Summary of select research projects (Early 2013)molmodbasics
This document summarizes select research projects from the Jensen group, which focuses on method development and simulations in a 50/50 split. Key projects discussed include using PROPKA to predict pH-dependent properties of proteins, using quantum chemistry to predict enzymatic reaction mechanisms and the effects of mutations on barrier heights and enzyme design, estimating barrier heights, rapid screening of mutants, and determining protein structures from chemical shifts.
Predicting accurate absolute binding energies in aqueous solution: thermodyn...molmodbasics
This document discusses sources of error in predicting accurate absolute binding energies in aqueous solution using electronic structure methods. It identifies several potential sources of error, including imaginary frequencies, anharmonic effects, treatment of ions, explicit solvation modeling, conformational sampling, treatment of protonation states, and harmonic approximations. It recommends using methods like DFT-D3/QZVP, PM6-D3H, and COSMO-RS to calculate solvation energies and binding affinities while addressing these error sources.
Este documento resume las herramientas y consejos de enseñanza de Jan H. Jensen del Departamento de Química de la Universidad de Copenhague. Describe cómo usar la votación en clase, videos de lectura, y preguntas para crear un salón de clases invertido y aprendizaje activo. También recomienda limitar la carga cognitiva a 7 nuevos conceptos a la vez y usar evaluaciones formativas y aprendizaje espaciado para mejorar la comprensión conceptual.
The document discusses hydrogen bond strength between two complexes, R-A and R-B. It states that the N-H⋯⋯O hydrogen bond strength is the same for both complexes when translational, rotational, and vibrational contributions are ignored. When 1 mole each of R, A, and B are mixed, more of complex R-B would form than R-A because there are more ways for R-B to form based on the various arrangements and bonding configurations possible.
Blurring the boundary between linear scaling QM, QM/MM and polarizable force ...molmodbasics
The document describes the Effective Fragment Molecular Orbital (EFMO) method, which blurs the boundary between linear scaling quantum mechanics, quantum mechanics/molecular mechanics, and polarizable force fields. EFMO extracts properties from monomer self-consistent field calculations and then computes many-body polarization and Coulomb interactions classically. It has been implemented in GAMESS and shown to provide more accurate energies and gradients than the Fragment Molecular Orbital method for a test case of the Trp cage protein. EFMO is presented as a promising method for efficiently treating large biomolecular systems through a quantum-mechanics-like treatment of short-range effects combined with a classical treatment of long-range interactions.
Quantum Biochemistry: the rise of semiempirical methodsmolmodbasics
CCSD(T)/CBS can now provide numerically exact solutions for small molecules, allowing it to be used for benchmarking and parameterizing faster computational chemistry methods. These faster semi-empirical methods calibrated against CCSD(T)/CBS are now sufficiently accurate and fast to enable high-throughput screening of chemical reactions, protein structures, and enzyme mutants (up to 1,000,000 calculations). The increased accuracy and automation of computational chemistry methods through high-throughput screening is opening new possibilities for molecular design in chemistry.
Summary of select research projects (Early 2013)molmodbasics
This document summarizes select research projects from the Jensen group, which focuses on method development and simulations in a 50/50 split. Key projects discussed include using PROPKA to predict pH-dependent properties of proteins, using quantum chemistry to predict enzymatic reaction mechanisms and the effects of mutations on barrier heights and enzyme design, estimating barrier heights, rapid screening of mutants, and determining protein structures from chemical shifts.
Predicting accurate absolute binding energies in aqueous solution: thermodyn...molmodbasics
This document discusses sources of error in predicting accurate absolute binding energies in aqueous solution using electronic structure methods. It identifies several potential sources of error, including imaginary frequencies, anharmonic effects, treatment of ions, explicit solvation modeling, conformational sampling, treatment of protonation states, and harmonic approximations. It recommends using methods like DFT-D3/QZVP, PM6-D3H, and COSMO-RS to calculate solvation energies and binding affinities while addressing these error sources.
Este documento resume las herramientas y consejos de enseñanza de Jan H. Jensen del Departamento de Química de la Universidad de Copenhague. Describe cómo usar la votación en clase, videos de lectura, y preguntas para crear un salón de clases invertido y aprendizaje activo. También recomienda limitar la carga cognitiva a 7 nuevos conceptos a la vez y usar evaluaciones formativas y aprendizaje espaciado para mejorar la comprensión conceptual.
The document discusses hydrogen bond strength between two complexes, R-A and R-B. It states that the N-H⋯⋯O hydrogen bond strength is the same for both complexes when translational, rotational, and vibrational contributions are ignored. When 1 mole each of R, A, and B are mixed, more of complex R-B would form than R-A because there are more ways for R-B to form based on the various arrangements and bonding configurations possible.
Blurring the boundary between linear scaling QM, QM/MM and polarizable force ...molmodbasics
The document describes the Effective Fragment Molecular Orbital (EFMO) method, which blurs the boundary between linear scaling quantum mechanics, quantum mechanics/molecular mechanics, and polarizable force fields. EFMO extracts properties from monomer self-consistent field calculations and then computes many-body polarization and Coulomb interactions classically. It has been implemented in GAMESS and shown to provide more accurate energies and gradients than the Fragment Molecular Orbital method for a test case of the Trp cage protein. EFMO is presented as a promising method for efficiently treating large biomolecular systems through a quantum-mechanics-like treatment of short-range effects combined with a classical treatment of long-range interactions.
Quantum Biochemistry: the rise of semiempirical methodsmolmodbasics
CCSD(T)/CBS can now provide numerically exact solutions for small molecules, allowing it to be used for benchmarking and parameterizing faster computational chemistry methods. These faster semi-empirical methods calibrated against CCSD(T)/CBS are now sufficiently accurate and fast to enable high-throughput screening of chemical reactions, protein structures, and enzyme mutants (up to 1,000,000 calculations). The increased accuracy and automation of computational chemistry methods through high-throughput screening is opening new possibilities for molecular design in chemistry.
Can semiempirical methods be used for high throughput screening (for enzyme m...molmodbasics
This document discusses the potential for using semiempirical methods for high-throughput screening of enzyme mutants. It notes some early attempts showed mixed results for predicting reaction mechanisms and identifying mutants with high activity. High-throughput screening can identify interesting mutants for further study. Open questions remain about efficiently handling large numbers of molecules, conformational searching for proteins, automatic transition state searching, improving accuracy with machine learning, and experimental validation.
The 2013 Bjerrum-Brønsted-Lang Lecture: quantum biochemistry and the rise of ...molmodbasics
1. The document appears to be slides from a 2013 lecture on developments in computational chemistry methods and their applications.
2. Advances in computational methods like CCSD(T)/CBS now allow for highly accurate quantum mechanical calculations for benchmarking and parameterizing semi-empirical methods.
3. New semi-empirical methods fitted using CCSD(T)/CBS data rather than experiments have achieved sufficient accuracy and speed for applications like high-throughput screening of enzyme mutants and protein structure determination from NMR data.
This document discusses the importance of accounting for protonation states in drug design and how they impact ligand binding. Protonation states determine the charge and hydrogen bonding properties of both ligands and proteins. Computational tools can predict ligand pKa values using methods like Hammett-Taft rules, while protein residue pKa values may differ from standard values due to their local environment. Protonation states can change upon ligand binding, affecting the binding affinity and thermodynamics in a pH-dependent manner. This implies docking scores need corrections if static protonation states are used.
Este documento proporciona varias herramientas y consejos para la enseñanza de química. Describe estrategias como votaciones en clase usando Socrave, exámenes previos a clase con Absalon, creación de videos de lectura, y retroalimentación inmediata en tareas usando PeerWise. También discute conceptos como aprendizaje activo, aula invertida, carga cognitiva y evaluación formativa, con el objetivo de mejorar la comprensión conceptual de los estudiantes más que la memorización.
This document discusses quantum biochemistry and the effective fragment molecular orbital (EFMO) method. The EFMO method blurs the boundary between linear scaling quantum mechanics, quantum mechanics/molecular mechanics, and polarizable force fields. It involves calculating properties of molecular fragments individually and then combining them to model intermolecular interactions through induced dipoles, allowing efficient modeling of large systems. Recent applications of EFMO to reaction barrier calculations showed good agreement with experiment. Further developments could include modeling solvent effects, open shell systems, vibrational frequencies, and more efficient geometry optimizations.
Este documento resume los principales conceptos y herramientas para implementar un enfoque de aprendizaje activo en lugar de conferencias tradicionales. En primer lugar, propone que los estudiantes adquieran conocimientos antes de clase a través de lecturas y videos. Luego, en clase, se utilizan preguntas y discusiones entre pares para aplicar y reforzar el conocimiento. Finalmente, el documento recomienda el uso de herramientas como quizzes, videos de conferencias y aplicaciones móviles para proporcionar retroalimentación in
Protein structure determination & refinement using QM-derived chemical shiftsmolmodbasics
1) The document discusses using quantum mechanical (QM)-derived chemical shifts to determine protein structures by NMR. Chemical shifts are more sensitive to structure than empirical models suggest.
2) Researchers are developing a QM-based chemical shift predictor (ProCS) and refining protein structures using ProCS and molecular dynamics simulations. This improves hydrogen bond geometries compared to X-ray structures.
3) The work aims to calculate chemical shifts for all nuclei to determine larger protein structures solely from chemical shifts, requiring millions of QM calculations. Additional constraints like SAXS may still be needed for very large proteins.
The document lists reasons for blogging, both for oneself and for others. For oneself, reasons include getting thoughts straight, feedback, getting things out of one's head, and remembering information. For others, reasons are being useful, sharing information openly as it's hard to predict what may help others. The document also asks if students would post class writings or research data online before publication.
The document discusses the various ways that Twitter can be used, including as a way to stay up to date with news and announcements, participate in discussions, get help and feedback, build networks and communities, and find stress relief through jokes. Twitter is compared to constantly attending conferences and allows users to live tweet talks, meet new people, and build their professional network and community.
xyz2mol converts xyz files to RDKit mol objects and can be used to summarize organometallic compounds from xyz files by determining the charge of metal ions using information from the localized molecular orbitals (LMOs). While LMOs provide information on how electrons are distributed among atoms, xyz2mol is still needed to construct the molecular structure and assign charges to individual metal atoms in multi-metal systems. This allows organometallic structures to be analyzed in cheminformatics tools.
This document discusses using genetic algorithms and other machine learning techniques to explore the vast chemical space of possible molecules. It notes that while there are over 10^60 possible small molecules, only around 10^8 have been made so far, leaving most of chemical space unexplored. It provides examples of how genetic algorithms with additive scoring functions have been able to rediscover specific target molecules and molecules with desired properties like light absorption or protein docking. With continued improvements in scoring functions, these techniques may eventually be able to efficiently search the entire chemical space to discover new molecules with useful applications.
This document discusses ChemRxiv, an open access preprint server for chemistry, and Plan S, an initiative to make scientific publications openly available. Some key points:
- ChemRxiv allows quick dissemination of manuscripts and establishes priority before formal publication. Researchers can post pre-acceptance versions and new versions of manuscripts.
- Preprints are good for science by allowing open sharing, but journals remain important for career evaluations like CVs. Journals are expensive while preprint servers are much cheaper.
- Plan S aims to eliminate publication paywalls by requiring open access. It is a response to rising journal prices outstripping budgets and limiting access to research. Open access publishing models can be affordable.
A Quantum Chemist Meets Cheminformaticsmolmodbasics
This document discusses how a quantum chemist began using RDKit to automate workflows for computational chemistry. The chemist describes how RDKit helped automate tasks like generating protonation states, conformers, and finding lowest energy structures. RDKit was also used to build tools like RegioSQM and xyz2mol. More recently, the chemist used RDKit to implement a genetic algorithm approach for designing novel light-absorbing molecules. In conclusion, RDKit has significantly changed the chemist's research by enabling automation that reduces manual work and mistakes, though some "QM-needs" like improved conformational searching and solvation models could further benefit computational chemistry within RDKit.
Can We Automate Computational Studies of Enzymes? Lessons from Small-Molecul...molmodbasics
This document discusses the potential for automating computational studies of enzymes based on lessons from studies of small molecule combustion and atmospheric chemistry. It notes that catalytic mechanisms can now be automatically extracted from reaction networks and that semiempirical quantum mechanics methods may allow automated conformational sampling and reaction setup for enzymes, though their accuracy needs testing. Tools developed so far in the author's group include methods for automatically extracting elementary steps from reaction networks and databases of enzyme reaction energies and barriers calculated at different levels of theory.
Este documento describe la investigación del profesor Jan H. Jensen sobre el plegamiento y modelado computacional de proteínas. Explica que las estructuras de proteínas son importantes para el desarrollo de medicamentos y que, aunque son difíciles de medir, ahora es posible calcularlas computacionalmente usando ecuaciones como la ecuación de Schrödinger dependiente del tiempo. También presenta varios programas y sitios web que permiten simulaciones y modelado de proteínas a nivel atómico.
Using semiempirical methods for fast and automated predictionsmolmodbasics
The document discusses using semiempirical methods for fast and automated predictions of regioselectivity in electrophilic aromatic substitution reactions of heteroaromatic systems. It describes a workflow that uses RDKit and RegioSQM to predict the site of protonation and substitution in 6 steps: (1) generating protonated isomers, (2) conformational search, (3) finding lowest energy isomer, (4) checking for proton transfer, (5) running calculations, and (6) displaying results. This approach achieved a 90% success rate on a test set of 520 compounds. Lessons learned include asking the "right" questions to improve accuracy, the importance of cheminformatics tools for automation, and ensuring
Jan H. Jensen is a physical chemist at the University of Copenhagen who studies computational chemistry methods. His research focuses on predicting the regioselectivity of C-H functionalization reactions and developing computational strategies to guide synthesis. He also works to automate calculations and make them accessible online. Jensen teaches thermodynamics and Python programming and uses active learning tools. He writes blogs and books on molecular modeling basics and maintains several websites to share computational chemistry research.
This document asks several questions about the differences and advantages of various computational chemistry methods like DFT, HF, and MP2. It also asks about the definitions and examples of different types of density functional approximations like LDA, GGA, hybrid, and meta-GGA functionals. Finally, it questions why DFT energies may not be exact, whether DFT is variational, and how to select the best functional for a given project.
Peer instructions questions for basic quantum mechanicsmolmodbasics
The document discusses the development of quantum mechanics from Planck/Einstein's quantization of energy to Schrodinger's wave equation. It presents the time-dependent and time-independent Schrodinger equations and their application to particles in a box, harmonic oscillators, and the hydrogen atom. The hydrogen atom energy levels and wavefunctions of the 1s and 2s orbitals are shown.
Can semiempirical methods be used for high throughput screening (for enzyme m...molmodbasics
This document discusses the potential for using semiempirical methods for high-throughput screening of enzyme mutants. It notes some early attempts showed mixed results for predicting reaction mechanisms and identifying mutants with high activity. High-throughput screening can identify interesting mutants for further study. Open questions remain about efficiently handling large numbers of molecules, conformational searching for proteins, automatic transition state searching, improving accuracy with machine learning, and experimental validation.
The 2013 Bjerrum-Brønsted-Lang Lecture: quantum biochemistry and the rise of ...molmodbasics
1. The document appears to be slides from a 2013 lecture on developments in computational chemistry methods and their applications.
2. Advances in computational methods like CCSD(T)/CBS now allow for highly accurate quantum mechanical calculations for benchmarking and parameterizing semi-empirical methods.
3. New semi-empirical methods fitted using CCSD(T)/CBS data rather than experiments have achieved sufficient accuracy and speed for applications like high-throughput screening of enzyme mutants and protein structure determination from NMR data.
This document discusses the importance of accounting for protonation states in drug design and how they impact ligand binding. Protonation states determine the charge and hydrogen bonding properties of both ligands and proteins. Computational tools can predict ligand pKa values using methods like Hammett-Taft rules, while protein residue pKa values may differ from standard values due to their local environment. Protonation states can change upon ligand binding, affecting the binding affinity and thermodynamics in a pH-dependent manner. This implies docking scores need corrections if static protonation states are used.
Este documento proporciona varias herramientas y consejos para la enseñanza de química. Describe estrategias como votaciones en clase usando Socrave, exámenes previos a clase con Absalon, creación de videos de lectura, y retroalimentación inmediata en tareas usando PeerWise. También discute conceptos como aprendizaje activo, aula invertida, carga cognitiva y evaluación formativa, con el objetivo de mejorar la comprensión conceptual de los estudiantes más que la memorización.
This document discusses quantum biochemistry and the effective fragment molecular orbital (EFMO) method. The EFMO method blurs the boundary between linear scaling quantum mechanics, quantum mechanics/molecular mechanics, and polarizable force fields. It involves calculating properties of molecular fragments individually and then combining them to model intermolecular interactions through induced dipoles, allowing efficient modeling of large systems. Recent applications of EFMO to reaction barrier calculations showed good agreement with experiment. Further developments could include modeling solvent effects, open shell systems, vibrational frequencies, and more efficient geometry optimizations.
Este documento resume los principales conceptos y herramientas para implementar un enfoque de aprendizaje activo en lugar de conferencias tradicionales. En primer lugar, propone que los estudiantes adquieran conocimientos antes de clase a través de lecturas y videos. Luego, en clase, se utilizan preguntas y discusiones entre pares para aplicar y reforzar el conocimiento. Finalmente, el documento recomienda el uso de herramientas como quizzes, videos de conferencias y aplicaciones móviles para proporcionar retroalimentación in
Protein structure determination & refinement using QM-derived chemical shiftsmolmodbasics
1) The document discusses using quantum mechanical (QM)-derived chemical shifts to determine protein structures by NMR. Chemical shifts are more sensitive to structure than empirical models suggest.
2) Researchers are developing a QM-based chemical shift predictor (ProCS) and refining protein structures using ProCS and molecular dynamics simulations. This improves hydrogen bond geometries compared to X-ray structures.
3) The work aims to calculate chemical shifts for all nuclei to determine larger protein structures solely from chemical shifts, requiring millions of QM calculations. Additional constraints like SAXS may still be needed for very large proteins.
The document lists reasons for blogging, both for oneself and for others. For oneself, reasons include getting thoughts straight, feedback, getting things out of one's head, and remembering information. For others, reasons are being useful, sharing information openly as it's hard to predict what may help others. The document also asks if students would post class writings or research data online before publication.
The document discusses the various ways that Twitter can be used, including as a way to stay up to date with news and announcements, participate in discussions, get help and feedback, build networks and communities, and find stress relief through jokes. Twitter is compared to constantly attending conferences and allows users to live tweet talks, meet new people, and build their professional network and community.
xyz2mol converts xyz files to RDKit mol objects and can be used to summarize organometallic compounds from xyz files by determining the charge of metal ions using information from the localized molecular orbitals (LMOs). While LMOs provide information on how electrons are distributed among atoms, xyz2mol is still needed to construct the molecular structure and assign charges to individual metal atoms in multi-metal systems. This allows organometallic structures to be analyzed in cheminformatics tools.
This document discusses using genetic algorithms and other machine learning techniques to explore the vast chemical space of possible molecules. It notes that while there are over 10^60 possible small molecules, only around 10^8 have been made so far, leaving most of chemical space unexplored. It provides examples of how genetic algorithms with additive scoring functions have been able to rediscover specific target molecules and molecules with desired properties like light absorption or protein docking. With continued improvements in scoring functions, these techniques may eventually be able to efficiently search the entire chemical space to discover new molecules with useful applications.
This document discusses ChemRxiv, an open access preprint server for chemistry, and Plan S, an initiative to make scientific publications openly available. Some key points:
- ChemRxiv allows quick dissemination of manuscripts and establishes priority before formal publication. Researchers can post pre-acceptance versions and new versions of manuscripts.
- Preprints are good for science by allowing open sharing, but journals remain important for career evaluations like CVs. Journals are expensive while preprint servers are much cheaper.
- Plan S aims to eliminate publication paywalls by requiring open access. It is a response to rising journal prices outstripping budgets and limiting access to research. Open access publishing models can be affordable.
A Quantum Chemist Meets Cheminformaticsmolmodbasics
This document discusses how a quantum chemist began using RDKit to automate workflows for computational chemistry. The chemist describes how RDKit helped automate tasks like generating protonation states, conformers, and finding lowest energy structures. RDKit was also used to build tools like RegioSQM and xyz2mol. More recently, the chemist used RDKit to implement a genetic algorithm approach for designing novel light-absorbing molecules. In conclusion, RDKit has significantly changed the chemist's research by enabling automation that reduces manual work and mistakes, though some "QM-needs" like improved conformational searching and solvation models could further benefit computational chemistry within RDKit.
Can We Automate Computational Studies of Enzymes? Lessons from Small-Molecul...molmodbasics
This document discusses the potential for automating computational studies of enzymes based on lessons from studies of small molecule combustion and atmospheric chemistry. It notes that catalytic mechanisms can now be automatically extracted from reaction networks and that semiempirical quantum mechanics methods may allow automated conformational sampling and reaction setup for enzymes, though their accuracy needs testing. Tools developed so far in the author's group include methods for automatically extracting elementary steps from reaction networks and databases of enzyme reaction energies and barriers calculated at different levels of theory.
Este documento describe la investigación del profesor Jan H. Jensen sobre el plegamiento y modelado computacional de proteínas. Explica que las estructuras de proteínas son importantes para el desarrollo de medicamentos y que, aunque son difíciles de medir, ahora es posible calcularlas computacionalmente usando ecuaciones como la ecuación de Schrödinger dependiente del tiempo. También presenta varios programas y sitios web que permiten simulaciones y modelado de proteínas a nivel atómico.
Using semiempirical methods for fast and automated predictionsmolmodbasics
The document discusses using semiempirical methods for fast and automated predictions of regioselectivity in electrophilic aromatic substitution reactions of heteroaromatic systems. It describes a workflow that uses RDKit and RegioSQM to predict the site of protonation and substitution in 6 steps: (1) generating protonated isomers, (2) conformational search, (3) finding lowest energy isomer, (4) checking for proton transfer, (5) running calculations, and (6) displaying results. This approach achieved a 90% success rate on a test set of 520 compounds. Lessons learned include asking the "right" questions to improve accuracy, the importance of cheminformatics tools for automation, and ensuring
Jan H. Jensen is a physical chemist at the University of Copenhagen who studies computational chemistry methods. His research focuses on predicting the regioselectivity of C-H functionalization reactions and developing computational strategies to guide synthesis. He also works to automate calculations and make them accessible online. Jensen teaches thermodynamics and Python programming and uses active learning tools. He writes blogs and books on molecular modeling basics and maintains several websites to share computational chemistry research.
This document asks several questions about the differences and advantages of various computational chemistry methods like DFT, HF, and MP2. It also asks about the definitions and examples of different types of density functional approximations like LDA, GGA, hybrid, and meta-GGA functionals. Finally, it questions why DFT energies may not be exact, whether DFT is variational, and how to select the best functional for a given project.
Peer instructions questions for basic quantum mechanicsmolmodbasics
The document discusses the development of quantum mechanics from Planck/Einstein's quantization of energy to Schrodinger's wave equation. It presents the time-dependent and time-independent Schrodinger equations and their application to particles in a box, harmonic oscillators, and the hydrogen atom. The hydrogen atom energy levels and wavefunctions of the 1s and 2s orbitals are shown.
Teaching protein folding to high school students in less than a day
1. I
dag
Hvad
er
computa.onal
chemistry?
Proteinfoldning
med
Molecular
Workbench
Proteinfoldning
med
Foldit
Frokost
Proteinfoldning
med
Foldit
14:00
Rundvisning
med
Jes
Andersen
(45
min)
Proteinfoldning
med
Foldit
16:00
Slut
Netværk:
KU
Guest
/
Brugernavn:
xxx/
Password:
xxx
1
3. Computa6onal
Chemistry
The
6me-‐dependent
Schrödinger
Equa6on
(1926)
∂ ˆ
i Ψ = H Ψ
∂t
The
underlying
physical
laws
necessary
for
the
mathema.cal
theory
of
a
large
part
of
physics
and
the
whole
of
chemistry
are
thus
completely
known,
and
the
difficulty
is
only
that
the
exact
applica.on
of
these
laws
leads
to
equa.ons
much
too
complicated
to
be
soluble.
Paul
Dirac,
1929
3
4. Approxima6ons
Probability
∂ ˆ
i Ψ = H Ψ p(x,t) = ( Ψ(x,t))
2
∂t
Ψ(x,t) ≈ Ψ(x)e−iEt /
∂
i Ψ = EΨ
∂t
ˆ
H Ψ(x) = EΨ(x)
Molecular
Mechanics
Force
Fields
Sta6s6cal
Mechanics
E(x) ≈ 1 c 2 (x − xo )2 + ...
2
ˆ
H Ψ n (x) = En Ψ n (x) (probability
of
state
n)
e− En / kT
pn =
Molecular
Dynamics
∑ e− En / kT
n
Δt 2 ⎛ ∂E ⎞
x(t + Δt) = 2x(t) − x(t − Δt) − ⎜ ⎟
m ⎝ ∂x ⎠
4
7. hQp://m.socra6ve.com
Room
number
9076
En
alpha-‐helix
er
stabiliseret
af:
A. Hydrofobe
interak.oner
B. Hydrogenbindinger
C. Interak.oner
mellem
ladede
aminosyre
D. Ved
ikke
7
8. hQp://m.socra6ve.com
Room
number
9076
En
alpha-‐helix
er
stabiliseret
af:
A. Hydrofobe
interak.oner
B. Hydrogenbindinger
C. Interak.oner
mellem
ladede
aminosyre
D. Ved
ikke
8
9. Hvad
er
den
mest
sandsynlige
posi.on
for
isoleucine:
A. I
kernen
af
proteiner
B. På
ydersiden
af
proteinet
C. Som
den
første
aminosyre
i
proteinet
D. Ved
ikke
9
10. Hvad
er
den
mest
sandsynlige
posi.on
for
isoleucine:
A. I
kernen
af
proteinet
B. På
ydersiden
af
proteinet
C. Som
den
første
aminosyre
i
proteinet
D. Ved
ikke
10
11. hQp://m.socra6ve.com
Room
number
9076
Et
beta-‐sheet
er
stabiliseret
af
hydrogenbindinger:
A. Mellem
sidekæde
atomer
B. Mellem
sidekæde
og
backbone
atomer
C. Mellem
backbone
atomer
D. Ved
ikke
11
12. hQp://m.socra6ve.com
Room
number
9076
Et
beta-‐sheet
er
stabiliseret
af
hydrogenbindinger:
A. Mellem
sidekæde
atomer
B. Mellem
sidekæde
og
backbone
atomer
C. Mellem
backbone
atomer
D. Ved
ikke
12
