Spinach on the ceiling: a theoretical chemist's return to biology

Theory vs. experiment: The rise of the dynamic view of proteins

Over the past century, the scientific conception of the protein has evolved significantly. This paper focuses on the most recent stage of this evolution, namely, the origin of the dynamic view of proteins and the challenge it posed to the static view of classical molecular biology. Philosophers and scientists have offered two hypotheses to explain the origin of the dynamic view and its slow reception by structural biologists. Some have argued that the shift from the static to the dynamic view was a Kuhnian revolution, driven by the accumulation of dynamic anomalies, while others have argued that the shift was caused by new empirical findings made possible by technological advances. I analyze this scientific episode and ultimately reject both of these empiricist accounts. I argue that focusing primarily on technological advances and empirical discoveries overlooks the important role of theory in driving this scientific change. I show how the application of general thermodynamic principles to proteins gave rise to the dynamic view, and a commitment to these principles then led early adopters to seek out the empirical examples of protein dynamics, which would eventually convince their peers. My analysis of this historical case shows that empiricist accounts of modern scientific progress-at least those that aim to explain developments in the molecular life sciences-need to be tempered in order to capture the interplay between theory and experiment.

Modeling the Homologous Recombination Process: Methods, Successes and Challenges

Homologous recombination (HR) is a fundamental process common to all species. HR aims to faithfully repair DNA double strand breaks. HR involves the formation of nucleoprotein filaments on DNA single strands (ssDNA) resected from the break. The nucleoprotein filaments search for homologous regions in the genome and promote strand exchange with the ssDNA homologous region in an unbroken copy of the genome. HR has been the object of intensive studies for decades. Because multi-scale dynamics is a fundamental aspect of this process, studying HR is highly challenging, both experimentally and using computational approaches. Nevertheless, knowledge has built up over the years and has recently progressed at an accelerated pace, borne by increasingly focused investigations using new techniques such as single molecule approaches. Linking this knowledge to the atomic structure of the nucleoprotein filament systems and the succession of unstable, transient intermediate steps that takes place during the HR process remains a challenge; modeling retains a very strong role in bridging the gap between structures that are stable enough to be observed and in exploring transition paths between these structures. However, working on ever-changing long filament systems submitted to kinetic processes is full of pitfalls. This review presents the modeling tools that are used in such studies, their possibilities and limitations, and reviews the advances in the knowledge of the HR process that have been obtained through modeling. Notably, we will emphasize how cooperative behavior in the HR nucleoprotein filament enables modeling to produce reliable information.

Folding and self-assembly of short intrinsically disordered peptides and protein regions

Proteins and peptide fragments are highly relevant building blocks in self-assembly for nanostructures with plenty of applications. Intrinsically disordered proteins (IDPs) and protein regions (IDRs) are defined by the absence of a well-defined secondary structure, yet IDPs/IDRs show a significant biological activity. Experimental techniques and computational modelling procedures for the characterization of IDPs/IDRs are discussed. Directed self-assembly of IDPs/IDRs allows reaching a large variety of nanostructures. Hybrid materials based on the derivatives of IDPs/IDRs show a promising performance as alternative biocides and nanodrugs. Cell mimicking, in vivo compartmentalization, and bone regeneration are demonstrated for IDPs/IDRs in biotechnological applications. The exciting possibilities of IDPs/IDRs in nanotechnology with relevant biological applications are shown.

Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

Solving the Schrödinger equation of atoms and molecules: Chemical-formula theory, free-complement chemical-formula theory, and intermediate variational theory

Chemistry is governed by the principle of quantum mechanics as expressed by the Schrödinger equation (SE) and Dirac equation (DE). The exact general theory for solving these fundamental equations is therefore a key for formulating accurately predictive theory in chemical science. The free-complement (FC) theory for solving the SE of atoms and molecules proposed by one of the authors is such a general theory. On the other hand, the working theory most widely used in chemistry is the chemical formula that refers to the molecular structural formula and chemical reaction formula, collectively. There, the central concepts are the local atomic concept, transferability, and from-atoms-to-molecule concept. Since the chemical formula is the most successful working theory in chemistry ever existed, we formulate our FC theory to have the structure reflecting the chemical formula. Our basic postulate is that as far as the SE is the principle of chemistry, its solutions for chemistry should have the structure that can be related to the chemical formulas. So, in this paper, we first formulate a theory that designs the wave function to reflect the structure of the chemical formula. We call this theory chemical formula theory (CFT). In the CFT, we place the valence ground and excited states of each atom at each position of the chemical formula of the molecule and let them interact using their free valences to form the ground and excited states of the molecule. The principle there is the variational principle so that the ground and excited states obtained satisfy the orthogonality and Hamiltonian-orthogonality relations. Then, we formulate the exact FC theory starting from the initial functions produced by the CFT. This FC theory is referred to as free-complement chemical-formula theory (FC-CFT), which is expected to describe efficiently the solution of the SE by the above reason. The FC-CFT wave function is modified from that of CFT. Since this modification is done by the exact SE, its analysis may give some insights to chemists that assist their chemistry. Thus, this theory would be not only exact but also conceptually useful. Furthermore, the intermediate theory between CFT and FC-CFT would also be useful. There, we use only integratable functions and apply the variational principle so that we refer to this theory as FC-CFT-variational (FC-CFT-V). It is an advanced theory of CFT. Since the variational method is straightforward and powerful, we can do extensive chemical studies in a reasonable accuracy. After finishing such studies, if we still need an exact level of solutions, we add the remaining functions of the FC-CFT and perform the exact calculations. Furthermore, when we deal with large and even giant molecules, the inter-exchange (iExg) theory for the antisymmetry rule introduced previously leads to a large simplification. There, the inter-exchanges between distant electron pairs fade away so that only Coulombic interactions survive. Further in giant systems, even an electrostatic description becomes possible. Then, the FC-CFT for exactly solving the SE would behave essentially to order N for large and giant molecular systems, though the pre-factor should be very large and must be minimized.

Large-scale QM/MM calculations of the CaMn4O5 cluster in the S3 state of the oxygen evolving complex of photosystem II. Comparison between water-inserted and no water-inserted structures

Large-scale QM/MM calculations were performed to elucidate an optimized geometrical structure of a CaMn₄O₅ cluster with and without water insertion in the S₃ state of the oxygen evolving complex (OEC) of photosystem II (PSII). The left (L)-opened structure was found to be stable under the assumption of no hydroxide anion insertion in the S₃ state, whereas the right (R)-opened structure became more stable if one water molecule is inserted to the Mn₄Ca cluster. The optimized Mn_a(4)–Mn_d(1) distance determined by QM/MM was about 5.0 Å for the S₃ structure without an inserted hydroxide anion, but this is elongated by 0.2–0.3 Å after insertion. These computational results are discussed in relation to the possible mechanisms of O–O bond formation in water oxidation by the OEC of PSII.

Challenges in computational studies of enzyme structure, function and dynamics

In this review we give an overview of the field of Computational enzymology. We start by describing the birth of the field, with emphasis on the work of the 2013 chemistry Nobel Laureates. We then present key features of the state-of-the-art in the field, showing what theory, accompanied by experiments, has taught us so far about enzymes. We also briefly describe computational methods, such as quantum mechanics-molecular mechanics approaches, reaction coordinate treatment, and free energy simulation approaches. We finalize by discussing open questions and challenges.

Eppur si muove! The 2013 Nobel Prize in Chemistry

The 2013 Nobel Prize in Chemistry has been awarded to Martin Karplus, Michael Levitt, and Arieh Warshel for their work on developing computational methods to study complex chemical systems. Their work has led to mechanistic critical insights into chemical systems both large and small and has enabled progress in a number of different fields, including structural biology.