A Firefly-inspired method for protein structure prediction in lattice models

We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function valuations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models

A Firefly-inspired method for protein structure prediction in lattice models

We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function valuations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models

A hybrid approach to protein folding problem integrating constraint programming with local search

<p>Abstract</p> <p>Background</p> <p>The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.</p> <p>Results</p> <p>Using the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach.</p> <p>Conclusion</p> <p>Constraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.</p

Design of Toy Proteins Capable to Rearrange Conformations in a Mechanical Fashion

We design toy protein mimicking a machine-like function of an enzyme. Using an insight gained by the study of conformation space of compact lattice polymers, we demonstrate the possibility of a large scale conformational rearrangement which occurs (i) without opening a compact state, and (ii) along a linear (one-dimensional) path. We also demonstrate the possibility to extend sequence design method such that it yields a "collective funnel" landscape in which the toy protein (computationally) folds into the valley with rearrangement path at its bottom. Energies of the states along the path can be designed to be about equal, allowing for diffusion along the path. They can also be designed to provide for a significant bias in one certain direction. Together with a toy ligand molecule, our "enzimatic" machine can perform the entire cycle, including conformational relaxation in one direction upon ligand binding and conformational relaxation in the opposite direction upon ligand release. This model, however schematic, should be useful as a test ground for phenomenological theories of machine-like properties of enzymes.Comment: 13 pages, 12 figure

Analysis and application of evolutionary processes to tackle HIV-1 entry

Im Laufe der Jahrmillionen hat die Evolution durch einige einfache Mechanismen wie Mutation, Selektion oder auch Vererbung eine erstaunliche Artenvielfalt hervorgebracht. Diese Prinzipien können auch beim computergestützten Entwurf von Proteinen und/oder Proteinsequenzen mit gewünschten Eigenschaften, wie z.B. Stabilität oder Funktionalität einer Proteinstruktur, angewandt werden. Da jedoch der mögliche Konformations- und Sequenzraum für bereits kleine Proteine immens groß wird, werden hier vereinfachte Gitterproteinmodelle verwendet. Im ersten Teil der Promotionsarbeit werden evolutionäre Algorithmen, im Besonderen S Metric Selection - Evolutionary Multi-objective Optimisation Algorithm (SMS-EMOA), implementiert und angewandt um möglichst optimale evolutionäre Parameter zu identifizieren, z.B. Populationsgröße oder Mutationsrate. Interessanterweise spielt die richtige Auswahl der evolutionären Parameter eine entscheidende Rolle bezüglich der Effizienz der Algorithmen. Im zweiten Teil der Arbeit wird die Evolution von Proteinen beobachtet und analysiert. Ein besonderes Augenmerk wird dabei auf Positionen gelegt, die nicht konserviert sind. Gleichwohl können diese mit kompensatorischen Mutationen an anderen Stellen im Protein strukturell wichtige Funktionen einnehmen. Hierbei werden verschiedene Koevolutionsmethoden, wie z.B. die Mutual Information (MI) oder die Direct Coupling Analysis (DCA), weiterentwickelt und verglichen. Anschließend wird die DCA-Methode mit einer neu verbesserten Gewichtung angewandt um koevolvierende Positionen im Humanen Immundefizienz-Virus (HIV) Hüllprotein-Komplex (Env) vorherzusagen. Bemerkenswerterweise wurden dabei sowohl bereits in der Literatur beschriebene als auch noch unbekannte Positionen identifiziert, die eine entscheidende Rolle im Eintritt des Viruses in die humane Wirtszelle spielen können. Schließlich wurden die koevolvierenden Positionen bei der Erstellung eines Homologiemodells des Protein-Komplexes verwendet

Digitized-Counterdiabatic Quantum Algorithm for Protein Folding

We propose a hybrid classical-quantum digitized-counterdiabatic algorithm to tackle the protein folding problem on a tetrahedral lattice. Digitized-counterdiabatic quantum computing is a paradigm developed to compress quantum algorithms via the digitization of the counterdiabatic acceleration of a given adiabatic quantum computation. Finding the lowest energy configuration of the amino acid sequence is an NP-hard optimization problem that plays a prominent role in chemistry, biology, and drug design. We outperform state-of-the-art quantum algorithms using problem-inspired and hardware-efficient variational quantum circuits. We apply our method to proteins with up to 9 amino acids, using up to 17 qubits on quantum hardware. Specifically, we benchmark our quantum algorithm with Quantinuum's trapped ions, Google's and IBM's superconducting circuits, obtaining high success probabilities with low-depth circuits as required in the NISQ era

The prospects of quantum computing in computational molecular biology

Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to "hype", and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.Comment: 23 pages, 3 figure

Pump-Probe and Mix-and-Inject Experiments at X-Ray Free Electron Lasers

Time resolved serial femtosecond crystallography (TR-SFX) utilizes X-ray crystallography to visualize the reaction of molecules in real time at the atomic level. Crystals of biological macromolecules are exposed to powerful X-ray pulses. The X-ray radiation emitted by the crystal is then measured by an X-ray sensitive area detector that produces an image called a diffraction pattern. These patterns are analyzed to determine a three-dimensional atomic structure of the biological macromolecule.The ultimate goal of TR-SFX is to make a “molecular movie” that shows the reaction dynamics of a biological process. For this, a reaction is started in a macromolecular crystal and its three-dimensional atomic structures at different time intervals are determined. When these structures are played in a timely order, a molecular movie is recorded. A perfect analogy of this in a real life would be taking pictures of someone dancing, and playing the succession of pictures to observe the dance. TR-SFX is a method that requires an X-ray Free Electron Laser (XFEL). The XFEL produces X-ray pulses of tens of femtosecond duration with 1012 photons per pulse. These pulses are so strong that the crystals are destroyed after being exposed to a single pulse. Since the X-ray pulses ii are so short, diffraction patterns are collected by the detector just before the crystals are destroyed which is called “diffraction before destruction”. Most impressively, at XFELs, each diffraction pattern is obtained from a fresh crystal. As a result, both reversible and non-reversible reactions are placed in an equal footing and can be studied similarly. Pump-probe TR-SFX and mix and inject crystallography (MISC) are two cornerstones of TR-SFX. In pump-probe TR-SFX, photoactive macromolecules within the crystals are activated using an optical laser called the pump, and the reaction is probed by XFEL pulses. Whereas in MISC, biomolecular crystals are mixed with a substrate, and the structural changes are probed by XFEL pulses in a time resolved fashion. This dissertation presents the time-resolved studies of an enzyme called b-lactamase (BlaC) and two photoactive proteins - photoactive yellow protein (PYP) and phytochromes. MISC was used for the study of the enzymatic reaction of BlaC with an antibiotic called Ceftriaxone (CEF). This proof-of-principle experiment conducted at Linac Coherent Light Source (LCLS) operated by Stanford University in Menlo Park, California, showed how CEF binds at the active site of BlaC. Similarly, a pump-probe experiment on PYP was accomplished at the first megahertz XFEL, European XFEL (EuXFEL), in Hamburg, Germany. This experiment was performed to explore the picosecond regime of the photocycle of PYP and test the feasibility of TR-SFX at high repetition rates XFEL. Finally, another pump-probe experiment on phytochromes was conducted at Spring-8 Angstrom Compact free electron Laser (SACLA), in Harima, Japan. With this experiment, we have observed the Z-E isomerization of phytochromes for the first time and determined the previously unknown time-resolved structure at 1ps. iii This dissertation also presents data acquisition techniques and processing methods used at XFELs. It explains methods to analyze millions of patterns obtained during an experiment, with the goal of solving the X-ray structures of different intermediates of the reaction. In summary, my dissertation explains everything from the beginning of the experiment to the production of a molecular movie