2,725 research outputs found
Computational structureâbased drug design: Predicting target flexibility
The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in threeâdimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant
from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft
Two polymorphisms facilitate differences in plasticity between two chicken major histocompatibility complex class I proteins
Major histocompatibility complex class I molecules (MHC I) present peptides to cytotoxic T-cells at the surface of almost all nucleated cells. The function of MHC I molecules is to select high affinity peptides from a large intracellular pool and they are assisted in this process by co-factor molecules, notably tapasin. In contrast to mammals, MHC homozygous chickens express a single MHC I gene locus, termed BF2, which is hypothesised to have co-evolved with the highly polymorphic tapasin within stable haplotypes. The BF2 molecules of the B15 and B19 haplotypes have recently been shown to differ in their interactions with tapasin and in their peptide selection properties. This study investigated whether these observations might be explained by differences in the protein plasticity that is encoded into the MHC I structure by primary sequence polymorphisms. Furthermore, we aimed to demonstrate the utility of a complimentary modelling approach to the understanding of complex experimental data. Combining mechanistic molecular dynamics simulations and the primary sequence based technique of statistical coupling analysis, we show how two of the eight polymorphisms between BF2*15:01 and BF2*19:01 facilitate differences in plasticity. We show that BF2*15:01 is intrinsically more plastic than BF2*19:01, exploring more conformations in the absence of peptide. We identify a protein sector of contiguous residues connecting the membrane bound ?3 domain and the heavy chain peptide binding site. This sector contains two of the eight polymorphic residues. One is residue 22 in the peptide binding domain and the other 220 is in the ?3 domain, a putative tapasin binding site. These observations are in correspondence with the experimentally observed functional differences of these molecules and suggest a mechanism for how modulation of MHC I plasticity by tapasin catalyses peptide selection allosterically
Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution
Directed evolution (DE) creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding, and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of the art of molecular modeling, discussing their strengths, limitations, and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques and a synergic effort between simulations and experimental validation.Peer ReviewedPostprint (author's final draft
The Interplay between Chemistry and Mechanics in the Transduction of a Mechanical Signal into a Biochemical Function
There are many processes in biology in which mechanical forces are generated.
Force-bearing networks can transduce locally developed mechanical signals very
extensively over different parts of the cell or tissues. In this article we
conduct an overview of this kind of mechanical transduction, focusing in
particular on the multiple layers of complexity displayed by the mechanisms
that control and trigger the conversion of a mechanical signal into a
biochemical function. Single molecule methodologies, through their capability
to introduce the force in studies of biological processes in which mechanical
stresses are developed, are unveiling subtle intertwining mechanisms between
chemistry and mechanics and in particular are revealing how chemistry can
control mechanics. The possibility that chemistry interplays with mechanics
should be always considered in biochemical studies.Comment: 50 pages, 18 figure
Computational protein design with evolutionary-based and physics-inspired modeling: current and future synergies
Computational protein design facilitates discovery of novel proteins with
prescribed structure and functionality. Exciting designs were recently reported
using novel data-driven methodologies that can be roughly divided into two
categories: evolutionary-based and physics-inspired approaches. The former
infer characteristic sequence features shared by sets of evolutionary-related
proteins, such as conserved or coevolving positions, and recombine them to
generate candidates with similar structure and function. The latter estimate
key biochemical properties such as structure free energy, conformational
entropy or binding affinities using machine learning surrogates, and optimize
them to yield improved designs. Here, we review recent progress along both
tracks, discuss their strengths and weaknesses, and highlight opportunities for
synergistic approaches
Monte Carlo Techniques for Drug Design: The Success Case of PELE
This chapter summarizes the most representative software packages that readily allow running Monte Carlo (MC) simulations in relevant scenarios for drug design. It explores in detail the Protein Energy Landscape Exploration (PELE) program, providing first the main characteristics of the technique, followed by an analysis of the different application studies in mapping proteinâligand interactions. The ligand, formed by a rigid core and a set of rotatable side chains, is perturbed by translating and rotating it. PELE creates a list of perturbation poses, and then chooses the one with the lowest system energy. PELE was originally designed to map ligand migration pathways: its first applications aimed at finding exit pathways starting from ligandâbound crystallographic structures. Additional applied studies have centered on modeling enzymatic mechanisms and engineering; the same techniques applied in mapping proteinâdrug interactions can be used in the study of substrate recognition by enzymes.Along the development of PELE in the last years, we gratefully acknowledge financial support from the European Union (in particular from the ERC program) and from the Catalan and Spanish Governments. In addition we want to thank all present and past members from the EAPM lab. at BSC for their dedication and work.Peer ReviewedPostprint (author's final draft
Structural Dynamics of Free Proteins in Diffraction
Among the macromolecular patterns of biological significance, right-handed α-helices are perhaps the most abundant structural motifs. Here, guided by experimental findings, we discuss both ultrafast initial steps and longer-time-scale structural dynamics of helix-coil
transitions induced by a range of temperature jumps in large, isolated macromolecular ensembles of an α-helical protein segment thymosin ÎČ_9 (TÎČ_9), and elucidate the comprehensive picture of (un)folding. In continuation of an earlier theoretical work from this laboratory that utilized a simplistic structure-scrambling algorithm combined
with a variety of self-avoidance thresholds to approximately model helix-coil transitions in TÎČ_9, in the present contribution we focus on the actual dynamics of unfolding as obtained from massively distributed ensemble-convergent MD simulations which provide an unprecedented scope of information on the nature of transient macromolecular structures, and with atomic-scale spatiotemporal resolution. In addition to the use of radial distribution functions of ultrafast electron diffraction (UED) simulations in gaining an insight into the elementary steps of conformational interconversions, we also investigate the structural dynamics of the protein via
the native (α-helical) hydrogen bonding contact metric which is an intuitive coarse graining approach. Importantly, the decay of α-helical motifs and the (globular) conformational annealing in TÎČ_9 occur consecutively or competitively, depending on the
magnitude of temperature jump
New Monte Carlo Based Technique To Study DNAâLigand Interactions
We present a new all-atom Monte Carlo technique capable of performing quick and accurate DNAâligand conformational sampling. In particular, and using the PELE software as a frame, we have introduced an additional force field, an implicit solvent, and an anisotropic network model to effectively map the DNA energy landscape. With these additions, we successfully generated DNA conformations for a test set composed of six DNA fragments of A-DNA and B-DNA. Moreover, trajectories generated for cisplatin and its hydrolysis products identified the best interacting compound and binding site, producing analogous results to microsecond molecular dynamics simulations. Furthermore, a combination of the Monte Carlo trajectories with Markov State Models produced noncovalent binding free energies in good agreement with the published molecular dynamics results, at a significantly lower computational cost. Overall our approach will allow a quick but accurate sampling of DNAâligand interactions.The authors thank the Barcelona Supercomputing Center for computational resources. This work was supported by grants from the European Research Councilâ2009-Adg25027-PELE European project and the Spanish Ministry of Economy and Competitiveness CTQ2013-48287 and âJuan de la Ciervaâ to F.L.Peer ReviewedPostprint (author's final draft
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