Finding the needle in the haystack: towards solving the protein-folding problem computationally

Conformational fluidity of intrinsically disordered proteins in crowded environment: a molecular dynamics simulation study

The class of intrinsically disordered proteins lacks stable three-dimensional structures. Their flexibility allows them to engage in a wide variety of interactions with other biomolecules thus making them biologically relevant and efficient. The intrinsic disorders of these proteins, which undergo binding-induced folding, allow alterations in their topologies while conserving their binding sites. Due to the lack of well-defined three-dimensional structures in the absence of their physiological partners, the folding and the conformational dynamics of these proteins remained poorly understood. Particularly, it is unclear how these proteins exist in the crowded intracellular milieu. In the present study, molecular dynamic simulations of two intrinsically unstructured proteins and two controls (folded proteins) were conducted in the presence and absence of molecular crowders to obtain an in-depth insight into their conformational flexibility. The present study revealed that polymer crowders stabilize the disordered proteins through enthalpic as well as entropic effects that are significantly more than their monomeric counterpart. Taken together, the study delves deep into crowding effects on intrinsically disordered proteins and provides insights into how molecular crowders induce a significantly diverse ensemble of dynamic scaffolds needed to carry out diverse functions.Communicated by Ramaswamy H. Sarma.

Stochastic Approximation to MBAR and TRAM: Batchwise Free Energy Estimation

The dynamics of molecules are governed by rare event transitions between long-lived (metastable) states. To explore these transitions efficiently, many enhanced sampling protocols have been introduced that involve using simulations with biases or changed temperatures. Two established statistically optimal estimators for obtaining unbiased equilibrium properties from such simulations are the multistate Bennett acceptance ratio (MBAR) and the transition-based reweighting analysis method (TRAM). Both MBAR and TRAM are solved iteratively and can suffer from long convergence times. Here, we introduce stochastic approximators (SA) for both estimators, resulting in SAMBAR and SATRAM, which are shown to converge faster than their deterministic counterparts, without significant accuracy loss. Both methods are demonstrated on different molecular systems.

Machine Learning Diffusion Monte Carlo Forces

Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of "DMC forces" is to machine learn these forces from DMC energy calculations. In this work, we employ Behler-Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio density functional theory (DFT) simulations and machine-learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules─C₂, H₂O, and CH₃Cl─that machine-learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at one hundredth of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints.

Biology and medicine in the landscape of quantum advantages

Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

Protein folding in vitro and in the cell: From a solitary journey to a team effort

Correct protein folding is essential for the health and function of living organisms. Yet, it is not well understood how unfolded proteins reach their native state and avoid aggregation, especially within the cellular milieu. Some proteins, especially small, single-domain and apparent two-state folders, successfully attain their native state upon dilution from denaturant. Yet, many more proteins undergo misfolding and aggregation during this process, in a concentration-dependent fashion. Once formed, native and aggregated states are often kinetically trapped relative to each other. Hence, the early stages of protein life are absolutely critical for proper kinetic channeling to the folded state and for long-term solubility and function. This review summarizes current knowledge on protein folding/aggregation mechanisms in buffered solution and within the bacterial cell, highlighting early stages. Remarkably, teamwork between nascent chain, ribosome, trigger factor and Hsp70 molecular chaperones enables all proteins to overcome aggregation propensities and reach a long-lived bioactive state.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Refolding of Lysozyme in Glycerol as Studied by Fast Scanning Calorimetry

The folding of lysozyme in glycerol was monitored by the fast scanning calorimetry technique. Application of a temperature-time profile with an isothermal segment for refolding allowed assessment of the state of the non-equilibrium protein ensemble and gave information on the kinetics of folding. We found that the non-equilibrium protein ensemble mainly contains a mixture of unfolded and folded protein forms and partially folded intermediates, and enthalpic barriers control the kinetics of the process. Lysozyme folding in glycerol follows the same or similar triangular mechanism described in the literature for folding in water. The unfolding enthalpy of the intermediate must be no lower than 70% of the folded form, while the activation barrier for the unfolding of the intermediate (ca. 140 kJ/mol) is about 100 kJ/mol lower than that of the folded form (ca. 240-260 kJ/mol).

Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates

The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi-scale network, i.e., Res2Net, for improved prediction of inter-residue geometries, including distance and orientations. The second is an attention-based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM-score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi-scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/.

Substrate Channeling by a Rationally Designed Fusion Protein in a Biocatalytic Cascade

Substrate channeling, where an intermediate in a multistep reaction is directed toward a reaction center rather than freely diffusing, offers several advantages when employed in catalytic cascades. Here we present a fusion enzyme comprised of an alcohol and aldehyde dehydrogenase, that is computationally designed to facilitate electrostatic substrate channeling using a cationic linker bridging the two structures. Rosetta protein folding software was utilized to determine an optimal linker placement, added to the truncated termini of the proteins, which is as close as possible to the active sites of the enzymes without disrupting critical catalytic residues. With improvements in stability, product selectivity (90%), and catalyst turnover frequency, representing 500-fold increased activity compared to the unbound enzymes and nearly 140-fold for a neutral-linked fusion enzyme, this design strategy holds promise for making other multistep catalytic processes more sustainable and efficient.

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins

Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble). MASSP integrates a multilayer two-dimensional convolutional neural network (2D-CNN) with a long short-term memory (LSTM) neural network into a multitasking framework. Our comparison shows that MASSP performs equally well or better than the state-of-the-art methods in predicting residue-level secondary structures, boundaries of transmembrane segments, and topology. Furthermore, it achieves outstanding accuracy in predicting protein-level structural classes. MASSP automatically distinguishes the structural classes of input sequences and identifies transmembrane segments and topologies if present, making it broadly applicable to different classes of proteins. In summary, MASSP's good performance and broad applicability make it well suited for annotating residue-level attributes and protein-level structural classes at the proteome scale.

3D Interaction Homology: Hydropathic Analyses of the "π-Cation" and "π-π" Interaction Motifs in Phenylalanine, Tyrosine, and Tryptophan Residues

Three-dimensional (3D) maps of the hydropathic environments of protein amino acid residues are information-rich descriptors of preferred conformations, interaction types and energetics, and solvent accessibility. The interactions made by each residue are the primary factor for rotamer selection and the secondary, tertiary, and even quaternary protein structure. Our evolving basis set of environmental data for each residue type can be used to understand the protein structure. This work focuses on the aromatic residues phenylalanine, tyrosine, and tryptophan and their structural roles. We calculated and analyzed side chain-to-environment 3D maps for over 70,000 residues of these three types that reveal, with respect to hydrophobic and polar interactions, the environment around each. After binning with backbone ϕ/ψ and side chain χ₁, we clustered each bin by 3D similarities between map-map pairs. For each of the three residue types, four bins were examined in detail: one in the β-pleat, two in the right-hand α-helix, and one in the left-hand α-helix regions of the Ramachandran plot. For high degrees of side chain burial, encapsulation of the side chain by hydrophobic interactions is ubiquitous. The more solvent-exposed side chains are more likely to be involved in polar interactions with their environments. Evidence for π-π interactions was observed in about half of the residues surveyed [phenylalanine (PHE): 53.3%, tyrosine (TYR): 34.1%, and tryptophan (TRP): 55.7%], but on an energy basis, this contributed to only ∼4% of the total. Evidence for π-cation interactions was observed in 14.1% of PHE, 8.3% of TYR, and 26.8% of TRP residues, but on an energy basis, this contributed to only ∼1%. This recognition of even these subtle interactions in the 3D hydropathic environment maps is key support for our interaction homology paradigm of protein structure elucidation and possibly prediction.

Applications of Discrete Synthetic Macromolecules in Life and Materials Science: Recent and Future Trends

In the last decade, the field of sequence-defined polymers and related ultraprecise, monodisperse synthetic macromolecules has grown exponentially. In the early stage, mainly articles or reviews dedicated to the development of synthetic routes toward their preparation have been published. Nowadays, those synthetic methodologies, combined with the elucidation of the structure-property relationships, allow envisioning many promising applications. Consequently, in the past 3 years, application-oriented papers based on discrete synthetic macromolecules emerged. Hence, material science applications such as macromolecular data storage and encryption, self-assembly of discrete structures and foldamers have been the object of many fascinating studies. Moreover, in the area of life sciences, such structures have also been the focus of numerous research studies. Here, it is aimed to highlight these recent applications and to give the reader a critical overview of the future trends in this area of research.

Exploring the folding process of human βB2-crystallin using multiscale molecular dynamics and the Markov state model

Adequate knowledge of protein conformations is crucial for understanding their function and their association properties with other proteins. The cataract disease is correlated with conformational changes in key proteins called crystallins. These changes are due to mutations or post-translational modifications that may lead to protein unfolding, and thus the formation of aggregate states. Human βB2-crystallin (HβB2C) is found in high proportion in the eye lens, and its mutations are related to some cataracts. HβB2C also associates into dimers, tetramers, and other higher-order supramolecular complexes. However, it is the only protein of the βγ-crystallin family that has been found in an extended conformation. Therefore, we hypothesize that the extended conformation is not energetically favourable and that HβB2C may adopt a closed (completely folded) conformation, similar to the other members of the βγ-crystallin family. To corroborate this hypothesis, we performed extensive molecular dynamics simulations of HβB2C in its monomeric and dimeric conformations, using all-atom and coarse-grained scales. We employed Markov state model (MSM) analysis to characterize the conformational and kinetically relevant states in the folding process of monomeric HβB2C. The MSM analysis clearly shows that HβB2C adopts a completely folded structure, and this conformation is the most kinetically and energetically favourable one. In contrast, the extended conformations are kinetically unstable and energetically unfavourable. Our MSM analysis also reveals a key metastable state, which is particularly interesting because it is from this state that the folded state is reached. The folded state is stabilized by the formation of two salt bridges between the residue-pairs E74-R187 and R97-E166 and the two hydrophobic residue-pairs V59-L164 and V72-V151. Furthermore, free energy surface (FES) analysis revealed that the HβB2C dimer with both monomers in a closed conformation (face-en-face dimer) is energetically more stable than the domain-swapped dimer (crystallographic structure). The results presented in this report shed light on the molecular details of the folding mechanism of HβB2C in an aqueous environment and may contribute to interpreting different experimental findings. Finally, a detailed knowledge of HβB2C folding may be key to the rational design of potential molecules to treat cataract disease.

Deep Learning in Protein Structural Modeling and Design

Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence → structure → function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.

Recent advances in computational methods for biosensor design

Biosensors are analytical tools with a great application in healthcare, food quality control, and environmental monitoring. They are of considerable interest to be designed by using cost-effective and efficient approaches. Designing biosensors with improved functionality or application in new target detection has been converted to a fast-growing field of biomedicine and biotechnology branches. Experimental efforts have led to valuable successes in the field of biosensor design; however, some deficiencies restrict their utilization for this purpose. Computational design of biosensors is introduced as a promising key to eliminate the gap. A set of reliable structure prediction of the biosensor segments, their stability, and accurate descriptors of molecular interactions are required to computationally design biosensors. In this review, we provide a comprehensive insight into the progress of computational methods to guide the design and development of biosensors, including molecular dynamics simulation, quantum mechanics calculations, molecular docking, virtual screening, and a combination of them as the hybrid methodologies. By relying on the recent advances in the computational methods, an opportunity emerged for them to be complementary or an alternative to the experimental methods in the field of biosensor design.

Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.

Peptide science: A "rule model" for new generations of peptidomimetics

Peptides have been heavily investigated for their biocompatible and bioactive properties. Though a wide array of functionalities can be introduced by varying the amino acid sequence or by structural constraints, properties such as proteolytic stability, catalytic activity, and phase behavior in solution are difficult or impossible to impart upon naturally occurring α-L-peptides. To this end, sequence-controlled peptidomimetics exhibit new folds, morphologies, and chemical modifications that create new structures and functions. The study of these new classes of polymers, especially α-peptoids, has been highly influenced by the analysis, computational, and design techniques developed for peptides. This review examines techniques to determine primary, secondary, and tertiary structure of peptides, and how they have been adapted to investigate peptoid structure. Computational models developed for peptides have been modified to predict the morphologies of peptoids and have increased in accuracy in recent years. The combination of in vitro and in silico techniques have led to secondary and tertiary structure design principles that mirror those for peptides. We then examine several important developments in peptoid applications inspired by peptides such as pharmaceuticals, catalysis, and protein-binding. A brief survey of alternative backbone structures and research investigating these peptidomimetics shows how the advancement of peptide and peptoid science has influenced the growth of numerous fields of study. As peptide, peptoid, and other peptidomimetic studies continue to advance, we will expect to see higher throughput structural analyses, greater computational accuracy and functionality, and wider application space that can improve human health, solve environmental challenges, and meet industrial needs. STATEMENT OF SIGNIFICANCE: Many historical, chemical, and functional relations draw a thread connecting peptides to their recent cognates, the "peptidomimetics". This review presents a comprehensive survey of this field by highlighting the width and relevance of these familial connections. In the first section, we examine the experimental and computational techniques originally developed for peptides and their morphing into a broader analytical and predictive toolbox. The second section presents an excursus of the structures and properties of prominent peptidomimetics, and how the expansion of the chemical and structural diversity has returned new exciting properties. The third section presents an overview of technological applications and new families of peptidomimetics. As the field grows, new compounds emerge with clear potential in medicine and advanced manufacturing.

Energetic Self-Folding Mechanism in α-Helices

A novel energetic route driving the folding of a polyalanine peptide from an extended conformation to its α-helix native conformation is described, supported by a new method to compute mean potential energy surfaces accurately in terms of the dihedral angles of the peptide chain from extensive molecular dynamics simulations. The energetic self-folding (ESF) route arises specifically from the balance between the intrinsic propensity of alanine residues toward the α_R conformation and two, opposite, effects: the destabilizing interaction with neighbor residues and the stabilizing formation of native hydrogen bonds, with the latter being dominant for large peptide lengths. The ESF mechanism provides simple but robust support to the nucleation-elongation or zipper models and offers a quantitative energetic funnel picture of the folding process. The mechanism is validated by the reasonable agreement between the computed folding energies and the experimental values.

Targeting epigenetic machinery: Emerging novel allosteric inhibitors

Epigenetics has emerged as an extremely exciting fast-growing area of biomedical research in post genome era. Epigenetic dysfunction is tightly related with various diseases such as cancer and aging related degeneration, potentiating epigenetics modulators as important therapeutics targets. Indeed, inhibitors of histone deacetylase and DNA methyltransferase have been approved for treating blood tumor malignancies, whereas inhibitors of histone methyltransferase and histone acetyl-lysine recognizer bromodomain are in clinical stage. However, it remains a great challenge to discover potent and selective inhibitors by targeting catalytic site, as the same subfamily of epigenetic enzymes often share high sequence identity and very conserved catalytic core pocket. It is well known that epigenetic modifications are usually carried out by multi-protein complexes, and activation of catalytic subunit is often tightly regulated by other interactive protein component, especially in disease conditions. Therefore, it is not unusual that epigenetic complex machinery may exhibit allosteric regulation site induced by protein-protein interactions. Targeting allosteric site emerges as a compelling alternative strategy to develop epigenetic drugs with enhanced druggability and pharmacological profiles. In this review, we highlight recent progress in the development of allosteric inhibitors for epigenetic complexes through targeting protein-protein interactions. We also summarized the status of clinical applications of those inhibitors. Finally, we provide perspectives of future novel allosteric epigenetic machinery modulators emerging from otherwise undruggable single protein target.

Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking

Protein-protein interaction (PPI) is an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of average hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs, especially in obligate complexes. We also develop a neural network-based method, with an area under the receiver operating characteristic curve of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are <2.5 Å for these fourteen cases. The structural analysis conducted in this work provides molecular details about the interface between α-helical IMPs and the WCN restraints represent an efficient means to score α-helical IMP docking candidates.

Toward Ab Initio Protein Folding: Inherent Secondary Structure Propensity of Short Peptides from the Bioinformatics and Quantum-Chemical Perspective

By combining bioinformatics with quantum-chemical calculations, we attempt to address quantitatively some of the physical principles underlying protein folding. The former allowed us to identify tripeptide sequences in existing protein three-dimensional structures with a strong preference for either helical or extended structure. The selected representatives of pro-helical and pro-extended sequences were converted into "isolated" tripeptides-capped at N- and C-termini-and these were subjected to an extensive conformational sampling and geometry optimization (typically thousands to tens of thousands of conformers for each tripeptide). For each conformer, the QM(DFT-D3)/COSMO-RS free-energy value was then calculated, G_conf(solv). The Δ G_conf(solv) is expected to provide an objective, unbiased, and quantitatively accurate measure of the conformational preference of the particular tripeptide sequence. It has been shown that irrespective of the helical vs extended preferences of the selected tripeptide sequences in context of the protein, most of the low-energy conformers of isolated tripeptides prefer the R-helical structure. Nevertheless, pro-helical tripeptides show slightly stronger helix preference than their pro-extended counterparts. Furthermore, when the sampling is repeated in the presence of a partner tripeptide to mimic the situation in a β-sheet, pro-extended tripeptides (exemplified by the VIV) show a larger free-energy benefit than pro-helical tripeptides (exemplified by the EAM). This effect is even more pronounced in a hydrophobic solvent, which mimics the less polar parts of a protein. This is in line with our bioinformatic results showing that the majority of pro-extended tripeptides are hydrophobic. The preference for a specific secondary structure by the studied tripeptides is thus governed by the plasticity to adopt to its environment. In addition, we show that most of the "naturally occurring" conformations of tripeptide sequences, i.e., those found in existing three-dimensional protein structures, are within ∼10 kcal·mol^-1 from their global minima. In summary, our "ab initio" data suggest that complex protein structures may start to emerge already at the level of their small oligopeptidic units, which is in line with a hierarchical nature of protein folding.

Design of a structure-based model for protein folding from flexible conformations

We introduce a coarse-grained, structure-based model for protein folding that considers the flexibility of the native state in the definition of the model interactions.

Third generation antibody discovery methods: in silico rational design

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

Comparison of Peptide Ion Conformers Arising from Non-Helical and Helical Peptides Using Ion Mobility Spectrometry and Gas-Phase Hydrogen/Deuterium Exchange

The dominant gas-phase conformer of [M+3H]³⁺ ions of the model peptide acetyl-PSSSSKSSSSKSSSSKSSSSK has been examined with ion mobility spectrometry (IMS), gas-phase hydrogen deuterium exchange (HDX), and mass spectrometry (MS) techniques. The [M+3H]³⁺ peptide ions are observed predominantly as a relatively compact conformer type. Upon subjecting these ions to electron transfer dissociation (ETD), the level of protection for each amino acid residue in the peptide sequence is assessed. The overall per-residue deuterium uptake is observed to be relatively more efficient for the neutral residues than for the model peptide acetyl-PAAAAKAAAAKAAAAKAAAAK. In comparison, the N-terminal and C-terminal regions of the serine peptide show greater relative protection compared with interior residues. Molecular dynamics (MD) simulations have been used to generate candidate structures for collision cross section and HDX reactivity matching. Hydrogen accessibility scoring (HAS) for select structural candidates from MD simulations has been used to suggest conformer types that could contribute to the observed HDX patterns. The results are discussed with respect to recent studies employing extensive MD simulations of gas-phase structure establishment of a peptide system. Graphical Abstract ᅟ.

Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?

Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences.