

Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.

Importance of protein intrinsic conformational dynamics and transient nature of non-covalent interactions in ligand binding affinity

We have recently identified BEN1 as a protein interactor of seryl-tRNA synthetase (SerRS) from model plant Arabidopsis thaliana. BEN1 contains an NADP⁺ binding domain and possesses acidic N-terminal extension essential for interaction with A. thaliana SerRS. This extension, specific for BEN1 homologues from Brassicaceae family, is solvent-exposed and distant to the nucleotide-binding site. We prepared a truncated BEN1 variant ΔN17BEN1 lacking the first 17 amino acid of this N-terminal extension as well as full-length BEN1 to investigate how the truncation affects the binding affinity towards coenzyme NADP⁺. By performing microscale thermophoresis (MST) experiments we have shown that both BEN1 variants bind the NADP⁺ cofactor, however, truncated BEN1 showed 34-fold higher affinity towards NADP⁺ indicating that its core protein structure is not just preserved but it binds NADP⁺ even stronger. To further corroborate the obtained results, we opted for a computational approach based on classical molecular dynamics simulations of both complexes. Our results have shown that both truncated and intact BEN1 variants form the same number of interactions with the NADP⁺ cofactor; however, it was the interaction occupancy that was affected. Namely, three independent MD simulations showed that the ΔN17BEN1 variant in complex with NADP⁺ has significantly higher interaction occupancy thus binds NADP⁺ with more than one order of magnitude higher affinity. Contrary to our expectations, the truncation of this distant region that does not communicate with the nucleotide-binding site didn't result in the gain of interaction but affected the intrinsic conformational dynamics which in turn fine-tuned the binding affinity by increasing the interaction occupancy and strength of the key conserved cation-π interaction between Arg69 and adenine of NADP⁺ and hydrogen bond between Ser244 and phosphate of NADP⁺.

Priming of NLRP3 inflammasome activation by Msn kinase MINK1 in macrophages

The nucleotide-binding domain, leucine-rich-repeat containing family, pyrin domain-containing 3 (NLRP3) inflammasome is essential in inflammation and inflammatory disorders. Phosphorylation at various sites on NLRP3 differentially regulates inflammasome activation. The Ser725 phosphorylation site on NLRP3 is depicted in multiple inflammasome activation scenarios, but the importance and regulation of this site has not been clarified. The present study revealed that the phosphorylation of Ser725 was an essential step for the priming of the NLRP3 inflammasome in macrophages. We also showed that Ser725 was directly phosphorylated by misshapen (Msn)/NIK-related kinase 1 (MINK1), depending on the direct interaction between MINK1 and the NLRP3 LRR domain. MINK1 deficiency reduced NLRP3 activation and suppressed inflammatory responses in mouse models of acute sepsis and peritonitis. Reactive oxygen species (ROS) upregulated the kinase activity of MINK1 and subsequently promoted inflammasome priming via NLRP3 Ser725 phosphorylation. Eliminating ROS suppressed NLRP3 activation and reduced sepsis and peritonitis symptoms in a MINK1-dependent manner. Altogether, our study reveals a direct regulation of the NLRP3 inflammasome by Msn family kinase MINK1 and suggests that modulation of MINK1 activity is a potential intervention strategy for inflammasome-related diseases.

Computationally Designed ACE2 Decoy Receptor Binds SARS-CoV-2 Spike (S) Protein with Tight Nanomolar Affinity

Even with the availability of vaccines, therapeutic options for COVID-19 still remain highly desirable, especially in hospitalized patients with moderate or severe disease. Soluble ACE2 (sACE2) is a promising therapeutic candidate that neutralizes SARS CoV-2 infection by acting as a decoy. Using computational mutagenesis, we designed a number of sACE2 derivatives carrying three to four mutations. The top-predicted sACE2 decoy based on the in silico mutagenesis scan was subjected to molecular dynamics and free-energy calculations for further validation. After illuminating the mechanism of increased binding for our designed sACE2 derivative, the design was verified experimentally by flow cytometry and BLI-binding experiments. The computationally designed sACE2 decoy (ACE2-FFWF) bound the receptor-binding domain of SARS-CoV-2 tightly with low nanomolar affinity and ninefold affinity enhancement over the wild type. Furthermore, cell surface expression was slightly greater than wild-type ACE2, suggesting that the design is well-folded and stable. Having an arsenal of high-affinity sACE2 derivatives will help to buffer against the emergence of SARS CoV-2 variants. Here, we show that computational methods have become sufficiently accurate for the design of therapeutics for current and future viral pandemics.

NbX: Machine Learning-Guided Re-Ranking of Nanobody-Antigen Binding Poses

Modeling the binding pose of an antibody is a prerequisite to structure-based affinity maturation and design. Without knowing a reliable binding pose, the subsequent structural simulation is largely futile. In this study, we have developed a method of machine learning-guided re-ranking of antigen binding poses of nanobodies, the single-domain antibody which has drawn much interest recently in antibody drug development. We performed a large-scale self-docking experiment of nanobody–antigen complexes. By training a decision tree classifier through mapping a feature set consisting of energy, contact and interface property descriptors to a measure of their docking quality of the refined poses, significant improvement in the median ranking of native-like nanobody poses by was achieved eightfold compared with ClusPro and an established deep 3D CNN classifier of native protein–protein interaction. We further interpreted our model by identifying features that showed relatively important contributions to the prediction performance. This study demonstrated a useful method in improving our current ability in pose prediction of nanobodies.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

Molecular flexibility in computational protein design: an algorithmic perspective

Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.

Determination of amino acids that favour the αL region using Ramachandran propensity plots. Implications for α-sheet as the possible amyloid intermediate

In amyloid diseases an insoluble amyloid fibril forms via a soluble oligomeric intermediate. It is this intermediate that mediates toxicity and it has been suggested, somewhat controversially, that it has the α-sheet structure. Nests and α-strands are similar peptide motifs in that alternate residues lie in the α_R and γ_L regions of the Ramachandran plot for nests, or α_R and α_L regions for α-strands. In nests a concavity is formed by the main chain NH atoms whereas in α-strands the main chain is almost straight. Using "Ramachandran propensity plots" to focus on the α_L/γ_L region, it is shown that glycine favours γ_L (82% of amino acids are glycine), but disfavours α_L (3% are glycine). Most charged and polar amino acids favour α_L with asparagine having by far the highest propensity. Thus, glycine favours nests but, contrary to common expectation, should not favour α-sheet. By contrast most charged or polar amino acids should favour α-sheet by their propensity for the α_L conformation, which is more discriminating amongst amino acids than the α_R conformation. Thus, these results suggest the composition of sequences that favour α-sheet formation and point towards effective prediction of α-sheet from sequence.

Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders

The prediction of peptide binders to Major Histocompatibility Complex (MHC) class II receptors is of great interest to study autoimmune diseases and for vaccine development. Most approaches predict the affinities using sequence-based models trained on experimental data and multiple alignments from known peptide substrates. However, detecting activity differences caused by single-point mutations is a challenging task. In this work, we used interactions calculated from simulations to build scoring matrices for quickly estimating binding differences by single-point mutations. We modelled a set of 837 peptides bound to an MHC class II allele, and optimized the sampling of the conformations using the Rosetta backrub method by comparing the results to molecular dynamics simulations. From the dynamic trajectories of each complex, we averaged and compared structural observables for each amino acid at each position of the 9°mer peptide core region. With this information, we generated the scoring-matrices to predict the sign of the binding differences. We then compared the performance of the best scoring-matrix to different computational methodologies that range in computational costs. Overall, the prediction of the activity differences caused by single mutated peptides was lower than 60% for all the methods. However, the developed scoring-matrix in combination with existing methods reports an increase in the performance, up to 86% with a scoring method that uses molecular dynamics.

Molecular free energy optimization on a computational graph

Free energy is arguably the most important property of molecular systems. Despite great progress in both its efficient estimation by scoring functions/potentials and more rigorous computation based on extensive sampling, we remain far from accurately predicting and manipulating biomolecular structures and their interactions. There are fundamental limitations, including accuracy of interaction description and difficulty of sampling in high dimensional space, to be tackled. Computational graph underlies major artificial intelligence platforms and is proven to facilitate training, optimization and learning. Combining autodifferentiation, coordinates transformation and generalized solvation free energy theory, we construct a computational graph infrastructure to realize seamless integration of fully trainable local free energy landscape with end to end differentiable iterative free energy optimization. This new framework drastically improves efficiency by replacing local sampling with differentiation. Its specific implementation in protein structure refinement achieves superb efficiency and competitive accuracy when compared with state of the art all-atom mainstream methods.

Improving SARS-CoV-2 structures: Peer review by early coordinate release

This work builds upon the record-breaking speed and generous immediate release of new experimental three-dimensional structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins and complexes, which are crucial to downstream vaccine and drug development. We have surveyed those structures to catch the occasional errors that could be significant for those important uses and for which we were able to provide demonstrably higher-accuracy corrections. This process relied on new validation and correction methods such as CaBLAM and ISOLDE, which are not yet in routine use. We found such important and correctable problems in seven early SARS-CoV-2 structures. Two of the structures were soon superseded by new higher-resolution data, confirming our proposed changes. For the other five, we emailed the depositors a documented and illustrated report and encouraged them to make the model corrections themselves and use the new option at the worldwide Protein Data Bank for depositors to re-version their coordinates without changing the Protein Data Bank code. This quickly and easily makes the better-accuracy coordinates available to anyone who examines or downloads their structure, even before formal publication. The changes have involved sequence misalignments, incorrect RNA conformations near a bound inhibitor, incorrect metal ligands, and cis-trans or peptide flips that prevent good contact at interaction sites. These improvements have propagated into nearly all related structures done afterward. This process constitutes a new form of highly rigorous peer review, which is actually faster and more strict than standard publication review because it has access to coordinates and maps; journal peer review would also be strengthened by such access.

Recent advances in de novo protein design: Principles, methods, and applications

The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.

qFit 3: Protein and ligand multiconformer modeling for X-ray crystallographic and single-particle cryo-EM density maps

New X-ray crystallography and cryo-electron microscopy (cryo-EM) approaches yield vast amounts of structural data from dynamic proteins and their complexes. Modeling the full conformational ensemble can provide important biological insights, but identifying and modeling an internally consistent set of alternate conformations remains a formidable challenge. qFit efficiently automates this process by generating a parsimonious multiconformer model. We refactored qFit from a distributed application into software that runs efficiently on a small server, desktop, or laptop. We describe the new qFit 3 software and provide some examples. qFit 3 is open-source under the MIT license, and is available at https://github.com/ExcitedStates/qfit-3.0.

Histone Deacetylase 3 Couples Mitochondria to Drive IL-1β-Dependent Inflammation by Configuring Fatty Acid Oxidation

Immune cell function depends on specific metabolic programs dictated by mitochondria, including nutrient oxidation, macromolecule synthesis, and post-translational modifications. Mitochondrial adaptations have been linked to acute and chronic inflammation, but the metabolic cues and precise mechanisms remain unclear. Here we reveal that histone deacetylase 3 (HDAC3) is essential for shaping mitochondrial adaptations for IL-1β production in macrophages through non-histone deacetylation. In vivo, HDAC3 promoted lipopolysaccharide-induced acute inflammation and high-fat diet-induced chronic inflammation by enhancing NLRP3-dependent caspase-1 activation. HDAC3 configured the lipid profile in stimulated macrophages and restricted fatty acid oxidation (FAO) supported by exogenous fatty acids for mitochondria to acquire their adaptations and depolarization. Rather than affecting nuclear gene expression, HDAC3 translocated to mitochondria to deacetylate and inactivate an FAO enzyme, mitochondrial trifunctional enzyme subunit α. HDAC3 may serve as a controlling node that balances between acquiring mitochondrial adaptations and sustaining their fitness for IL-1β-dependent inflammation.

N6-Adenosine methylation on mRNA is recognized by YTH2 domain protein of human malaria parasite Plasmodium falciparum

Background

Plasmodium falciparum exhibits high translational plasticity during its development in RBCs, yet the regulation at the post-transcriptional level is not well understood. The N⁶-methyl adenosine (m6A) is an important epigenetic modification primarily present on mRNA that controls the levels of transcripts and efficiency of translation in eukaryotes. Recently, the dynamics of m6A on mRNAs at all three developmental stages of P. falciparum in RBCs have been profiled; however, the proteins that regulate the m6A containing mRNAs in the parasites are unknown.

Results

Using sequence analysis, we computationally identified that the P. falciparum genome encodes two putative YTH (YT521-B Homology) domain-containing proteins, which could potentially bind to m6A containing mRNA. We developed a modified methylated RNA immunoprecipitation (MeRIP) assay using PfYTH2 and find that it binds selectively to m6A containing transcripts. The PfYTH2 has a conserved aromatic amino acid cage that forms the methyl-binding pocket. Through site-directed mutagenesis experiments and molecular dynamics simulations, we show that F98 residue is important for m6A binding on mRNA. Fluorescence depolarization assay confirmed that PfYTH2 binds to methylated RNA oligos with high affinity. Further, MeRIP sequencing data revealed that PfYTH2 has more permissive sequence specificity on target m6A containing mRNA than other known eukaryotic YTH proteins. Taken together, here we identify and characterize PfYTH2 as the major protein that could regulate m6A containing transcripts in P. falciparum.

Conclusion

Plasmodium spp. lost the canonical m6A-specific demethylases in their genomes, however, the YTH domain-containing proteins seem to be retained. This study presents a possibility that the YTH proteins are involved in post-transcriptional control in P. falciparum, and might orchestrate the translation of mRNA in various developmental stages of P. falciparum. This is perhaps the first characterization of the methyl-reading function of YTH protein in any parasites.

Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning

Protein-protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. However, accurate predictions of the effects of interfacial mutations to identify "hotspots" have remained elusive despite the myriad of modeling and machine learning methods tested. Here, for the first time, we demonstrate that nonlinear reweighting of energy terms from Rosetta, through the use of machine learning, exhibits improved predictability of ΔΔG values associated with interfacial mutations.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

Current developments in Coot for macromolecular model building of Electron Cryo-microscopy and Crystallographic Data

Coot is a tool widely used for model building, refinement, and validation of macromolecular structures. It has been extensively used for crystallography and, more recently, improvements have been introduced to aid in cryo‐EM model building and refinement, as cryo‐EM structures with resolution ranging 2.5–4 A are now routinely available. Model building into these maps can be time‐consuming and requires experience in both biochemistry and building into low‐resolution maps. To simplify and expedite the model building task, and minimize the needed expertise, new tools are being added in Coot. Some examples include morphing, Geman‐McClure restraints, full‐chain refinement, and Fourier‐model based residue‐type‐specific Ramachandran restraints. Here, we present the current state‐of‐the‐art in Coot usage.

Affinity-matured variants derived from nimotuzumab keep the original fine specificity and exhibit superior biological activity

Nimotuzumab is a humanized monoclonal antibody against the Epidermal Growth Factor Receptor with a long history of therapeutic use, recognizing an epitope different from the ones targeted by other antibodies against the same antigen. It is also distinguished by much less toxicity resulting in a better safety profile, which has been attributed to its lower affinity compared to these other antibodies. Nevertheless, the ideal affinity window for optimizing the balance between anti-tumor activity and toxic effects has not been determined. In the current work, the paratope of the phage-displayed nimotuzumab Fab fragment was evolved in vitro to obtain affinity-matured variants. Soft-randomization of heavy chain variable region CDRs and phage selection resulted in mutated variants with improved binding ability. Two recombinant antibodies were constructed using these variable regions, which kept the original fine epitope specificity and showed moderate affinity increases against the target (3-4-fold). Such differences were translated into a greatly enhanced inhibitory capacity upon ligand-induced receptor phosphorylation on tumor cells. The new antibodies, named K4 and K5, are valuable tools to explore the role of affinity in nimotuzumab biological properties, and could be used for applications requiring a fine-tuning of the balance between binding to tumor cells and healthy tissues.

New tools in MolProbity validation: CaBLAM for CryoEM backbone, UnDowser to rethink "waters," and NGL Viewer to recapture online 3D graphics

The MolProbity web service provides macromolecular model validation to help correct local errors, for the structural biology community worldwide. Here we highlight new validation features, and also describe how we are fighting back against outside developments which compromise that mission. Our new tool called UnDowser analyzes the properties and context of clashing HOH "waters" to diagnose what they might actually represent; a dozen distinct scenarios are illustrated and described. We now treat alternate conformations more thoroughly, and switching to the Neo4j database (graphical rather than relational) enables cleaner, more comprehensive, and much larger reference datasets. A problematic outside change is that refinement software now increasingly restrains traditional validation criteria (geometry, clashes, rotamers, and even Ramachandran) in order to supplement the sparser experimental data at 3-4 Å resolutions typical of modern cryoEM. But unfortunately the broad density allows model optimization without fixing underlying problems, which means these structures often score much better on validation than they really are. CaBLAM, our tool designed for evaluating peptide orientations at lower resolutions, was described in the previous Tools issue, and here we demonstrate its effectiveness in diagnosing local errors even when other validation outliers have been artificially removed. Sophisticated hacking of the MolProbity server has required continual monitoring and various security measures short of restricting user access. The deprecation of Java applets now prevents KiNG interactive online display of outliers on the 3D model during a MolProbity run, but that important functionality has now been recaptured with a modified version of the Javascript NGL Viewer.

Improved chemistry restraints for crystallographic refinement by integrating the Amber force field into Phenix

The refinement of biomolecular crystallographic models relies on geometric restraints to help to address the paucity of experimental data typical in these experiments. Limitations in these restraints can degrade the quality of the resulting atomic models. Here, an integration of the full all-atom Amber molecular-dynamics force field into Phenix crystallographic refinement is presented, which enables more complete modeling of biomolecular chemistry. The advantages of the force field include a carefully derived set of torsion-angle potentials, an extensive and flexible set of atom types, Lennard-Jones treatment of nonbonded interactions and a full treatment of crystalline electrostatics. The new combined method was tested against conventional geometry restraints for over 22 000 protein structures. Structures refined with the new method show substantially improved model quality. On average, Ramachandran and rotamer scores are somewhat better, clashscores and MolProbity scores are significantly improved, and the modeling of electrostatics leads to structures that exhibit more, and more correct, hydrogen bonds than those refined using traditional geometry restraints. In general it is found that model improvements are greatest at lower resolutions, prompting plans to add the Amber target function to real-space refinement for use in electron cryo-microscopy. This work opens the door to the future development of more advanced applications such as Amber-based ensemble refinement, quantum-mechanical representation of active sites and improved geometric restraints for simulated annealing.

B, Berger JM, Gray JJ. Toward the computational design of protein crystals with improved resolution

Substantial advances have been made in the computational design of protein interfaces over the last 20 years. However, the interfaces targeted by design have typically been stable and high-affinity. Here, we report the development of a generic computational design method to stabilize the weak interactions at crystallographic interfaces. Initially, we analyzed structures reported in the Protein Data Bank to determine whether crystals with more stable interfaces result in higher resolution structures. We found that for 22 variants of a single protein crystallized by a single individual, the Rosetta-calculated `crystal score' correlates with the reported diffraction resolution. We next developed and tested a computational design protocol, seeking to identify point mutations that would improve resolution in a highly stable variant of staphylococcal nuclease (SNase). Using a protocol based on fixed protein backbones, only one of the 11 initial designs crystallized, indicating modeling inaccuracies and forcing us to re-evaluate our strategy. To compensate for slight changes in the local backbone and side-chain environment, we subsequently designed on an ensemble of minimally perturbed protein backbones. Using this strategy, four of the seven designed proteins crystallized. By collecting diffraction data from multiple crystals per design and solving crystal structures, we found that the designed crystals improved the resolution modestly and in unpredictable ways, including altering the crystal space group. Post hoc, in silico analysis of the three observed space groups for SNase showed that the native space group was the lowest scoring for four of six variants (including the wild type), but that resolution did not correlate with crystal score, as it did in the preliminary results. Collectively, our results show that calculated crystal scores can correlate with reported resolution, but that the correlation is absent when the problem is inverted. This outcome suggests that more comprehensive modeling of the crystallographic state is necessary to design high-resolution protein crystals from poorly diffracting crystals.

Upgraded molecular models of the human KCNQ1 potassium channel

The voltage-gated potassium channel KCNQ1 (K_V7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, I_KS, which is critical for membrane repolarization as part of the cardiac action potential. Loss-of-function (LOF) mutations in KCNQ1 are the most common cause of congenital long QT syndrome (LQTS), type 1 LQTS, an inherited genetic predisposition to cardiac arrhythmia and sudden cardiac death. A detailed structural understanding of KCNQ1 is needed to elucidate the molecular basis for KCNQ1 LOF in disease and to enable structure-guided design of new anti-arrhythmic drugs. In this work, advanced structural models of human KCNQ1 in the resting/closed and activated/open states were developed by Rosetta homology modeling guided by newly available experimentally-based templates: X. leavis KCNQ1 and various resting voltage sensor structures. Using molecular dynamics (MD) simulations, the capacity of the models to describe experimentally established channel properties including state-dependent voltage sensor gating charge interactions and pore conformations, PIP2 binding sites, and voltage sensor–pore domain interactions were validated. Rosetta energy calculations were applied to assess the utility of each model in interpreting mutation-evoked KCNQ1 dysfunction by predicting the change in protein thermodynamic stability for 50 experimentally characterized KCNQ1 variants with mutations located in the voltage-sensing domain. Energetic destabilization was successfully predicted for folding-defective KCNQ1 LOF mutants whereas wild type-like mutants exhibited no significant energetic frustrations, which supports growing evidence that mutation-induced protein destabilization is an especially common cause of KCNQ1 dysfunction. The new KCNQ1 Rosetta models provide helpful tools in the study of the structural basis for KCNQ1 function and can be used to generate hypotheses to explain KCNQ1 dysfunction.

Comparative dynamics of tropomyosin in vertebrates and invertebrates

Tropomyosin (Tpm) is an extended α-helical coiled-coil homodimer that regulates actinomyosin interactions in muscle. Molecular simulations of four Tpms, two from the vertebrate class Mammalia (rat and pig), and two from the invertebrate class Malacostraca (shrimp and lobster), showed that despite extensive sequence and structural homology across metazoans, dynamic behavior-particularly long-range structural fluctuations-were clearly distinct. Vertebrate Tpms were more flexible and sampled complex, multi-state conformational landscapes. Invertebrate Tpms were more rigid, sampling a highly constrained harmonic landscape. Filtering of trajectories by principle component analysis into essential subspaces showed significant overlap within but not between phyla. In vertebrate Tpms, hinge-regions decoupled long-range interhelical motions and suggested distinct domains. In contrast, crustacean Tpms did not exhibit long-range dynamic correlations-behaving more like a single rigid rod on the nanosecond time scale. These observations suggest there may be divergent mechanisms for Tpm binding to actin filaments, where conformational flexibility in mammalian Tpm allows a preorganized shape complementary to the filament surface, and where rigidity in the crustacean Tpm requires concerted bending and binding.

Spatiotemporal dynamics of GEF-H1 activation controlled by microtubule- and Src-mediated pathways

Rho family GTPases are activated with precise spatiotemporal control by guanine nucleotide exchange factors (GEFs). Guanine exchange factor H1 (GEF-H1), a RhoA activator, is thought to act as an integrator of microtubule (MT) and actin dynamics in diverse cell functions. Here we identify a GEF-H1 autoinhibitory sequence and exploit it to produce an activation biosensor to quantitatively probe the relationship between GEF-H1 conformational change, RhoA activity, and edge motion in migrating cells with micrometer- and second-scale resolution. Simultaneous imaging of MT dynamics and GEF-H1 activity revealed that autoinhibited GEF-H1 is localized to MTs, while MT depolymerization subadjacent to the cell cortex promotes GEF-H1 activation in an ~5-µm-wide peripheral band. GEF-H1 is further regulated by Src phosphorylation, activating GEF-H1 in a narrower band ~0-2 µm from the cell edge, in coordination with cell protrusions. This indicates a synergistic intersection between MT dynamics and Src signaling in RhoA activation through GEF-H1.

Longitudinal MRI findings in patient with SLC25A12 pathogenic variants inform disease progression and classification

Aspartate-glutamate carrier 1 (AGC1) is one of two exchangers within the malate-aspartate shuttle. AGC1 is encoded by the SLC25A12 gene. Three patients with pathogenic variants in SLC25A12 have been reported in the literature. These patients were clinically characterized by neurodevelopmental delay, epilepsy, hypotonia, cerebral atrophy, and hypomyelination; however, there has been discussion in the literature as to whether this hypomyelination is primary or secondary to a neuronal defect. Here we report a 12-year-old patient with variants in SLC25A12 and magnetic resonance imaging (MRI) at multiple ages. Novel compound heterozygous, recessive variants in SLC25A12 were identified: c.1295C>T (p.A432V) and c.1447-2_1447-1delAG. Clinical presentation is characterized by severe intellectual disability, nonambulatory, nonverbal status, hypotonia, epilepsy, spastic quadriplegia, and a happy disposition. The serial neuroimaging findings are notable for cerebral atrophy with white matter involvement, namely, early hypomyelination yet subsequent progression of myelination. The longitudinal MRI findings are most consistent with a leukodystrophy of the leuko-axonopathy category, that is, white matter abnormalities that are most suggestive of mechanisms that result from primary neuronal defects. We present here the first case of a patient with compound heterozygous variants in SLC25A12, including brain MRI findings, in the oldest individual reported to date with this neurogenetic condition.

Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions

Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.