Alignment-based Protein Mutational Landscape Prediction: Doing More with Less

The wealth of genomic data has boosted the development of computational methods predicting the phenotypic outcomes of missense variants. The most accurate ones exploit multiple sequence alignments, which can be costly to generate. Recent efforts for democratizing protein structure prediction have overcome this bottleneck by leveraging the fast homology search of MMseqs2. Here, we show the usefulness of this strategy for mutational outcome prediction through a large-scale assessment of 1.5M missense variants across 72 protein families. Our study demonstrates the feasibility of producing alignment-based mutational landscape predictions that are both high-quality and compute-efficient for entire proteomes. We provide the community with the whole human proteome mutational landscape and simplified access to our predictive pipeline.

Building alternative splicing and evolution-aware sequence-structure maps for protein repeats

Alternative splicing of repeats in proteins provides a mechanism for rewiring and fine-tuning protein interaction networks. In this work, we developed a robust and versatile method, ASPRING, to identify alternatively spliced protein repeats from gene annotations. ASPRING leverages evolutionary meaningful alternative splicing-aware hierarchical graphs to provide maps between protein repeats sequences and 3D structures. We re-think the definition of repeats by explicitly accounting for transcript diversity across several genes/species. Using a stringent sequence-based similarity criterion, we detected over 5,000 evolutionary conserved repeats by screening virtually all human protein-coding genes and their orthologs across a dozen species. Through a joint analysis of their sequences and structures, we extracted specificity-determining sequence signatures and assessed their implication in experimentally resolved and modelled protein interactions. Our findings demonstrate the widespread alternative usage of protein repeats in modulating protein interactions and open avenues for targeting repeat-mediated interactions.

LEVELNET to visualize, explore, and compare protein-protein interaction networks

Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organizing such data. LEVELNET is a versatile and interactive tool for visualizing, exploring, and comparing protein-protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multi-layered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localization of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties.

Mega-scale experimental analysis of protein folding stability in biology and design

Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale1. However, the energetics driving folding are invisible in these structures and remain largely unknown2. The hidden thermodynamics of folding can drive disease3,4, shape protein evolution5-7 and guide protein engineering8-10, and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40-72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.

Functional mapping of N-terminal residues in the yeast proteome uncovers novel determinants for mitochondrial protein import

N-terminal ends of polypeptides are critical for the selective co-translational recruitment of N-terminal modification enzymes. However, it is unknown whether specific N-terminal signatures differentially regulate protein fate according to their cellular functions. In this work, we developed an in-silico approach to detect functional preferences in cellular N-terminomes, and identified in S. cerevisiae more than 200 Gene Ontology terms with specific N-terminal signatures. In particular, we discovered that Mitochondrial Targeting Sequences (MTS) show a strong and specific over-representation at position 2 of hydrophobic residues known to define potential substrates of the N-terminal acetyltransferase NatC. We validated mitochondrial precursors as co-translational targets of NatC by selective purification of translating ribosomes, and found that their N-terminal signature is conserved in Saccharomycotina yeasts. Finally, systematic mutagenesis of the position 2 in a prototypal yeast mitochondrial protein confirmed its critical role in mitochondrial protein import. Our work highlights the hydrophobicity of MTS N-terminal residues and their targeting by NatC as important features for the definition of the mitochondrial proteome, providing a molecular explanation for mitochondrial defects observed in yeast or human NatC-depleted cells. Functional mapping of N-terminal residues thus has the potential to support the discovery of novel mechanisms of protein regulation or targeting.

Deep Local Analysis deconstructs protein-protein interfaces and accurately estimates binding affinity changes upon mutation

MOTIVATION

The spectacular recent advances in protein and protein complex structure prediction hold promise for reconstructing interactomes at large-scale and residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel the impact of sequence variations on the strength of the association.

RESULTS

In this work, we report on Deep Local Analysis, a novel and efficient deep learning framework that relies on a strikingly simple deconstruction of protein interfaces into small locally oriented residue-centered cubes and on 3D convolutions recognizing patterns within cubes. Merely based on the two cubes associated with the wild-type and the mutant residues, DLA accurately estimates the binding affinity change for the associated complexes. It achieves a Pearson correlation coefficient of 0.735 on about 400 mutations on unseen complexes. Its generalization capability on blind datasets of complexes is higher than the state-of-the-art methods. We show that taking into account the evolutionary constraints on residues contributes to predictions. We also discuss the influence of conformational variability on performance. Beyond the predictive power on the effects of mutations, DLA is a general framework for transferring the knowledge gained from the available non-redundant set of complex protein structures to various tasks. For instance, given a single partially masked cube, it recovers the identity and physicochemical class of the central residue. Given an ensemble of cubes representing an interface, it predicts the function of the complex.

AVAILABILITY AND IMPLEMENTATION

Source code and models are available at http://gitlab.lcqb.upmc.fr/DLA/DLA.git.

Deep Local Analysis evaluates protein docking conformations with locally oriented cubes

MOTIVATION

With the recent advances in protein 3D structure prediction, protein interactions are becoming more central than ever before. Here, we address the problem of determining how proteins interact with one another. More specifically, we investigate the possibility of discriminating near-native protein complex conformations from incorrect ones by exploiting local environments around interfacial residues.

RESULTS

Deep Local Analysis (DLA)-Ranker is a deep learning framework applying 3D convolutions to a set of locally oriented cubes representing the protein interface. It explicitly considers the local geometry of the interfacial residues along with their neighboring atoms and the regions of the interface with different solvent accessibility. We assessed its performance on three docking benchmarks made of half a million acceptable and incorrect conformations. We show that DLA-Ranker successfully identifies near-native conformations from ensembles generated by molecular docking. It surpasses or competes with other deep learning-based scoring functions. We also showcase its usefulness to discover alternative interfaces.

AVAILABILITY AND IMPLEMENTATION

http://gitlab.lcqb.upmc.fr/dla-ranker/DLA-Ranker.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multiple profile models extract features from protein sequence data and resolve functional diversity of very different protein families

Functional classification of proteins from sequences alone has become a critical bottleneck in understanding the myriad of protein sequences that accumulate in our databases. The great diversity of homologous sequences hides, in many cases, a variety of functional activities that cannot be anticipated. Their identification appears critical for a fundamental understanding of the evolution of living organisms and for biotechnological applications. ProfileView is a sequence-based computational method, designed to functionally classify sets of homologous sequences. It relies on two main ideas: the use of multiple profile models whose construction explores evolutionary information in available databases, and a novel definition of a representation space in which to analyse sequences with multiple profile models combined together. ProfileView classifies protein families by enriching known functional groups with new sequences and discovering new groups and subgroups. We validate ProfileView on seven classes of widespread proteins involved in the interaction with nucleic acids, amino acids and small molecules, and in a large variety of functions and enzymatic reactions. Profile-View agrees with the large set of functional data collected for these proteins from the literature regarding the organisation into functional subgroups and residues that characterise the functions. In addition, ProfileView resolves undefined functional classifications and extracts the molecular determinants underlying protein functional diversity, showing its potential to select sequences towards accurate experimental design and discovery of novel biological functions. On protein families with complex domain architecture, ProfileView functional classification reconciles domain combinations, unlike phylogenetic reconstruction. ProfileView proves to outperform the functional classification approach PANTHER, the two k-mer based methods CUPP and eCAMI and a neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.

Protein sequence-to-structure learning: Is this the end(-to-end revolution)?

The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.

Assessing conservation of alternative splicing with evolutionary splicing graphs

Understanding how protein function has evolved and diversified is of great importance for human genetics and medicine. Here, we tackle the problem of describing the whole transcript variability observed in several species by generalizing the definition of splicing graph. We provide a practical solution to construct parsimonious evolutionary splicing graphs where each node is a minimal transcript building block defined across species. We show a clear link between the functional relevance, tissue regulation, and conservation of alternative transcripts on a set of 50 genes. By scaling up to the whole human protein-coding genome, we identify a few thousand genes where alternative splicing modulates the number and composition of pseudorepeats. We have implemented our approach in ThorAxe, an efficient, versatile, robust, and freely available computational tool.

HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches. We present HOPMA, a novel method to predict protein functional states and transitions by using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.

Predicting substitutions to modulate disorder and stability in coiled-coils

Background

Coiled-coils are described as stable structural motifs, where two or more helices wind around each other. However, coiled-coils are associated with local mobility and intrinsic disorder. Intrinsically disordered regions in proteins are characterized by lack of stable secondary and tertiary structure under physiological conditions in vitro. They are increasingly recognized as important for protein function. However, characterizing their behaviour in solution and determining precisely the extent of disorder of a protein region remains challenging, both experimentally and computationally.

Results

In this work, we propose a computational framework to quantify the extent of disorder within a coiled-coil in solution and to help design substitutions modulating such disorder. Our method relies on the analysis of conformational ensembles generated by relatively short all-atom Molecular Dynamics (MD) simulations. We apply it to the phosphoprotein multimerisation domains (PMD) of Measles virus (MeV) and Nipah virus (NiV), both forming tetrameric left-handed coiled-coils. We show that our method can help quantify the extent of disorder of the C-terminus region of MeV and NiV PMDs from MD simulations of a few tens of nanoseconds, and without requiring an extensive exploration of the conformational space. Moreover, this study provided a conceptual framework for the rational design of substitutions aimed at modulating the stability of the coiled-coils. By assessing the impact of four substitutions known to destabilize coiled-coils, we derive a set of rules to control MeV PMD structural stability and cohesiveness. We therefore design two contrasting substitutions, one increasing the stability of the tetramer and the other increasing its flexibility.

Conclusions

Our method can be considered as a platform to reason about how to design substitutions aimed at regulating flexibility and stability.

Targeting Solute Carrier Transporters through Functional Mapping

Solute carrier (SLC) transporters are emerging drug targets. Identifying the molecular determinants responsible for their specific and selective transport activities and describing key interactions with their ligands are crucial steps towards the design of potential new drugs. A general functional mapping across more than 400 human SLC transporters would pave the way to the rational and systematic design of molecules modulating cellular transport.

Predicting Protein Functional Motions: an Old Recipe with a New Twist

Large macromolecules, including proteins and their complexes, very often adopt multiple conformations. Some of them can be seen experimentally, for example with x-ray crystallography or cryo-electron microscopy. This structural heterogeneity is not occasional and is frequently linked with specific biological function. Thus, the accurate description of macromolecular conformational transitions is crucial for understanding fundamental mechanisms of life's machinery. We report on a real-time method to predict such transitions by extrapolating from instantaneous eigen motions, computed using the normal mode analysis, to a series of twists. We demonstrate the applicability of our approach to the prediction of a wide range of motions, including large collective opening-closing transitions and conformational changes induced by partner binding. We also highlight particularly difficult cases of very small transitions between crystal and solution structures. Our method guarantees preservation of the protein structure during the transition and allows accessing conformations that are unreachable with classical normal mode analysis. We provide practical solutions to describe localized motions with a few low-frequency modes and to relax some geometrical constraints along the predicted transitions. This work opens the way to the systematic description of protein motions, whatever their degree of collectivity. Our method is freely available as a part of the NOn-Linear rigid Block (NOLB) package.

Transcripts' Evolutionary History and Structural Dynamics Give Mechanistic Insights into the Functional Diversity of the JNK Family

Alternative splicing and alternative initiation/termination transcription sites have the potential to greatly expand the proteome in eukaryotes by producing several transcript isoforms from the same gene. Although these mechanisms are well described at the genomic level, little is known about their contribution to protein evolution and their impact at the protein structure level. Here, we address both issues by reconstructing the evolutionary history of transcripts and by modeling the tertiary structures of the corresponding protein isoforms. We reconstruct phylogenetic forests relating 60 protein-coding transcripts from the c-Jun N-terminal kinase (JNK) family observed in seven species. We identify two alternative splicing events of ancient origin and show that they induce subtle changes in the protein's structural dynamics. We highlight a previously uncharacterized transcript whose predicted structure seems stable in solution. We further demonstrate that orphan transcripts, for which no phylogeny could be reconstructed, display peculiar sequence and structural properties. Our approach is implemented in PhyloSofS (Phylogenies of Splicing Isoforms Structures), a fully automated computational tool freely available at https://github.com/PhyloSofS-Team/PhyloSofS.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Containing a measles outbreak in Minnesota, 2017: methods and challenges

AIMS

We report on a measles outbreak largely occurring in Minnesota's under-vaccinated Somali community in the spring of 2017. The outbreak was already into its third generation when the first two cases were confirmed, and rapid public health actions were needed. The aim of our response was to quickly end transmission and contain the outbreak.

METHODS

The state public health department performed laboratory testing on suspect cases and activated an Incident Command staffed by subject matter experts that was operational within 2 h of case confirmation. Epidemiologic interviews identified exposures in settings where risk of transmission was high, that is, healthcare, childcare, and school settings. Vaccination status of exposed persons was assessed, and postexposure prophylaxis (PEP) was offered, if applicable. Exposed persons who did not receive PEP were excluded from childcare centers or schools for 21 days. An accelerated statewide measles, mumps, and rubella (MMR) recommendation was made for Somali Minnesota children and children in affected outbreak counties. Partnerships with the Somali Minnesota community were deepened, building off outreach work done with the community since 2008.

RESULTS

Public health identified 75 measles cases from 30 March to 25 August 2017: 43% were female, 81% Somali Minnesotan, 91% unvaccinated, and 28% hospitalized. The median age of cases was 2 years (range: 3 months-57 years). Most transmission (78%) occurred in childcare centers and households. A secondary attack rate of 91% was calculated for unvaccinated household contacts. Over 51,000 doses of MMR were administered during the outbreak above expected baseline. At least 8490 individuals were exposed to measles; 155 individuals received PEP; and over 500 persons were excluded from childcare and school. State and key public health partners spent an estimated $2.3 million on response.

CONCLUSION

This outbreak demonstrates the necessity of immediate, targeted disease control actions and strong public health, healthcare, and community partnerships to end a measles outbreak.

GEMME: a simple and fast global epistatic model predicting mutational effects

The systematic and accurate description of protein mutational landscapes is a question of utmost importance in biology, bioengineering, and medicine. Recent progress has been achieved by leveraging on the increasing wealth of genomic data and by modeling intersite dependencies within biological sequences. However, state-of-the-art methods remain time consuming. Here, we present Global Epistatic Model for predicting Mutational Effects (GEMME) (www.lcqb.upmc.fr/GEMME), an original and fast method that predicts mutational outcomes by explicitly modeling the evolutionary history of natural sequences. This allows accounting for all positions in a sequence when estimating the effect of a given mutation. GEMME uses only a few biologically meaningful and interpretable parameters. Assessed against 50 high- and low-throughput mutational experiments, it overall performs similarly or better than existing methods. It accurately predicts the mutational landscapes of a wide range of protein families, including viral ones and, more generally, of much conserved families. Given an input alignment, it generates the full mutational landscape of a protein in a matter of minutes. It is freely available as a package and a webserver at www.lcqb.upmc.fr/GEMME/.

Decrypting protein surfaces by combining evolution, geometry, and molecular docking

The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physicochemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET² algorithm and assessed on a new dataset of 262 protein on which we performed CC-D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.

"Infostery" analysis of short molecular dynamics simulations identifies highly sensitive residues and predicts deleterious mutations

Characterizing a protein mutational landscape is a very challenging problem in Biology. Many disease-associated mutations do not seem to produce any effect on the global shape nor motions of the protein. Here, we use relatively short all-atom biomolecular simulations to predict mutational outcomes and we quantitatively assess the predictions on several hundreds of mutants. We perform simulations of the wild type and 175 mutants of PSD95’s third PDZ domain in complex with its cognate ligand. By recording residue displacements correlations and interactions, we identify “communication pathways” and quantify them to predict the severity of the mutations. Moreover, we show that by exploiting simulations of the wild type, one can detect 80% of the positions highly sensitive to mutations with a precision of 89%. Importantly, our analysis describes the role of these positions in the inter-residue communication and dynamical architecture of the complex. We assess our approach on three different systems using data from deep mutational scanning experiments and high-throughput exome sequencing. We refer to our analysis as “infostery”, from “info” - information - and “steric” - arrangement of residues in space. We provide a fully automated tool, COMMA2 (www.lcqb.upmc.fr/COMMA2), that can be used to guide medicinal research by selecting important positions/mutations.

Local Interaction Signal Analysis Predicts Protein-Protein Binding Affinity

Several models estimating the strength of the interaction between proteins in a complex have been proposed. By exploring the geometry of contact distribution at protein-protein interfaces, we provide an improved model of binding energy. Local interaction signal analysis (LISA) is a radial function based on terms describing favorable and non-favorable contacts obtained by density functional theory, the support-core-rim interface residue distribution, non-interacting charged residues and secondary structures contribution. The three-dimensional organization of the contacts and their contribution on localized hot-sites over the entire interaction surface were numerically evaluated. LISA achieves a correlation of 0.81 (and a root-mean-square error of 2.35 ± 0.38 kcal/mol) when tested on 125 complexes for which experimental measurements were realized. LISA's performance is stable for subsets defined by functional composition and extent of conformational changes upon complex formation. A large-scale comparison with 17 other functions demonstrated the power of the geometrical model in the understanding of complex binding.

Meet-U: Educating through research immersion

We present a new educational initiative called Meet-U that aims to train students for collaborative work in computational biology and to bridge the gap between education and research. Meet-U mimics the setup of collaborative research projects and takes advantage of the most popular tools for collaborative work and of cloud computing. Students are grouped in teams of 4–5 people and have to realize a project from A to Z that answers a challenging question in biology. Meet-U promotes "coopetition," as the students collaborate within and across the teams and are also in competition with each other to develop the best final product. Meet-U fosters interactions between different actors of education and research through the organization of a meeting day, open to everyone, where the students present their work to a jury of researchers and jury members give research seminars. This very unique combination of education and research is strongly motivating for the students and provides a formidable opportunity for a scientific community to unite and increase its visibility. We report on our experience with Meet-U in two French universities with master’s students in bioinformatics and modeling, with protein–protein docking as the subject of the course. Meet-U is easy to implement and can be straightforwardly transferred to other fields and/or universities. All the information and data are available at www.meet-u.org.

INTerface Builder: A Fast Protein-Protein Interface Reconstruction Tool

INTerface Builder (INTBuilder) is a fast, easy-to-use program to compute protein-protein interfaces. It is designed to retrieve interfaces from molecular docking software outputs in an empirically determined linear complexity. INTBuilder directly reads the output formats of popular docking programs like ATTRACT, HEX, MAXDo, and ZDOCK, as well as a more generic format and Protein Data Bank (PDB) files. It identifies interacting surfaces at both residue and atom resolutions. INTerface Builder is an open source software written in C and freely available for noncommercial use (CeCILL license) at https://www.lcqb.upmc.fr/INTBuilder .

JET2 Viewer: a database of predicted multiple, possibly overlapping, protein-protein interaction sites for PDB structures

The database JET2 Viewer, openly accessible at http://www.jet2viewer.upmc.fr/, reports putative protein binding sites for all three-dimensional (3D) structures available in the Protein Data Bank (PDB). This knowledge base was generated by applying the computational method JET² at large-scale on more than 20 000 chains. JET² strategy yields very precise predictions of interacting surfaces and unravels their evolutionary process and complexity. JET2 Viewer provides an online intelligent display, including interactive 3D visualization of the binding sites mapped onto PDB structures and suitable files recording JET² analyses. Predictions were evaluated on more than 15 000 experimentally characterized protein interfaces. This is, to our knowledge, the largest evaluation of a protein binding site prediction method. The overall performance of JET² on all interfaces are: Sen = 52.52, PPV = 51.24, Spe = 80.05, Acc = 75.89. The data can be used to foster new strategies for protein-protein interactions modulation and interaction surface redesign.

Protein social behavior makes a stronger signal for partner identification than surface geometry

Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc.

Tma108, a putative M1 aminopeptidase, is a specific nascent chain-associated protein in Saccharomyces cerevisiae

The discovery of novel specific ribosome-associated factors challenges the assumption that translation relies on standardized molecular machinery. In this work, we demonstrate that Tma108, an uncharacterized translation machinery-associated factor in yeast, defines a subpopulation of cellular ribosomes specifically involved in the translation of less than 200 mRNAs encoding proteins with ATP or Zinc binding domains. Using ribonucleoparticle dissociation experiments we established that Tma108 directly interacts with the nascent protein chain. Additionally, we have shown that translation of the first 35 amino acids of Asn1, one of the Tma108 targets, is necessary and sufficient to recruit Tma108, suggesting that it is loaded early during translation. Comparative genomic analyses, molecular modeling and directed mutagenesis point to Tma108 as an original M1 metallopeptidase, which uses its putative catalytic peptide-binding pocket to bind the N-terminus of its targets. The involvement of Tma108 in co-translational regulation is attested by a drastic change in the subcellular localization of ATP2 mRNA upon Tma108 inactivation. Tma108 is a unique example of a nascent chain-associated factor with high selectivity and its study illustrates the existence of other specific translation-associated factors besides RNA binding proteins.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

Genetic evidence of a precisely tuned dysregulation in the hypoxia signaling pathway during oncogenesis

The classic model of tumor suppression implies that malignant transformation requires full "two-hit" inactivation of a tumor-suppressor gene. However, more recent work in mice has led to the proposal of a "continuum" model that involves more fluid concepts such as gene dosage-sensitivity and tissue specificity. Mutations in the tumor-suppressor gene von Hippel-Lindau (VHL) are associated with a complex spectrum of conditions. Homozygotes or compound heterozygotes for the R200W germline mutation in VHL have Chuvash polycythemia, whereas heterozygous carriers are free of disease. Individuals with classic, heterozygous VHL mutations have VHL disease and are at high risk of multiple tumors (e.g., CNS hemangioblastomas, pheochromocytoma, and renal cell carcinoma). We report here an atypical family bearing two VHL gene mutations in cis (R200W and R161Q), together with phenotypic analysis, structural modeling, functional, and transcriptomic studies of these mutants in comparison with classical mutants involved in the different VHL phenotypes. We demonstrate that the complex pattern of disease manifestations observed in VHL syndrome is perfectly correlated with a gradient of VHL protein (pVHL) dysfunction in hypoxia signaling pathways. Thus, by studying naturally occurring familial mutations, our work validates in humans the "continuum" model of tumor suppression.

Hotspot mutations in KIT receptor differentially modulate its allosterically coupled conformational dynamics: impact on activation and drug sensitivity

Receptor tyrosine kinase KIT controls many signal transduction pathways and represents a typical allosterically regulated protein. The mutation-induced deregulation of KIT activity impairs cellular physiological functions and causes serious human diseases. The impact of hotspots mutations (D816H/Y/N/V and V560G/D) localized in crucial regulatory segments, the juxtamembrane region (JMR) and the activation (A-) loop, on KIT internal dynamics was systematically studied by molecular dynamics simulations. The mutational outcomes predicted in silico were correlated with in vitro and in vivo activation rates and drug sensitivities of KIT mutants. The allosteric regulation of KIT in the native and mutated forms is described in terms of communication between the two remote segments, JMR and A-loop. A strong correlation between the communication profile and the structural and dynamical features of KIT in the native and mutated forms was established. Our results provide new insight on the determinants of receptor KIT constitutive activation by mutations and resistance of KIT mutants to inhibitors. Depiction of an intra-molecular component of the communication network constitutes a first step towards an integrated description of vast communication pathways established by KIT in physiopathological contexts.

Receptor tyrosine kinase KIT plays a crucial role in the regulation of cell signaling. This allosterically controlled activity may be affected by gain-of-function mutations that promote the development of several cancers. Identification of the molecular basis of KIT constitutive activation and allosteric regulation has inspired computational study of KIT hotspot mutations. In the present contribution, we investigated the mutation-induced effects on KIT conformational dynamics and intra-protein communication conditionally on the mutation location and the nature of the substituting amino acid. Our data elucidate that all studied mutations stabilize an inactive non-autoinhibited state of KIT over the inactive auto-inhibited state prevalent for the native protein. This shift in the protein conformational landscape promotes KIT constitutive activation. Our in silico analysis established correlations between the structural and dynamical effects induced by oncogenic mutations and the mutants auto-activation rates and drug sensitivities measured in vitro and in vivo. Particularly, the A-loop mutations stabilize the drug-resistant forms, while the JMR mutations may facilitate inhibitors binding to the active site. Cross-correlations established between local and long-range structural and dynamical effects demonstrate the allosteric character of the gain-of-function mutations mode of action.

Allosteric pathway identification through network analysis: from molecular dynamics simulations to interactive 2D and 3D graphs

Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach - MOdular NETwork Analysis (MONETA) - based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non-activated STAT5 proteins. Our theoretical prediction based on results obtained with MONETA was validated for KIT by in vitro experiments. MONETA is a versatile analytical and visualization tool entirely devoted to the understanding of the functioning/malfunctioning of allosteric regulation in proteins - a crucial basis to guide the discovery of next-generation allosteric drugs.

Differential effects of CSF-1R D802V and KIT D816V homologous mutations on receptor tertiary structure and allosteric communication

The colony stimulating factor-1 receptor (CSF-1R) and the stem cell factor receptor KIT, type III receptor tyrosine kinases (RTKs), are important mediators of signal transduction. The normal functions of these receptors can be compromised by gain-of-function mutations associated with different physiopatological impacts. Whereas KIT D816V/H mutation is a well-characterized oncogenic event and principal cause of systemic mastocytosis, the homologous CSF-1R D802V has not been identified in human cancers. The KIT D816V oncogenic mutation triggers resistance to the RTK inhibitor Imatinib used as first line treatment against chronic myeloid leukemia and gastrointestinal tumors. CSF-1R is also sensitive to Imatinib and this sensitivity is altered by mutation D802V. Previous in silico characterization of the D816V mutation in KIT evidenced that the mutation caused a structure reorganization of the juxtamembrane region (JMR) and facilitated its departure from the kinase domain (KD). In this study, we showed that the equivalent CSF-1R D802V mutation does not promote such structural effects on the JMR despite of a reduction on some key H-bonds interactions controlling the JMR binding to the KD. In addition, this mutation disrupts the allosteric communication between two essential regulatory fragments of the receptors, the JMR and the A-loop. Nevertheless, the mutation-induced shift towards an active conformation observed in KIT D816V is not observed in CSF-1R D802V. The distinct impact of equivalent mutation in two homologous RTKs could be associated with the sequence difference between both receptors in the native form, particularly in the JMR region. A local mutation-induced perturbation on the A-loop structure observed in both receptors indicates the stabilization of an inactive non-inhibited form, which Imatinib cannot bind.

Protein-protein interactions in a crowded environment: an analysis via cross-docking simulations and evolutionary information

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/

Protein-protein interactions (PPI) are at the heart of the molecular processes governing life and constitute an increasingly important target for drug design. Given their importance, it is vital to determine which protein interactions have functional relevance and to characterize the protein competition inherent to crowded environments, as the cytoplasm or the cellular organelles. We show that combining coarse-grain molecular cross-docking simulations and binding site predictions based on evolutionary sequence analysis is a viable route to identify true interacting partners for hundreds of proteins with a variate set of protein structures and interfaces. Also, we realize a large-scale analysis of protein binding promiscuity and provide a numerical characterization of partner competition and level of interaction strength for about 28000 false-partner interactions. Finally, we demonstrate that binding site prediction is useful to discriminate native partners, but also to scale up the approach to thousands of protein interactions. This study is based on the large computational effort made by thousands of internautes helping World Community Grid over a period of 7 months. The complete dataset issued by the computation and the analysis is released to the scientific community.

Raltegravir flexibility and its impact on recognition by the HIV-1 IN targets

HIV-1 IN is a pertinent target for the development of AIDS chemotherapy. The first IN-specific inhibitor approved for the treatment of HIV/AIDS, RAL, was designed to block the ST reaction. We characterized the structural and conformational features of RAL and its recognition by putative HIV-1 targets - the unbound IN, the vDNA, and the IN•vDNA complex - mimicking the IN states over the integration process. RAL binding to the targets was studied by performing an extensive sampling of the inhibitor conformational landscape and by using four different docking algorithms: Glide, Autodock, VINA, and SurFlex. The obtained data evidenced that: (i) a large binding pocket delineated by the active site and an extended loop in the unbound IN accommodates RAL in distinct conformational states all lacking specific interactions with the target; (ii) a well-defined cavity formed by the active site, the vDNA, and the shortened loop in the IN•vDNA complex provide a more optimized inhibitor binding site in which RAL chelates Mg(2+) cations; (iii) a specific recognition between RAL and the unpaired cytosine of the processed DNA is governed by a pair of strong H-bonds similar to those observed in DNA base pair G-C. The identified RAL pose at the cleaved vDNA shed light on a putative step of RAL inhibition mechanism. This modeling study indicates that the inhibition process may include as a first step RAL recognition by the processed vDNA bound to a transient intermediate IN state, and thus provides a potentially promising route to the design of IN inhibitors with improved affinity and selectivity.

Allosteric communication across the native and mutated KIT receptor tyrosine kinase

A fundamental goal in cellular signaling is to understand allosteric communication, the process by which signals originated at one site in a protein propagate dependably to affect remote functional sites. Here, we describe the allosteric regulation of the receptor tyrosine kinase KIT. Our analysis evidenced that communication routes established between the activation loop (A-loop) and the distant juxtamembrane region (JMR) in the native protein were disrupted by the oncogenic mutation D816V positioned in the A-loop. In silico mutagenesis provided a plausible way of restoring the protein communication detected in the native KIT by introducing a counter-balancing second mutation D792E. The communication patterns observed in the native and mutated KIT correlate perfectly with the structural and dynamical features of these proteins. Particularly, a long-distance effect of the D816V mutation manifested as an important structural re-organization of the JMR in the oncogenic mutant was completely vanished in the double mutant D816V/D792E. This detailed characterization of the allosteric communication in the different forms of KIT, native and mutants, was performed by using a modular network representation composed of communication pathways and independent dynamic segments. Such representation permits to enrich a purely mechanistic interaction-based model of protein communication by the introduction of concerted local atomic fluctuations. This method, validated on KIT receptor, may guide a rational modulation of the physiopathological activities of other receptor tyrosine kinases.

The majority of functionally important biological processes are regulated by allosteric communication within individual proteins and across protein complexes. Receptor tyrosine kinases (RTKs) control signal transduction pathways and consequently represent a typical paradigm. The mutation-induced deregulation of RTK activity impairs crucial cellular physiological functions and causes serious human diseases. The present study focuses on the allosteric communication across the three-dimensional structure of the RTK KIT cytoplasmic region. Combining a mechanistic model of information transmission with the analysis of concerted local atomic fluctuations we examined and compared the communication profiles in the native and D816V-mutated proteins. This approach permitted to localize and visualize communication routes in the native KIT and revealed that these routes were disrupted in the mutant D816V. We proposed in silico mutagenesis as a mean to restore the communication detected in the native KIT. Our work sheds light on the allosteric communication in RTKs, a phenomenon playing an essential role in signaling pathways albeit experiments do not provide the atomic details of the path followed in going from one structural element to the other. A rational understanding of the molecular determinants underlying the effects of disease-related kinase mutations may contribute to the improvement of targeted therapies.

Mutation D816V alters the internal structure and dynamics of c-KIT receptor cytoplasmic region: implications for dimerization and activation mechanisms

The type III receptor tyrosine kinase (RTK) KIT plays a crucial role in the transmission of cellular signals through phosphorylation events that are associated with a switching of the protein conformation between inactive and active states. D816V KIT mutation is associated with various pathologies including mastocytosis and cancers. D816V-mutated KIT is constitutively active, and resistant to treatment with the anti-cancer drug Imatinib. To elucidate the activating molecular mechanism of this mutation, we applied a multi-approach procedure combining molecular dynamics (MD) simulations, normal modes analysis (NMA) and binding site prediction. Multiple 50-ns MD simulations of wild-type KIT and its mutant D816V were recorded using the inactive auto-inhibited structure of the protein, characteristic of type III RTKs. Computed free energy differences enabled us to quantify the impact of D816V on protein stability in the inactive state. We evidenced a local structural alteration of the activation loop (A-loop) upon mutation, and a long-range structural re-organization of the juxta-membrane region (JMR) followed by a weakening of the interaction network with the kinase domain. A thorough normal mode analysis of several MD conformations led to a plausible molecular rationale to propose that JMR is able to depart its auto-inhibitory position more easily in the mutant than in wild-type KIT and is thus able to promote kinase mutant dimerization without the need for extra-cellular ligand binding. Pocket detection at the surface of NMA-displaced conformations finally revealed that detachment of JMR from the kinase domain in the mutant was sufficient to open an access to the catalytic and substrate binding sites.

Protein kinases are involved in a huge amount of cellular processes through phosphorylation, a crucial mechanism in cell signaling, and their misregulation often results in disease. The deactivation of protein tyrosine kinases (PTKs) or their oncogenic activation arises from mutations which affect the protein primary structure and the configuration of the enzymatic site apparently by stabilizing the activation loop (A-loop) extended conformation. Particularly, mutation D816V of receptor tyrosine kinase (RTK) KIT, found in patients with pediatric mastocytosis, acute leukemia or germ cell tumors, can be considered as the archetype of mutation inducing a displacement of the population equilibrium toward the active conformation. We present a comprehensive computational study of the activating mechanism(s) of this mutation. Our multi-approach in silico procedure evidenced a local alteration of the A-loop structure, and a long-range structural re-organization of the juxta-membrane region (JMR) followed by a weakening of the interaction network with the kinase domain. Our results provided a plausible conception of how the observed departure of JMR from kinase domain in the mutant promotes kinase mutant dimerization without requiring extra-cellular ligand binding. The pocket profiles we obtained suggested putative allosteric binding sites that could be targeted by ligands/modulators that trap the mutated enzyme.

Chemical stability studies of thermally-carbonized porous silicon

We have studied chemical stability of thermally-carbonized porous silicon (PS). The initial hydrogen termination of PS has been replaced by carbon using thermal dissociation of acetelyne molecules. This kind of carbonized surface has been found to be at least as stable in humid atmosphere as a thermally-oxidized PS surface. It is also found to be stable in an aqueous KOH and HF. In-vitro studies of tissue compatible in simulated human fluid indicate improved stability and that the carbonized surface could be bioactive.

Dispersive Fourier Transform Spectroscopy Of Free-Standing Porous Silicon Films

We have investigated optical constants of free-standing porous silicon films by dispersive Fourier transform spectroscopy (DFTS) in the NIR-VIS range. This allows the spectral variation of both the absorption coefficient and the refractive index of a material to be determined from the measurements of the attenuation and phase shift imposed on an electromagnetic wave by its interaction with a specimen. Using these optical constants, we have studied the complex dielectric function and the complex conductivity. To avoid the additive error in the absorption spectra arising from the pseudocoherence, we measured the transmission spectra by conventional Fourier transform spectroscopy (FTS). Using the refraction spectrum derived from the DFTS measurements, we have corrected for reflection losses in calculation of the absorption spectrum from the FTS transmission spectrum. The changes in the absorption coefficient and the refractive index due to oxidation, which is the most common aging phenomenon in porous silicon, have been studied using samples with different types of oxidization.

Investigations of Oxidation Dependence on Type of Porous Silicon Near Room Temperature by Isothermal Microcalorimeter

Oxidation of porous silicon has been studied using thermal activity monitoring, i.e. isothermal microcalorimeter. It was found that, at room temperature (25 °C) the micro-calorimetric signal from the oxidation of the p+-type porous silicon (PS) reduces exponentially, while in the case of n-type PS, the signal starts to increase slowly, reaching its highest value after some hours. This kind of behaviour is typical of autocatalytic reactions. To clarify the origin of the difference, we varied the preparation parameters of the porous silicon. We determined the activation energy from the measurements near the room temperature (25–70 °C). The results of this research have been compared with the previous observations and the possible origin of the difference has been discussed.

Activation of the edema factor of Bacillus anthracis by calmodulin: evidence of an interplay between the EF-calmodulin interaction and calcium binding

Calmodulin (CaM) is a remarkably flexible protein which can bind multiple targets in response to changes in intracellular calcium concentration. It contains four calcium-binding sites, arranged in two globular domains. The calcium affinity of CaM N-terminal domain (N-CaM) is dramatically reduced when the complex with the edema factor (EF) of Bacillus anthracis is formed. Here, an atomic explanation for this reduced affinity is proposed through molecular dynamics simulations and free energy perturbation calculations of the EF-CaM complex starting from different crystallographic models. The simulations show that electrostatic interactions between CaM and EF disfavor the opening of N-CaM domains usually induced by calcium binding. Relative calcium affinities of the N-CaM binding sites are probed by free energy perturbation, and dissociation probabilities are evaluated with locally enhanced sampling simulations. We show that EF impairs calcium binding on N-CaM through a direct conformational restraint on Site 1, by an indirect destabilization of Site 2, and by reducing the cooperativity between the two sites.

ATP conformations and ion binding modes in the active site of anthrax edema factor: a computational analysis

The Edema Factor (EF), one of the virulence factors of anthrax, is an adenylyl cyclase that promotes the overproduction of cyclic-AMP (cAMP) from ATP, and therefore perturbs cell signaling. Crystallographic structures of EF bound to ATP analogs and reaction products, cyclic-AMP, and Pyrophosphate (PPi), revealed different substrate conformations and catalytic-cation binding modes, one or two cations being observed in the active site. To shed light into the biological significance of these crystallographic structures, the energetics, geometry, and dynamics of the active site are analyzed using molecular dynamics simulations. The ATP conformation observed in the one-metal-ion structure allows stronger interactions with the catalytic ion, and ATP is more restrained than in the structure containing two Mg(2+) ions. Therefore, we propose that the conformation observed in the one-ion crystal structure is a more probable starting point for the reaction. The simulations also suggest that a C3'-endo sugar pucker facilitates nucleophilic attack. Additionally, the two-cation binding mode restrains the mobility of the reaction products, and thus their tendency to dissociate.

Dynamics and energetics: a consensus analysis of the impact of calcium on EF-CaM protein complex

We have studied the relationship between dynamical correlations and energetic contributions in an attempt to model the transmission of information inside protein-protein complexes. The complex formed between the edema factor (EF) of Bacillus anthracis and calmodulin (CaM) was taken as an example, as the formation and stability of the complex depend on the calcium complexation level. The effect of calcium through EF-CaM residue network has been investigated with various approaches: 1), the elastic network model; 2), the local feature analysis; 3), the generalized correlations; and 4), the energetic dependency maps (EDMs), on 15-ns molecular dynamics simulations of the complex loaded with 0, 2, or 4 Ca2+ ions. The elastic network model correctly describes the basic architecture of the complex but is poorly sensitive to the level of calcium compared to the other methods. The local feature analysis allows us to characterize the local dynamics of the complex and the propagation of the calcium signal through CaM. The analyses of global dynamics and energetics--through generalized correlations and EDMs--provide a comprehensive picture of EF-CaM architecture and can be unified by using the concept of residue network connectedness. A medium connectedness, defined as the ability of each residue to communicate with all remaining parts of the complex, is observed for the 2Ca2+ level, which was experimentally identified as the most stable form of EF-CaM. The hierarchy of relative stabilities given by the EDMs sheds a new light on the EF-CaM interaction mechanism described experimentally and supports an organization of the complex architecture centered around nucleation points.