Flexible protein folding by ant colony optimization

Protein structure prediction is one of the most challenging topics in bioinformatics. As the protein structure is found to be closely related to its functions, predicting the folding structure of a protein to judge its functions is meaningful to the humanity. This chapter proposes a flexible ant colony (FAC) algorithm for solving protein folding problems (PFPs) based on the hydrophobic-polar (HP) square lattice model. Different from the previous ant algorithms for PFPs, the pheromones in the proposed algorithm are placed on the arcs connecting adjacent squares in the lattice. Such pheromone placement model is similar to the one used in the traveling salesmen problems (TSPs), where pheromones are released on the arcs connecting the cities. Moreover, the collaboration of effective heuristic and pheromone strategies greatly enhances the performance of the algorithm so that the algorithm can achieve good results without local search methods. By testing some benchmark two-dimensional hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed algorithm is quite competitive compared with some other well-known methods for solving the same protein folding problems

Computational structure‐based 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 three‐dimensional 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

Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.

ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation

Explicit factorization of external coordinates in constrained Statistical Mechanics models

If a macromolecule is described by curvilinear coordinates or rigid constraints are imposed, the equilibrium probability density that must be sampled in Monte Carlo simulations includes the determinants of different mass-metric tensors. In this work, we explicitly write the determinant of the mass-metric tensor G and of the reduced mass-metric tensor g, for any molecule, general internal coordinates and arbitrary constraints, as a product of two functions; one depending only on the external coordinates that describe the overall translation and rotation of the system, and the other only on the internal coordinates. This work extends previous results in the literature, proving with full generality that one may integrate out the external coordinates and perform Monte Carlo simulations in the internal conformational space of macromolecules. In addition, we give a general mathematical argument showing that the factorization is a consequence of the symmetries of the metric tensors involved. Finally, the determinant of the mass-metric tensor G is computed explicitly in a set of curvilinear coordinates specially well-suited for general branched molecules.Comment: 22 pages, 2 figures, LaTeX, AMSTeX. v2: Introduccion slightly extended. Version in arXiv is slightly larger than the published on

Analiza porównawcza własności fizycznych miejsc wiązania antybiotyków aminoglikozydowych w RNA i białkach

Aminoglycoside antibiotics have been in use for more than 60 years, helping combat severe bacterial infections. Due to this long time of usage, more and more bacteria become resistant to one or several drugs from this group. This spread of resistant species is alarming and additionally, there is little knowledge about the mechanisms of bacterial resistance. In order to broaden our understanding of how bacteria combat aminoglycosides, we performed computer simulations of various molecules that bind aminoglycosides in a bacterial cell: (i) the primary binding site, called the A-site and located in ribosomal RNA, wild type and with mutations that decrease the aminoglycoside binding affinity; and (ii) the aminoglycoside modifying enzymes (AMEs), which are produced by bacteria to inactivate these drugs. The mutations of the RNA A-site were chosen based on previous experimental studies on whole bacteria. These studies showed that even single base substitutions were sufficient to make bacteria resistant, but did not explain how this resistance was gained on an atomic level. There are many AMEs and they vary a lot among themselves, yet they all have a narrow specificity towards aminoglycosides, which are quite homogeneous group. The two main questions we have posed in our research are: (i) what are the physical grounds of bacteria becoming less susceptible to aminoglycosides due to RNA A-site mutations; and (ii) how different AMEs attract aminoglycosides and interact with them? We performed all-atom molecular dynamics (MD) simulations of the A-site model with selected mutations and of AME representatives. In addition, the complexes of these biomolecules with aminoglycosides were simulated. For comparison, we also performed simulations of the wild type A-site model and of the aminoglycosides in water. We used various biophysical methods to analyze these simulations and to study: internal dynamics of the biomolecules; electrostatic potential, shape, and volume of the binding pockets; types of interactions with aminoglycosides; and changes in conformations of aminoglycosides. In addition, we developed and implemented an algorithm that helps describe molecular motions. We found that different A-site mutations affect different features of the RNA binding site. Some of them changed the mobility of the nucleic bases, and therefore the shape of the A-site was altered. Other mutations changed the electrostatic potential inside the binding site, thus making it almost unrecognizable to aminoglycosides. The study of AMEs showed that apart from their structural and sequence-related diversity, they differ in the internal movement patterns. However, these enzymes interact with aminoglycosides very similarly, using mainly electrostatic interactions. Interestingly, we noticed that these interactions were copied from the RNA:aminoglycoside complex. Our findings were in agreement with experimental studies and also helped to explain some of their outcomes. The results presented in this dissertation may help design new antibiotics that would overcome the bacterial resistance.Od ponad 60 lat antybiotyki aminoglikozydowe są z powodzeniem stosowane w szpitalach przeciwko ciężkim infekcjom bakteryjnym. Jednak pojawianie się coraz większej liczby przypadków bakterii opornych na stosowane aminoglikozydy sprawia, że badania mechanizmów oporności u bakterii stają kluczowe w dalszej skutecznej walce z infekcjami tego typu. Przeprowadziłam komputerowe symulacje biomolekuł, które oddziałują z antybiotykami aminoglikozydowymi we wnętrzu komórek bakteryjnych. Badanymi obiektami są: (i) główne miejsce wiązania aminoglikozydów, zwane miejscem A, w rybosomalnym RNA; natywne oraz z mutacjami powodującymi wzrost oporności u bakterii; a także (ii) enzymy modyfikujące aminoglikozydy (ang. aminoglycoside modifying enzymes, AME}), produkowane przez bakterie w celu chemicznej dezaktywacji tych leków. Motywacją do badań nad zmutowanym miejscem A był brak informacji o zmianach jakie zachodzą w fizycznych własnościach miejsca A po różnych zamianach nukleotydów. Wiadomo jakie mutacje prowadzą do oporności oraz że nawet pojedyncze zamiany nukleotydu mogą mieć bardzo wymierne skutki, ale nie wyjaśniono jakie są tego podstawy. Natomiast, w przypadku AME, celem prowadzenia symulacji było wyjaśnienie w jaki sposób ta grupa białek jest w stanie być jednocześnie bardzo zróżnicowana i wysoce specyficzna względem aminoglikozydów. Przeprowadziłam symulacje dynamiki molekularnej (MD) modelu miejsca A z wybranymi mutacjami oraz reprezentatywnych enzymów z trzech największych rodzin AME. Aby uzyskać opis oddziaływań między tymi miejscami wiążącymi a aminoglikozydami, przeprowadziłam również symulacje MD tych biomolekuł w kompleksach z wybranymi antybiotykami. W celu analizy symulacji użyłam metodologii z zakresu biofizyki teoretycznej. Badałam wiele własności fizykochemicznych wybranych biomolekuł i ich kompleksów, m.in.: dynamikę wewnętrzną, własności elektrostatyczne, kształt i objętość miejsc wiązania aminoglikozydów, a także rodzaje oddziaływań z aminoglikozydami. Ponadto, stworzyłam nową metodę analizy zmian konformacyjnych w molekułach, która dokonuje podziału biomolekuł na tzw. dynamiczne domeny, na podstawie danych pochodzących z symulacji lub eksperymentów. Z analizy symulacji rybosomalnego miejsca A wynika, że mutacje różnych zasad wpływają na różne własności fizyczne tego fragmentu RNA. W zależności od położenia mutowanej zasady, zmieniał się rozkład ładunków cząstkowych w miejscu wiążącym lub kształt tego miejsca. Mutacje wpływały również na dynamikę ruchów wewnętrznych miejsca A. Analiza symulacji cząsteczek AME wskazała, że oprócz różnorodności struktur trzeciorzędowych i sekwencji, występuje w tej grupie również różnorodność w ruchach wewnętrznych. Pomimo tych różnic, wszystkie enzymy oddziaływały z aminoglikozydami w bardzo podobny sposób, głównie elektrostatycznie. Ponadto, te oddziaływania wydają się być kopiowane z kompleksów, jakie aminoglikozydy tworzą z miejscem A. Rezultaty moich badań są zgodne z poprzednimi doniesieniami eksperymentalnymi, a także pomagają wyjaśnić niektóre z nich. Wyniki opisane w tej pracy mogą być podstawą do zaprojektowania zmodyfikowanych aminoglikozydów, które mogłyby być aktywne nawet wobec opornych bakterii

Inverted critical adsorption of polyelectrolytes in confinement

What are the fundamental laws for the adsorption of charged polymers onto oppositely charged surfaces, for convex, planar, and concave geometries? This question is at the heart of surface coating applications, various complex formation phenomena, as well as in the context of cellular and viral biophysics. It has been a long-standing challenge in theoretical polymer physics; for realistic systems the quantitative understanding is however often achievable only by computer simulations. In this study, we present the findings of such extensive Monte-Carlo in silico experiments for polymer-surface adsorption in confined domains. We study the inverted critical adsorption of finite-length polyelectrolytes in three fundamental geometries: planar slit, cylindrical pore, and spherical cavity. The scaling relations extracted from simulations for the critical surface charge density $\sigma_c$-defining the adsorption-desorption transition-are in excellent agreement with our analytical calculations based on the ground-state analysis of the Edwards equation. In particular, we confirm the magnitude and scaling of $\sigma_c$ for the concave interfaces versus the Debye screening length $1/\kappa$ and the extent of confinement $a$ for these three interfaces for small $\kappa a$ values. For large $\kappa a$ the critical adsorption condition approaches the planar limit. The transition between the two regimes takes place when the radius of surface curvature or half of the slit thickness $a$ is of the order of $1/\kappa$. We also rationalize how $\sigma_c(\kappa)$ gets modified for semi-flexible versus flexible chains under external confinement. We examine the implications of the chain length onto critical adsorption-the effect often hard to tackle theoretically-putting an emphasis on polymers inside attractive spherical cavities.Comment: 12 pages, 10 figures, RevTe

Quantum mechanical calculation of the effects of stiff and rigid constraints in the conformational equilibrium of the Alanine dipeptide

If constraints are imposed on a macromolecule, two inequivalent classical models may be used: the stiff and the rigid one. This work studies the effects of such constraints on the Conformational Equilibrium Distribution (CED) of the model dipeptide HCO-L-Ala-NH2 without any simplifying assumption. We use ab initio Quantum Mechanics calculations including electron correlation at the MP2 level to describe the system, and we measure the conformational dependence of all the correcting terms to the naive CED based in the Potential Energy Surface (PES) that appear when the constraints are considered. These terms are related to mass-metric tensors determinants and also occur in the Fixman's compensating potential. We show that some of the corrections are non-negligible if one is interested in the whole Ramachandran space. On the other hand, if only the energetically lower region, containing the principal secondary structure elements, is assumed to be relevant, then, all correcting terms may be neglected up to peptides of considerable length. This is the first time, as far as we know, that the analysis of the conformational dependence of these correcting terms is performed in a relevant biomolecule with a realistic potential energy function.Comment: 37 pages, 4 figures, LaTeX, BibTeX, AMSTe

Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies

Advances and Challenges in Protein-Ligand Docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion