Bluues: a program for the analysis of the electrostatic properties of proteins based on generalized Born radii

Chemical Synthesis and Structure-Activity Relationship Studies of the Coagulation Factor Xa Inhibitor Tick Anticoagulant Peptide from the Hematophagous Parasite Ornithodoros moubata

Tick Anticoagulant Peptide (TAP), a 60-amino acid protein from the soft tick Ornithodoros moubata, inhibits activated coagulation factor X (fXa) with almost absolute specificity. Despite TAP and Bovine Pancreatic Trypsin Inhibitor (BPTI) (i.e., the prototype of the Kunitz-type protease inhibitors) sharing a similar 3D fold and disulphide bond topology, they have remarkably different amino acid sequence (only ~24% sequence identity), thermal stability, folding pathways, protease specificity, and even mechanism of protease inhibition. Here, fully active and correctly folded TAP was produced in reasonably high yields (~20%) by solid-phase peptide chemical synthesis and thoroughly characterised with respect to its chemical identity, disulphide pairing, folding kinetics, conformational dynamics, and fXa inhibition. The versatility of the chemical synthesis was exploited to perform structure-activity relationship studies on TAP by incorporating non-coded amino acids at positions 1 and 3 of the inhibitor. Using Hydrogen-Deuterium Exchange Mass Spectrometry, we found that TAP has a remarkably higher conformational flexibility compared to BPTI, and propose that these different dynamics could impact the different folding pathway and inhibition mechanisms of TAP and BPTI. Hence, the TAP/BPTI pair represents a nice example of divergent evolution, while the relative facility of TAP synthesis could represent a good starting point to design novel synthetic analogues with improved pharmacological profiles.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and nonenzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or nonenzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90%-97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic

Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and non-enzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or non-enzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90 - 97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

Significance statement

Identification of enzyme active sites on proteins with unsolved crystallographic structures can accelerate discovery of novel biochemical reactions, which can impact healthcare, industrial processes, and environmental remediation. Our lab has developed an ML tool for predicting sites on computationally generated protein structures as enzymatic and non-enzymatic. We have made our tool available on a webserver, allowing the scientific community to rapidly search previously unknown protein function space.

Connection between MHC class II binding and aggregation propensity: The antigenic peptide 10 of Paracoccidioides brasiliensis as a benchmark study

The aggregation of epitopes that are also able to bind major histocompatibility complex (MHC) alleles raises questions around the potential connection between the formation of epitope aggregates and their affinities to MHC receptors. We first performed a general bioinformatic assessment over a public dataset of MHC class II epitopes, finding that higher experimental binding correlates with higher aggregation-propensity predictors. We then focused on the case of P10, an epitope used as a vaccine candidate against Paracoccidioides brasiliensis that aggregates into amyloid fibrils. We used a computational protocol to design variants of the P10 epitope to study the connection between the binding stabilities towards human MHC class II alleles and their aggregation propensities. The binding of the designed variants was tested experimentally, as well as their aggregation capacity. High-affinity MHC class II binders in vitro were more disposed to aggregate forming amyloid fibrils capable of binding Thioflavin T and congo red, while low affinity MHC class II binders remained soluble or formed rare amorphous aggregates. This study shows a possible connection between the aggregation propensity of an epitope and its affinity for the MHC class II cleft.

Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimized Binders

Nanobodies (VHHs) are engineered fragments of the camelid single-chain immunoglobulins. The VHH domain contains the highly variable segments responsible for antigen recognition. VHHs can be easily produced as recombinant proteins. Their small size is a good advantage for in silico approaches. Computer methods represent a valuable strategy for the optimization and improvement of their binding affinity. They also allow for epitope selection offering the possibility to design new VHHs for regions of a target protein that are not naturally immunogenic. Here we present an in silico mutagenic protocol developed to improve the binding affinity of nanobodies together with the first step of their in vitro production. The method, already proven successful in improving the low Kd of a nanobody hit obtained by panning, can be employed for the ex novo design of antibody fragments against selected protein target epitopes.

Multiple-Allele MHC Class II Epitope Engineering by a Molecular Dynamics-Based Evolution Protocol

Epitopes that bind simultaneously to all human alleles of Major Histocompatibility Complex class II (MHC II) are considered one of the key factors for the development of improved vaccines and cancer immunotherapies. To engineer MHC II multiple-allele binders, we developed a protocol called PanMHC-PARCE, based on the unsupervised optimization of the epitope sequence by single-point mutations, parallel explicit-solvent molecular dynamics simulations and scoring of the MHC II-epitope complexes. The key idea is accepting mutations that not only improve the affinity but also reduce the affinity gap between the alleles. We applied this methodology to enhance a Plasmodium vivax epitope for multiple-allele binding. In vitro rate-binding assays showed that four engineered peptides were able to bind with improved affinity toward multiple human MHC II alleles. Moreover, we demonstrated that mice immunized with the peptides exhibited interferon-gamma cellular immune response. Overall, the method enables the engineering of peptides with improved binding properties that can be used for the generation of new immunotherapies.

Biallelic ATOH1 Gene Variant in Siblings With Pontocerebellar Hypoplasia, Developmental Delay, and Hearing Loss

Background and Objectives

To report on the novel association of biallelic variant in atonal basic helix-loop-helix transcription factor 1 (ATOH1) gene and pontocerebellar hypoplasia (PCH), severe global developmental delay, intellectual disability, and hearing loss in a family with 2 affected siblings.

Methods

A detailed clinical assessment and exome sequencing of peripheral blood sample were performed. Segregation analysis with Sanger sequencing and structural modeling of the variant was performed to support the pathogenicity of the variant.

Results

A homozygous missense variant (NM_005172.1:c.481C>G) in the ATOH1 gene was identified in the proband and his affected sister. The segregation analysis subsequently confirmed its segregation with an apparently recessive PCH in this family. ATOH1 encodes for the atonal basic helix-loop-helix (bHLH) transcription factor 1, a core transcription factor in the developing cerebellum, brainstem, and dorsal spinal cord, and in the ear. The identified variant results in the p.(Arg161Gly) amino acid substitution in the evolutionarily conserved DNA-binding bHLH domain of the ATOH1 protein. Biallelic missense variants in this domain were previously reported to result in disordered cerebellar development and hearing loss in animal models. In silico homology modeling revealed that p.Arg161Gly in ATOH1 protein probably disrupts a salt bridge with DNA backbone phosphate and increases the flexibility of the bHLH helix-both of which together affect the binding capability of the bHLH domain to the DNA.

Discussion

Based on the sequencing results and evidence from structural modeling of the identified variant, as well as with previous reports of ATOH1 gene disruption, we conclude that ATOH1 may represent a novel candidate gene associated with the phenotype of PCH, global developmental delay, and hearing loss in humans.

Interference of p53:Twist1 interaction through competing nanobodies

Twist1 promote the bypass of p53 response by interacting with p53 and facilitating its MDM2-mediated degradation. We reasoned that reagents able to interfere with the p53:Twist1 complex might alleviate Twist1 inhibitory effect over p53, thus representing potential therapeutic tools in p53 wild type tumors. From a pre-immune library of llama nanobodies (VHH), we isolated binders targeting the p53 C-terminal region (p53-CTD) involved in the interaction with Twist1 by using recombinant Twist1 as an epitope-specific competitor during elution. Positive hits were validated by proving their capacity to immunoprecipitate p53 and to inhibit Twist1:p53 binding in vitro. Molecular modeling confirmed a preferential docking of positive hits with p53-CTD. D11 VHH activity was validated in human cell models, succeeded in immunoprecipitating endogenous p53 and, similarly to Twist1 knock-down, interfered with p53 turnover, p53 phosphorylation at Serine 392 and affected cell viability. Despite the limited functional effect determined by D11 expression in target cells, our results provide the proof of principle that nanobodies ectopically expressed within a cell, have the capacity to target the assembly of the pro-tumorigenic Twist1:p53 complex. These results disclose novel tools for dissecting p53 biology and lay down the grounds for the development of innovative targeted therapeutic approaches.

Computational Evolution Protocol for Peptide Design

Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.

Complementary experimental/docking approach for determining chitosan and carboxymethylchitosan ability for the formation of active polymer-β-galactosidase adducts

The spontaneous aggregation of chitosan and carboxymethylchitosan polymers can be advantageous for the enzyme confinement on these colloidal systems during immobilization processes. The initial crucial step involves the polymer-enzyme adduct formation. The objective here is to determine the interactions that drive the adduct formation between these polymers and β-galactosidase from Bacillus circulans. The chemical characterization of chitosan and its carboxymethyl-derivate allowed to explain their colloidal behavior and design the four-unit fragments ligands used for the docking study. The deacetylation degree (0.6 times lower), isoelectric point (5.2 instead 6.4) and substitution degree (DS_O = 1.779 and DS_2N = 0.441) of carboxymenthylchitosan are due to the hydroxide concentration (>25%) and 30 °C modification conditions. Favorable Van der Waals and H-bond interactions between chitosan-β-galactosidase and contribution of electrostatic attraction mediated by calcium ions for carboxymethylchitosan-β-galactosidase explained the zeta potential and dynamic light scattering results at pH 7.0. These interactions occur onto the external surface of this galactosidase, without affecting the catalytic activity. A cross-linked enzyme aggregates-type model was proposed for the formation of the adducts, based on the complementary experimental-docking results. They contribute understanding the behavior of polyelectrolyte chitosan-derived matrices for enzyme immobilization.

Pathogenic D76N Variant of β2-Microglobulin: Synergy of Diverse Effects in Both the Native and Amyloid States

Simple Summary

Elevated β₂-microglobulin (β2m) serum levels cause serious complications in patients on long-term kidney dialysis by depositing in the form of amyloid fibrils in the osteoarticular system. Recently, a hereditary systemic amyloidosis was discovered, caused by a naturally occurring D76N β2m mutant exhibiting normal serum levels and a distinct, visceral deposition pattern. D76N β2m showed a structure remarkably similar to the wild-type (WT) protein, albeit with decreased thermodynamic stability and increased amyloidogenicity. Despite the extensive research, the molecular bases of the aberrant aggregation of β2m in vivo remains elusive. Here, using a variety of biophysical techniques, we investigated the role of the pathogenic D76N mutation in the amyloid formation of β2m by point mutations affecting the stabilizing ion-pairs of β2m. We found that, relative to WT β2m, the exceptional amyloidogenicity of the pathogenic D76N β2m variant is realized by the synergy of diverse effects of destabilized native structure, higher sensitivity to negatively charged amphiphilic molecules and polyphosphate, more effective fibril nucleation, higher conformational stability of fibrils, and elevated affinity for extracellular matrix proteins. Understanding the underlying molecular mechanisms might help to find target points for effective treatments against diseases associated with the deleterious aggregation of proteins.

Abstract

β₂-microglobulin (β2m), the light chain of the MHC-I complex, is associated with dialysis-related amyloidosis (DRA). Recently, a hereditary systemic amyloidosis was discovered, caused by a naturally occurring D76N β2m variant, which showed a structure remarkably similar to the wild-type (WT) protein, albeit with decreased thermodynamic stability and increased amyloidogenicity. Here, we investigated the role of the D76N mutation in the amyloid formation of β2m by point mutations affecting the Asp76-Lys41 ion-pair of WT β2m and the charge cluster on Asp38. Using a variety of biophysical techniques, we investigated the conformational stability and partial unfolding of the native state of the variants, as well as their amyloidogenic propensity and the stability of amyloid fibrils under various conditions. Furthermore, we studied the intermolecular interactions of WT and mutant proteins with various binding partners that might have in vivo relevance. We found that, relative to WT β2m, the exceptional amyloidogenicity of the pathogenic D76N β2m variant is realized by the deleterious synergy of diverse effects of destabilized native structure, higher sensitivity to negatively charged amphiphilic molecules (e.g., lipids) and polyphosphate, more effective fibril nucleation, higher conformational stability of fibrils, and elevated affinity for extracellular components, including extracellular matrix proteins.

Quantitative Description of Surface Complementarity of Antibody-Antigen Interfaces

Antibodies have the remarkable ability to recognise their cognate antigens with extraordinary affinity and specificity. Discerning the rules that define antibody-antigen recognition is a fundamental step in the rational design and engineering of functional antibodies with desired properties. In this study we apply the 3D Zernike formalism to the analysis of the surface properties of the antibody complementary determining regions (CDRs). Our results show that shape and electrostatic 3DZD descriptors of the surface of the CDRs are predictive of antigen specificity, with classification accuracy of 81% and area under the receiver operating characteristic curve (AUC) of 0.85. Additionally, while in terms of surface size, solvent accessibility and amino acid composition, antibody epitopes are typically not distinguishable from non-epitope, solvent-exposed regions of the antigen, the 3DZD descriptors detect significantly higher surface complementarity to the paratope, and are able to predict correct paratope-epitope interaction with an AUC = 0.75.

Machine learning differentiates enzymatic and non-enzymatic metals in proteins

Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate between them. Yet distinguishing these two classes is critical for the identification of both native and designed enzymes. Because of similarities between catalytic and non-catalytic  metal binding sites, finding physicochemical features that distinguish these two types of metal sites can indicate aspects that are critical to enzyme function. In this work, we develop the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. We then use a decision-tree ensemble machine learning model to classify metals bound to proteins as enzymatic or non-enzymatic with 92.2% precision and 90.1% recall. Our model scores electrostatic and pocket lining features as more important than pocket volume, despite the fact that volume is the most quantitatively different feature between enzyme and non-enzymatic sites. Finally, we find our model has overall better performance in a side-to-side comparison against other methods that differentiate enzymatic from non-enzymatic sequences. We anticipate that our model's ability to correctly identify which metal sites are responsible for enzymatic activity could enable identification of new enzymatic mechanisms and de novo enzyme design.

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.

Insights into the Interaction Mechanism of DTP3 with MKK7 by Using STD-NMR and Computational Approaches

GADD45β/MKK7 complex is a non-redundant, cancer cell-restricted survival module downstream of the NF-kB survival pathway, and it has a pathogenically critical role in multiple myeloma, an incurable malignancy of plasma cells. The first-in-class GADD45β/MKK7 inhibitor DTP3 effectively kills MM cells expressing its molecular target, both in vitro and in vivo, by inducing MKK7/JNK-dependent apoptosis with no apparent toxicity to normal cells. DTP3 combines favorable drug-like properties, with on-target-specific pharmacology, resulting in a safe and cancer-selective therapeutic effect; however, its mode of action is only partially understood. In this work, we have investigated the molecular determinants underlying the MKK7 interaction with DTP3 by combining computational, NMR, and spectroscopic methods. Data gathered by fluorescence quenching and computational approaches consistently indicate that the N-terminal region of MKK7 is the optimal binding site explored by DTP3. These findings further the understanding of the selective mode of action of GADD45β/MKK7 inhibitors and inform potential mechanisms of drug resistance. Notably, upon validation of the safety and efficacy of DTP3 in human trials, our results could also facilitate the development of novel DTP3-like therapeutics with improved bioavailability or the capacity to bypass drug resistance.

A novel strategy for molecular interfaces optimization: The case of Ferritin-Transferrin receptor interaction

Protein-protein interactions regulate almost all cellular functions and rely on a fine tune of surface amino acids properties involved on both molecular partners. The disruption of a molecular association can be caused even by a single residue mutation, often leading to a pathological modification of a biochemical pathway. Therefore the evaluation of the effects of amino acid substitutions on binding, and the ad hoc design of protein-protein interfaces, is one of the biggest challenges in computational biology. Here, we present a novel strategy for computational mutation and optimization of protein-protein interfaces. Modeling the interaction surface properties using the Zernike polynomials, we describe the shape and electrostatics of binding sites with an ordered set of descriptors, making possible the evaluation of complementarity between interacting surfaces. With a Monte Carlo approach, we obtain protein mutants with controlled molecular complementarities. Applying this strategy to the relevant case of the interaction between Ferritin and Transferrin Receptor, we obtain a set of Ferritin mutants with increased or decreased complementarity. The extensive molecular dynamics validation of the method results confirms its efficacy, showing that this strategy represents a very promising approach in designing correct molecular interfaces.

Generalized Born radii computation using linear models and neural networks

MOTIVATION

Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples.

RESULTS

GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of 'perfect' GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with 'perfect' GB radii of 0.97 and ca. 20% smaller root mean square error.

AVAILABILITY AND IMPLEMENTATION

We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein electrostatics: From computational and structural analysis to discovery of functional fingerprints and biotechnological design

Computationally driven engineering of proteins aims to allow them to withstand an extended range of conditions and to mediate modified or novel functions. Therefore, it is crucial to the biotechnological industry, to biomedicine and to afford new challenges in environmental sciences, such as biocatalysis for green chemistry and bioremediation. In order to achieve these goals, it is important to clarify molecular mechanisms underlying proteins stability and modulating their interactions. So far, much attention has been given to hydrophobic and polar packing interactions and stability of the protein core. In contrast, the role of electrostatics and, in particular, of surface interactions has received less attention. However, electrostatics plays a pivotal role along the whole life cycle of a protein, since early folding steps to maturation, and it is involved in the regulation of protein localization and interactions with other cellular or artificial molecules. Short- and long-range electrostatic interactions, together with other forces, provide essential guidance cues in molecular and macromolecular assembly. We report here on methods for computing protein electrostatics and for individual or comparative analysis able to sort proteins by electrostatic similarity. Then, we provide examples of electrostatic analysis and fingerprints in natural protein evolution and in biotechnological design, in fields as diverse as biocatalysis, antibody and nanobody engineering, drug design and delivery, molecular virology, nanotechnology and regenerative medicine.

Molecular Dynamics Simulations Reveal Canonical Conformations in Different pMHC/TCR Interactions

The major defense system against microbial pathogens in vertebrates is the adaptive immune response and represents an effective mechanism in cancer surveillance. T cells represent an essential component of this complex system. They can recognize myriads of antigens as short peptides (p) originated from the intracellular degradation of foreign proteins presented by major histocompatibility complex (MHC) proteins. The clonotypic T-cell antigen receptor (TCR) is specialized in recognizing pMHC and triggering T cells immune response. It is still unclear how TCR engagement to pMHC is translated into the intracellular signal that initiates T-cell immune response. Some work has suggested the possibility that pMHC binding induces in the TCR conformational changes transmitted to its companion CD3 subunits that govern signaling. The conformational changes would promote phosphorylation of the CD3 complex ζ chain that initiates signal propagation intracellularly. Here, we used all-atom molecular dynamics simulations (MDs) of 500 ns to analyze the conformational behavior of three TCRs (1G4, ILA1 and ILA1α1β1) interacting with the same MHC class I (HLA-A*02:01) bound to different peptides, and modelled in the presence of a lipid bilayer. Our data suggest a correlation between the conformations explored by the β-chain constant regions and the T-cell response experimentally determined. In particular, independently by the TCR type involved in the interaction, the TCR activation seems to be linked to a specific zone of the conformational space explored by the β-chain constant region. Moreover, TCR ligation restricts the conformational space the MHC class I groove.

A consensus protocol for the in silico optimisation of antibody fragments

We present an in silico mutagenetic protocol for improving the binding affinity of single domain antibodies (or nanobodies, VHHs). The method iteratively attempts random mutations in the interacting region of the protein and evaluates the resulting binding affinity towards the target by scoring, with a collection of scoring functions, short explicit solvent molecular dynamics trajectories of the binder-target complexes. The acceptance/rejection of each attempted mutation is carried out by a consensus decision-making algorithm, which considers all individual assessments derived from each scoring function. The method was benchmarked by evolving a single complementary determining region (CDR) of an anti-HER2 VHH hit obtained by direct panning of a phage display library. The optimised VHH mutant showed significantly enhanced experimental affinity with respect to the original VHH it matured from. The protocol can be employed as it is for the optimization of peptides, antibody fragments, and (given enough computational power) larger antibodies.

Structure of Turnip mosaic virus and its viral-like particles

Turnip mosaic virus (TuMV), a potyvirus, is a flexible filamentous plant virus that displays a helical arrangement of coat protein copies (CPs) bound to the ssRNA genome. TuMV is a bona fide representative of the Potyvirus genus, one of most abundant groups of plant viruses, which displays a very wide host range. We have studied by cryoEM the structure of TuMV virions and its viral-like particles (VLPs) to explore the role of the interactions between proteins and RNA in the assembly of the virions. The results show that the CP-RNA interaction is needed for the correct orientation of the CP N-terminal arm, a region that plays as a molecular staple between CP subunits in the fully assembled virion.

Predicting the Affinity of Peptides to Major Histocompatibility Complex Class II by Scoring Molecular Dynamics Simulations

Predicting the binding affinity of peptides able to interact with major histocompatibility complex (MHC) molecules is a priority for researchers working in the identification of novel vaccines candidates. Most available approaches are based on the analysis of the sequence of peptides of known experimental affinity. However, for MHC class II receptors, these approaches are not very accurate, due to the intrinsic flexibility of the complex. To overcome these limitations, we propose to estimate the binding affinity of peptides bound to an MHC class II by averaging the score of the configurations from finite-temperature molecular dynamics simulations. The score is estimated for 18 different scoring functions, and we explored the optimal manner for combining them. To test the predictions, we considered eight peptides of known binding affinity. We found that six scoring functions correlate with the experimental ranking of the peptides significantly better than the others. We then assessed a set of techniques for combining the scoring functions by linear regression and logistic regression. We obtained a maximum accuracy of 82% for the predicted sign of the binding affinity using a logistic regression with optimized weights. These results are potentially useful to improve the reliability of in silico protocols to design high-affinity binding peptides for MHC class II receptors.

Novel Pathogenic PRSS1 Variant p.Glu190Lys in a Case of Chronic Pancreatitis

Mutations in the PRSS1 (serine protease 1) gene encoding human cationic trypsinogen cause hereditary pancreatitis or may be associated with sporadic chronic pancreatitis. The mutations exert their pathogenic effect either by increasing intra-pancreatic trypsinogen activation (trypsin pathway) or by causing proenzyme misfolding and endoplasmic reticulum stress (misfolding pathway). Here we report a novel heterozygous c.568G>A (p.Glu190Lys) variant identified in a case with chronic pancreatitis. The parents of the index patient had no history of pancreatitis but were unavailable for genetic testing. Functional characterization revealed 2.5-fold increased autoactivation of the mutant trypsinogen relative to wild type. Unlike many other clinically relevant PRSS1 mutations, p.Glu190Lys did not alter the chymotrypsin C (CTRC)-dependent degradation of trypsinogen nor did it increase CTRC-mediated processing of the trypsinogen activation peptide. Cellular secretion of the mutant protein was unchanged indicating normal folding behavior. Based on the genetic and functional evidence, we classify the p.Glu190Lys PRSS1 variant as likely pathogenic, which stimulates autoactivation of cationic trypsinogen independently of CTRC.

Dynamics and Thermodynamics of Transthyretin Association from Molecular Dynamics Simulations

Molecular dynamics simulations are used in this work to probe the structural stability and the dynamics of engineered mutants of transthyretin (TTR), i.e., the double mutant F87M/L110M (MT-TTR) and the triple mutant F87M/L110M/S117E (3M-TTR), in relation to wild-type. Free energy analysis from end-point simulations and statistical effective energy functions are used to analyze trajectories, revealing that mutations do not have major impact on protein structure but rather on protein association, shifting the equilibria towards dissociated species. The result is confirmed by the analysis of 3M-TTR which shows dissociation within the first 10 ns of the simulation, indicating that contacts are lost at the dimer-dimer interface, whereas dimers (formed by monomers which pair to form two extended β-sheets) appear fairly stable. Overall the simulations provide a detailed view of the dynamics and thermodynamics of wild-type and mutant transthyretins and a rationale of the observed effects.

Binding affinity prediction of nanobody-protein complexes by scoring of molecular dynamics trajectories

Nanobodies offer a viable alternative to antibodies for engineering high affinity binders. Their small size has an additional advantage: it allows exploiting computational protocols for optimizing their biophysical features, such as the binding affinity. The efficient prediction of this quantity is still considered a daunting task especially for modelled complexes. We show how molecular dynamics can successfully assist in the binding affinity prediction of modelled nanobody-protein complexes. The approximate initial configurations obtained by in silico design must undergo large rearrangements before achieving a stable conformation, in which the binding affinity can be meaningfully estimated. The scoring functions developed for the affinity evaluation of crystal structures will provide accurate estimates for modelled binding complexes if the scores are averaged over long finite temperature molecular dynamics simulations.

The α4β1/EMILIN1 interaction discloses a novel and unique integrin-ligand type of engagement

EMILIN1, a homo-trimeric adhesive ECM glycoprotein, interacts with the α4β1 integrin through its gC1q domain. Uniquely among the C1q family members, the EMILIN1 gC1q presents only nine-stranded β-sandwich fold and the missing strand is substituted by a disordered 19-residue long segment spanning from Y927 to G945 at the apex of the gC1q domain. This unstructured loop exposes to the solvent the acidic residue E933, which plays a key role in the α4β1 integrin mediated interaction. Here, we experimentally determined that the three E933 residues (one from each monomer) are all required for ligand binding. By docking the NMR structure of the gC1q to a virtual α4β1 crystal structure based on the known structures of α4β7 and α5β1 integrins we built a model of α4β1-gC1q complex where three E933 residues are smoothly forced to coordinate the Mg²⁺ ion at the βI MIDAS site of the integrin. By bringing the three E933 close in space, the trimeric supramolecular organization of gC1q allows the formation of a proper 3D geometry and suggests a quaternary-structure-dependent mode of interaction. Furthermore, we experimentally identified R904 as a synergistic residue for cell adhesion. Accordingly, the model showed that this residue is able to form potential stabilizing intra-chain salt bridges with residues E928 and E930. This mode of interaction likely accounts for a more stable and durable α4β1-gC1q interaction in comparison with the prototypic CS1 ligand. To our knowledge, this is the first report describing the simultaneous involvement of all the three acidic residues of a trimeric ligand in the formation of a dimeric complex with the integrin βI domain.

Potyvirus virion structure shows conserved protein fold and RNA binding site in ssRNA viruses

Potyviruses constitute the second largest genus of plant viruses and cause important economic losses in a large variety of crops; however, the atomic structure of their particles remains unknown. Infective potyvirus virions are long flexuous filaments where coat protein (CP) subunits assemble in helical mode bound to a monopartite positive-sense single-stranded RNA [(+)ssRNA] genome. We present the cryo-electron microscopy (cryoEM) structure of the potyvirus watermelon mosaic virus at a resolution of 4.0 Å. The atomic model shows a conserved fold for the CPs of flexible filamentous plant viruses, including a universally conserved RNA binding pocket, which is a potential target for antiviral compounds. This conserved fold of the CP is widely distributed in eukaryotic viruses and is also shared by nucleoproteins of enveloped viruses with segmented (-)ssRNA (negative-sense ssRNA) genomes, including influenza viruses.

Compacting de Bruijn graphs from sequencing data quickly and in low memory

MOTIVATION

As the quantity of data per sequencing experiment increases, the challenges of fragment assembly are becoming increasingly computational. The de Bruijn graph is a widely used data structure in fragment assembly algorithms, used to represent the information from a set of reads. Compaction is an important data reduction step in most de Bruijn graph based algorithms where long simple paths are compacted into single vertices. Compaction has recently become the bottleneck in assembly pipelines, and improving its running time and memory usage is an important problem.

RESULTS

We present an algorithm and a tool bcalm 2 for the compaction of de Bruijn graphs. bcalm 2 is a parallel algorithm that distributes the input based on a minimizer hashing technique, allowing for good balance of memory usage throughout its execution. For human sequencing data, bcalm 2 reduces the computational burden of compacting the de Bruijn graph to roughly an hour and 3 GB of memory. We also applied bcalm 2 to the 22 Gbp loblolly pine and 20 Gbp white spruce sequencing datasets. Compacted graphs were constructed from raw reads in less than 2 days and 40 GB of memory on a single machine. Hence, bcalm 2 is at least an order of magnitude more efficient than other available methods.

AVAILABILITY AND IMPLEMENTATION

Source code of bcalm 2 is freely available at: https://github.com/GATB/bcalm

CONTACT

rayan.chikhi@univ-lille1.fr.

Molecular mapping of α-thrombin (αT)/β2-glycoprotein I (β2GpI) interaction reveals how β2GpI affects αT functions

β2-Glycoprotein I (β2GpI) is the major autoantigen in the antiphospholipid syndrome, a thrombotic autoimmune disease. Nonetheless, the physiological role of β2GpI is still unclear. In a recent work, we have shown that β2GpI selectively inhibits the procoagulant functions of human α-thrombin (αT; i.e. prolongs fibrin clotting time, t_c, and inhibits αT-induced platelet aggregation) without affecting the unique anticoagulant activity of the protease, i.e. the proteolytic generation of the anticoagulant protein C (PC) from the PC zymogen, which interacts with αT exclusively at the protease catalytic site. Here, we used several different biochemical/biophysical techniques and molecular probes for mapping the binding sites in the αT-β2GpI complex. Our results indicate that αT exploits the highly electropositive exosite-II, which is also responsible for anchoring αT on the platelet GpIbα (platelet receptor glycoprotein Ibα) receptor, for binding to a continuous negative region on β2GpI structure, spanning domain IV and (part of) domain V, whereas the protease active site and exosite-I (i.e. the fibrinogen-binding site) remain accessible for substrate/ligand binding. Furthermore, we provided evidence that the apparent increase in t_c, previously observed with β2GpI, is more likely caused by alteration in the ensuing fibrin structure rather than by the inhibition of fibrinogen hydrolysis. Finally, we produced a theoretical docking model of αT-β2GpI interaction, which was in agreement with the experimental results. Altogether, these findings help to understand how β2GpI affects αT interactions and suggest that β2GpI may function as a scavenger of αT for binding to the GpIbα receptor, thus impairing platelet aggregation while enabling normal cleavage of fibrinogen and PC.

Loop Electrostatics Asymmetry Modulates the Preexisting Conformational Equilibrium in Thrombin

Thrombin exists as an ensemble of active (E) and inactive (E*) conformations that differ in their accessibility to the active site. Here we show that redistribution of the E*-E equilibrium can be achieved by perturbing the electrostatic properties of the enzyme. Removal of the negative charge of the catalytic Asp102 or Asp189 in the primary specificity site destabilizes the E form and causes a shift in the 215-217 segment that compromises substrate entrance. Solution studies and existing structures of D102N document stabilization of the E* form. A new high-resolution structure of D189A also reveals the mutant in the collapsed E* form. These findings establish a new paradigm for the control of the E*-E equilibrium in the trypsin fold.

Structural Studies of AAV2 Rep68 Reveal a Partially Structured Linker and Compact Domain Conformation

Adeno-associated virus (AAV) nonstructural proteins Rep78 and Rep68 carry out all DNA transactions that regulate the AAV life cycle. They share two multifunctional domains: an N-terminal origin binding/nicking domain (OBD) from the HUH superfamily and a SF3 helicase domain. A short linker of ∼20 amino acids that is critical for oligomerization and function connects the two domains. Although X-ray structures of the AAV5 OBD and AAV2 helicase domains have been determined, information about the full-length protein and linker conformation is not known. This article presents the solution structure of AAV2 Rep68 using small-angle X-ray scattering (SAXS). We first determined the X-ray structures of the minimal AAV2 Rep68 OBD and of the OBD with the linker region. These X-ray structures reveal novel features that include a long C-terminal α-helix that protrudes from the core of the protein at a 45° angle and a partially structured linker. SAXS studies corroborate that the linker is not extended, and we show that a proline residue in the linker is critical for Rep68 oligomerization and function. SAXS-based rigid-body modeling of Rep68 confirms these observations, showing a compact arrangement of the two domains in which they acquire a conformation that positions key residues in all domains on one face of the protein, poised to interact with DNA.

Thrombin inhibits the anti-myeloperoxidase and ferroxidase functions of ceruloplasmin: relevance in rheumatoid arthritis

Human ceruloplasmin (CP) is a multifunctional copper-binding protein produced in the liver. CP oxidizes Fe(2+) to Fe(3+), decreasing the concentration of Fe(2+) available for generating harmful oxidant species. CP is also a potent inhibitor of leukocyte myeloperoxidase (MPO) (Kd=130nM), a major source of oxidants in vivo. Rheumatoid arthritis (RA) is an inflammatory autoimmune disease affecting flexible joints and characterized by activation of both inflammatory and coagulation processes. Indeed, the levels of CP, MPO, and thrombin are markedly increased in the synovial fluid of RA patients. Here we show that thrombin cleaves CP in vitro at (481)Arg-Ser(482) and (887)Lys-Val(888) bonds, generating a nicked species that retains the native-like fold and the ferroxidase activity of the intact protein, whereas the MPO inhibitory function of CP is abrogated. Analysis of the synovial fluid of 24 RA patients reveals that CP is proteolytically degraded to a variable extent, with a fragmentation pattern similar to that observed with thrombin in vitro, and that proteolysis is blocked by hirudin, a highly potent and specific thrombin inhibitor. Using independent biophysical techniques, we show that thrombin has intrinsic affinity for CP (Kd=60-270nM), independent of proteolysis, and inhibits CP ferroxidase activity (KI=220±20nM). Mapping of thrombin binding sites with specific exosite-directed ligands (i.e., hirugen, fibrinogen γ'-peptide) and thrombin analogues having the exosites variably compromised (i.e., prothrombin, prethrombin-2, βT-thrombin) reveals that the positively charged exosite-II of thrombin binds to the negatively charged upper region of CP, while the protease active site and exosite-I remain accessible. These results suggest that thrombin can exacerbate inflammation in RA by impairing the MPO inhibitory function of CP via proteolysis and by competitively inhibiting CP ferroxidase activity. Notably, local administration of hirudin, a highly potent and specifc thrombin inhibitor, reduces the concentration of active MPO in the synovial fluid of RA patients and has a beneficial effect on the clinical symptoms of the disease.

Probing the influence of citrate-capped gold nanoparticles on an amyloidogenic protein

Nanoparticles (NPs) are known to exhibit distinct physical and chemical properties compared with the same materials in bulk form. NPs have been repeatedly reported to interact with proteins, and this interaction can be exploited to affect processes undergone by proteins, such as fibrillogenesis. Fibrillation is common to many proteins, and in living organisms, it causes tissue-specific or systemic amyloid diseases. The nature of NPs and their surface chemistry is crucial in assessing their affinity for proteins and their effects on them. Here we present the first detailed structural characterization and molecular mechanics model of the interaction between a fibrillogenic protein, β2-microglobulin, and a NP, 5 nm hydrophilic citrate-capped gold nanoparticles. NMR measurements and simulations at multiple levels (enhanced sampling molecular dynamics, Brownian dynamics, and Poisson-Boltzmann electrostatics) explain the origin of the observed protein perturbations mostly localized at the amino-terminal region. Experiments show that the protein-NP interaction is weak in the physiological-like, conditions and do not induce protein fibrillation. Simulations reproduce these findings and reveal instead the role of the citrate in destabilizing the lower pH protonated form of β2-microglobulin. The results offer possible strategies for controlling the desired effect of NPs on the conformational changes of the proteins, which have significant roles in the fibrillation process.

Accuracy of continuum electrostatic calculations based on three common dielectric boundary definitions

We investigate the influence of three common definitions of the solute/solvent dielectric boundary (DB) on the accuracy of the electrostatic solvation energy ΔG_el computed within the Poisson Boltzmann and the generalized Born models of implicit solvation. The test structures include small molecules, peptides and small proteins; explicit solvent ΔG_el are used as accuracy reference. For common atomic radii sets BONDI, PARSE (and ZAP9 for small molecules) the use of van der Waals (vdW) DB results, on average, in considerably larger errors in ΔG_el than the molecular surface (MS) DB. The optimal probe radius ρ_w for which the MS DB yields the most accurate ΔG_el varies considerably between structure types. The solvent accessible surface (SAS) DB becomes optimal at ρ_w ~ 0.2 Å (exact value is sensitive to the structure and atomic radii), at which point the average accuracy of ΔG_el is comparable to that of the MS-based boundary. The geometric equivalence of SAS to vdW surface based on the same atomic radii uniformly increased by ρ_w gives the corresponding optimal vdW DB. For small molecules, the optimal vdW DB based on BONDI + 0.2 Å radii can yield ΔG_el estimates at least as accurate as those based on the optimal MS DB. Also, in small molecules, pairwise charge-charge interactions computed with the optimal vdW DB are virtually equal to those computed with the MS DB, suggesting that in this case the two boundaries are practically equivalent by the electrostatic energy criteria. In structures other than small molecules, the optimal vdW and MS dielectric boundaries are not equivalent: the respective pairwise electrostatic interactions in the presence of solvent can differ by up to 5 kcal/mol for individual atomic pairs in small proteins, even when the total ΔG_el are equal. For small proteins, the average decrease in pairwise electrostatic interactions resulting from the switch from optimal MS to optimal vdW DB definition can be mimicked within the MS DB definition by doubling of the solute dielectric constant. However, the use of the higher interior dielectric does not eliminate the large individual deviations between pairwise interactions computed within the two DB definitions. It is argued that while the MS based definition of the dielectric boundary is more physically correct in some types of practical calculations, the choice is not so clear in some other common scenarios.

The oligomycin-sensitivity conferring protein of mitochondrial ATP synthase: emerging new roles in mitochondrial pathophysiology

The oligomycin-sensitivity conferring protein (OSCP) of the mitochondrial F(O)F1 ATP synthase has long been recognized to be essential for the coupling of proton transport to ATP synthesis. Located on top of the catalytic F1 sector, it makes stable contacts with both F1 and the peripheral stalk, ensuring the structural and functional coupling between F(O) and F1, which is disrupted by the antibiotic, oligomycin. Recent data have established that OSCP is the binding target of cyclophilin (CyP) D, a well-characterized inducer of the mitochondrial permeability transition pore (PTP), whose opening can precipitate cell death. CyPD binding affects ATP synthase activity, and most importantly, it decreases the threshold matrix Ca²⁺ required for PTP opening, in striking analogy with benzodiazepine 423, an apoptosis-inducing agent that also binds OSCP. These findings are consistent with the demonstration that dimers of ATP synthase generate Ca²⁺-dependent currents with features indistinguishable from those of the PTP and suggest that ATP synthase is directly involved in PTP formation, although the underlying mechanism remains to be established. In this scenario, OSCP appears to play a fundamental role, sensing the signal(s) that switches the enzyme of life in a channel able to precipitate cell death.

Structure, folding dynamics, and amyloidogenesis of D76N β2-microglobulin: roles of shear flow, hydrophobic surfaces, and α-crystallin

Systemic amyloidosis is a fatal disease caused by misfolding of native globular proteins, which then aggregate extracellularly as insoluble fibrils, damaging the structure and function of affected organs. The formation of amyloid fibrils in vivo is poorly understood. We recently identified the first naturally occurring structural variant, D76N, of human β₂-microglobulin (β₂m), the ubiquitous light chain of class I major histocompatibility antigens, as the amyloid fibril protein in a family with a new phenotype of late onset fatal hereditary systemic amyloidosis. Here we show that, uniquely, D76N β₂m readily forms amyloid fibrils in vitro under physiological extracellular conditions. The globular native fold transition to the fibrillar state is primed by exposure to a hydrophobic-hydrophilic interface under physiological intensity shear flow. Wild type β₂m is recruited by the variant into amyloid fibrils in vitro but is absent from amyloid deposited in vivo. This may be because, as we show here, such recruitment is inhibited by chaperone activity. Our results suggest general mechanistic principles of in vivo amyloid fibrillogenesis by globular proteins, a previously obscure process. Elucidation of this crucial causative event in clinical amyloidosis should also help to explain the hitherto mysterious timing and location of amyloid deposition.

Generalized Born forces: surface integral formulation

Generalized Born (GB) models offer a convenient alternative to Poisson-Boltzmann based models. In the last decade, the GB radii computed based on the exact results obtained for a charge embedded in a conducting sphere have proven to be accurate also for the complex molecular shapes of proteins. The surface integral formulation of the theory has been much less explored than the volume integral formulation. In this work, we provide the exact equations for the GB solvation forces in the surface integral formulation, which are non-trivial due to the non-negligible dependence of GB radii on atomic positions and due to the discontinuity in the derivative of the solvent accessible surface point positions with respect to atomic positions. The equations derived here provide a useful reference for developing faster approximations.

Dimers of mitochondrial ATP synthase form the permeability transition pore

Here we define the molecular nature of the mitochondrial permeability transition pore (PTP), a key effector of cell death. The PTP is regulated by matrix cyclophilin D (CyPD), which also binds the lateral stalk of the FOF1 ATP synthase. We show that CyPD binds the oligomycin sensitivity-conferring protein subunit of the enzyme at the same site as the ATP synthase inhibitor benzodiazepine 423 (Bz-423), that Bz-423 sensitizes the PTP to Ca(2+) like CyPD itself, and that decreasing oligomycin sensitivity-conferring protein expression by RNAi increases the sensitivity of the PTP to Ca(2+). Purified dimers of the ATP synthase, which did not contain voltage-dependent anion channel or adenine nucleotide translocator, were reconstituted into lipid bilayers. In the presence of Ca(2+), addition of Bz-423 triggered opening of a channel with currents that were typical of the mitochondrial megachannel, which is the PTP electrophysiological equivalent. Channel openings were inhibited by the ATP synthase inhibitor AMP-PNP (γ-imino ATP, a nonhydrolyzable ATP analog) and Mg(2+)/ADP. These results indicate that the PTP forms from dimers of the ATP synthase.

Bioinformatics in Italy: BITS2011, the Eighth Annual Meeting of the Italian Society of Bioinformatics

The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research.

This preface provides a brief overview of the meeting and introduces the peer-reviewed manuscripts that were accepted for publication in this Supplement.

Bluues server: electrostatic properties of wild-type and mutated protein structures

MOTIVATION

Electrostatic calculations are an important tool for deciphering many functional mechanisms in proteins. Generalized Born (GB) models offer a fast and convenient computational approximation over other implicit solvent-based electrostatic models. Here we present a novel GB-based web server, using the program Bluues, to calculate numerous electrostatic features including pKa-values and surface potentials. The output is organized allowing both experts and beginners to rapidly sift the data. A novel feature of the Bluues server is that it explicitly allows to find electrostatic differences between wild-type and mutant structures.

AVAILABILITY

The Bluues server, examples and extensive help files are available for non-commercial use at URL: http://protein.bio.unipd.it/bluues/.