Biology subjects

Laine, E.

Publications and source records attributed to Laine, E..

24 records · Page 2Linked to original sources

bioRxiv2019

Multiple protein-DNA interfaces unravelled by evolutionary information, physico-chemical and geometrical properties

The usage made of protein surfaces by nucleic acids still remains largely unknown, due to the lack of available structural data and the inherent complexity associated to protein surface deformability and evolution. In this work, we present a method that contributes to decipher such complexity by predicting protein-DNA interfaces and characterizing their properties. It relies on three biologically and physically meaningful descriptors, namely evolutionary conservation, physico-chemical properties and surface geometry. We carefully assessed its performance on several hundreds of protein structures. We achieve a higher sensitivity compared to state-of-the-art methods, and similar precision. Importantly, we show that our method is able to unravel hidden binding sites by applying it to unbound protein structures and to proteins binding to DNA via multiple sites and in different conformations. It is implemented as a fully automated tool, [Formula], freely accessible at: http://www.lcqb.upmc.fr/JET2DNA. We also provide a new reference dataset of 187 protein-DNA complex structures, representative of all types of protein-DNA interactions, along with a subset of associated unbound structures: http://www.lcqb.upmc.fr/PDNAbenchmarks.

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bioRxiv2019

ProfileView: multiple probabilistic models resolve protein families functional diversity

Sequence functional classification 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 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 probabilistic models whose construction explores evolutionary information in available databases, and a new definition of a representation space where to look at sequences from the point of view of probabilistic models combined together. ProfileView classifies families of proteins for which functions should be discovered or characterised within known groups. 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. ProfileView agrees with the large set of functional data collected for these proteins from the literature regarding the organisation into functional subgroups and residues that characterize the functions. Furthermore, 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 new biological functions. ProfileView proves to outperform three functional classification approaches, CUPP, PANTHER, and a recently developed neural network approach based on Restricted Boltzmann Machines. It overcomes time complexity limitations of the latter.

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bioRxiv2019

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 lifes 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 guaranties preservation of the protein structure during the transition and allows to access 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 available as a part of the NOn-Linear rigid Block (NOLB) package at https://team.inria.fr/nano-d/software/nolb-normal-modes/.\n\nSignificance StatementProteins perform their biological functions by changing their shapes and interacting with each other. Getting access to these motions is challenging. In this work, we present a method that generates plausible physics-based protein motions and conformations. We model a protein as a network of atoms connected by springs and deform it along the least-energy directions. Our main contribution is to perform the deformations in a nonlinear way, through a series of twists. This allows us to produce a wide range of motions, some of them previously inaccessible, and to preserve the structure of the protein during the motion. We are able to simulate the opening or closing of a protein and the changes it undergoes to adapt to a partner.

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bioRxiv2019

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

AbstractsThe 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 inter-site dependencies within biological sequences. However, state-of-the-art methods require numerous highly variable sequences and remain time consuming. Here, we present GEMME (www.lcqb.upmc.fr/GEMME), a method that overcomes these limitations 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. Assessed against 41 experimental high-throughput mutational scans, GEMME overall performs similarly or better than existing methods and runs faster by several orders of magnitude. It greatly improves predictions for viral sequences and, more generally, for very conserved families. It uses only a few biologically meaningful and interpretable parameters, while existing methods work with hundreds of thousands of parameters.

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bioRxiv2018

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 proteins surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physico-chemical 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/.\n\nAuthor summaryThe multiplicity and versatility of protein interactions make protein surfaces complex biological objects. For instance, a protein may interact with several partners at different moments via the same region at its surface, or it may use several regions, with different shapes and evolutionary origins, to establish different types of interactions. In addition, interfaces can re-adjust depending on environmental conditions. In this work, we introduce the notion of interaction region\", in contrast to interaction site\", which accounts for the interface variability coming from molecular flexibility and for the multiple usage of the protein surface by several partners. Moreover, we use four biologically meaningful descriptors to delineate protein surface patches. Three of them, namely evolutionary conservation, physico-chemical composition and local geometry, are computed for a given protein, without any knowledge on its potential partners. The fourth property is inferred from the behavior of the protein with respect to other proteins, partners or not, in docking simulations. We show that predicted patches match well with known interacting regions, that the four descriptors are complementary and that they enable capturing most of the signal relevant to protein interactions. We further exploit the way patches are grown to precisely localize interaction regions, and to estimate whether a protein have a few or many partners.

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bioRxiv2017

Transcripts evolutionary conservation and structural dynamics give insights into the role of alternative splicing for 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 transcripts from the c-Jun N-terminal kinase (JNK) family observed in 7 species. We identify two alternative splicing events of ancient origin and show that they induce subtle changes on the proteins 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.

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