MF Schneider, This is not about the molecules--On the Violation of Momentum Conservation in Biology. A short comment, Arxiv.Org, 2020.
Conservation laws are the pillars of physics. It's what we held on to when our imagination was challenged during the days of relativity or quantum mechanics. Their violation leads to the most absurd models, so excellently exercised in the history of the perpetuum mobile. Importantly, it is not at all sufficient to merely accept the existence of conservation laws. Intention to obey them is required when models are developed, as conservation laws are not obeyed simply by accident. However evident this demand may appear, its application turns …
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Saima Ahmed et al., Aqueous solvation from the water perspective, The Journal Of Chemical Physics, 148 (2018) 234505-8.
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ChenChen Wu, Molcontroller: a VMD Graphical User Interface for Manipulating Molecules, Arxiv Preprint, 2020 pp. 1-9.
Visual Molecular Dynamics (VMD) is one of the most widely used molecular graphics software in the community of theoretical simulations. So far, however, it still lacks a graphical user interface (GUI) for molecular manipulations when doing some modeling tasks. For instance, translation or rotation of a selected molecule(s) or part(s) of a molecule, which are currently only can be achieved using tcl scripts. Here, we use tcl script develop a user-friendly GUI for VMD, named Molcontroller, which is featured by allowing users to quickly and conveniently perform various molecular manipulations. This GUI might be helpful for improving the modeling efficiency of VMD users.
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Anon. (2019), Lab leads effort to model proteins tied to cancer - Lab leads effort to model proteins tied to cancer, Labportal.Llnl.Gov, 2019 pp. 1-4.
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Helen M Deeks et al., Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking., Plos One, 2020 PMC7065745, 15 (3) p. e0228461.
Simulating drug binding and unbinding is a challenge, as the rugged energy landscapes that separate bound and unbound states require extensive sampling that consumes significant computational resources. Here, we describe the use of interactive molecular dynamics in virtual reality (iMD-VR) as an accurate low-cost strategy for flexible protein-ligand docking. We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5-10 minutes. Following a brief training phase, our studies shown that iMD-VR novices were able to generate unbinding and rebinding pathways on similar timescales as iMD-VR experts, with the majority able to recover binding poses within 2.15 Å RMSD of the crystallographic binding pose. These results indicate that iMD-VR affords sufficient control for users to carry out the detailed atomic manipulations required to dock flexible ligands into dynamic enzyme active sites and recover crystallographic poses, offering an interesting new approach for simulating drug docking and generating binding hypotheses.
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Ron Unger and John Moult, Finding the lowest free energy conformation of a protein is an NP-hard problem: Proof and implications, Bulletin Of Mathematical Biology, 55 (1993) 1183-1198.
The protein folding problem and the notion of NP-completeness and NP-hardness are discussed. A lattice model is suggested to capture the essece of protein folding. For this model we present a proof...
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Zachary Wu et al., Machine learning-assisted directed protein evolution with combinatorial libraries, Proceedings Of The National Academy Of Sciences Of The United States Of America, 116 (2019) 8852-8858.
To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si–H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79% ee (enantiomeric excess). By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.
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CC King, One Man’s Meat Is Another Man’s Person, , 2004 pp. 1-143.
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Hans C Anderson, Rattle, Journal Of Computational Physics, 52 (1983) 24-34.
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Andrzej Kloczkowski and Andrzej Kolinski, Theoretical Models and Simulations of Polymer Chains, , 2019 pp. 1-2.
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Marco Giulini, An information theory–based approach for optimal model reduction of biomolecules, Arxiv Preprint, 2020 pp. 1-22.
In the theoretical modelling of a physical system a crucial step consists in the identification of those degrees of freedom that enable a synthetic, yet informative representation of it. While in some cases this selection can be carried out on the basis of intuition and experience, a straightforward dis- crimination of the important features from the negligible ones is difficult for many complex systems, most notably heteropolymers and large biomolecules. We here present a thermodynamics-based theoretical framework to gauge the effectiveness of a given simplified representation by measuring its information content. We employ this method to identify those reduced descriptions of proteins, in terms of a subset of their atoms, that retain the largest amount of information from the original model; we show that these highly informative representations share common features that are intrin- sically related to the biological properties of the proteins under examination, thereby establishing a bridge between protein structure, energetics, and function.
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Anton Robert et al., Resource-Efficient Quantum Algorithm for Protein Folding, Arxiv.Org, 2019.
Predicting the three-dimensional (3D) structure of a protein from its primary sequence of amino acids is known as the protein folding (PF) problem. Due to the central role of proteins' 3D structures in chemistry, biology and medicine applications (e.g., in drug discovery) this subject has been intensively studied for over half a century. Although classical algorithms provide practical solutions, sampling the conformation space of small proteins, they cannot tackle the intrinsic NP-hard complexity of the problem, even reduced to its simplest Hydrophobic-Polar model. While fault-tolerant quantum computers are still beyond reach for state-of-the-art quantum technologies, there is evidence that quantum algorithms can be successfully used on Noisy Intermediate-Scale Quantum (NISQ) computers to accelerate energy optimization in frustrated systems. In this work, we present a model Hamiltonian with $\mathcal{O}(N^4)$ scaling and a corresponding quantum variational algorithm for the folding of a polymer chain with $N$ monomers on a tetrahedral lattice. The model reflects many physico-chemical properties of the protein, reducing the gap between coarse-grained representations and mere lattice models. We use a robust and versatile optimisation scheme, bringing together variational quantum algorithms specifically adapted to classical cost functions and evolutionary strategies (genetic algorithms), to simulate the folding of the 10 amino acid Angiotensin peptide on 22 qubits. The same method is also successfully applied to the study of the folding of a 7 amino acid neuropeptide using 9 qubits on an IBM Q 20-qubit quantum computer. Bringing together recent advances in building gate-based quantum computers with noise-tolerant hybrid quantum-classical algorithms, this work paves the way towards accessible and relevant scientific experiments on real quantum processors.
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Andreas Kukol, Molecular Modeling of Proteins, , 2008 pp. 1-389.
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Steven Weinberg, Lindblad decoherence in atomic clocks, Physical Review A, 2016.
In searching for an interpretation of quantum mechanics we seem to be faced with nothing but bad choices [1]. To avoid both the dualism of the Copenhagen interpretation and the endless creation of inconceivably many branches of history of the many-worlds approach, while at the same time holding on to a realist description of the evolution of physical states from moment to moment, we may try to modify quantum mechanics so that during measurement the density matrix of even an isolated system undergoes a collapse of the sort called for by the Copenhagen …
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HJC Berendsen and D van der Spoel, GROMACS: a message-passing parallel molecular dynamics implementation, Studies In History And Philosophy Of Science Part B, 91 (1995) 43-56.
A parallel message-passing implementation of a molecular dynamics (MD) program that is useful for bio (macro) molecules in aqueous environment is described. The software has been developed for a custom-designed 32-processor ring GROMACS (GROningen MAchine for Chemical Simulation) with communication to and from left and right neighbours, but can run on any parallel system onto which aa ring of processors can be mapped and which supports PVM-like block send and receive calls. The GROMACS software consists of a …
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Michael Levitt and Miriam Hirshberg, Potential Energy Function and Parameters for Simulations of the Molecular Dynamics of Proteins and Nucleic Acids in Solution, Computer Physics Communications, 91 (1995) 215-231.
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Jaydeep P Bardhan, Numerical solution of boundary-integral equations for molecular electrostatics, The Journal Of Chemical Physics, 2009 B7A359D5-C504-4E4B-B2E3-152707523CAD, 130 (9), 3 p. 094102.
Numerous molecular processes, such as ion permeation through channel proteins, are governed by relatively small changes in energetics. As a result, theoretical investigations of these processes require accurate numerical methods. In the present paper, we evaluate the accuracy of two approaches to simulating boundary-integral equations for continuum models of the electrostatics of solvation. The analysis emphasizes boundary-element method simulations of the integral-equation formulation known as the apparent-surface-charge (ASC) method or polarizable-continuum model (PCM). In many numerical implementations of the ASC/PCM model, one forces the integral equation to be satisfied exactly at a set of discrete points on the boundary. We demonstrate in this paper that this approach to discretization, known as point collocation, is significantly less accurate than an alternative approach known as qualocation. Furthermore, the qualocation method offers this improvement in accuracy without increasing simulation time. Numerical examples demonstrate that electrostatic part of the solvation free energy, when calculated using the collocation and qualocation methods, can differ significantly; for a polypeptide, the answers can differ by as much as 10 kcal/mol (approximately 4% of the total electrostatic contribution to solvation). The applicability of the qualocation discretization to other integral-equation formulations is also discussed, and two equivalences between integral-equation methods are derived.Numerous molecular processes, such as ion permeation through channel proteins, are governed by relatively small changes in energetics. As a result, theoretical investigations of these processes require accurate numerical methods. In the present paper, we evaluate the accuracy of two approaches to simulating boundary-integral equations for continuum models of the electrostatics of solvation. The analysis emphasizes boundary-element method simulations of the integral-equation formulation known as the apparent-surface-charge (ASC) method or polarizable-continuum model (PCM). In many numerical implementations of the ASC/PCM model, one forces the integral equation to be satisfied exactly at a set of discrete points on the boundary. We demonstrate in this paper that this approach to discretization, known as point collocation, is significantly less accurate than an alternative approach known as qualocation. Furthermore, the qualocation method offers this improvement in accuracy without increasing simulation time...
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Nawaf Bou-Rabee, Time Integrators for Molecular Dynamics, Entropy, 16 (2014) 138-162.
This paper invites the reader to learn more about time integrators for Molecular Dynamics simulation through a simple MATLAB implementation. An overview of methods is provided from an algorithmic viewpoint that emphasizes long-time stability and finite-time dynamic accuracy. The given software simulates Langevin dynamics using an explicit, second-order (weakly) accurate integrator that exactly reproduces the Boltzmann-Gibbs density. This latter feature comes from adding a Metropolis acceptance-rejection step to the integrator. The paper discusses in detail the properties of the integrator. Since these properties do not rely on a specific form of a heat or pressure bath model, the given algorithm can be used to simulate other bath models including, e.g., the widely used v-rescale thermostat.
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Ginka S Buchner et al., Dynamics of protein folding: Probing the kinetic network of folding–unfolding transitions with experiment and theory, Bba - Proteins And Proteomics, 1814 (2011) 1001-1020.
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Nicola Calonaci et al., Machine learning a model for RNA structure prediction, Arxiv.Org, 2020 2004.00351v1, q-bio.BM.
RNA function crucially depends on its structure. Thermodynamic models that are used for secondary structure prediction report a large number of structures in a limited energy range, often failing in identifying the correct native structure unless complemented by auxiliary experimental data. In this work we build an automatically trainable model that is based on a combination of thermodynamic parameters, chemical probing data (Selective 2^ Hydroxyl Acylation analyzed via Primer Extension, SHAPE), and co-evolutionary data (Direct Coupling Analysis, DCA). Perturbations are trained on a suitable set of systems for which the native structure is known. A convolutional window is used to include neighboring reactivities in the SHAPE nodes of the network, and regularization terms limit overfitting improving transferability. The most transferable model is chosen with a cross-validation strategy that allows to automatically optimize the relative importance of heterogenous input datasets. The model architecture enlightens the structural information content of SHAPE reactivities and their dependence on local conformational ensembles. By using the selected model, we obtain enhanced populations for reference native structures and more sensitive and precise predicted structures in an independent validation set not seen during training. The flexibility of the approach allows the model to be easily retrained and adapted to incorporate arbitrary experimental information.
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Henrik Christiansen et al., Accelerating molecular dynamics simulations with population annealing, Arxiv.Org, 2018 1806.06016v2, physics.comp-ph (6) p. 060602.
Population annealing is a powerful tool for large-scale Monte Carlo simulations. We adapt this method to molecular dynamics simulations and demonstrate its excellent accelerating effect by simulating the folding of a short peptide commonly used to gauge the performance of algorithms. The method is compared to the well established parallel tempering approach and is found to yield similar performance for the same computational resources. In contrast to other methods, however, population annealing scales to a nearly arbitrary number of parallel processors and it is thus a unique tool that enables molecular dynamics to tap into the massively parallel computing power available in supercomputers that is so much needed for a range of difficult computational problems.
Published in: Phys. Rev. Lett. 122, 060602 (2019)
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Feng Ding et al., Ab Initio Folding of Proteins with All-Atom Discrete Molecular Dynamics, Structure, 16 (2008) 1010-1018.
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Peter Eastman et al., OpenMM 7: Rapid development of high performance algorithms for molecular dynamics, Plos Computational Biology, 13 (2017) e1005659-17.
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Roger Edberg et al., Constrained molecular dynamics: Simulations of liquid alkanes with a new algorithm, The Journal Of Chemical Physics, 84 (1986) 6933-6939.
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Marshall Fixman, The Poisson–Boltzmann equation and its application to polyelectrolytes, The Journal Of Chemical Physics, 70 (1979) 4995-5005.
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Marco Giulini et al., An information theory-based approach for optimal model reduction of biomolecules, Arxiv.Org, 2020.
Coarse-grained models are simplified representations of systems in terms of fewer particles or sites with respect to more accurate reference descriptions, e.g., atomically-detailed. The definition of the effective interaction sites is commonly based on chemical intuition, and no systematic analysis is usually performed over the many different possible representations to assess the quality of this choice using quantitative metrics. We here present a theoretical framework to gauge the effectiveness of a given coarse-grained description by measuring the model's information content. The quantity employed, namely mapping entropy, is defined in terms of multi-dimensional integrals of probability distributions over the system's configurational space, and normally is exceedingly difficult to calculate with conventional methods. In this work we illustrate how controllable approximations can be made that enable the computationally efficient calculation of mapping entropy. We employ this method to identify the representations of proteins that entail the largest amount of information from the original systems. It is shown that representations with low values of mapping entropy share common features that are intrinsically related to the biological properties of the proteins under examination, thereby establishing a bridge between protein structure, energetics, and function.
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Fredrik Hedman, Algorithms for Molecular Dynamics Simulations, , 2006 pp. 1-107.
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Scott A Hollingsworth and Ron O Dror, Molecular Dynamics Simulation for All, Neuron, 99 (2018) 1129-1143.
The impact of molecular dynamics (MD) simulations in molecular biology and drug discovery has expanded dramatically in recent years. These simulations capture the behavior of proteins and other biomolecules in full atomic detail and at very fine temporal resolution. Major improvements in simulation speed, accuracy, and accessibility, together with the proliferation of experimental structural data, have increased the appeal of bio- molecular simulation to experimentalists—a trend particularly noticeable in, although certainly not limited to, neuroscience. Simulations have proven valuable in deciphering functional mechanisms of proteins and other biomolecules, in uncovering the structural basis for disease, and in the design and optimization of small mol- ecules, peptides, and proteins. Here we describe, in practical terms, the types of information MD simulations can provide and the ways in which they typically motivate further experimental work.
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Hiqmet Kamberaj, Faster protein folding using enhanced conformational sampling of molecular dynamics simulation, Journal Of Molecular Graphics And Modelling, 81 (2018) 32-49.
In this study, we applied swarm particle-like molecular dynamics (SPMD) approach to enhance conformational sampling of replica exchange simulations. In particular, the approach showed significant improvement in sampling efficiency of conformational phase space when combined with replica ex- change method (REM) in computer simulation of peptide/protein folding. First we introduce the augmented dynamical system of equations, and demonstrate the stability of the algorithm. Then, we illustrate the approach by using different fully atomistic and coarse-grained model systems, comparing them with the standard replica exchange method. In addition, we applied SPMD simulation to calculate the time correlation functions of the transitions in a two dimensional surface to demonstrate the enhancement of transition path sampling. Our results showed that folded structure can be obtained in a shorter simulation time using the new method when compared with non-augmented dynamical system. Typically, in less than 0.5 ns using replica exchange runs assuming that native folded structure is known and within simulation time scale of 40 ns in the case of blind structure prediction. Furthermore, the root mean square deviations from the reference structures were less than 2 #A. To demonstrate the perfor- mance of new method, we also implemented three simulation protocols using CHARMM software. Comparisons are also performed with standard targeted molecular dynamics simulation method.
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Hyun-Seok Kim et al., Optimal determination of force field parameters for reduced molecular dynamics model, Computer Physics Communications, 236 (2019) 86-94.
Using a gradient-based optimization method, the time-consuming atomistic model of substrate is replaced by computationally efficient Lennard-Jones (L-J) potential walls whose parameters are determined to appropriately represent the interactions between the nanoparticles and the substrate. To obtain the required design sensitivity with respect to design variables for the constant temperature molecular dynamics (MD) simulations that use the Nosé–Hoover thermostat, the finite difference method is impractical due to the huge amount of computational costs. Thus, we developed an adjoint design sensitivity analysis (DSA) method that is efficient for the system of many design variables. In numerical examples, we replace the complicated and time-consuming silicate structure to a multiple layer model of L-J potential wall, through the design optimization that includes the design variables of ε, σ , and the positions of each layer. The objective is to minimize the squared difference of time averaged performance between the full and the reduced models during the whole time span. The proposed method could lead to a significant reduction of computational costs, together with comparable outcomes from MD simulations.
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Michael Kotelyanskii and Doros N Theodorou, Simulation Methods for Polymers, , 2006 pp. 1-619.
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Indu Kumari et al., Molecular Dynamics Simulations, Challenges and Opportunities: A Biologist’s Prospective, Current Protein & Peptide Science, 18 (2017) 1-48.
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Carsten Kutzner et al., More Bang for Your Buck: Improved use of GPU Nodes for GROMACS 2018, Arxiv.Org, 2019 1903.05918v2, cs.DC.
We identify hardware that is optimal to produce molecular dynamics trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more towards the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift towards GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware.
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Joseph Laureanti et al., Visualizing biomolecular electrostatics in virtual reality with UnityMol-APBS, , 2019.
Virtual reality is a powerful tool with the ability to immerse a user within a completely external environment. This immersion is particularly useful when visualizing and analyzing interactions between small organic molecules, molecular inorganic complexes, and biomolecular systems such as redox proteins and enzymes. A common tool used in the biomedical community to analyze such interactions is the APBS software, which was developed to solve the equations of continuum electrostatics for large biomolecular assemblages. Numerous applications exist for using APBS in the biomedical community including analysis of protein ligand interactions and APBS has enjoyed widespread adoption throughout the biomedical community. Currently, typical use of the full APBS toolset is completed via the command line followed by visualization using a variety of two-dimensional external molecular visualization software. This process has inherent limitations: visualization of three-dimensional objects using a two-dimensional interface masks important information within the depth component. Herein, we have developed a single application, UnityMol-APBS, that provides a dual experience where users can utilize the full range of the APBS toolset, without the use of a command line interface, by use of a simple \ac{GUI} for either a standard desktop or immersive virtual reality experience.
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M R Leal and G Weber, Sharp DNA denaturation in 3D mesoscopic DNA, Arxiv.Org, .
The mesoscopic Peyrard-Bishop DNA Hamiltonian describes the main molecular interactions with simple potentials, resulting in an efficient calculation of the melting of the duplex helix. It is based on a 2D model which can be simplified to calculate the classical partition function with the transfer-integral technique. At the heart of this approach are simplifications that leave only a single variable to integrate, but also make it impossible to apply the model to situations where a more detailed structural description is needed. Here …
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T Lelièvre et al., Computation of free energy differences through nonequilibrium stochastic dynamics: The reaction coordinate case, Journal Of Computational Physics, 222 (2007) 624-643.
The computation of free energy differences through an exponential weighting of out-of- equilibrium paths (known as the Jarzynski equality [C. Jarzynski, Equilibrium free energy differences from nonequilibrium measurements: a master equation approach, Phys. Rev. E 56 (5)(1997) 5018–5035, C. Jarzynski, Nonequilibrium equality for free energy differences, Phys. Rev. Lett. 78 (14)(1997) 2690–2693]) is often used for transitions between states described by an external parameter in the Hamiltonian. An extension to transitions between …
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Huiyu Li and Ao Ma, Kinetic energy flows in activated dynamics of biomolecules, Arxiv.Org, 2020 2007.06733v1, physics.bio-ph.
Protein conformational changes are activated processes essential for protein functions. Activation in a protein differs from activation in a small molecule in that it involves directed and systematic energy flows through preferred channels encoded in the protein structure. Understanding the nature of these energy flow channels and how energy flows through them during activation is critical for understanding protein conformational changes. We recently developed a rigorous statistical mechanical framework for understanding potential energy flows. Here we complete this theoretical framework with a rigorous theory for kinetic energy flows: potential and kinetic energy inter-convert when impressed forces oppose inertial forces whereas kinetic energy transfers directly from one coordinate to another when inertial forces oppose each other. This theory is applied to analyzing a prototypic system for biomolecular conformational dynamics: the isomerization of an alanine dipeptide. Among the two essential energy flow channels for this process, dihedral phi confronts the activation barrier, whereas dihedral theta receives energy from potential energy flows. Intriguingly, theta helps phi to cross the activation barrier by transferring to phi via direct kinetic energy flow all the energy it received: increase in theta caused by potential energy flow converts into increase in phi. As a compensation, theta receives kinetic energy from bond angle alpha via direct mechanism and bond angle beta via indirect mechanism.
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Xiaoliang Ma et al., Physical Folding Codes for Proteins, Arxiv.Org, 2019 1901.00991v1, q-bio.BM.
Exploring and understanding the protein-folding problem has been a long-standing challenge in molecular biology. Here, using molecular dynamics simulation, we reveal how parallel distributed adjacent planar peptide groups of unfolded proteins fold reproducibly following explicit physical folding codes in aqueous environments due to electrostatic attractions. Superfast folding of protein is found to be powered by the contribution of the formation of hydrogen bonds. Temperature-induced torsional waves propagating along unfolded proteins break the parallel distributed state of specific amino acids, inferred as the beginning of folding. Electric charge and rotational resistance differences among neighboring side-chains are used to decipher the physical folding codes by means of which precise secondary structures develop. We present a powerful method of decoding amino acid sequences to predict native structures of proteins. The method is verified by comparing the results available from experiments in the literature.
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James E Mark, Physical Properties of Polymers Handbook, , 2013 pp. 1-1050.
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Stefans Mezulis et al., The Phyre2 web portal for protein modeling, prediction and analysis, Nature Protocols, 10 (2015) 845-858.
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Milad Hobbi Mobarhan, Ab Initio Molecular Dynamics, , 2014 pp. 1-215.
In this thesis, we perform ab initio molecular dynamics (MD) simulations at the Hartree-Fock level, where the forces are computed on-the-fly using the Born-Oppenheimer approximation. The theory behind the Hartree-Fock method is discussed in detail and an implementation of this method based on Gaussian basis functions is explained. We also demonstrate how to calculate the analytic energy derivatives needed for obtaining the forces acting on the nuclei. Hartree-Fock calculations on the ground state energy, dipole moment, ionization potential, and population analysis are done for H2, N2, FH, CO, NH3, H2O, and CH4. These results are in perfect agreement with the literature. Ab initio MD calculations with different Gaussian basis sets, are performed on the diatomic systems H2, N2, F2, FH, and CO, for equilibrium bond length and vibration frequency analysis. Finally, a study on the reaction dynamics of the nucleophilic sub- stitution reaction H– + CH4 −−→ CH4 + H– is done, illustrating the importance of the initial vibrational energy of the methane molecule for the reaction to occur.
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Ariane Nunes-Alves et al., Recent progress in molecular simulation methods for drug binding kinetics, Arxiv.Org, 2020 2002.08983v2, q-bio.QM.
Due to the contribution of drug-target binding kinetics to drug efficacy, there is a high level of interest in developing methods to predict drug-target binding kinetic parameters. During the review period, a wide range of enhanced sampling molecular dynamics simulation-based methods has been developed for computing drug-target binding kinetics and studying binding and unbinding mechanisms. Here, we assess the performance of these methods considering two benchmark systems in detail: mutant T4 lysozyme-ligand complexes and a large set of N-HSP90-inhibitor complexes. The results indicate that some of the simulation methods can already be usefully applied in drug discovery or lead optimization programs but that further studies on more high-quality experimental benchmark datasets are necessary to improve and validate computational methods.
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Dinesh K Pai, STRANDS: Interactive Simulation of Thin Solids using Cosserat Models, Computer Graphics Forum, 21 (2002) 347-352.
Strandsare thin elastic solids that are visually well approximated as smooth curves, and yet possess essential physical behaviors characteristic of solid objects such as twisting. Common examples in computer graphics include: sutures, catheters, and tendons in surgical simulation; hairs, ropes, and vegetation in animation. Physical models based on spring meshes or 3D finite elements for such thin solids are either inaccurate or inefficient for interactive simulation. In this paper we show that models based on the Cosserat theory of …
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Sanghyun Park and Klaus Schulten, Calculating potentials of mean force from steered molecular dynamics simulations, The Journal Of Chemical Physics, 120 (2004) 5946-5961.
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Stefano Piana et al., ScienceDirect Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations, Current Opinion In Structural Biology, 24 (2014) 98-105.
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Steven S Plotkin and José N Onuchic, Understanding protein folding with energy landscape theory Part II: Quantitative aspects, Quarterly Reviews Of Biophysics, 35 (2003) 205-286.
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Matthias Post et al., Principal component analysis of nonequilibrium molecular dynamics simulations, The Journal Of Chemical Physics, 150 (2019) 204110-11.
Principal component analysis (PCA) represents a standard approach to identify collective variables {xi} = x, which can be used to construct the free energy landscape ∆G(x) of a molecular system. While PCA is routinely applied to equilibrium molecular dynamics (MD) simulations, it is less obvious as to how to extend the approach to nonequilibrium simulation techniques. This includes, e.g., the definition of the statistical averages employed in PCA as well as the relation between the equilibrium free energy landscape ∆G(x) and the energy landscapes ∆G(x) obtained from nonequilibrium MD. As an example for a nonequilibrium method, “targeted MD” is considered which employs a moving distance constraint to enforce rare transitions along some biasing coordinate s. The introduced bias can be described by a weighting function P(s), which provides a direct relation between equilibrium and nonequilibrium data, and thus establishes a well-defined way to perform PCA on nonequilibrium data. While the resulting distribution P(x) and energy ∆G ∝ ln P will not reflect the equilibrium state of the system, the nonequilibrium energy landscape ∆G(x) may directly reveal the molecular reaction mechanism. Applied to targeted MD simulations of the unfolding of decaalanine, for example, a PCA performed on backbone dihedral angles is shown to discriminate several unfolding pathways. Although the formulation is in principle exact, its practical use depends critically on the choice of the biasing coordinate s, which should account for a naturally occurring motion between two well-defined end-states of the system.
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P L Privalov, Stability of Proteins Small Globular Proteins, Advances In Protein Chemistry Volume 33, 33 (1979) 167-241.
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Arvind Ramanathan and Christopher James Langmead, Dynamic Invariants in Protein Folding Pathways Revealed by Tensor Analysis , , 2005.
Recent advances in molecular dynamics simulation technologies (e.g., Folding@Home, NAMD, Desmond/Anton) have, for the first time, enabled scientists to perform all-atom simulations over timescales relevant to protein folding. Unfortunately, the concomitant increase in the size of the resulting data sets presents a barrier to understanding the molecular basis of folding. In particular, long simulations make it harder to identify and characterize important microstates, and the collective conformational dynamics that influence and enable the transitions between them. We address these problems by introducing a novel tensor-based method for performing a spatio-temporal analysis of protein folding pathways. We applied our method to folding simulations of the villin head-piece generated by the Pande group using Folding@Home. Using our method, we were able to identify three regions in this protein that exhibit similar collective behaviors across multiple simulations. We were also able to identify cross-over points in these simulations leading to different conformational subspaces. Our results indicate that these three regions may act as folding units, and that the observed collective motions may represent important dynamic invariants in the folding process. Thus, our spatio-temporal analysis method shows promise as a means for obtaining novel insights into protein folding pathways.
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Sebastian Raschka and Benjamin Kaufman, Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition, , 2020.
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.
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Maryam Rouhani et al., Molecular dynamics simulation for rational protein engineering: Present and future prospectus, Journal Of Molecular Graphics And Modelling, 84 (2018) 43-53.
ecently protein engineering has been used as a pivotal tool for designing proteins with improved characteristics. While the experimental methods might be laborious and time-consuming, in silico protein design is a time and cost-effective approach. Moreover, in some cases, protein modeling might be the only way to obtain structural information where the experimental techniques are inapplicable. Molecular dynamics (MD) simulation is a method that allows the motion of protein to be simulated in defined conditions on the basis of classical molecular dynamics. MD simulation could widely be used when protein design needs accurate modeling of the target protein dynamics and also descriptions of the relation between conformational changes and function of protein at the atomic level. In this review, the effectiveness and the power of MD simulation in designing proteins with improved characteristics will be discussed.
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Gil Santos et al., A relational-constructionist account of protein macrostructure and function, Foundations Of Chemistry, 104 (2020) 11963-28.
One of the foundational problems of biochemistry concerns the conceptualisation of the relationship between the composition, structure and function of macromolecules like proteins. Part of the recent philosophical literature displays a reductionist bias, that is, the endorsement of a form of microstructuralismmirroring an out-dated biochemical conceptualisation. We shall argue that such microstructuralist approaches are ultimately committed to a potentialist form of micro-predeterminism whereby the macrostructure and function of proteins is accounted for solely in terms of the intrinsic properties and potentialities of the components of the primary structure as if they were self- contained or essentially immutable entities. We shall instead suggest that a conceptualisation of the relationship between proteins’ composition, structure and function consistent with contemporary biochemical practice should account also for the causal role of the cellular, organismal and environmental relations in protein development. The analysis of the folding process we propose suggests that microstructure-laden reductionist approaches are ontologically indefensible. Rather than a potentialist form of micro-predeterminism, our analysis ultimately supports a relational-construction-based view of protein development and potentialities formation, which requires an indispensable analysis of the dynamical interplay between the micro-level of the parts and the macro-level of the relational structures of their systems.
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William Robert Saunders et al., Fast electrostatic solvers for kinetic Monte Carlo simulations, Arxiv.Org, 2019 1905.04065v1, physics.comp-ph.
Kinetic Monte Carlo (KMC) is an important computational tool in theoretical physics and chemistry. In contrast to standard Monte Carlo, KMC permits the description of time dependent dynamical processes and is not restricted to systems in equilibrium. Compared to Molecular Dynamics, it allows simulations over significantly longer timescales. Recently KMC has been applied successfully in modelling of novel energy materials such as Lithium-ion batteries and organic/perovskite solar cells. Motivated by this, we consider general solid state systems which contain free, interacting particles which can hop between localised sites in the material. The KMC transition rates for those hops depend on the change in total potential energy of the system. For charged particles this requires the frequent calculation of electrostatic interactions, which is usually the bottleneck of the simulation. To avoid this issue and obtain results in reasonable times, many studies replace the long-range potential by a phenomenological short range approximation, which leads to systematic errors and unphysical results. On the other hand standard electrostatic solvers such as Ewald summation or fast Poisson solvers are highly inefficient in the KMC setup or introduce uncontrollable systematic errors at high resolution. In this paper we describe a new variant of the Fast Multipole Method by Greengard and Rokhlin which overcomes this issue by dramatically reducing computational costs. We construct an algorithm which scales linearly in the number of charges for each KMC step, something which had not been deemed to be possible before. We demonstrate the performance and parallel scalability of the method by implementing it in a performance portable software library. We describe the high-level Python interface of the code, which makes it easy to adapt to specific use cases.
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David Schaller and S Pach, PyRod–Tracing Water Molecules in Molecular Dynamics Simulations, The Journal Of Physical Chemistry Letters, 59 (2019) 2818-2829.
Ligands entering a protein binding pocket essentially compete with water molecules for binding to the protein. Hence, the location and thermodynamic properties of water molecules in protein structures have gained increased attention in the drug design community. Including corresponding data into 3D pharmacophore modeling is essential for efficient high throughput virtual screening. Here, we present PyRod, a free and open-source python software that allows for visualization of pharmacophoric binding pocket characteristics …
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Martin K Scherer et al., Variational Selection of Features for Molecular Kinetics, Arxiv.Org, 2018 1811.11714v2, physics.bio-ph (19) p. 194108.
The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years. The variational principle has opened the door for a nearly fully automated toolkit for selecting models that predict the long-time kinetics from molecular dynamics simulations. However, one yet-unoptimized step of the pipeline involves choosing the features, or collective variables, from which the model should be constructed. In order to build intuitive models, these collective variables are often sought to be interpretable and familiar features, such as torsional angles or contact distances in a protein structure. However, previous approaches for evaluating the chosen features rely on constructing a full MSM, which in turn requires additional hyperparameters to be chosen, and hence leads to a computationally expensive framework. Here, we present a method to optimize the feature choice directly, without requiring the construction of the final kinetic model. We demonstrate our rigorous preprocessing algorithm on a canonical set of twelve fast-folding protein simulations, and show that our procedure leads to more efficient model selection.
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Henri Orland, Searching for Transition Paths (in Protein Folding), , 2019 pp. 1-65.
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Andrew W Senior et al., Improved protein structure prediction using potentials from deep learning, Nature, 2020 pp. 1-22.
Protein structure prediction can be used to determine the three-dimensional shape of
1 a protein from its amino acid sequence . This problem is of fundamental importance
as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous
3 sequences, which aids in the prediction of protein structures . Here we show that we
can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no 7 structures for homologous proteins have been experimentally determined .
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Simmons, Walter A, Protein Folding and Machine Learning: Fundamentals , , 2018 pp. 1-18.
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T Simonson and David Perahia, Dielectric properties of proteins from simulations: tools and techniques, Computer Physics Communications, 91 (1995) 291-303.
Tools and techniques to analyze the dielectric properties of proteins are described. Microscopic dielectric properties are determined by a susceptibility tensor of order 3n, where n is the number of protein atoms. For perturbing charges not too close to the protein, the dielectric relaxation free energy is directly related to the dipole-dipole correlation matrix of the unperturbed protein, or equivalently to the covariance matrix of its atomic displacements. These are straightforward to obtain from existing molecular dynamics packages such as …
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Klaus Schulten, Steered Molecular Dynamics Introduction and Examples, , 2006.
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Jelena Stefanović and Constantinos C Pantelides, Molecular Dynamics as a Mathematical Mapping. I. Differentiable Force Functions, Molecular Simulation, 26 (2001) 237-271.
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Jelena Stefanović and Constantinos C Pantelides, Molecular Dynamics as a Mathematical Mapping. III. Efficient Evaluation of the Differentiable Force Functions and Their Derivatives, Molecular Simulation, 26 (2006) 323-352.
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Brigita Urbanc et al., Ab initio Discrete Molecular Dynamics Approach to Protein Folding and Aggregation, Essay: On The Easy Dismissal Of Interactionism, 412 (2006) 314-338.
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Jesse Vig et al., BERTology Meets Biology: Interpreting Attention in Protein Language Models, Arxiv.Org, 2020.
Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. Through the lens of attention, we analyze the inner workings of the Transformer and explore how the model discerns structural and functional properties of proteins. We show that attention (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of proteins, and (3) focuses on progressively more complex biophysical properties with increasing layer depth. We also present a three-dimensional visualization of the interaction between attention and protein structure. Our findings align with known biological processes and provide a tool to aid discovery in protein engineering and synthetic biology. The code for visualization and analysis is available at https://github.com/salesforce/provis.
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Jorge A Vila, Guessing the upper bound free-energy difference between native-like structures, , 2018.
Use of a combination of statistical thermodynamics and the Gershgorin theorem enable us to guess, in the thermodynamic limit, a plausible value for the upper bound free-energy difference between native-like structures of monomeric globular proteins. Support to our result in light of both the observed free-energy change between the native and denatured states and the microstability free-energy values obtained from the observed micro-unfolding tendency of nine globular proteins, will be here discussed.
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Jorge A Vila, Forecasting the upper bound free energy difference between protein native-like structures, Physica A, 2019 vol. 533 p. 122053.
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H Wang et al., DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics, Studies In History And Philosophy Of Science Part B, 228 (2018) 178-184.
Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to …
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Steffen Wolf et al., Multisecond ligand dissociation dynamics from atomistic simulations, Arxiv.Org, 2020 2001.04212v2, physics.bio-ph.
Coarse-graining of fully atomistic molecular dynamics simulations is a long-standing goal in order to allow the description of processes occurring on biologically relevant timescales. For example, the prediction of pathways, rates and rate-limiting steps in protein-ligand unbinding is crucial for modern drug discovery. To achieve the enhanced sampling necessary for coarse-graining, we first perform dissipation-corrected targeted molecular dynamics simulations, which yield free energy and friction profiles of the molecular process under consideration. In a second step, we use these fields to perform Langevin simulations which account for the desired molecular kinetics. By introducing the concept of 'temperature boosting' of the Langevin simulations, this combination of methods allows simulation of biomolecular processes occurring on multisecond timescales and beyond. Adopting the dissociation of solvated sodium chloride as well as trypsin-benzamidine and Hsp90-inhibitor protein-ligand complexes as test problems, we are able to reproduce the rates from atomistic molecular dynamics simulation and experiments within a factor of 1.5-4 for unbinding times up to the range of milliseconds and of 1.2-10 in the range of seconds. Analysis of the friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.
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Shanshan Wu and Ao Ma, A novel mechanism for energy activation in biomolecules, Arxiv.Org, 2020 2007.15061v1, physics.bio-ph.
An activated process consists of energy activation and barrier crossing; the former is a prerequisite for the latter. Barrier crossing has been studied extensively, but energy activation has been overlooked due to a lack of means to gauge its progress. We define reaction stability as the probability that reactive trajectories pass a vicinity in phase space; it enabled us to analyze energy activation of a biomolecular isomerization. This process follows a mechanism fundamentally different from presumed mechanisms in standard reaction rate theories: it features accumulation of high kinetic energy in reaction coordinates, achieved by precise synergy between them coordinated by momentum space.
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Dong Xu and Yang Zhang, Improving the Physical Realism and Structural Accuracy of Protein Models by a Two-Step Atomic-Level Energy Minimization, Biophysj, 101 (2011) 2525-2534.
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Daniel M Zuckerman, Key biology you should have learned in physics class: Using ideal-gas mixtures to understand biomolecular machines, American Journal Of Physics, 88 (2020) 182-193.
The biological cell exhibits a fantastic range of behaviors, but ultimately these are governed by a handful of physical and chemical principles. Here we explore simple theory, known for decades and based on the simple thermodynamics of mixtures of ideal gases, which illuminates several key functions performed within the cell. Our focus is the free-energy-driven import and export of molecules, such as nutrients and other vital compounds, via transporter proteins. Complementary to a thermodynamic picture is a description of transporters via “mass-action” chemical kinetics, which lends further insights into biological machinery and free energy use. Both thermodynamic and kinetic descriptions can shed light on the fundamental non-equilibrium aspects of transport. On the whole, our biochemical-physics discussion will remain agnostic to chemical details, but we will see how such details ultimately enter a physical description through the example of the cellular fuel ATP.
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José García de la Torre et al., SIMUFLEX: Algorithms and Tools for Simulation of the Conformation and Dynamics of Flexible Molecules and Nanoparticles in Dilute Solution, Journal Of Chemical Theory And Computation, 5 (2009) 2606-2618.
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W F van Gunsteren et al., Computer simulation of protein motion, Studies In History And Philosophy Of Science Part B, .
The application of molecular dynamics computer simulation methods to study the dynamics of proteins is reviewed with an eye to its possibilities and limitations. Examples are given, mainly using nanosecond trajectories of the proteins bovine pancreatic trypsin inhibitor and lysozyme, of the different protein properties, of which the dynamics can be or cannot be sampled on a nanosecond time scale. It is concluded that the major asset of the simulation technique is that the different factors contributing to the dynamics of a particular process can …
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