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Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.

Theory, computation and experiment have matched up for the folding of small proteins in vitro, a difficult feat because folding energy landscapes are fairly smooth and free energy differences between states are small. Smoothness means that protein structure and folding are susceptible to the local environment inside living cells. Theory, computation and experiment are now exploring cellular modulation of energy landscapes. Interesting concepts have emerged, such as co-evolution of protein surfaces with their cellular environment to reduce detrimental interactions. Here we look at very recent work beginning to bring together theory, simulations and experiments in the area of protein landscape modulation, to see what problems might be solved in the near future by combining these approaches.

Many aspects of the study of protein folding and dynamics have been affected by the recent advances in machine learning. Methods for the prediction of protein structures from their sequences are now heavily based on machine learning tools. The way simulations are performed to explore the energy landscape of protein systems is also changing as force-fields are started to be designed by means of machine learning methods. These methods are also used to extract the essential information from large simulation datasets and to enhance the sampling of rare events such as folding/unfolding transitions. While significant challenges still need to be tackled, we expect these methods to play an important role on the study of protein folding and dynamics in the near future. We discuss here the recent advances on all these fronts and the questions that need to be addressed for machine learning approaches to become mainstream in protein simulation.

Developments in the design of small peptides that mimic proteins in complexity, recent advances in nanosecond time-resolved spectroscopy methods to study peptides and the development of modern, highly parallel simulation algorithms have come together to give us a detailed picture of peptide folding dynamics. Two newly implemented simulation techniques, parallel replica dynamics and replica exchange molecular dynamics, can now describe directly from simulations the kinetics and thermodynamics of peptide formation, respectively. Given these developments, the simulation community now has the tools to verify and validate simulation protocols and models (forcefields).

By following a consistent line of physical reasoning, some fundamental understanding about the foldability of proteins has been achieved. In recent years, this has led to the development of a number of successful algorithms for optimizing potential energy functions for folding protein models. The differences between the folding mechanisms of simple, contact-based lattice proteins and more traditional, realistic protein models, however, still call for further development of the potentials in addition to the optimization approaches.

The computational techniques of sorting out protein folds (these techniques include dynamic programming, self-consistent field theory, etc.) have already ceased to be the bottleneck of predictions. The main problem is that all the methods of recognition and prediction of protein structure can actually use only some part of the interactions operating in the chain, and that even their energies are not known precisely. This is the principal source of errors now. The errors can be reduced by employment of many distant homologues, but this opens a possibility to predict a generalized folding pattern rather than a particular fold with all its details.

Transition states in protein folding are being analyzed experimentally and by computer simulation to reveal the sequence of events in the folding and unfolding of proteins at high resolution. Results of this analysis have enabled the reconstruction of folding pathways in vitro and open the prospect of analyzing pathways in vivo.

While there have been impressive advances in understanding protein folding over the past few decades, we are still far from the goal of solving the protein folding problem: predicting the folding pathway and final structure entirely from the amino acid sequence. One reason for this shortcoming may be the lack of understanding of the complexity of the unfolded state before folding and earliest steps in the process. Recent technological advances and applications of cutting edge techniques in novel ways have begun to reveal this complexity. Comparing the kinetics with recent molecular dynamics simulations on the microsecond timescale may lead to more detailed and predictive folding models.

Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.

Protein folding is an important problem in structural biology with significant medical implications, particularly for misfolding disorders like Alzheimer's disease. Solving the folding problem will ultimately require a combination of theory and experiment, with theoretical models providing a comprehensive view of folding and experiments grounding these models in reality. Here we review progress towards this goal over the past decade, with an emphasis on recent theoretical advances that are empowering chemically detailed models of folding and the new results these technologies are providing. In particular, we discuss new insights made possible by Markov state models (MSMs), including the role of non-native contacts and the hub-like character of protein folded states.

The protein folding problem can be viewed as three different problems: defining the thermodynamic folding code; devising a good computational structure prediction algorithm; and answering Levinthal's question regarding the kinetic mechanism of how proteins can fold so quickly. Once regarded as a grand challenge, protein folding has seen much progress in recent years. Folding codes are now being used to successfully design proteins and non-biological foldable polymers; aided by the Critical Assessment of Techniques for Structure Prediction (CASP) competition, protein structure prediction has now become quite good. Even the once-challenging Levinthal puzzle now seems to have an answer--a protein can avoid searching irrelevant conformations and fold quickly by making local independent decisions first, followed by non-local global decisions later.

The pace of data accumulation in glycobiology has lately rapidly increased, largely due to high-throughput technologies. In this increasingly data-rich environment, computer science started to play a central role in handling the data, extracting significant biological information, and probing the missing parts of the 'scenery' by prediction, modelling or simulation. Investigating and comparing glycomes by bioinformatics and structural methods has great practical value and sharply increased in popularity in the past couple of years. In this context, advances have also been made with regard to structural aspects of protein N-glycosylation and consequences for glycoprotein folding. In these areas, however, an approach that integrates glycobiology with protein science is necessary.

The basic rules governing the folding of small, single-domain proteins are being discovered. New algorithms that can predict the major features of the folding process give the opportunity to design and optimise protein folding in a rational way. Recent experimental works suggest that sequence-specific features should be integrated in folding models to improve their performance.

Highlights • In-depth sequence analysis reveals that the protein fold universe is more evolutionarily connected than previously assumed. • Short ancestral fragments are observed to have propagated to many modern proteins and hint at possible evolutionary pathways. • Experimental reconstruction of such events by chimeragenesis and directed evolution allows to test evolutionary relationships. • Detailed knowledge of folding landscapes helps to understand evolutionary history and improve protein engineering. • The ubiquitous and versatile TIM-barrel fold is a model system to explore evolution, folding, and design., Proteins are chief actors in life that perform a myriad of exquisite functions. This diversity has been enabled through the evolution and diversification of protein folds. Analysis of sequences and structures strongly suggest that numerous protein pieces have been reused as building blocks and propagated to many modern folds. This information can be traced to understand how the protein world has diversified. In this review, we discuss the latest advances in the analysis of protein evolutionary units, and we use as a model system one of the most abundant and versatile topologies, the TIM-barrel fold, to highlight the existing common principles that interconnect protein evolution, structure, folding, function, and design.