The prospects of quantum computing in computational molecular biology

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

AI3SD Video: How good are protein structure prediction methods at predicting folding pathways?

Deep learning has achieved unprecedented success in predicting a protein’s crystal structure, but whether this achievement relates to a better modelling of the folding process is an open question. In this work, we compare the dynamic pathways from six state-of-the-art protein structure prediction methods to experimental folding data. We find evidence of a weak correlation between simulated dynamics and formal kinetics; however, many of the structures of the predicted intermediates are incompatible with available hydrogen-deuterium exchange experiments. These results suggest that recent advances in protein structure prediction do not provide an enhanced understanding of the principles underpinning protein folding

Current structure predictors are not learning the physics of protein folding

Summary Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state. Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding. Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding

Investigating the potential for a limited quantum speedup on protein lattice problems

Protein folding is a central challenge in computational biology, with important applications in molecular biology, drug discovery and catalyst design. As a hard combinatorial optimisation problem, it has been studied as a potential target problem for quantum annealing. Although several experimental implementations have been discussed in the literature, the computational scaling of these approaches has not been elucidated. In this article, we present a numerical study of quantum annealing applied to a large number of small peptide folding problems, aiming to infer useful insights for near-term applications. We present two conclusions: that even naive quantum annealing, when applied to protein lattice folding, has the potential to outperform classical approaches, and that careful engineering of the Hamiltonians and schedules involved can deliver notable relative improvements for this problem. Overall, our results suggest that quantum algorithms may well offer improvements for problems in the protein folding and structure prediction realm.Comment: 45 pages, 18 figure

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design

Revitalizing the Concept of Bond Order Through Delocalization Measures in Real Space

Ab initio quantum chemistry is an independent source of information supplying an ever widening group of experimental chemists. However, bridging the gap between these ab initio data and chemical insight remains a challenge. In particular, there is a need for a bond order index that characterizes novel bonding patterns in a reliable manner, while recovering the familiar effects occurring in well-known bonds. In this article, through a large body of calculations, we show how the delocalization index derived from Quantum Chemical Topology (QCT) serves as such a bond order. This index is defined in a parameter-free, intuitive and consistent manner, and with little qualitative dependency on the level of theory used. The delocalization index is also able to detect the subtler bonding effects that underpin most practical organic and inorganic chemistry. We explore and connect the properties of this index and open the door for its extensive usage in the understanding and discovery of novel chemistry

Cognitive Evolution of a Patient Who Suffered a Subarachnoid Haemorrhage Eight Years Ago, after Being Treated with Growth Hormone, Melatonin and Neurorehabilitation

To describe the cognitive evolution of a patient who suffered a subarachnoid haemorrhage resulting in a total loss of his cognitive functions. The patient was initially treated with GH (0.8 mg/day), melatonin (50 mg/day) and neurorehabilitation 1 year after his brain damage, during 3 months. Then continued with GH (0.5 mg/day, 6 months/year, during 2 years) and melatonin treatments and neurorehabilitation (3 days/week). 5 years later the patient came back to our Centre due to the absence of recent memory and personal and spatio-temporal orientation and he received an intensive specific neurorehabilitation, including EINA (Auditory Stimulation and Neurosensory Integration), together with GH (0.8 mg/day) and melatonin, for 6 months. At discharge of his first treatment period cognitive functions showed very poor changes but these had been improved when he came back 5 years later. A review carried out 8 years after SHA demonstrated that the patient significantly recovered in all the cognitive functions and he was able to live an independent life. GH plays a key role on cognition, including its actions on recent memory. Melatonin, in turn, helps as a neuroprotective agent. A specific neurostimulation must be performed so that the effects of GH can be expressed. Within neurostimulation, EINA seems to play a very important role for enhancing the effects of medical and rehabilitative treatments on brain plasticity